To support passive Internet-of-Things (IoT) for future 6G networks, backscatter communication (BC) has emerged as a promising solution due to its ultra-low-power consumption nature. In this paper, we propose a novel multistatic passive IoT architecture which allows the reader to recover the information from multiple backscatter devices (BDs), excited by multiple remote continuous-waveform (CW) transmitters. At each BD, multiple backscatter antennas, in conjunction with cyclic delay transmission (CDT) and interleaved frequency division multiple access (IFDMA) framework, are deployed to achieve antenna gain, diversity advantage, as well as transmission orthogonality among the BDs. Furthermore, transmit beamforming at the CW transmitters and cyclic delays across the BDs are optimized to enhance the performance and ensure fairness among the BDs. Extensive simulation results have validated the effectiveness of the proposed framework under various scenarios, demonstrating substantial improvements in both reliability and fairness compared with conventional schemes.

In this paper, a multi-static integrated sensing, identification and backscatter communication (ISIBC) system is proposed for Internet-of-Things (IoT) networks, where the backscatter devices (BDs) attached to the targets utilize ambient radio frequency (RF) signals for data transmission and multiple receivers are used for target localization. Each receiver estimates the directions of arrival (DoAs) of the incident signals from the RF source, the targets with BDs and the environmental scatterers, detects the activities of each BD, and demodulates the symbols transmitted by each active BD. With the DoA estimation and activity detection results, the receivers are required to identify the targets, i.e., associating the estimated DoAs with the corresponding targets attached with active BDs. These tasks, however, are challenging as the ambient RF signal is unknown. To tackle this issue, we propose a semi-blind generalized likelihood ratio test (GLRT) detector to estimate the activities of the BDs, which tests the homogeneity of the received signal samples over different BD symbol periods without requiring any information about the ambient RF signal and backscattering channels. A constant false alarm rate (CFAR) threshold is derived for the proposed GLRT detector. Further, we propose a novel semi-blind eigenvector-based identification method to identify the targets without using knowledge of the ambient RF signal. The eigenvector-based demodulator is proposed to demodulate the symbols transmitted by active BDs. Finally, the estimation and identification results from each receiver are utilized to jointly localize the active BDs. Extensive simulation results are presented to show the effectiveness of the proposed methods for the studied ISIBC system.

This paper presents a Dual-Security-Assured Computation Offloading (DSACO) scheme for space-air-ground networks (SAGNs), which exploits the integrated sensing, communication, and computing (ISCC) capability of the low Earth orbit (LEO) satellite to support secure and efficient computation offloading. In the proposed scheme, the air-ground mode serves as the default edge-processing strategy due to its low latency and energy consumption, while the LEO satellite senses the malicious aerial eavesdropper and protects the ground device (GD)-to-UAV links through directional anti-eavesdropping jamming. When such protection becomes insufficient, the system switches to direct GD-to-LEO offloading via dedicated frequency bands. Accordingly, the secure offloading process is formulated as a worst-case average long-term energy minimization problem under sensing uncertainty. To solve the resulting dual-timescale mixed-integer nonlinear problem, we develop a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework for the joint optimization of sensing duration, UAV trajectories, computation offloading, and resource allocation. Simulation results demonstrate that the proposed scheme significantly enhances system security while maintaining offloading efficiency, and that the H-MADRL framework outperforms benchmark methods.

Backscatter communication is emerging as a promising solution for enabling low-power and large-scale IoT applications. However, it faces challenges in terms of widespread deployment, wireless resource management, and quality of service (QoS). In this paper, we first propose a secure transmission architecture to integrate the backscatter network with the existing 5G/IoT infrastructure. Next, we introduce a multi-device scheduling and time-switching strategy aimed at optimizing both capacity and secure throughput. In the time-switching scheme, BDs primarily operate in symbiotic mode without requiring additional spectrum, but can dynamically switch to opportunistic spectrum access mode when necessary, with adaptive time allocation to improve QoS. For multi-device scheduling, BDs are assigned to function as a master transmission node, cooperation node, or spoofing/jamming node, thereby enhancing the system’s resistance to proactive eavesdropping. The optimization problem is formulated to minimize spectrum resource usage while ensuring QoS and following the energy constraint. To solve this, we introduce an enumeration-based interior-point algorithm (EIA) and design a novel progressive greedy algorithm (PGA). The EIA method provides optimal solutions, while the PGA algorithm achieves high-quality suboptimal solutions with lower complexity. Extensive simulation results demonstrate that the proposed strategy stands out in ensuring QoS, enhancing security, and reducing spectrum resource usage.

Integrating sensing capabilities into base stations (BSs) represents a crucial research direction for 6G wireless communications. Given limited transmit power and the complex perception environment, it is necessary to enhance the sensing performance of BSs in low signal-to-noise ratio (SNR) conditions. Aiming at this challenge, this paper proposes a track-before-detect (TBD) scheme based on the extended particle filter (EPF) and multi-BS collaboration. In this scheme, the likelihood ratio derived from the incoherent fusion of multi-BS signals is utilized in the EPF to jointly perform target number estimation and trajectory tracking. In the EPF, to improve tracking accuracy and accelerate the detection of newborn targets, sampling approaches that leverages the characteristics of OFDM signals are introduced to generate independent measurements. Then, an efficient importance density is designed for managing target birth, survival, and death respectively to enhance the effectiveness of particles. Finally, the particle parameter sharing (PPS) strategy is proposed to reduce computational complexity, addressing key challenges in PF algorithms. With the well-designed measurements and importance density, the proposed EPF-TBD exhibits excellent detection and tracking performance under low SNR conditions, outperforming existing DBT and TBD algorithms in simulations.

In industrial wireless networks with resource-constrained and densely deployed devices, link scheduling is a challenging task. Traditional optimization methods have high computational complexity and low scalability. Graph learning offers a promising approach, yet it also comes with limitations of capturing multivariate relationships from interference, leading to ineffective link scheduling. In this article, hypergraphs are additionally introduced to model cumulative interference from concurrent transmissions. Considering the constructed comprehensive interference model, we propose a Lightweight link scheduling algorithm based on hybrid binary Graph and HyperGraph representation learning (L-GHG) in an unsupervised manner. Thereinto, the L-GHG algorithm extracts and fuses features from various interference relationships for link scheduling decisions. A graph partitioning based on channel state information is proposed to reduce redundant search space arising from cumulative interference. Simulations demonstrate that our proposed algorithm outperforms benchmark schemes regarding generalizability and scalability. We also validate the availability of the proposed algorithm in practical experiments.

Task-oriented semantic communication, leveraging learning-based joint source-channel coding (JSCC), has emerged as a key paradigm for low-latency, high-precision edge-assisted Internet of Things systems. However, the direct mapping of source data to continuous channel symbols in JSCC poses a great challenge in compatibility with existing digital systems. To address this, we propose a digital semantic communication scheme, i.e., an AdaptiveSemanticCompression with jointResourceAllocation andModulation (Adaptive-SCRAM) optimization scheme for multi-user image classification. This scheme, with the semantics quantized by a compressed codebook, enables the discrete semantic transmission with adaptive modulation, while achieving high accuracy and low latency with transmission resources optimized in multi-user classification task. Specifically, we first design a vector quantized-variational autoencoder-based digital JSCC framework with regional quantization, by jointly maximizing the semantic entropy and minimizing the codebook training loss with various SNRs and modulation orders considered in Rayleigh fading. Then based on the well trained end-to-end architecture, we mathematically fit the classification accuracy with respect to the effects of both compressed codebook size and received SNR under different modulation orders, providing an effective premise for the task performance optimization. Finally, we consider to maximize the overall multi-user classification accuracy under the transmission delay constraint, by optimizing the compression, modulation, power and bandwidth allocation for each user. To address the highly non-convex issue, we develop a dual-layer optimization algorithm. The outer-layer problem, which optimizes the compressed codebook size and modulation order, is solved by a cross-entropy-based learning algorithm. While for the inner-layer problem, a successive convex approximation method is used to optimize the power and bandwidth allocation. Simulation results show that our JSCC framework significantly reduces the semantic codebook size without compromising the classification accuracy, which is applicable to practical digital transmission systems. More importantly, compared to most existing comparable optimization schemes for image classification, our Adaptive-SCRAM optimization scheme with adaptive compression, modulation, and resource allocation can achieve much higher classification accuracy for multi-user tasks, while guaranteeing the transmission efficiency.

A deep reinforcement learning (DRL) method is proposed for hybrid beamforming in near-field wideband communication systems, aiming to alleviate the harmful near-field beam splitting issue caused by the spatial-wideband effect. Compared to the far-field beam splitting issue that occurs only in the angle domain, the near-field beam splitting issue extends to the distance domain and is thus more challenging to address. A hybrid beamforming architecture with true-time delayers (TTDs) is exploited to address the near-field beam splitting issue and facilitate the maximization of spectral efficiency (SE) across large bandwidth for downlink multi-user communications. However, the conventional iterative hybrid beamforming optimization methods, such as the weighted minimum mean-square method, often exhibit high computational complexity and are thus difficult to adapt the dynamic channel in a real-time manner. Hence, a DRL-based algorithm is propose to select fully-digital codewords from the designed near-field wideband beamforming codebook. To further approximate the selected codewords in the considered hybrid-beamforming architecture, a three-stage hybrid beamforming approximation (HBA) algorithm is proposed to minimize the fully-digital approximation error by jointly optimizing the TTD-based analog beamformers and baseband digital beamformers. Numerical results show that: (1) the proposed DRL-HBA solution for hybrid beamforming significantly increases the SE; (2) the hybrid beamforming architecture with TTDs can eliminate the near-field beam splitting effect and achieve beam focusing across wide band.

In opportunistic cognitive radio networks, when the primary signal is very weak compared to the background noise, the secondary user requires a long sensing time to achieve reliable spectrum sensing, leaving little time for secondary transmission. To tackle this issue, we propose an active reconfigurable intelligent surface (RIS)-assisted spectrum sensing system, where the received signal strength from the target primary user can be enhanced and underlying interference within the background noise can be mitigated. In comparison with the passive RIS, the active RIS not only adjusts the phase shifts of the reflecting elements but also amplifies the incident signals. Notably, we study the optimization of the reflecting coefficient matrix (RCM) to improve the detection probability given a maximum tolerable false alarm probability and limited sensing time. Then, we show that the formulated problem can be equivalently transformed into a weighted mean square error minimization problem using the principle of the weighted minimum mean square error (WMMSE) algorithm, and an iterative optimization approach is proposed. In addition, to fairly compare passive RIS and active RIS, we study the required power budget of the RIS to achieve a target detection probability under a special case where the direct links are negligible and the RIS-related channels are line-of-sight. The conclusions drawn from this special case are further validated through simulations under more general channel conditions. Furthermore, the effectiveness of the WMMSE-based RCM optimization approach is demonstrated via extensive simulations. The results also reveal that the active RIS can outperform the passive RIS when the interference is relatively weak, whereas the passive RIS performs better in strong interference scenarios due to its ability to support a large number of reflecting elements under the same power budget.

The space-terrestrial integrated network (STIN) is crucial for achieving ubiquitous connectivity in the 6G era. However, leveraging full potential of STIN is challenging due to the distinct characteristics and objectives of constituent networks. Inspired by symbiotic communication (SC), this paper proposes a service exchange-based symbiotic STIN system that optimizes objectives of different networks by exploiting their complementary features. Specifically, the ground network provides task offloading services to the space network, while the space network reciprocates with communication services. To minimize computation delay in the space network and maximize the energy efficiency (EE) of the ground network, we formulate a multi-objective optimization problem (MOOP) that jointly optimizes task offloading, resource allocation, and beamforming. We first transform the MOOP into a single-objective optimization problem (SOOP) via the ε-constraint method and then develop a successive convex approximation (SCA) algorithm to characterize its fundamental performance, which requires future state information. As obtaining such non-causal information is hard, we design a more practical multi-agent reinforcement learning (MARL) algorithm based on insights from the SCA. Besides, to address the challenges of storing multiple MARL policies for different EE-delay trade-offs, we develop a diffusion model-based behavior cloning (BC) algorithm to obtain a general policy suitable for varying trade-offs. Simulation results show that proposed algorithms outperform benchmarks and confirm that the proposed service exchange realizes a symbiotic STIN.

In this paper, we propose a novel integrated sensing and backscatter communication (ISABC) system in which each moving target is attached with a backscatter device (BD) to facilitate simultaneous target identification and parameter estimation. When the base station (BS) transmits signals to its desired user, each BD attached to the target transmits the target identification information to the BS via backscatter communication, which concurrently enhances the echo signal strength. The BS needs to detect the BD symbols and to estimate the target parameters using the echoes. This task, however, is challenging due to the coupling between the BD symbols and the target parameters. To address this issue, we propose a novel iterative detection and estimation (IDE) framework, which involves the following two processes alternately: 1) Utilizing a modified maximum likelihood (ML) estimator to perform parameter estimation with the detected BD symbols; 2) Employing the ML detector to detect the BD symbols with the estimated target parameters. Since the inter-carrier interference (ICI) of the OFDM signal is independent of the BD symbols, but contains the delay and Doppler shift information, we develop a target parameter initialization method using such ICI component to improve the performance of the proposed IDE scheme. Moreover, the closed-form Miller-Chang bound is derived to demonstrate the theoretical performance for the target parameter estimation. Finally, simulation results are provided to validate the effectiveness of the proposed designs.

Symbiotic radio (SR) is a promising technology to achieve ultra-low-power transmission for the next generation wireless network. However, its enabling technique, ambient backscatter transmission, suffers from double fading and path loss that severely restricts its service range. In this work, the reconfigurable holographic surface (RHS) is introduced at the transmitter to tackle this problem. Unlike the widely studied reconfigurable intelligent surface (RIS), the RHS that uses amplitude control can offer higher integration and lower power consumption. Seeing that the treatment of the direct-link interference in the received signal is the key problem for the receiver of the backscatter devices to achieve successful signal detection, we propose two strategies. InStrategy I, the ambient data symbol is detected for canceling the direct-link interference, while inStrategy II, we design the data stream sent by the ambient transmitter and its transmission frame in collaboration with zero-forcing beamforming to alleviate the interference among all ambient users and backscatter devices. Based on the two strategies, respectively, problems are formulated to jointly optimize the transmit power, the digital beamforming, and the holographic beamforming of the RHS such that the energy efficiency of the SR network (SRN) is maximized. For each of the two problems, we develop specific hybrid beamforming algorithm that decomposes the original problem into three sub-problems. These sub-problems respectively optimize the transmit power, the digital beamforming, and the holographic beamforming that are solved in an alternative manner. Simulation results verify that the proposed strategies can well exploit the potential of the RHS in enhancing the transmission performance of the SRN.

This paper investigates a symbiotic radio (SR) system composed of a multi-antenna radio frequency (RF) source, a backscatter device (BD), and a receiver. SR is an energy- and spectrum-efficient technology with great potential to enable passive Internet of Things (IoT). However, due to the double path loss in the reflecting link, the received BD signal is typically much weaker than the received RF source signal. As the dynamic range (DR) of the analog-to-digital converter (ADC) at the receiver is limited, it is challenging to recover the weak BD signal in the presence of a strong RF source signal after quantization. Additionally, when the low-resolution ADC is employed at the receiver, the introduced quantization noise (QN) will further deteriorate the bit error rate (BER) performance of the system. To mitigate these effects, we adopt optimal quantizers at the receiver and derive a linear quantized signal model, which considers practical modulation schemes and decoding processes. Based on this model, we define the DRs of both the ADC and the received signal, and derive analytical BER expressions for both the BD and RF source signals under the effect of QN. Subsequently, we formulate an optimization problem to minimize the BER of the BD signal via transmit beamforming, while accounting for the impact of ADC DR and QN. To solve the fractional optimization problem, we employ Dinkelbach’s algorithm in conjunction with the semidefinite relaxation (SDR) technique. Finally, simulation results demonstrate the effectiveness of the proposed beamforming design method and the performance gains achieved by employing optimal quantizers at the receiver.

Symbiotic radio (SR) is an emerging technology that exhibits high energy and spectral efficiency, where a cooperative receiver is designed to decode the primary and the secondary signals jointly. However, the ambiguity problem for joint detection arises due to the multiplication of the primary and secondary signals, which leads to a degradation in detection performance. To address this problem, the secondary transmitter (STx) could adaptively adjust the modulation scheme based on the channel responses of the direct and the reflecting links. Multiple antennas enable to reconfigure these channel responses through active beamforming at the primary transmitter (PTx) and passive beamforming at the STx, leading to the coupling of the beamforming and the adaptive modulation. Considering this coupling, we propose a joint beamforming and adaptive modulation design scheme for SR systems with multiple antennas. To enhance both the performance of primary and secondary transmissions, we focus on the composite signal, which includes the primary and the secondary signals. Accordingly, we formulate an optimization problem as maximizing the minimum Euclidean distance of the composite signal. This problem involves jointly optimizing the active beamforming at the PTx, along with the adaptive modulation and passive beamforming at the STx, subject to the passive reflection constraint at the STx and the average power constraint at the PTx. As the formulated problem is non-convex, we decompose the original problem into two subproblems, which are then solved iteratively using semi-definite programming and difference-of-convex algorithms. To reduce the computational complexity, we further propose a suboptimal scheme. Moreover, we derive the theoretical symbol error rate for the proposed scheme, gaining insights into the role of active beamforming at the PTx. Finally, simulation results show the superiority of the proposed scheme and verify the accuracy of the theoretical analysis.

In reconfigurable intelligent surface (RIS)-assisted symbiotic radios (SRs), an RIS serves not only as a transmitter to deliver its information by reflecting signals from a primary transmitter (PTx) but also as a helper to assist this primary transmission via reconfiguring wireless environments. In this paper, we are interested in an RIS-assisted multiple-input multiple-output (MIMO) SR system, where the RIS has two functions: (1) balancing the channel gains of multiple data streams from the PTx and (2) delivering its own multiple data streams. Considering the passive reflective nature of the RIS, we propose a novel multi-data-stream block delivery scheme, where the reflecting elements of the RIS are segmented into multiple blocks and each block transmits one data stream. The association between the data streams and the corresponding reflecting elements is represented by an indicator matrix. With this information delivery scheme, upper and lower bounds on the sum rate of RIS-assisted MIMO SR are determined and the primary and secondary transmissions’ achievable rates are characterized. Then, we jointly design the transmit covariance matrix at the PTx, the indicator matrix at the RIS, and the passive beamforming vector at the RIS by maximizing the derived upper bound on the sum rate. To show the advantages of RIS-assisted MIMO SR, we examine the traditional precoding matrix-based multi-data-stream delivery scheme of the RIS and explore a special case where the direct link is blocked. Finally, extensive numerical results demonstrate that when the RIS transmits multiple data streams, the system can achieve a higher sum rate compared with the case where the RIS purely enhances the primary transmission, thanks to the additional degree-of-freedom introduced by the RIS transmission.

This paper investigates a multi-user multiple-input single-output (MU-MISO) symbiotic radio (SR) system empowered by multiple reconfigurable intelligent surfaces (RISs). In this system, each RIS adopts a multi-functional reflection modulation scheme to simultaneously transmit data from Internet of Things (IoT) sensors and enhance the primary transmission. However, allocating all reflected power exclusively to IoT data transmission could compromise the capability of RISs to enhance the primary transmission. To address this issue, we propose a flexible power allocation scheme that partitions the power reflected from the RISs into two components: one for enhancing the primary transmission and the other for supporting IoT communication, fully exploiting the multi-functional potential of RISs. Subsequently, we formulate a joint optimization problem aimed at minimizing the transmit power subject to the rate requirements for both primary and RIS transmissions, involving the joint optimization of the beamforming at the base station (BS) and the reflection modulation design at the RISs. Given the non-convex constraints and the coupled variables within the problem, we develop an alternating optimization algorithm combined with difference-of-convex programming to efficiently solve the problem and determine the power allocation scheme. Furthermore, to reduce computational complexity, we consider uniform power allocation across all reflecting elements and introduce weighted parameters to flexibly balance between assisting primary transmission and supporting IoT communication. Simulation results demonstrate that our proposed power allocation scheme effectively balances the demands of the primary and RIS transmission, significantly reducing the required transmit power.

This paper investigates the passive multiple trajectories tracking with multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) signals transmitted by the base station (BS) in the rich scattering environment. In the proposed framework, multiple radar receivers track multiple trajectories cooperatively with the assistance of a fusion center. To cope with high dimensional and superimposed echoes received by each radar receiver, we propose a parameter estimation method based on Bayesian learning to obtain the coarse estimations of targets and clutter. Then, these estimations are utilized as inputs for the trajectory probability hypothesis density (TPHD) filter for trajectory tracking. Considering the potential for a target to generate multiple measurements in overlapping areas of sweeping beams and non-Poisson characteristics of the estimated clutter/false measurements, we derive the multi-detection TPHD (MD-TPHD) filter with non-Poisson clutter and its Gaussian mixture implementation, where the clutter is separated into a Poisson and a discrete distributed component. As the field of view (FoV) of each radar receiver is finite, we fuse the trajectories in a fusion center utilizing Arithmetic Average (AA) method. The fused TPHDs are then fed back to each radar receiver to assist more accurate tracking. Simulation results demonstrate the superiority of MD-TPHD filter assisted by trajectory fusion in the multi-detection and FoV limited scenarios.

Cognitive aerial-terrestrial networks (CATN) hold promise in addressing the spectrum shortage challenges posed by thriving aerial networks, where aerial users (AUs) requiring high-quality downlink communications suffer severe interference from numerous terrestrial base stations (BSs). To alleviate such interference, we propose jointly optimizing the user association and coordinated beamforming (CBF) of the terrestrial network, thereby maximizing the sum rate of the secondary terrestrial users (TUs) under the interference temperature constraints of the primary AUs. Traditional iterative optimization schemes are impractical for this problem due to their high computational complexity and information exchange overhead. Although deep reinforcement learning (DRL)-based schemes offer a viable alternative, their performance is sensitive to the weight of the constraint violation penalty in the reward. To overcome these limitations, we propose a safe DRL-based user association and CBF scheme for CATN, which avoids multiple training attempts to find the optimal penalty weight before actual deployment and reduces expenses. Specifically, the studied system is modeled as a networked constrained partially observable Markov game, where each TU agent chooses its associated BS, and each BS agent decides its beamforming vectors, aiming to maximize the reward while satisfying the safety constraints to protect the AUs. Simulation results show that the proposed scheme can achieve a higher sum rate of TUs than a two-stage optimization scheme while the average received interference power of the AUs is generally below the threshold.

Symbiotic radio is an energy- and spectrum-efficient transmission technology to achieve seamless connection in the next generation wireless network. To tackle the transmission distance limitation of the backscatter devices (BD) in the symbiotic radio network (SRN), in this work, reconfigurable holographic surface (RHS) is introduced to accommodate the multiple BDs by assisting their concurrent transmission. Compared to the traditional reconfigurable intelligent surface, RHS has the advantages of higher energy efficiency and integration with lower hardware cost. An optimization problem is formulated to jointly optimize the digital beamforming of the radio-frequency chains and the holographic pattern of the RHS such that the achievable sum rate of the received signal at the sink node (SN) which is used to collect the information of all the BDs is maximized under the achievable-rate constraint of the ambient mobile user. This challenging non-convex problem is transformed into semi-definite programming by successive convex approximation and sequential parametric convex approximation, which are solved in an alternating way. The effectiveness of using the RHS to assist the concurrent transmission of the multiple BDs in the SRN has been validated by the numerical results.

Symbiotic radio (SR) is an emerging technology that supports the secondary transmitter (STx) to share the spectrum and energy with the primary transmission. However, the STx’s reflection modulation may lead to detection ambiguity when jointly decoding the primary and the secondary signals. To address this problem, the STx could employ adaptive modulation to dynamically vary its modulation scheme according to the channel responses of both the direct and reflecting links. Multiple antennas provide a way to reconfigure the channel through active beamforming at the primary transmitter (PTx) and passive beamforming at the STx, which in turn impact the adaptive modulation design. Considering such interdependence, this paper proposes a joint beamforming and adaptive modulation design scheme for SR with multiple antennas. We formulate a problem that maximizes the minimum Euclidean distance of the composite signal subject to the passive reflection constraint of the STx and the transmit power constraint of the PTx. Due to the nonconvexity of the formulated problem, we decouple the original problem into two subproblems and then iteratively solve them by semi-definite programming and difference-of-convex algorithms. Moreover, the nearest neighbor union bound of the symbol error rate for the proposed scheme is analyzed. Finally, simulation results show that the proposed scheme can achieve a significant performance gain compared with the existing schemes.

Semantic communication (SemCom) has emerged as a key paradigm for next-generation wireless networks. However, existing SemCom systems face challenges in adapting to dynamic Semantic Entities of Interest (SEI) and suffer from significant computational overhead. This paper proposes a novel Prompt-guided SemCom (PSC) framework that leverages textual prompts to dynamically align transmitted image features with the receiver’s SEI, enabling adaptive compression and enhanced generalization. The proposed PSC system introduces two key features. Specifically, a cross-modal policy network dynamically selects image tokens based on textual prompts, thereby minimizing the transmission overhead. Moreover, a Mixture of Experts (MoE) architecture enables parallel processing with sparse expert activation, efficiently handling heterogeneous features while reducing computational complexity during inference. To train the PSC system, we introduce an iterative algorithm combining self-supervised learning and Group Relative Policy Optimization (GRPO), unifying Deep Learning (DL) and Deep Reinforcement Learning (DRL) for the first time in the context of SEI extraction and selection. Experiments demonstrate the superiority of PSC over conventional methods, achieving enhanced reconstruction performance and robustness. Furthermore, results validate the effectiveness of the policy network and MoE architecture, enabling PSC to achieve 94.80% reconstruction performance while reducing communication overhead by 44.90% and the number of activated parameters by 47.04%.

Backscatter communication has emerged as a promising technology for future Internet of Things (IoT) due to its ultra-low power consumption. However, its performance is limited by the double fading effect in the backscatter channel. In this paper, we incorporate multiple-input multiple-output (MIMO) technique into backscatter communications, where the sinusoidal radio frequency (RF) source, backscatter device (BD), and receiver are all equipped with multiple antennas. Furthermore, cyclic delay diversity (CDD) is employed across antennas at the BD to achieve array gain as well as transmit diversity gain. To cope with the frequency-selectivity introduced by CDD, two frequency-domain signal detection schemes are utilized based on the maximum ratio combining (MRC) and minimum mean square error (MMSE) criteria, and the closed-form expressions for the bit error rates (BERs) of backscatter communications are derived. The transmit beamformer at the RF source is optimized to minimize the BER of the backscatter communication. With properly designed beamforming, the overall backscatter link can be effectively strengthened, thereby enabling more reliable backscatter communications. Simulation results show that deploying multiple antennas at both the RF source and the receiver significantly improves the BER performance, while the CDD-based transmission scheme can help to achieve additional array gain as well as transmit diversity gain for backscatter communications.

In this paper, we propose a novel frequency-modulated continuous wave (FMCW)-enabled integrated sensing, identification, and backscatter communication (ISIBC) system design for 6G Internet of Things (IoT). In this system, during the sensing stage, the base station (BS) emits the FMCW signal, and from the echo signal, it estimates the angles of arrival (AoAs) and ranges of multiple targets to obtain their positions while simultaneously identifying these targets through detecting the symbols transmitted by the attached backscatter devices (BDs). In particular, we first formulate precise models for the echo signal and the discrete beat signal at the uniform linear array (ULA). To eliminate the inter-symbol interference (ISI), we propose a zero-padded BD symbol pattern. Subsequently, by leveraging the proposed BD symbol pattern and a truncation operation, we further reformulate the discrete beat signal model into an explicit third-order tensor model. Next, building on the reformulated tensor model, we propose a canonical polyadic decomposition (CPD)-based algorithm for joint parameter estimation and BD symbol detection. Finally, numerous simulation results are provided to validate the effectiveness and excellent performance of the proposed FMCW-enabled ISIBC system design.

This paper introduces a novel frequency-modulated continuous wave (FMCW)-enabled integrated sensing, identification, and backscatter communication (ISIBC) system to support the emerging low-altitude economy (LAE). In this system, during the sensing stage, the ground base station (GBS) utilizes the FMCW radar to estimate the range and radial velocity of the unmanned aerial vehicle (UAV), and concurrently to identify the UAV through detecting the symbols transmitted by the attached backscatter device (BD). In particular, we first formulate explicit signal models for the echo signal and the discrete beat signal. To mitigate the inter-symbol interference (ISI), we propose a zero-padded BD symbol pattern. Subsequently, the beat signal model is reformulated into a rank-1 matrix model through the proposed BD symbol pattern and the truncation operation. Next, based on the rank-1 matrix model, we propose a singular value decomposition (SVD)-based algorithm for joint parameter estimation and BD symbol detection. Finally, extensive simulation results are provided to demonstrate the effectiveness and superior performance of the proposed ISIBC system design for LAE.

This paper presents a passive multiple trajectories tracking system with multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) signals transmitted by the base station (BS). Firstly, we propose a Bayesian learning method to obtain the coarse estimations of targets and clutter, which are utilized for trajectories tracking. Considering the potential for a target to generate multiple measurements in overlapping areas of sweeping beams and non-Poisson clutter, we extend the trajectory probability hypothesis density (TPHD) filter to multi-detection TPHD (MD-TPHD) filter with non-Poisson clutter and provide its Gaussian mixture implementation. Simulation results show the performance of the algorithm.

Ambient IoT, which integrates backscatter communication and energy harvesting techniques, has emerged as a promising technology for massive connectivity in 6G. Symbiotic radio (SR), a novel backscatter communication paradigm, has shown great potential in advancing ambient IoT. Nevertheless, existing multiple access schemes for SR are limited by low transmission rates when enabling massive ambient IoT transmissions, primarily due to time-division access or inter-device interference. Additionally, the impact of energy harvesting on the multiple access system is rarely investigated. In this paper, we propose a code division multiple access (CDMA)-based symbiotic radio system to support interference-free simultaneous access for massive IoT devices. In the proposed system, multiple IoT devices first harvest energy from cellular signals, modulate their information onto the incident signal using unique spreading codes, and then transmit the modulated signals to the receiver. Two decoding schemes are proposed to retrieve the information from the IoT devices, and the throughput is analyzed accordingly. Moreover, we derive a closedform expression of the ergodic sum throughput, and reveal that there exists an optimal energy harvesting time that maximizes overall IoT transmission throughput. Finally, simulation results validate the performance of the proposed system and demonstrate the tradeoff between energy harvesting and data transmission.

In symbiotic radio (SR) systems, backscatter devices (BDs) are able to achieve ultra-low-power transmissions by backscattering the primary signal of a primary transmission, offering a promising solution for future passive Internet-ofThings (IoT). This paper explores the use of multiple antennas at the BD, which allows it to combat fading in the cascaded channel of the backscatter link. To leverage the potential, the BD generates the secondary signal using Alamouti space-time coding across the multiple antennas. This enables the BD to transmit information without requiring channel state information (CSI) of the cascaded channel. Considering the secondary symbol period is identical to the primary symbol period in the SR system, we propose a theoretical model to evaluate the diversity order for both the primary and the secondary transmissions using their pairwise error probability (PEP). Interestingly, based on the theoretical results, it is found that the secondary signal cannot obtain the diversity gain provided by the space-time coding with the conventional non-biased constellation but can obtain it with a direct current (DC)-biased constellation. Extensive numerical results are provided to demonstrate the accuracy of the theoretical analysis and to show how to obtain the diversity gain for SR with space-time coding.

The emerging integrated sensing and backscatter communication (ISABC), is expected to provide a new paradigm for Internet of Things (IoT) applications. In this paper, we propose a novel radar-enabled ISABC (R-ISABC) system design, where the signal processing center (SPC) can simultaneously perform localization for multiple targets and symbol detection for multiple backscatter devices (BDs) without requiring knowledge of the exact waveform of the radar signal. In particular, we first characterize the received signal models using the angle of arrivals (AOAs) of targets, symbol vectors transmitted by BDs, and the environment radar reverberation, and then an explicit third-order tensor model is elaborated by leveraging the periodicity of the radar reverberation and rearranging the sampled received signals. Then, we propose a novel CANDECOMP/PARAFAC decomposition (CPD)-assisted joint angle estimation and BD symbol detection algorithm based on the formulated third-order tensor model with the differential coding adopted at each BD. Numerous simulation results verify the feasibility and effectiveness of the R-ISABC system design.

This paper considers a multi-reconfigurable intelligent surface (RIS) empowered symbiotic radio (SR) system, where multiple RISs, operating as Internet-of-Things (IoT) devices, are used to transmit their modulated information bits by backscattering the incident primary signal and to assist the primary system simultaneously. Most existing works consider the modulation design for the single RIS scenario while lacking indepth investigation into the multi-RIS scenario. To fill this gap, we are interested in cooperatively optimizing the signal constellation and the associated phase shifts of all IoT devices to enhance the overall symbol error rate performance of both the primary and IoT transmissions. Towards this end, we formulate a problem to maximize the minimum Euclidean distance of the received noise-free signal from a signal detection perspective, subject to the peak amplitude constraints of the signal constellation and the passive reflection constraints of phase shifts. Due to the non-convexity of the formulated problem, an iterative algorithm is proposed to solve it. Besides, the structure of the optimized signal constellations in the absence of the direct link is sketched to draw useful insights. Finally, simulation results are provided to validate the superiority of the proposed cooperative constellation design methodology over the classic constellation design.

Symbiotic radio (SR) has emerged as a promising technology that enables ambient Internet of Things (IoT) devices to passively access wireless networks for data transmission. To support massive low-activity IoT devices, this paper proposes a novel low-activity code-division multiple access (LACDMA) based SR system, where a large number of low-activity backscatter devices (BDs) simultaneously transmit information in a CDMA manner by backscattering signals from a primary transmitter (PTx). To jointly detect the signals from both the PTx and the low-activity BDs, we first develop an optimal sparsity-aware maximum a posteriori (S-MAP) detector, which performs an exhaustive search over all candidates in the transmit alphabet. To address the prohibitive complexity of this approach, we further propose a low-complexity sparsity-aware iterative successive interference cancellation (S-SIC) detector. In this design, the PTx's signal is first decoded by treating the backscatter signals as interference. Then, the BDs' signals are recovered by exploiting their sparse activity patterns with the estimated PTx signal. Subsequently, the PTx's signal is reestimated by regarding the backscatter link as a multi-path with the estimated BDs' signal. Simulation results are provided to evaluate the proposed detectors. It is demonstrated that the developed detectors can realize the multipath gain contributed by the BDs to enhance the primary transmission and the proposed S-SIC detector can closely approach the performance of the SMAP detector, particularly in scenarios with a large number of receive antennas.

Cognitive radio (CR) has been identified as a highly promising spectrum-sharing technology for enhancing the spectrum efficiency of future wireless networks. Recently, the emerging reconfigurable intelligent surface (RIS) has been integrated into CR networks to further advance spectrum efficiency. However, existing works primarily focus on conventional RIS architectures, which can only adjust the reflection of incident wireless signals on one side of the RIS. As a result, these architectures cannot tune the wireless signals to improve performance across the full 360∘ coverage. Additionally, most existing studies concentrate on enhancing the transmission of the secondary user (SU), with the potential benefits to the transmission of the primary user (PU) remaining unclear. In this paper, we investigate a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-empowered opportunistic CR network, where the STAR-RIS can tune the wireless signals on both sides simultaneously. To unleash the potential of this system, we study the reflection and transmission coefficients (RTCs) optimization problem to maximize the SU’s average achievable rate while ensuring the target sensing performance and meeting the PU’s rate requirement. We then introduce a two-stage RTC optimization framework, which decouples the optimization process into sensing and transmission stages. Specifically, the RTCs in the sensing stage are optimized to improve the PU’s transmission while achieving the desired detection and false alarm probabilities. In the transmission stage, the RTCs are optimized to maximize the SU’s average achievable rate while ensuring that the PU’s rate requirements are satisfied. Furthermore, we propose a single-stage optimization method to reduce the implementation complexity caused by frequent RTC reconfigurations. Extensive simulations demonstrate the performance improvements enabled by STAR-RIS and validate the effectiveness of the proposed RTC optimization methods.

Reconfigurable intelligent surfaces (RIS) are considered as a kind of potential technology of 6G communication, which can affect wireless communication channels by manipulating the reflection units. RIS is a kind of passive devices, which causes channel estimation to require large pilot overhead, and MIMO systems also bring huge power consumption and hardware cost. Therefore, we consider a RIS-aided mmWave MIMO communication system based on mixed-resolution quantization schemes to solve these problems. Considering the row and column sparse structure in the mmWave channel and the influence of mixed-resolution quantization, a channel estimation scheme based on the compressive sensing (CS) algorithm is proposed. However, if the angles of arrival (AoAs) or the angles of departure (AoDs) are not located on discrete grids, the sparse structure of angular channel matrices will leak. Taking full account of the particular leakage structure, a Leakage Structure Orthogonal Matching Pursuit (LSOMP) based channel estimation scheme is proposed. The simulation results show that the proposed preprocessing method can greatly improve the channel estimation accuracy, and the proposed algorithms with the mixed-resolution quantization schemes can not only reduce the circuit device power consumption of receivers, but also reduce pilot overhead and improve the performance stability especially in high SNR.

By integrating Cognitive Radio (CR) functionality into the Internet of Things (IoT), the CR-based IoT network offers a promising solution to the spectrum scarcity problem faced by traditional IoT systems. However, accurate and fair Cooperative Spectrum Sensing (CSS) faces many challenges, such as malicious nodes’ presence, sensor behaviour reliability, and IoT devices’ limited energy. In this paper, we propose a lightweight blockchain-enabled CSS system that enhances transparency and reliability in sensing report exchange and fusion. Specifically, we introduce a comprehensive trust evaluation algorithm and a fair sensor selection method to assess the reliability of sensors and fairly assign spectrum sensing tasks. To ensure the decentralization and security of IoT networks, we propose a lightweight consensus mechanism which utilizes trust values and deposits to determine voting weights during producer elections while removing malicious nodes by blocklist mechanism. A smart contract is also designed to automate the entire CSS process, including spectrum sensing, deposit management, and trust management. Finally, security analysis and numerical simulations are conducted to demonstrate the effectiveness and robustness of the proposed blockchain-enabled CSS system.

Cooperative spectrum sensing (CSS) plays a crucial role in this era of explosive Internet of Things with scarce spectrum resources, since it can effectively enhance the sensing accuracy with the cooperation of secondary users (SUs). However, most existing CSS algorithms primarily focus on increasing the cooperative detection accuracy, while neglecting the computational complexity or sensing latency. Therefore, we propose a deep learning (DL) driven CSS scheme with the consideration of dynamic multi-model selection and suitable resource allocation. Specifically, we first derive the closed-form expressions to fit and characterize the detection and false alarm probabilities of three popular DL models, including the convolutional neural network, the long short-term memory (LSTM) network and the hybrid convolutional LSTM network. Then, the problem of minimizing the cooperative sensing error is formulated under the constrains of limited computational resource and sensing latency. Finally, the cross-entropy algorithm is employed to dynamically select the most suitable cooperating SU set and their correspondingly matched models, to balance the sensing accuracy and computational complexity. Simulation results demonstrate that our CSS scheme are much more robust and computationally efficient compared to some well-known CSS algorithms, especially in achieving extremely high sensing accuracy at low transmit power or low received signal-to-noise ratio.

This paper proposes a partition-based reconfigurable intelligent surface (RIS)-assisted multi-user multiple-input single-output (MU-MISO) symbiotic radio (SR) system. Unlike conventional RIS designs in SR systems that utilize all elements for secondary transmissions, which restricts its ability to support the primary transmission, the RIS is divided into two subsurfaces: one enhances primary transmissions while the other transmits IoT information via spectrum sharing. We formulate a joint optimization problem to minimize transmit power under rate constraints for both primary and IoT transmissions through active beamforming and passive RIS phase-shift design. To tackle the non-convex constraints and variable coupling in the formulated problem, we propose efficient optimization techniques, including alternating optimization and difference-of-convex methods. Furthermore, we propose a low-complexity interference-free scheme leveraging partitioned RIS to eliminate inter-user interference, which is unachievable with conventional RIS design. Simulation results reveal that compared to the conventional scheme, which can be viewed as a special case of partition-based RIS where all the reflecting elements are used to transmit IoT information, partition-based RIS demonstrates superior performance, thereby validating the advantages of RIS partitioning in SR systems.

Symbiotic radio (SR) has emerged as a promising technology for enabling efficient spectrum and power sharing between active and backscattering transmissions. In this paper, we investigate the reconfigurable intelligent surface (RIS)-assisted SR system, where the primary transmission uses orthogonal frequency division multiplexing (OFDM) and the RIS transmits the secondary signal by backscattering the primary signal. The primary OFDM block and the secondary symbol have identical symbol periods but may not be perfectly synchronized, which can introduce inter-carrier interference (ICI) in the received OFDM blocks, thereby hindering joint signal detection. To address this issue, we propose a novel pilot structure and receiver design for SR. Specifically, the RIS sent a training sequence at the beginning of the secondary transmission, enabling the receiver to detect the presence of ICI and estimate essential parameters. If ICI is detected, two effective methods for synchronization offset estimation are proposed. Then, joint signal detection is improved by properly decoupling primary and secondary signals, mitigating the impact of synchronization offsets. On the other hand, if ICI is absent, the secondary signal arrival is identified using the training sequence, and joint signal detection is directly performed without suffering ICI. Simulation results validate the accuracy of the proposed estimation methods and show that the proposed detection methods ensure the reliable detection of both primary and secondary signals, even in the presence of ICI.

When complex networks are used for distributed estimation, spatial anisotropy, particularly the potential heterogeneity in noise distributions across multiple nodes, poses significant challenges in practice. In such intricate environments, while state-of-the-art algorithms may excel at specialized nodes, their inability to maintain consistent performance over the entire network often results in systemic degradation. To address this fundamental limitation, an innovative framework is proposed in this letter, wherein the radial basis function neural networks (RBFNNs) are uniformly deployed at each node, serving as universal approximators for various noise profiles. Leveraging this framework, a recursive-type diffusion algorithm is introduced to achieve enhanced network-wide performance. Owing to RBFNN's remarkable modeling capacity, the proposed algorithm can sustain consistent and superior performance in intricate network environments. Simulations undoubtedly validate its superiority over advanced competing alternatives.

In this paper, we investigate a novel symbiotic radio (SR)-aided high-speed railway (HSR) wireless network, in which the Internet of Things (IoT) device, operating as a secondary transmitter, transmits its own information to the mobile relay (MR) on the HSR by backscattering radio frequency (RF) signals from the base station (BS). With the assistance of SR, the designed network facilitates the transmission of locally collected environmental sensing messages from the IoT network to the HSR, simultaneously enhancing the primary communication between the BS and MRs. Aiming to maximize the sum transmission rate of the primary and the IoT network, we focus on a joint power control and device access (JPCDA) problem. Specifically, each IoT device accesses the network through appropriate time slot selection and appropriate power control, thereby achieving satisfactory overall network performance. However, since the fast channel variations arising from the high mobility of HSRs make it impractical to acquire accurate channel state information (CSI), it is challenging to achieve an optimal resource allocation scheme. To address this challenge, we develop a distributed deep reinforcement learning (DRL)-based algorithm that utilizes historical CSI to infer real-time CSI for decision making. In particular, each computing unit of the agent performs action selection for only one IoT device at one time based on the current local observation information. Numerical results illustrate that our proposed algorithm outperforms other baselines, and still works effectively when the environment changes.

The rapid development of High-Speed Railway (HSR) puts higher requirements on comprehensive perception and reliable transmission in tunnel scenarios. To realize efficient and reliable perception information transmission of HSR in the tunnel, we propose a multisensor perception communication system, which consists of an Access Point (AP) deployed on each carriage for perception information transmission and self-powered wireless sensors. The AP remote transmits the perception information through the leaky cable deployed in the tunnel. We construct an optimization problem for minimizing the transmission time of the whole system’s perception information in the multi-network system and the adjacent area of the carriage. A Multi-Agent Cooperation-based Deep Reinforcement Learning (MA-CDRL) algorithm is proposed to get the optimal scheduling strategy for reducing the transmission time. We construct the CDRL neural network for the algorithm to introduce the states of other APs, resulting in the system making more efficient transmission strategies. In the simulations, the proposed algorithm gets a better performance than the comparison algorithms and is verified in various dynamic HSR scenarios, such as different travel speeds and sensor distributions.

Cooperative spectrum sensing (CSS) is a promising approach to improve the detection of primary users (PUs) using multiple sensors. However, there are several challenges for existing combination methods, i.e., performance degradation and ceiling effect for hard-decision fusion (HDF), as well as significant uploading latency and non-robustness to noise in the reporting channel for soft-data fusion (SDF). To address these issues, an integrated communication and computation (ICC) framework is proposed in this paper. Specifically, distributed semantic communication (DSC) jointly optimizes multiple sensors and the fusion center to minimize the transmitted data without degrading detection performance. Moreover, over-the-air computation (AirComp) is utilized to further reduce spectrum occupation in reporting channel, taking advantage of characteristics of wireless channel to enable data aggregation. Under the ICC framework, a particular system, namely ICC-CSS, is designed and implemented, which is theoretically proved to be equivalent to the optimal estimator-correlator (E-C) detector with equal gain SDF when the PU signal samples are independent and identically distributed. Extensive simulations verify the superiority of ICC-CSS compared with various conventional CSS schemes in terms of detection performance, robustness to SNR variations in both sensing and reporting channels, as well as scalability with respect to the number of samples and sensors.

Reconfigurable intelligent surface (RIS) empowered symbiotic radio (SR) holds the potential to support ambient Internet-of-Things (IoT) due to its spectrum- and energy-efficient characteristics. In this system, the RIS not only assists the primary system but also transmits IoT device information. While most existing works focus on the modulation design for single RIS scenarios, there is a lack of investigation into multi-RIS scenarios. This paper addresses this gap by considering a multi-RIS-empowered SR system, where multiple RISs backscatter the incident primary signal to transmit IoT information to the receiver. We aim to cooperatively optimize the signal constellation and phase shifts of all RISs to improve the overall symbol error rate performance for both primary and IoT transmissions. To achieve this, we formulate a problem to maximize the minimum Euclidean distance of the received noise-free signal from a signal detection perspective, subject to constraints on the peak amplitude of the IoT signal constellation and the passive reflection capabilities of the RISs. Given the non-convex nature of the problem, we propose an efficient iterative algorithm. Additionally, we sketch the structure of the optimized IoT signal constellations in the absence of a direct link to provide essential insights. We also develop a low-complexity algorithm and signal detection method by leveraging the received signal structure. Finally, simulation results demonstrate the superiority of our cooperative constellation design methodology over the traditional PSK constellation designs.

The rapid advancement of Software-Defined Networks (SDN) and Network Function Virtualization (NFV) has popularized the adoption of the Service Function Chain (SFC) paradigm for efficient network service delivery. This paradigm leverages the flexibility and cost-effectiveness of deploying Virtual Network Functions (VNFs) as software entities or virtual machines on off-the-shelf servers. Chaining VNFs together allows traffic to be directed through the network as required. However, existing algorithms for traffic steering and routing path computation in SFC suffer from many challenges, including complexity, lack of scalability, and low time efficiency. This paper focuses on addressing the challenges associated with VNF placement and SFC chaining in SDN/NFV-enabled networks. Our objective is to identify an optimal solution for VNF placement that maximizes the utilization of network resources. We formulate the problem as a Binary Integer Programming (BIP) model to accomplish this. Additionally, we propose a novel algorithm called DeepSelector, which incorporates deep learning techniques and an intelligent node selection network to determine the optimal placement of VNFs for SFC requests. Through performance evaluation, we demonstrate that DeepSelector achieves high network resource utilization and offers efficient VNF placement computation, significantly improving overall network performance.

In this paper, we investigate the spectrum sharing and resource allocation scheme for air-ground integrated wireless network which consists of multiple unmanned aerial vehicles (UAVs) and a high altitude platform (HAP). We consider the UAVs are required to provide services or execute certain missions in the area that HAP owns the spectrum and other resources. Correspondingly, we propose an energy efficient spectrum sharing and resource allocation scheme so that the UAVs can flexibly utilize the radio resources within the area without degrading the quality of service (QoS) of the HAP. In the proposed scheme, we jointly optimize pricing of spectrum and transmit power to maximize the utility of both the HAP and UAVs in the considered system in an energy efficient manner. A game theoretic approach is then presented to find the spectrum sharing and resource allocation strategies for both HAP and UAVs and the problem has been addressed via convex optimization. Our extensive simulations demonstrate marked improvements in system utility, spectrum and energy efficiency, and also highlight the effectiveness of the proposed scheme.

Time-Critical Wireless Network (TCWN) is a promising communication technology that can satisfy the low latency, high reliability, and deterministic requirements of mission-critical applications. Multiple TCWNs required by various applications inevitably coexist with each other. Most existing works aim to achieve acceptable latency or consider the simplest topology (i.e., line topology). As latency requirements become more stringent, exploring the minimum data collection latency becomes an interesting problem. In this paper, the coexisting system consists of multiple tree-topology-based TCWNs. We first establish a conversion framework to convert an arbitrary tree topology into multiple analogous line topologies to reduce the analysis complexity. We then propose a Time-Critical wireless network Scheduling (TCS) algorithm to minimize the data collection latency of coexisting TCWNs. The TCS algorithm consists of two phases. In the internetwork scheduling phase, we strictly derive a general expression to characterize the practical network requirements. In the intranetwork scheduling phase, we design two levels of priority assignment algorithms to accurately characterize the critical states and resource requirements of different nodes. We conduct extensive simulations to verify the effectiveness of the TCS algorithm. The evaluation results show that the TCS algorithm can achieve minimum data collection latency in more than 99.956% cases, and the maximum difference compared to the optimal value is one time slot.

Extremely large antenna array (ELAA) not only effectively enhances system communication performance but also improves the sensing capabilities of communication systems, making it one of the key enabling technologies in 6G wireless networks. This paper investigates the multiuser localization problem in an uplink Multiple Input Multiple Output (MIMO) system, where the base station (BS) is equipped with an ELAA to receive signals from multiple single-antenna users. We exploit analog beamforming to reduce the number of radio frequency (RF) chains. We first develop a comprehensive near-field ELAA channel model that accounts for the antenna radiation pattern and free space path loss. Due to the large aperture of the ELAA, the angular resolution of the array is high, which improves user localization accuracy. However, it also makes the user localization problem highly non-convex, posing significant challenges when the number of RF chains is limited. To address this issue, we use an array partitioning strategy to divide the ELAA channel into multiple subarray channels and utilize the geometric constraints between user locations and subarrays for probabilistic modeling. To fully exploit these geometric constraints, we propose the array partitioning-based location estimation with limited measurements (APLE-LM) algorithm based on the message passing principle to achieve multiuser localization. We derive the Bayesian Cramér-Rao Bound (BCRB) as the theoretical performance lower bound for our formulated near-field multiuser localization problem. Extensive simulations under various parameter configurations validate the proposed APLE-LM algorithm. The results demonstrate that APLE-LM achieves superior localization accuracy compared to baseline algorithms and approaches the BCRB at high signal-to-noise ratio (SNR).

Reconfigurable intelligent surface (RIS) has shown its potential in terahertz (THz) communications, due to its capability to expand coverage and compensate for the severe attenuation of THz signals. This paper investigates a large-scale RIS-assisted THz communications system in the near-field. Nevertheless, the beam squint effect of RIS, caused by the frequency-independent phase shifting circuit, results in severe array gain loss across the wide bandwidth. While true time delays (TTDs) that generate frequency-dependent phase shifts can mitigate beam squint, they often suffer from high power consumption. To address the drawback, we introduce a set of fixed true time delays (FTTDs) with low power consumption and low insertion loss for the RIS. These FTTDs, shared by the elements of RIS, can generate frequency-dependent phase shifts, thereby addressing the beam squint effect. To overcome the limitation of FTTDs being unable to change delays, we propose a dynamic architecture that consists of a switch network and two-layer phase shifters, allowing the elements of RIS to select the FTTDs. Subsequently, we analyze the theoretical array gain of the proposed FTTD-equipped RIS and determine the minimum number of FTTDs required for effective mitigation. Then, we formulate a problem of maximizing the achievable rate and propose a two-stage algorithm. Specifically, in the first stage, we obtain the optimal wideband beamforming design for both the BS and the RIS. In the second stage, we approximate this optimal design with the beamforming design from our proposed architecture. Finally, simulation results demonstrate that our proposed RIS design, with a small number of FTTDs, can achieve a near-optimal achievable rate and higher energy efficiency.

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) system, where an RIS assists a primary transmission by passive beamforming and simultaneously acts as an information transmitter by periodically adjusting its reflection coefficients. Such RIS functions innately enable a new type of communication channel, called multiplicative multiple access channel (M-MAC), where the primary and secondary signals are superposed in a multiplicative manner. To pursue the fundamental performance limits, in this paper, we focus on characterizing the capacity region for the RIS-assisted SR system. Due to the reflection nature of RISs, the signal transmitted from the RIS elements should satisfy a passive reflection constraint. In particular, we consider two types of passive reflection constraints, one for the case that the amplitudes of the reflection coefficients are fixed but the phases are adjustable, while the other for the case that both the amplitudes and the phases can be adjusted. Under the passive reflection constraints at the RIS as well as the average power constraint at the primary transmitter (PTx), we characterize the capacity region of RIS-assisted SR when the direct link from the PTx to the receiver is blocked. It is observed that: 1) the number of sum-rate-optimal points on the boundary of the capacity region is infinite; 2) for the rate pairs with the maximum sum rate, the optimal amplitude distribution of the primary signal is a continuous Rayleigh distribution, while for the remaining rate pairs on the capacity region boundary, the optimal amplitude distribution of the primary signal is discrete; 3) when both the amplitudes and the phases of the reflection coefficients are adjusted for the RIS, the capacity region is enlarged as compared to the phase-adjusted-only case.

For millimeter wave (mmWave) or Terahertz (THz) communications, by leveraging the high spatial resolution offered by large antenna arrays and the multi-path sparsity of mmWave/THz channels, a novel inter-symbol interference (ISI) mitigation technique called delay alignment modulation (DAM) has been recently proposed. The key ideas of DAM aredelay pre-compensationandpath-based beamforming. However, existing research on DAM is mainly based on fully digital beamforming, which requires the number of radio frequency (RF) chains to be equal to the number of antennas. This paper proposes the hybrid analog/digital beamforming based DAM, including both fully and partially connected structures. The analog and digital beamforming matrices are designed to achieve performance close to DAM based on fully digital beamforming. While DAM was considered for the path-based channel model with integer delays in the previous work, this paper extends DAM to a more general tap-based model that accounts for fractional path delays. To further reduce the cost of channel estimation and improve the performance for wireless channels with fractional delays, DAM with codebook-based beam alignment and DAM-orthogonal frequency division multiplexing (DAM-OFDM) with hybrid beamforming are proposed. The effectiveness of the proposed techniques is verified by extensive simulation results.

In space-air-ground integrated networks (SAGINs), cognitive spectrum sharing has been regarded as a promising solution to meet the rapidly increasing spectrum demand of various applications, because it can significantly improve the spectrum efficiency by enabling a secondary network to access the spectrum of a primary network. However, different networks in SAGIN may have different quality of service (QoS) requirements, which can not be well satisfied with the traditional cognitive spectrum sharing architecture. To address this issue, in this paper, we propose a hierarchical cognitive spectrum sharing architecture (HCSSA) for SAGINs, where the secondary networks are divided into a preferential one and an ordinary one. Specifically, the aerial and terrestrial networks can access the spectrum of the satellite network under the condition that the caused interference to the satellite terminal is below a certain threshold. Besides, considering that the aerial network has a higher priority than the terrestrial network, we aim to use a rate constraint to ensure the performance of the aerial network. Subject to these two constraints, we consider a sum-rate maximization for the terrestrial network by jointly optimizing the transmit beamforming vectors of the aerial and terrestrial base stations. To solve this non-convex problem, we propose a penalty-based iterative beamforming (PIBF) scheme that uses the penalty method and the successive convex approximation technique. Moreover, we also develop three low-complexity schemes, where the beamforming vectors are obtained by optimizing the normalized beamforming vectors and power control. In addition, we consider the case where only statistical channel state information is available and the case where channel estimation errors exist, and propose the corresponding beamforming schemes. Finally, we provide extensive numerical simulations to evaluate the performance of the proposed beamforming schemes and demonstrate the advantages of the proposed HCSSA compared with the traditional cognitive spectrum sharing architecture.

Backscatter communication is a promising technology to support massive connectivity for passive Internet-of-Things (IoT) in a cost-effective and power-efficient manner. However, the double fading effect and the passive backscatter operation in backscatter devices (BDs) pose significant challenges to reliable transmissions and efficient multiple access. To overcome these challenges, in this paper, we propose a multiple access scheme for the multi-BD bistatic backscatter communication (BBC) system using multiple antennas. Specifically, the single-carrier frequency domain equalization (SC-FDE) transmission scheme with cyclic delay diversity (CDD) is adopted by each BD to combat the double fading. Furthermore, interleaved frequency division multiple access (IFDMA) is used to facilitate multiple access of BDs. By equipping BDs with multiple backscatter antennas and leveraging the CDD technique, our proposed scheme can not only increase the received signal-to-noise (SNR) performance but also introduce spatial diversity received by the reader. Frequency domain orthogonality among BDs is achieved by assigning unique frequency sets to each, which enables more efficient energy use and reduces synchronization requirements, thereby offering substantial improvements over traditional time division multiple access (TDMA). Besides, the proposed scheme thoughtfully considers BD hardware constraints, by optimizing the trade-off between the cost and connectivity. Simulation results have verified the effectiveness and robustness of the proposed multi-BD BBC system with various configurations.

In this paper, we consider a symbiotic radio (SR) system, which consists of a radio frequency (RF) source with multiple antennas, a backscatter device (BD), and a receiver, to support passive Internet of Things (IoT). The RF source transmits its signal (RF source signal) to the receiver via transmit beamforming while the BD transmits its signal (BD signal) to the receiver by modulating its information bits over the incident RF source signal. However, due to the double path loss of the reflecting link, the received BD signal is typically much weaker than the received RF source signal. Therefore, the dynamic range of the received signal may exceed that of the analog-to-digital converter (ADC), which will make the ADC unable to quantize the received BD signal. To address this issue, we exploit transmit beamforming to balance the strength of the received BD signal and the received RF source signal. To explore the effect of the ADC dynamic range on the system, we first quantitatively describe the dynamic ranges of both the ADC and the received signal and then analyze the bit error rates (BERs) of the BD signal and the RF source signal. Subsequently, we formulate an optimization problem to minimize the BER of the BD signal via transmit beamforming while considering the ADC dynamic range constraint. To solve the problem, we first adopt Dinkelbach’s algorithm to determine the minimum ADC resolution required to quantize the weak received BD signal, followed by semidefinite programming (SDP) to design the beamforming vector. Finally, simulation results reveal that the minimum ADC resolution is related to the transmit power and the relative strength between the direct link and the reflecting link.

To support the massive Internet-of- Things (IoT) network, symbiotic radio (SR) has emerged as a promising solution that enables passive IoT connections by exploiting active primary transmissions. Realizing the enhanced spectrum- and energy-efficiency promised by SR requires symbol synchronization between the primary and IoT signals, which, however, remains challenging for cost-limited IoT devices. In this paper, we investigate reconfigurable intelligent surface (RIS)-assisted SR (RSR) with imperfect symbol synchronization. Specifically, the primary transmission employs orthogonal frequency division multiplexing (OFDM), while the RIS enhances the primary transmission and concurrently transmits its secondary signal by passively backscattering the incident primary signal. Due to the unknown synchronization offset (SO) between primary and secondary signals, the reflected channel via the RIS exhibits variations within each OFDM block, consequently leading to inter-carrier interference (ICI) in the received signal. To mitigate this unfavorable effect, we propose a novel receiver design by utilizing virtual subcarriers within each OFDM block. By employing energy detection at the virtual subcarriers, the receiver can detect the arrival of the secondary signal based on the ICI. Furthermore, by compensating the loss of orthogonality in the received OFDM block, the receiver can blindly estimate the SO, thereby facilitating joint detection of primary and secondary signals. Simulation results validate that our proposed receiver significantly improves the bit error rate (BER) performance for RSR with imperfect symbol synchronization.

In this paper, we investigate the integrated sensing and backscatter communication (ISABC) system in mobility scenarios. Specifically, the backscatter devices (BDs) are attached to the moving targets, thus enhancing the signal strength of reflected echoes and concurrently passively transmitting supplementary information, such as identification details, to the ISABC terminal through backscatter communication. The ISABC terminal aims to detect signals from the BDs while concurrently estimating target parameters, such as delays and Doppler shifts, from the backscattered signals. However, it is quite challenging to concurrently achieve parameter estimation and signal detection from the received superposition of two disparate signals emanating from the structural and antenna components of the target equipped with BD. The challenge is exacerbated coupling between the symbols of BD and the estimated parameters, alongside the intercarrier interference (ICI) induced by the Doppler shift. To address these issues, we propose a novel joint parameter estimation and signal detection scheme by alternatively performing the following two processes: 1) Utilizing a modified maximum likelihood (ML) estimation algorithm to perform off-grid ICI-aware sensing with superimposed signals. 2) Employing the generalized likelihood ratio test (GLRT) detector for demodulating the symbols of the BD. Finally, simulation results are provided to demonstrate the performance of the proposed algorithm and validate that the estimation performance can be improved in the high SNR regime.

Reconfigurable intelligent surface (RIS) has been widely exploited for secure communications in physical layer security (PLS) by destructing the eavesdropper's channel via reflect beamforming. In this paper, we investigate RIS-aided secure communications with a novel RIS design scheme. The proposed design leverages RIS to increase the achievable secrecy rate via transmitting the artificial noise (AN) instead. To do so, RIS modulates its information over the incident signal and reflects it to the legitimate receiver and eavesdropper. The RIS modulation scheme is a prior knowledge available at the legitimate receiver, but not available at the eavesdropper. Thus, the reflected signal through the RIS is naturally an additional multi-path component for the legitimate user but a type of AN for the eavesdropper, yielding a mutualistic symbiosis between the RIS and the legitimate user but a parasitic symbiosis between the RIS and the eavesdropper as demonstrated in symbiotic radio (SR). From this SR perspective, we consider an achievable secrecy rate maximization problem by optimizing the RIS reflect beamforming. To this end, we use the path-following algorithm to solve the problem iteratively. Further-more, the comparison of the conventional destruct-channel (DC)- RIS design and the proposed AN - RIS design is conducted. Finally, simulation results show that the proposed AN - RIS design outperforms the DC- RIS design in general cases where the reflecting link is weaker than the direct link of eavesdropper.

In previous studies on reconfigurable intelligent surface (RIS)-aided spectrum sharing cognitive radio (CR), the potential of RIS in supporting secondary transmission may not be fully unleashed, due to the insufficient attention to its capacity to mitigate interference from the primary transmitter at the secondary receiver. To bridge this gap, this paper investigates a general active RIS (RIS)-aided CR system, in which the secondary user (SU) aims to minimize the transmit power while satisfying its own SINR constraint and the interference temperature constraint at the primary receivers. The SU needs to jointly optimize the transmit beamforming at the SU transmitter and the reflection coefficients at the active RIS. An improved alternating optimization (AO) algorithm is first proposed, which exploits the active RIS not only to enhance the transmission channel for the secondary transmission, as commonly addressed in most CR literatures, but also to mitigate interference from the primary transmitter. Additionally, a novel low-complexity channel customization (CC) algorithm is then proposed, which can efficiently customize the SU transmission channel toward a desired direction without the need for AO iterations. Simulation results show that the SU transmit power can be effectively reduced by exploiting the active RIS with the PU interference mitigation. Moreover, the proposed CC algorithm achieves the transmit power reduction with moderate performance loss as compared with the AO algorithm, but offers an efficient means to facilitate beamforming management in CR.

This paper characterizes the achievable rate region of the active reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) interference channel, where an active RIS is used to help multiple multi-antenna transmitters send information to their intended receivers in the presence of strong interference among them. All the transmitters are subject to the transmit power constraints, while the active RIS must satisfy the power budget constraint and the maximum amplitude constraint for each reflecting element (RE). Under these constraints, the rate-profile method is employed to approach the Pareto boundary of the rate region, which needs to solve a series of feasibility problems for a given rate profile. These problems can be solved by an alternating optimization algorithm. In each iteration, the sum of the rate tuples is sequentially optimized by the transmit beamforming vectors at the transmitters and the reflection coefficients matrix at the active RIS. Specifically, the transmit beamforming vectors are obtained by solving a sequence of second-order cone programming (SOCP) problems, and the reflection coefficients matrix is obtained by solving a sequence of semidefinite programming (SDP) problems along with Gaussian randomization. Simulation results show that, even with strong interference, the active RIS can offer a significant improvement in the rate region compared to the passive RIS under the same power budget.

In an active reconfigurable intelligent surface (RIS), each reflecting element (RE) reflects the incident signal with not only reconfigurable phase shift but also controllable amplitude amplification. In this paper, we are interested in active RIS-aided interference channels in which$K$user pairs share the same time and frequency resources with the help of the active RIS. Thanks to the promising amplitude amplification capability, activating a moderate number of REs, rather than all of them, is sufficient for the active RIS to mitigate the cross channel interferences. Motivated by this, we propose a power-aware sparse reflect beamforming design for the active RIS-aided interference channels, which allows the active RIS to flexibly adjust the number of activated REs for the sake of saving power. Specifically, we first establish the power consumption model in which only those activated REs consume the biasing and operation power that supports the amplitude amplification. Based on the proposed model, we formulate a problem to maximize the sum rate of the$K$user pairs by designing the sparse reflect beamforming vector under the maximum amplification gain and the limited power budget constraints on the active RIS. Towards this end, we propose an iterative reweighted$\ell_{1}$-norm method in combination with fractional programming to find a sparse solution for the reflect beamforming vector. Numerical results show that the proposed sparse design can notably increase the sum rate of the$K$user pairs in interference channels even with the limited power budget.

In reconfigurable intelligent surface (RIS)-assisted symbiotic radios (SRs), an RIS delivers its information by periodically reflecting the signal from a primary transmitter (PTx) and simultaneously the RIS assists this primary transmission by passive beamforming. In this paper, we are interested in characterizing the multiple-input multiple-output (MIMO) channel capacity for RIS-assisted SR, where both PTx and RIS transmit multiple data streams. To this end, we propose a novel multi-data-stream delivery scheme for the RIS, where the reflecting elements of the RIS are divided into multiple parts and each part is used to transmit one data stream by periodically adjusting its reflection coefficients. With this information delivery scheme, we derive an upper bound on the MIMO channel capacity of an RIS-assisted SR system. To achieve it, we then jointly design the transmit covariance matrix at the PTx and the passive beamforming vector at the RIS by using an alternating optimization algorithm. Considering the high computational complexity of solving the transmit covariance matrix, we further propose two low-complexity algorithms, i.e., upper bound-based algorithm and singular value-based algorithm. Finally, extensive numerical results are presented to show the effectiveness of the proposed algorithms and demonstrate the advantages of the RIS to transmit multiple data streams.

Cognitive spectrum sharing has been regarded as a promising solution to improve spectrum utilization efficiency for space-air-ground integrated networks (SAGINs). However, in SAGIN, different networks may have different quality of service (QoS) requirements, which pose challenges to the traditional cognitive spectrum sharing architecture. For example, the aerial network typically has high QoS requirements, which may not be met when it acts as a secondary network. To address this issue, we propose a hierarchical cognitive spectrum sharing architecture (HCSSA) for SAGIN, where the secondary networks are divided into a preferential one and an ordinary one. Specifically, in SAGIN, an aerial network and a terrestrial network share the spectrum of a satellite network. HCSSA gives higher priority to the aerial network by a QoS constraint, while the terrestrial network is the ordinary secondary network without protection. Besides, the satellite terminal requires the received interference to be below a threshold. Subject to these two constraints and the maximum transmit power constraints, we aim to maximize the sum rate of the terrestrial network by optimizing the transmit beamforming vectors of the aerial base station (BS) and the terrestrial BSs. To solve this non-convex problem, we propose an iterative beamforming scheme by exploiting the penalty method and the successive convex approximation scheme. Simulation results show the performance of the proposed beamforming scheme and illustrate the advantages of HCSSA compared with the traditional cognitive spectrum sharing architecture.

This paper investigates an active reconfigurable intelligent surface (RIS)-aided mmWave system, in which the base station (BS) proactively sends a positioning reference signal to localize the mobile station (MS). In particular, due to the propagation nature of mmWave, the direct link between the BS and the MS is usually blocked. Thus, the reference signal cannot directly reach the MS but can do so through the enhanced reflections provided by multiple active RISs in the surroundings. To harness the benefits of active RIS-aided wireless localization system, the multiple active RISs are designed to reflect the reference signal in a time-division manner, and the reflection coefficient matrix of each active RIS is designed via a codebook method. Then, based on the reflected reference signal, a root MUSIC-based angle of departure (AoD) estimation algorithm is proposed for the MS to estimate the AoD from each active RIS. After that, the MS is able to calculate its location with the estimated AoDs and the positions of the active RISs. In addition, the power splitting between the BS and the active RISs is considered to further improve the positioning accuracy when the total power consumption is given. The optimal power splitting ratio is obtained by minimizing Cramér-Rao lower bound of the AoD. Simulation results show the superiority of our proposed localization scheme and the significance of optimizing the power splitting ratio in the considered active RIS-aided system.

With promising benefits of splitting deep neural network (DNN) computation loads to the edge server, split computing has been a novel paradigm achieving high-quality artificial intelligence (AI) services for the energy-constrained user equipments (UEs). To satisfy the service demands of a large number of UEs, traditional edge-UE split computing evolves toward multitier split computing involving the edge and cloud servers with different capabilities, leading to a “complex” optimization involving communication and computation. To tackle this challenge, in this article, we propose a multitier deep reinforcement learning (DRL) decision-making scheme for distributed splitting point selection and computing resource allocation in the three-tier UE-edge-cloud split computing systems. With the proposed scheme, the high-dimensional optimization can be tackled by the UEs and an edge server with different control cycles through performing local decision-making tasks in a sequential manner. Based on the policies updated by the UEs and the edge server in successive stages, the overall performance of split computing can be continuously improved, which is justified through a theoretical convergence performance analysis. Comprehensive simulation studies show that the proposed multitier DRL decision-making scheme outperforms the conventional split computing schemes in terms of the overall latency, inference accuracy, and energy efficiency to practice multitier split computing.

In this article, we are interested in active reconfigurable intelligent surface (RIS)-aided interference channels in which K user pairs share the same time and frequency resources with the aid of active RIS. Thanks to the promising amplitude amplification capability, activating a moderate number of reflecting elements (REs) rather than all of them is sufficient for the active RIS to mitigate the cross-channel interferences. Motivated by this, we propose a power-aware sparse reflect beamforming (SRB) design for the active RIS-aided interference channels, which allows the active RIS to flexibly adjust the number of activated REs for the sake of reducing power costs. Specifically, we establish the power consumption model in which only those activated REs consume the biasing and operation power that supports the amplitude amplification, yielding an$\ell _{0}$-norm power consumption function. Based on the proposed model, we investigate a sum-rate maximization problem and an active RIS power minimization problem by carefully designing the SRB vector. To solve these problems, we first replace the nonconvex$\ell _{0}$-norm function with an iterative reweighted$\ell _{1}$-norm function. Subsequently, we employ fractional programming to solve the sum-rate maximization and utilize semidefinite programming combined with the difference-of-convex algorithm (DCA) to address the active RIS power minimization both of which are proven to converge well. Numerical results show that the proposed sparse designs can notably increase the sum rate of user pairs and decrease the power consumption of the active RIS in interference channels.

This paper investigates master-auxiliary unmanned aerial vehicles (UAVs)-enabled wireless-powered Internet-of-Things (WPIoT) networks, which overcome the inflexibility and site selection issues caused by traditional fixed-point intelligent reflecting surface (IRS). Specifically, multiple rechargeable Master UAVs (MUAVs) and IRS-integrated Auxiliary UAVs (AUAVs) are applied in pairs to cooperatively charge and collect data from IoT devices clustered in different subareas under cloud scheduling. Given the constraints of limited onboard battery capacity and complete data collection, we formulate a system energy minimization problem, which is then divided into three sub-problems. We first utilize a pair of U-nets with quantization layers trained by deep unsupervised learning (DUL) to output discrete downlink (DL) and uplink (UL) IRS phases separately. Gradient functions with specific features are also proposed to solve non-differentiable issue during training. Given the optimized IRS phase policies, off-policy deep reinforcement learning (DRL) is exploited to optimize intra-subarea data collection policy and inter-subarea multi-UAV scheduling scheme. Two assistive techniques, positive transition initialization (PTI) and action mask, are proposed to guide the learning of the agent and alleviate the burden of optimization. Numerical results indicate that our proposed approach combining DUL with DRL can improve performance by more than 90% compared to the pure DRL method. Furthermore, our proposed Master-Auxiliary-UAV collaborative data collection scheme (MACS) can achieve better energy performance than homogeneous MUAV data collection scheme (HMCS) in cases with low onboard battery capacity while halving task completion time.

Mobile crowdsourcing network is a promising paradigm to leverage mobile users (MUs) to perform large-scale sensing task. Due to limited sensing-computation resource and data security risk, it is necessary to design an efficient incentive mechanism to motivate MUs to complete crowdsourcing task. In this paper, a deep reinforcement learning (DRL)-based contract incentive mechanism is proposed by jointly considering participation contribution, sensing task, and computation resource of MUs. Specifically, considering the heterogeneous willingness of the MUs, we formulate a three-dimensional sensing-computation-reward contract incentive mechanism to obtain the maximum utility of mobile crowdsourcing platform. Moreover, based on the individual rationality and incentive compatibility constraints, we derive the optimal contract under the partial information asymmetry scenario. In the case of the complete information asymmetry scenario, we formulate the contract incentive issue as an Markov decision process. Considering the infinite and continuous action and state spaces, we develop the deep deterministic method to obtain the efficient sensing task, computation resource, and incentive reward policy. Finally, we conduct numerical simulation to demonstrate the feasibility of our DRL-based contract crowdsourcing incentive mechanism.

The space-terrestrial integrated network (STIN) is a pivotal architecture to support ubiquitous connectivity in the upcoming 6G era. Inter-operator resource and service sharing is a promising way to realize such a huge network, utilizing resources efficiently and reducing construction costs. Given the rationality of operators, the configuration of resources and services in STIN should focus on both the overall system performance and individual benefits of operators. Motivated by emerging symbiotic communication facilitating mutual benefits across different radio systems, we investigate the resource and service sharing in STIN from a symbiotic communication perspective in this paper. In particular, we consider a STIN consisting of a ground network operator (GNO) and a satellite network operator (SNO). Specifically, we aim to maximize the weighted sum rate (WSR) of the whole STIN by jointly optimizing the user association, resource allocation, and beamforming. Besides, we introduce a sharing coefficient to characterize the revenue of operators. Operators may suffer revenue loss when only focusing on maximizing the WSR. In pursuit of mutual benefits, we propose a mutual benefit constraint (MBC) to ensure that each operator obtains revenue gains. Then, we develop a centralized algorithm based on the successive convex approximation (SCA) method. Considering that the centralized algorithm is difficult to implement, we propose a distributed algorithm based on Lagrangian dual decomposition and the consensus alternating direction method of multipliers (ADMM). Finally, we provide extensive numerical simulations to demonstrate the effectiveness of the two proposed algorithms, and the distributed optimization algorithm can approach the performance of the centralized one. The results also reveal that the proposed MBCs can enable operators to achieve mutual benefits and realize a symbiotic resource and service sharing paradigm.

Wireless communication is vulnerable to malicious jamming attacks due to the inherent broadcasting nature of wireless channels. This paper investigates an anti-jamming communication system that employs a reconfigurable intelligent surface (RIS) to enhance desired signals and suppress jamming signals. To optimize the system performance while guaranteeing fairness, we maximize the minimum signal-to-interference-plus-noise ratio (SINR) at the legitimate user equipments by jointly optimizing the transmit beamforming vectors at the base station (BS) and the reflecting coefficients at the RIS, subject to the BS’s maximum transmit power constraint and the RIS’s reflecting coefficient constraints. To solve the non-convex max-min-fairness optimization problem, we propose an alternating-optimization (AO)-based approach that alternates between optimizing variables using a second-order-cone program and semi-definite relaxation techniques. Considering the piratical limitation of imperfect jammer-related channel state information (CSI), we also adopt the stochastic successive convex approximation technique for tackling imperfect CSI in the AO-based approach. Furthermore, we propose a deep-reinforcement-learning (DRL)-based solving approach that does not require the jammer-related CSI. Numerical results show that both approaches improve the minimum SINR performance significantly. Although the AO-based approach with real-time CSI slightly outperforms the DRL-based approach with historical CSI, the DRL-based approach uses the trained deep neural network to obtain the beamforming decision directly without solving optimization problems.

This paper studies a novel wireless information surveillance scenario, where the legitimate party aims to eavesdrop on multiple suspicious communication links with the help of multiple unmanned aerial vehicles (UAVs). Each suspicious link is comprised of a UAV (transmitter) and its fixed destination. To improve the eavesdropping ability, cooperative legitimate UAVs emit jamming signals to reduce the capacities of suspicious channels and plan the flight trajectory to enhance the capacity of the eavesdropping channels. Considering the system dynamics, it is natural to model this sequential decision-making problem as a Markov Decision Process (MDP), which might be solved by reinforcement learning (RL). However, it is difficult to design a policy in RL that determines jamming powers satisfying the considered eavesdropping constraints. Therefore, we decompose the optimization process into two phases, 1) obtaining the non-learning-based optimal solver for jamming power allocation under each state, and 2) optimizing the policy of moving action by RL. We will show this decoupled optimization process also holds the optimality. Considering the flying safety, we will determine the individual moving policy for each legitimate UAV rather than a centralized policy that controls all UAVs. Finally, extensive simulations are conducted to demonstrate the effectiveness of the proposed solution.

Constructing earth-fixed cells with low-earth orbit (LEO) satellites in non-terrestrial networks (NTNs) has been the most promising paradigm to enable global coverage. The limited computing capabilities on LEO satellites however render tackling resource optimization within a short duration a critical challenge. Although the sufficient computing capabilities of the ground infrastructures can be utilized to assist the LEO satellite, different time-scale control cycles and coupling decisions between the space- and ground-segments still obstruct the joint optimization design for computing agents at different segments. To address the above challenges, in this paper, a multi-time-scale deep reinforcement learning (DRL) scheme is developed for achieving the radio resource optimization in NTNs, in which the LEO satellite and user equipment (UE) collaborate with each other to perform individual decision-making tasks with different control cycles. Specifically, the UE updates its policy toward improving value functions of both the satellite and UE, while the LEO satellite only performs finite-step rollout for decision-makings based on the reference decision trajectory provided by the UE. Most importantly, rigorous analysis to guarantee the performance convergence of the proposed scheme is provided. Comprehensive simulations are conducted to justify the effectiveness of the proposed scheme in balancing the transmission performance and computational complexity.

Symbiotic radio (SR) is a promising solution to achieve high spectrum- and energy-efficiency due to its spectrum sharing and low-power consumption properties, in which the secondary system achieves data transmissions by backscattering the signal originating from the primary system. In this paper, we are interested in the pilot design and signal detection when the primary transmission adopts orthogonal frequency division multiplexing (OFDM). In particular, to preserve the channel orthogonality among the OFDM sub-carriers, each secondary symbol is designed to span an entire OFDM symbol. The comb-type pilot structure is employed by the primary transmission, while the preamble pilot structure is used by the secondary transmission. With the designed pilot structures, the primary signal can be detected via the conventional methods by treating the secondary signal as a part of the composite channel, i.e., the effective channel of the primary transmission. Furthermore, the secondary signal can be extracted from the estimated composite channel with the help of the detected primary signal. The bit error rate (BER) performance with both perfect and estimated CSI, the diversity orders of the primary and secondary transmissions, and the sensitivity to symbol synchronization error are analyzed. Simulation results show that the performance of the primary transmission is enhanced thanks to the backscatter link established by the secondary transmission. More importantly, even without the direct link, the primary and secondary transmissions can be supported via only the backscatter link.

Cooperative spectrum sensing (CSS) is a key enabling technology of cognitive radio networks with multiple secondary users (SUs). In conventional CSS systems, when the primary signals are weak, the SUs require long sensing time to achieve a high detection probability for protecting the transmission of the primary user (PU), leading to little remaining time for secondary transmissions. To address this issue, we propose a reconfigurable intelligent surface (RIS) assisted CSS system, where multiple RISs are employed to improve the CSS performance within limited sensing time. Considering that the dependency of the CSS performance on the received primary signal strengths at the SUs differs across various CSS schemes, the RIS configurations could also be optimized differently regarding these CSS schemes. Motivated by this, we investigate the phase shift matrix (PSM) optimization problems to maximize the cooperative detection probability given a maximum tolerable false alarm probability, and we consider two typical kinds of CSS schemes, namely, data fusion and decision fusion. As it is intractable to directly solve these problems due to the complex expressions of the cooperative detection probability with respect to the PSMs, we show that the solutions can be obtained by transforming these problems into channel gain-related optimization problems. Furthermore, we show that the proposed PSM optimization methods can be extended to the more practical scenarios where instantaneous channel state information (CSI) is unavailable. In such cases, we leverage statistical CSI to improve the CSS performance in the sense of expectation. Subsequently, we conduct a numerical analysis on the number of reflecting elements required to achieve a target detection probability in the statistical CSI case. Finally, simulation results demonstrate that the proposed PSM optimization methods can significantly improve the CSS performance within limited sensing time.

Massive multiple-input multiple-output (MIMO) is a key enabling technology for next-generation communication systems. In massive MIMO cellular networks, coordinated beamforming (CBF), which jointly designs the beamformers of multiple base stations (BSs), is an efficient method to enhance the network performance. In this paper, we investigate the sum rate maximization problem in a massive MIMO mobile cellular network, where in each cell a multi-antenna BS serves multiple mobile users simultaneously via downlink beamforming. Although existing optimization-based CBF algorithms can provide near-optimal solutions, they require real-time and global channel state information (CSI), in addition to their high computation complexity. Due to the non-negligible delay of practical backhaul networks and the high-complexity optimization process, it is almost impossible to apply them in mobile cellular networks. Noting that the considered problem under the practical constraints can be modeled as a networked distributed partially observable Markov decision process, we propose a deep reinforcement learning-based distributed dynamic coordinated beamforming (DDCBF) scheme, which enables each BS to determine the beamformers with only local CSI and some historical information from other BSs. Besides, the beamformers can be calculated with a considerably lower computational complexity by exploiting neural networks and expert knowledge, i.e., a solution structure observed from the iterative procedure of the centralized optimization algorithms. Moreover, we provide extensive numerical simulations to validate the effectiveness of the proposed DRL-based approach. With lower computational complexity and less required information, the results show that the proposed approach can achieve comparable performance to the centralized iterative optimization algorithms.

Spectrum and service sharing among multiple mobile network operators (MNOs) is regarded as a promising technology to construct future mobile networks, because it provides a higher utilization efficiency of the network resources, e.g., spectrum and network infrastructure. However, each individual MNO may not be able to benefit from this technology without joint optimizations. In this paper, we investigate a symbiotic spectrum and service sharing paradigm, which can improve the overall performance of the multi-MNO network while guaranteeing each MNO benefits as well. Particularly, we formulate two user association problems with novel mutual benefit constraints, namely, sum rate maximization and load balancing. Besides, we introduce a sharing coefficient for inter-MNO service sharing, which can be regarded as an inter-MNO service level agreement (SLA) accounting for the regulation of user association behaviors. Then, we propose a successive convex approximation (SCA) based algorithm and a fractional programming (FP) based algorithm to solve the sum rate maximization problems for different inter-MNO sharing strategies. In addition, we also develop a Lagrangian dual decomposition based algorithm for the load balancing problems. Finally, extensive numerical simulations are provided to demonstrate the effectiveness of the proposed algorithms. The results show that mutual benefit constraints can help multiple MNOs realize a symbiotic spectrum and service sharing paradigm. By comparing different inter-MNO sharing strategies, it can also be observed that the highest performance gain can be achieved when the multiple MNOs share their spectrum and service simultaneously.

Interference poses a significant challenge in wireless communications due to the broadcast and superposition nature of wireless media. Reconfigurable intelligent surfaces (RIS), a promising 6G technology, can manage interference by customizing radio propagation with controllable reflecting elements (REs). Active RIS, which amplifies signal amplitudes and adjusts phases with enhanced REs, shows potential in addressing strong interference. This paper investigates the active RIS-aided multiple-input single-output (MISO) interference channel, characterizing the achievable rate region under transmit-power constraints, maximum amplitude constraint on each RE, and a power budget constraint at the active RIS. Utilizing multiple antennas at transmitters and multiple enhanced REs at the active RIS, we explore the Pareto Boundary of the achievable rate region with the rate profile method. A general alternating optimization framework iteratively optimizes the transmit beamforming vector and the reflection coefficient matrix through convex optimization techniques. We also consider a stringent case where active RIS ensures interference-free transmissions, deriving a reflection coefficient matrix structure via subspace decomposition. Additionally, a suboptimal algorithm combining this structure with maximum-ratio-transmission (MRT) and zero-forcing (ZF) beamforming characterizes the achievable rate region. Simulation results demonstrate that active RIS significantly improves the rate region and ensures interference-free transmissions compared to passive RIS and relay under the same power budget.

In this paper, we investigate reconfigurable intelligent surface (RIS)-enabled full-duplex backscatter communications in multi-user symbiotic radio (SR) systems. Specifically, an RIS is integrated with a primary transmission in which the primary transmitter (PT) broadcasts common messages to multiple primary receivers (PRs) and the RIS. Thanks to the full-duplex backscatter communication nature, the RIS absorbs part of the PT signals to decode the PT messages and, meanwhile, reflects the remaining part to convey its own messages for the PRs. By doing so, the RIS and the PRs can receive common messages, like the pairing messages, from the PT, and simultaneously establish information links between them without requiring additional spectrum and radio resources. However, it brings a challenging task on how to properly design the reflection matrix to resolve the conflicts between the signal absorption and reflection in the RIS-enabled full-duplex backscatter communication. Towards this end, we formulate an optimization problem that aims to jointly optimize the PT transmit beamforming vector and the RIS reflection matrix subject to the transmission rate constraints for the PT and the RIS. The problem is solved by the proposed alternating optimization (AO) method combined with difference-of-convex (DC) algorithm. Simulation results are presented to show that the RIS-enabled full-duplex backscatter communication can efficiently save the transmit power as compared to its half-duplex counterpart.

Symbiotic radio (SR) has emerged as a spectrum- and energy-efficient paradigm to support massive Internet of Things (IoT) connections. Two multiple access schemes are proposed in this paper to facilitate massive IoT connections using the cellular network based on the SR technique, namely, the simultaneous access (SA) scheme and the selection diversity access (SDA) scheme. In the SA scheme, the base station (BS) transmits information to the receiver while multiple IoT devices transmit their information simultaneously by passively backscattering the BS signal to the receiver, while in the SDA scheme, only the IoT device with the strongest backscatter link transmits information. In both of the schemes, the receiver jointly decodes the information from the BS and IoT devices. To evaluate the above two schemes, the closed-form expressions of the ergodic rates in high signal-to-noise ratio (SNR) regimes and outage probabilities for cellular and IoT transmissions are derived by using extreme value theory, generalized-K distribution approximation and Gaussian-Chebyshev quadrature methods. Finally, numerical results are provided to verify the theoretical analysis and compare the proposed multiple access schemes. When the number of IoT devices is small, the SDA scheme is more appealing since it can significantly reduce the computational complexity while achieving equivalent performance to the SA scheme. When the number of IoT devices is large, the SA scheme is preferable since it guarantees a significantly better rate performance and a lower outage probability.

Symbiotic radio (SR) is a promising technique to support cellular Internet-of-Things (IoT) by forming a mutualistic relationship between IoT and cellular transmissions. In this paper, we propose a novel multi-user multi-IoT-device SR system to enable massive access in cellular IoT. In the considered system, the base station (BS) transmits information to multiple cellular users, and a number of IoT devices simultaneously backscatter their information to these users via the cellular signal. The cellular users jointly decode the information from the BS and IoT devices. Noting that the reflective links from the IoT devices can be regarded as the channel uncertainty of the direct links, we apply the robust design method to design the beamforming vectors at the BS. Specifically, the transmit power is minimized under the cellular transmission outage probability constraints and IoT transmission sum rate constraints. The algorithm based on semi-definite programming and difference-of-convex programming is proposed to solve the power minimization problem. Moreover, we consider a special case where each cellular user is associated with several adjacent IoT devices and propose a direction of arrival (DoA)-based beamforming design approach. The DoA-based approach requires only the DoA and angular spread (AS) of the direct links instead of the instantaneous channel state information (CSI) of the reflective link channels, leading to a significant reduction in the channel feedback overhead. Simulation results have substantiated the multi-user multi-IoT-device SR system and the effectiveness of the proposed beamforming design approaches. It is shown that the DoA-based beamforming approach achieves comparable performance as the CSI-based approach in the special case when the ASs are small.

Ambient backscatter communication (AmBC) has emerged as a promising technology for passive Internet of Things (IoT), which enables backscatter devices (BDs) to transmit information over ambient radio frequency (RF) signals. This paper investigates joint localization and signal detection for an AmBC system. The BDs modulate information over ambient orthogonal frequency division multiplexing (OFDM) signals, and the receiver realizes joint localization and signal detection for the BDs. A two-stage receiver algorithm is proposed to realize the joint localization and signal detection. In the first stage, the receiver estimates the delays and angles-of-arrival (AoAs) of the BDs through the orthogonal matching pursuit (OMP) based algorithm. Then attenuation coefficients of the backscatter link are estimated through the least squares (LS) method, based on which the information symbols transmitted by the BDs can be detected in the second stage. Further, to address the discretization error in the estimation of AoAs and delays through the OMP algorithm, we propose an sparse Bayesian learning (SBL) based algorithm to achieve off-grid estimation. Finally, the Cramér-Rao lower bound (CRLB) is derived to evaluate the performance of the algorithms. Simulation results have verified the effectiveness of the system model and the superiority of the SBL-based algorithm, and it is shown that larger bandwidth and antenna arrays are beneficial to improving the estimation accuracy.

Deep learning-empowered semantic communication is regarded as a promising candidate for future 6G networks. Although existing semantic communication systems have achieved superior performance compared to traditional methods, the end-to-end architecture adopted by most semantic communication systems is regarded as a black box, leading to the lack of explainability. To tackle this issue, in this paper, a novel semantic communication system with a shared knowledge base is proposed for text transmissions. Specifically, a textual knowledge base constructed by inherently readable sentences is introduced into our system. With the aid of the shared knowledge base, the proposed system integrates the message and corresponding knowledge from the shared knowledge base to obtain the residual information, which enables the system to transmit fewer symbols without semantic performance degradation. In order to make the proposed system more reliable, the semantic self-information and the source entropy are mathematically defined based on the knowledge base. Furthermore, the knowledge base construction algorithm is developed based on a similarity-comparison method, in which a pre-configured threshold can be leveraged to control the size of the knowledge base. Moreover, the simulation results have demonstrated that the proposed approach outperforms existing baseline methods in terms of transmitted data size and sentence similarity.

In reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR), the RIS acts as a secondary transmitter by modulating its information bits over the incident primary signal and simultaneously assists the primary transmission, then a cooperative receiver is used to jointly decode the primary and secondary signals. Most existing works of SR focus on using RIS to enhance the reflecting link while ignoring the ambiguity problem for the joint detection caused by the multiplication relationship of the primary and secondary signals. Particularly, in case of a blocked direct link, joint detection will suffer from severe performance loss due to the ambiguity, when using the conventional on-off keying and binary phase shift keying modulation schemes for RIS. To address this issue, we propose a novel modulation scheme for RIS-assisted SR that divides the phase-shift matrix into two components: the symbol-invariant and symbol-varying components, which are used to assist the primary transmission and carry the secondary signal, respectively. To design these two components, we focus on the detection of the composite signal formed by the primary and secondary signals, through which a problem of minimizing the bit error rate (BER) of the composite signal is formulated to improve both the BER performance of the primary and secondary ones. By solving the problem, we derive the closed-form solution of the optimal symbol-invariant and symbol-varying components, which is related to the channel strength ratio of the direct link to the reflecting link. Moreover, theoretical BER performance is analyzed. Finally, simulation results show the superiority of the proposed modulation scheme over its conventional counterpart.

This paper studies the reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) system, where an RIS acts as a secondary transmitter to transmit its information by leveraging the primary signal as its RF carrier and simultaneously assists the primary transmission. Conventionally, all reflecting elements of the RIS are used to transmit the secondary signal, which, however, would limit its capability for assisting the primary transmission. To address this issue, we propose a novel RIS partitioning scheme, where the RIS is partitioned into two sub-surfaces, one to assist the primary transmission and the other to transmit the secondary signal. Naturally, there exists a fundamental tradeoff between the assistance and transmission capabilities of RIS regarding the surface partitioning strategy. Considering the coupling effect between the primary and secondary transmissions, we focus on the detection of the composite signal formed by the primary and secondary ones, based on which we propose a novel two-step detector. Then, we formulate the assistance-transmission tradeoff problem to minimize the bit error rate (BER) of the composite signal by jointly optimizing the surface partitioning strategy and the phase shifts of the two sub-surfaces, such that the overall BER of RIS-assisted SR is minimized. By solving this problem, we show that the optimized surface partitioning strategy depends on the channel strength ratio of the direct link to the reflected link. Moreover, performance analysis shows that when the direct link is blocked, exchanging the number of reflecting elements used for assistance and transmission can still achieve almost the same BER of the composite signal thanks to the coupling effect. Finally, extensive simulations show that our proposed RIS partitioning scheme outperforms the conventional schemes which use all reflecting elements for either assistance or transmission.

Splitting the computation loads of a neural network (NN) training task to multiple stations, split computing has been the most promising technology to sustain high-accuracy model for resource-constrained user equipments (UEs) to empower real-time intelligent services. Nevertheless, different communication link variations and computation capabilities in different stations (including UE and servers) render the overall performance optimization in split computing a critical challenge. In this case, different stations should be able to infer the others' communication/computation capabilities to distributively decide the optimum splitting points of an NN. To this end, in this paper, we propose a multi-tier deep reinforcement learning (DRL) scheme for split computing, by which the UE and edge server can collaboratively and adaptively determine their splitting points and computation resources to optimize the long-term overall training latency through tackling different time-scale sub-optimizations in a sequential manner. With the image recognition task as experimental example, comprehensive simulations are conducted to justify the performances in terms of training latency, model accuracy and energy consumption of the proposed scheme for split computing.

This paper investigates the reconfigurable intelligent surface (RIS) assisted symbiotic radio (RSR) system, where the primary transmission adopts orthogonal frequency division multiplexing (OFDM), and the RIS enables the secondary transmission by backscattering the primary signal. In particular, we propose a novel transmission protocol for the RSR system. With the help of the proposed protocol, the primary signal can be successfully detected without the knowledge of the secondary transmission at the RIS. Moreover, based on the detected primary signals, efficient channel estimation is achieved with a scalable training overhead. Furthermore, with the estimated channel state information (CSI), the passive beamforming vector of the RIS is optimized to maximize the weighted sum-rate of the primary and secondary transmissions. To solve this problem, we develop an effective algorithm based on the direct fractional programming (FP) approach, enabling the RIS to enhance the primary transmission and simultaneously transmit its own secondary signal. Simulation results validate the effectiveness of our proposed scheme and demonstrate that our proposed scheme outperforms the conventional ones with the same training length.

In opportunistic cognitive radio networks, the secondary user (SU) requires long sensing time to achieve a reliable spectrum sensing performance when the primary signal is very weak, leading to little remaining time for the secondary transmission. To tackle this issue, we propose an active reconfigurable intelligent surface (RIS) assisted spectrum sensing system to enhance the received primary signal at the SU, therefore the required sensing time can be reduced. In contrast to the passive RIS, the active RIS can amplify the incident signal and hence is more efficient in terms of the required reflecting elements as well as power consumption. Particularly, we study the reflecting coefficient matrix (RCM) optimization problem to improve the performance of the active RIS assisted spectrum sensing system. With the knowledge of the spiked model from random matrix theory, the RCM optimization problem can be transformed to an equivalent problem maximizing the largest eigenvalue of the population covariance matrix of the sensing signal samples. Then, we adopt the weighted minimum mean square error (WMMSE) algorithm to obtain the optimal RCM. Besides, we also investigate the minimum power budget for the active RIS to realize a near-1 detection probability under a simplified case, where the direct link does not exist and line-of-sight RIS-related channels are considered. Simulation results show that the active RIS can outperform the passive RIS for the same power budget in the RIS-assisted spectrum sensing system.

To utilize network resources efficiently and reduce network construction costs, inter-operator spectrum and service sharing has been regarded as a promising technique for constructing future multi-operator networks. As the mobile operators are inherently competitors, one necessary prerequisite for inter-operator spectrum and service sharing is to guarantee each operator's revenue, namely, achieving mutual benefits among operators. Noting that the recently proposed symbiotic communication can achieve mutual benefits among different radio systems, in this paper, we investigate inter-operator spectrum and service sharing from a symbiotic communication perspective. Particularly, we propose a mutual benefit constraint to guarantee the revenue of each operator, and we aim to maximize the weighted sum rate (WSR) of the multi-operator network through joint optimization of the user association and beamforming design. Besides, considering the cost incurred by inter-operator spectrum and service sharing, we introduce a sharing coefficient to characterize the revenue of operators. Then, we develop an algorithm based on alternating optimization and successive convex approximation (SCA) methods for the WSR maximization problem. Simulation results demonstrate that the proposed mutual benefit constraints and optimization algorithms can enable operators to achieve mutual benefits.

In RIS-assisted symbiotic radio (SR) systems, the RIS transmits messages by using the spectrum and energy of the primary transmission and simultaneously assists the primary transmission by passive beamforming. Due to the functionality of information transmission, the RIS could not always enhance the primary transmission. Instead, We observe that the performance of the primary transmission in terms of bit error rate (BER) will drop first and then improve with the increase in the number of reflecting elements of the RIS, i.e.,$K$. Motivated by this, in this paper, we are interested in a mutualistic condition on$K$for RIS-assisted SR, through which, the primary transmission could obtain the performance gain from the RIS. First, we design a transmission scheme for the RIS, which uses the different phase shifts to represent different transmission bits. Then, we optimize the phase shifts of the RIS, based on which, we derive an upper bound on BER for both primary and secondary transmissions. Compared with the BER performance in the absence of the RIS, we derive the mutualistic condition on the number of the reflecting elements$K$. Finally, simulation results are provided to verify the effectiveness of the theoretical analysis.

This paper investigates heterogeneous unmanned aerial vehicles (UAVs)-enabled wireless-powered Internet-of-Things (WPIoT) networks, where a Master UAV (MUAV) and an intelligent reflecting surface (IRS)-integrated Auxiliary UAV (AUAV) cooperatively collect data from terrestrial IoT devices. Specifically, the MUAV leverages a successive hover-and-fly strat-egy for energy broadcasting and data collection, while the AUAV accompanies MUAV and assists in both wireless power transfer (WPT) and wireless information transmission (WIT) processes through adaptive adjustment of IRS. Given the stringent re-quirement of complete data collection, we divide the energy-efficiency maximization problem into two sub-problems: discrete phase control and joint trajectory design and time allocation. To address these issues, we propose combining deep unsupervised learning (DUL) and artificial replay buffer initialization (ARBI)-enhanced off-policy deep reinforcement learning (DRL). Numeri-cal results demonstrate that our proposed scheme achieves better performance in terms of energy-efficiency, task complete time and reward convergence.

Multiple-input multiple-output orthogonal time frequency space (MIMO-OTFS) systems have gained increasing attention due to their superior performance in double-selective channel scenarios. However, MIMO-OTFS systems typically suffer from high pilot overhead and complex channel estimation (CE) when the number of antennas is large. To tackle these issues, in this paper, we propose a novel transceiver design, consisting of a new transceiver architecture, a low-overhead pilot pattern, and the corresponding low-complexity CE algorithm. Specifically, firstly, we apply the Inverse Discrete Fourier Transformation (IDFT) module at the transmitter (TX) and the corresponding Discrete Fourier Transformation (DFT) module at the receiver (RX), by resorting to which the received signals can be separated effectively in the time-delay-angular (TDA) domain to greatly reduce the inter-path and inter-antenna interferences. Secondly, at the TX, we design a new pilot pattern that removes the guard region and the length of which does not increase with the number of transmit antennas (TAs), leading to significantly reduced pilot overhead and increased spectral efficiency. Thirdly, at the RX, we utilize the three-dimensional (3D) sparsity of the MIMO-OTFS channel in the delay-Doppler-angular (DDA) domain to correspondingly achieve a low-complexity CE algorithm. Extensive numerical results demonstrate the effectiveness of our proposed transceiver design.

This paper explores the potential of reconfigurable intelligent surface (RIS) to null interference in a multi-user multiple-input single-output (MU-MISO) symbiotic radio (SR) system. Due to the periodic variations of the RIS phase shifts in the SR systems, it is challenging to use it to achieve interference-free reception in SR systems. To address this issue, we propose a RIS partitioning design for MU-MISO SR. By splitting RIS into two parts, one serves as a relay and removes interference among users, and the other one acts as a secondary transmitter for the Internet of Things (IoT) system, transmitting IoT information by periodically changing its phase shifts. Through this design, the MU-MISO SR system can be decomposed into multiple parallel MISO SR subsystems, each of which only contains the desired user signal and the RIS signal without interference from other users. To do so, we first explore the relationship between the number of reflecting elements and their interference-free capability. Then we formulate a power minimization problem to jointly optimize the transmit beamforming and the phase shifts of partitioned RIS. Finally, simulation results show that the proposed RIS partitioning design could help reduce power consumption, while at the same time achieving interference-free reception in SR.

In reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) systems, the RIS transmits information by modulating its information bits over RF signals from a primary transmitter (PTx), and simultaneously, the RIS assists the primary transmission by passive beamforming. Considering the inevitable channel estimation errors arising in practice, in this paper, we are interested in quantifying the effects of imperfect knowledge of channels on the channel capacity for both primary and secondary transmissions in RIS-assisted SR. For the primary transmission, we first derive upper and lower bounds on the achievable rate with channel estimation errors. Based on the derived lower bound, we investigate the minimum number of reflecting elements of an RIS that can enable the performance enhancement of the primary transmission compared to the case without the RIS. For the secondary transmission, exact and asymptotic achievable rates are derived. Finally, extensive numerical results are presented to demonstrate the effects of the channel estimation errors together with the interrelationship between primary and secondary transmissions.

In the reconfigurable intelligent surface (RIS)-based symbiotic radio (SR) system, the RIS acts as a secondary transmitter by modulating its information over the primary signal and simultaneously assists the primary transmission, which leads to a mutualistic spectrum sharing relationship. To ensure that the primary transmission can gain more benefits from the secondary transmission, we propose a novel RIS design scheme for SR, which divides the phase-shift matrix into two sub-phase-shift matrices, one to assist the primary transmission and the other to transmit the secondary signal. To optimize the two sub-phase-shift matrices, we first introduce a mutualistic factor which describes the performance requirement of the primary transmission and then formulate the problem to minimize the bit error rate (BER) of the secondary transmission, under the constraint that the BER performance of the primary transmission is higher than the performance requirement controlled by the mutualistic factor. To solve this non-convex problem, we resort to the successive convex approximation technique to obtain a suboptimal solution. Finally, simulation results reveal an interesting tradeoff between the BER performance of the primary and secondary transmissions by adjusting the mutualistic factor.

To accommodate the growing number of heterogeneous radios with limited wireless resources, symbiotic communication (SC) inspired by biology has been recently proposed to establish a symbiotic radio (SR) ecosystem. In this SR ecosystem, through collaboratively optimizing service/resource exchange policies, radios can coevolve like organisms, thus enabling various radio resources (such as spectrum, energy, and computing power) to complement each other. However, one critical challenge is securing a trusted coevolution environment in an SR ecosystem since the SRs with different network operators should coevolve under unreliable wireless links with complex electromagnetic interference. Moreover, multidimensional resources participated and a wide array of service requirements pose additional challenges to service/resource exchange decision-making across massive SRs. In this paper, we propose a Blockchain-empowered Intelligent cOevolution scheme for SRs, named BIO-SR. Specifically, BIO-SR exploits the digital acyclic graph (DAG) blockchain consensus in securing a trusted environment of SRs and applies deep reinforcement learning (DRL) in service exchange decision-making. The simulation results show that the BIO-SR scheme outperforms conventional solutions in terms of transmission rate and latency under both non-attack and malicious attack scenarios.

Inter-operator resource and service sharing, which can utilize the network resources more efficiently and improve users' quality of services, is regarded as a promising method for the operators to construct future multi-operator networks. Particularly, an appropriate user association scheme is quite essential to enhance the network capacity. In this paper, our objective is to maximize the capacity of a multi-operator network by optimizing the user association scheme. Specifically, we investigate three inter-operator sharing scenarios, i.e., service-sharing scenario, spectrum-sharing scenario, and full-sharing scenario. Then, we propose a fractional programming (FP) based algorithm for the user association optimization problem. As inter-operator service sharing enables users to be served by other operators, a sharing coefficient is introduced for each operator to indicate its service level agreement (SLA) for regulating the users' association behaviors. As shown in the simulation results, the user association scheme obtained by the proposed algorithm can achieve a higher capacity than other alternative schemes. In comparison with the scenario without inter-operator sharing, both inter-operator spectrum sharing and service sharing can improve network capacity significantly, and the largest capacity is realized under the full-sharing scenario.

Symbiotic radio (SR) is envisioned to support both Internet-of-Things (IoT) and cellular networks by forming a mutualistic relationship between them. In this paper, we propose a multiuser multi-IoT-device SR system where the base station (BS) transmits information to multiple users, and a number of IoT devices simultaneously backscatter their information to the users via the BS signal. Since the IoT information changes rather quickly compared to the channel variation, the IoT transmission introduces channel uncertainty when the users decoding the cellular information. To leverage the channel uncertainty, we apply the robust design method to design the beamforming vector at the BS. To be specific, the transmit power at the BS is minimized under the cellular transmission outage probability constraints and IoT transmission rate constraints. S-lemma is then utilized to transform the challenging probability constraints into a convex semi-definite programming (SDP) form. To deal with the non-convex rank-one constraint in SDP, we exploit successive convex approximations to transform the SDP problem into a difference-of-convex (DC) problem and solve it iteratively with DC programming. Simulation results have substantiated the multiuser multi-IoT-device symbiotic radio system and the effectiveness of the proposed algorithm.

This paper considers an active reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) cognitive radio (CR) system where the active RIS is designed to assist the transmission of the secondary user (SU) by tuning its controllable reflecting elements (REs) with enhanced reflections. As each RE in active RISs can adjust the phases and amplify the incident signals, active RISs thus provide a more favorable channel condition for the considered CR system than conventional passive RISs do. We aim to maximize the achievable rate of the SU subject to the interference temperature (IT) constraints on the primary users (PUs) as well as the power budget constraint on the active RIS. Towards this end, we propose an alternating optimization algorithm to solve this rate maximization problem. More specifically, the transmit beamforming vector is obtained by solving a second-order core programming (SOCP) problem, and the reflect beamforming vector is obtained with the aid of the fractional programming (FP) technique. Numerical results are provided to compare the active RIS-aided CR system with the passive RIS-aided one, showing that under the same power budget, the active RIS notably outperforms the passive RIS when they are used to assist the transmission of the SU in the CR system.

In this paper, we are interested in reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) systems, where an RIS assists a primary transmission by passive beam-forming and simultaneously acts as a secondary transmitter to modulate its own information by periodically adjusting its reflecting coefficients. The above modulation scheme innately enables a new multiplicative multiple access channel (M-MAC), in which the primary and secondary signals are superposed in a multiplicative and additive manner. To pursue the fundamental performance limits of the M-MAC, we focus on the characterization of the capacity region of such systems. Due to the passive nature of RISs, the transmitted signal of the RIS should satisfy the peak power constraint. Under this constraint at the RIS as well as the average power constraint at the primary transmitter (PTx), we analyze the capacity-achieving distributions of the transmitted signals and the optimal reflecting coefficients of the RIS. Then, we derive the maximum achievable rates for both primary and secondary transmissions and characterize the rate region of the M-MAC. It is observed that the secondary transmission can achieve the maximum rate when the PTx transmits signals with the constant envelope. Furthermore, the rate region of the M - MAC is strictly convex and larger than that of the conventional TDMA scheme.

This paper considers the reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) system, in which the RIS, acting as a secondary transmitter (STx), transmits its information by leveraging the primary signal as its RF carrier and simultaneously assists the primary transmission. In such a system, when the RIS transmits information, it could only provide limited assistance for the primary transmission. Thus, there exists a fundamental tradeoff between the assistance and information transmission capabilities of the RIS. To study the tradeoff, in this paper, we propose a novel RIS design scheme which partitions the RIS surface into two sub-surfaces, one to assist the primary transmission and the other to transmit the secondary signal. Considering the coupling effect between these two transmissions in SR, we focus on the composite signal formed by the primary and secondary signals. Then, to optimize the surface partitioning strategy, we formulate the assistance-transmission tradeoff problem to minimize the bit error rate (BER) of the composite signal, so as to improve the overall BER performance of the primary and secondary signals. By solving the problem, we show that optimal surface partitioning is related to the strength ratio of the direct link to the reflected link. Finally, simulation results show that the proposed design outperforms the conventional design significantly, which provides the best tradeoff.

Non-terrestrial networks (NTNs) with low-earth orbit (LEO) satellites have been regarded as promising remedies to support global ubiquitous wireless services. Due to the rapid mobility of LEO satellite, inter-beam/satellite handovers happen frequently for a specific user equipment (UE). To tackle this issue, earth-fixed cell scenarios have been under studied, in which the LEO satellite adjusts its beam direction towards a fixed area within its dwell duration, to maintain stable transmission performance for the UE. Therefore, it is required that the LEO satellite performs real-time resource allocation, which however is unaffordable by the LEO satellite with limited computing capability. To address this issue, in this paper, we propose a two-time-scale collaborative deep reinforcement learning (DRL) scheme for beam management and resource allocation in NTNs, in which LEO satellite and UE with different control cycles update their decision-making policies through a sequential manner. Specifically, UE updates its policy subject to improving the value functions of both the agents. Furthermore, the LEO satellite only makes decisions through finite-step rollouts with a reference decision trajectory received from the UE. Simulation results show that the proposed scheme can effectively balance the throughput performance and computational complexity over traditional greedy-searching schemes.

In this paper, we investigate a dynamic coordinated beamforming (CBF) problem to enhance the sum rate of a massive multiple-input multiple-output (MIMO) cellular network. Although existing optimization-based algorithms can provide near-optimal solutions, they require real-time global channel state information (CSI) and have high computational complexity, making them not viable in practical mobile networks. To tackle this issue, we propose a deep reinforcement learning based distributed dynamic CBF framework, which allows each base station (BS) to determine the optimal beamformers with only local CSI and some historical information transferred from other BSs. Besides, the computational complexity is substantially reduced thanks to the exploitation of neural networks and expert knowledge, i.e., a known solution structure that can be observed from a closed-form optimization algorithm. Simulation results demonstrate that the proposed approach can outperform the closed-form optimization methods and achieve comparable performance to the state-of-the-art optimization algorithm.

In this paper, we are interested in a simultaneously transmitting and reflecting RIS (STAR-RIS)-assisted SR system, where all elements of a STAR-RIS are divided into two groups, one operating in the reflection mode to enhance the primary transmission, and the other operating in the transmission mode to transmit its own information bits. For such a system, we aim to jointly design the receiver and the phase shifts of the STAR-RIS. To avoid the ambiguity problem caused by the multiplication feature of SR, we employ ASK and PSK modulation schemes at the primary transmitter and the STAR-RIS, respectively. With such modulation schemes, we design the signal detection schemes for both primary and secondary transmissions and then analyze their corresponding symbol error rate (SER) performance. To prioritize the QoS of the primary transmission and simultaneously minimize the SER of the secondary transmission, we formulate one optimization problem to optimize the mode-switching scheme and phase shifts design scheme for STAR-RIS. Due to the non-convexity of the formulated problem, we employ the penalty method together with the successive convex approximation (SCA) method to iteratively solve it. Finally, extensive simulation results are presented to demonstrate the advantages of the STAR-RIS-assisted SR system and the effectiveness of the joint phase shifts and receiver design scheme.

In this work, we investigate a spectrum-sharing network, where an Internet of Things (IoT) system and a WiFi system coexist in the unlicensed band. The IoT devices can actively transmit signal by contending for the channel with the WiFi users. Alternatively, they can also convey information by passively backscattering the ambient WiFi signal, and thus form symbiosis with the WiFi during the transmission. Since the active and the passive transmission mode have their own merits under specific circumstances, how to select one of them adaptively and efficiently with guaranteeing the normal service of the WiFi system is of high importance. To solve this problem, we first investigate the effective throughput of the IoT system from the cross-layer perspective. Then, by jointly considering the effective throughput and the power consumption of the IoT system, we formulate the transmission mode selection problem. Two solutions are given depending on whether the signaling between the two systems is available. By entailing the intersystem parameter sharing, the model-based transmission mode selection scheme is proposed, which gives the closed-form optimal solution. When the intersystem parameter sharing is absent, the model-less transmission mode selection scheme is proposed by using the deep reinforcement learning (DRL) technique. Extensive simulation results validate the theoretical analysis, and demonstrate the effectiveness of the proposed transmission mode selection schemes.

Symbiotic radio (SR) is a promising energy-, spectrum-, and cost-efficient communication technology for Internet of Things. This article considers an SR network (SRN) in which backscatter devices (BDs) communicate with a primary receiver (PR) by riding on ambient radio frequency carriers from a primary transmitter (PT). First, both the achievable primary rate and backscatter-link sum rate are derived for BDs adopting spatial-division-multiple-access (SDMA) scheme and dynamic time-division-multiple-access (TDMA) scheme, respectively. Then, two optimization problems are formulated to maximize the BDs’ sum rate, by jointly optimizing the PTs’ beamforming matrix, and the BDs’ power reflection coefficients as well as the backscatter time allocation. For the single-antenna PR case, the optimal beamforming is obtained in semi-closed forms. For the multiantenna PR case, to solve the nonconvex problems with coupled variables, efficient iterative algorithms are proposed based on block coordinate descent and sequential convex approximation techniques. Finally, numerical results show that the proposed design enhances the SRNs’ throughput significantly, and give useful insights on the BDs’ multiple-access schemes.

Reconfigurable intelligent surface (RIS) has recently emerged as a promising technology for wireless communications, which intelligently controls the phase shift of each unit cell to form desired beams. Most prior works on RIS consider time-division duplexing (TDD) systems, in which the same phase shifts can be applied to both uplink and downlink due to the channel reciprocity. However, for frequency-division duplexing (FDD) systems, using the same phase shifts will result in beam misalignment, thereby leading to performance degradation. To address this issue, in this article, we study the practical RIS design and beamforming optimization for FDD systems. By representing the phase shifts of RIS with the equivalent circuit model which includes the resistance, inductances, and tunable capacitance, we propose a methodology to design the circuit parameters (i.e., inductances and capacitance) to meet the desired reflection requirements (i.e., phase tuning range, reflectivity, and zero phase slope) of both the uplink and downlink transmissions in FDD systems. Given the designed inductances, a practical binary RIS reflection model corresponding to two reflection states is then proposed. Furthermore, based on the proposed reflection model, a problem is formulated to jointly optimize the active and passive beamforming such that the minimum array response gain of the uplink and the downlink is maximized. An efficient iterative algorithm is proposed to obtain a suboptimal solution. Simulation results show that our proposed RIS design outperforms those benchmarks which design the circuits by only optimizing either uplink or downlink.

In conventional dual-function radar-communication (DFRC) systems, the radar and communication channels are routinely estimated at fixed time intervals based on their worst-case operation scenarios. Such situation-agnostic repeated estimations cause significant training overhead and dramatically degrade the system performance, especially for applications with dynamic sensing/communication demands and limited radio resources. In this paper, we leverage the channel aging characteristics to reduce training overhead and to design a situation-dependent channel re-estimation interval optimization-based resource allocation in a multi-target tracking DFRC system. Specifically, we exploit the channel temporal correlation to predict radar and communication channels for reducing the need for training preamble retransmission. Then, we characterize the channel aging effects on the Cramer-Rao lower bounds (CRLBs) for radar tracking performance analysis and achievable rates with maximum ratio transmission (MRT) and zero-forcing (ZF) transmit beamforming for communication performance analysis. In particular, the aged CRLBs and achievable rates are derived as closed-form expressions with respect to the channel aging time, bandwidth, and power. Based on the analyzed results, we optimize these factors to maximize the average total aged achievable rate subject to individual target tracking precision demand, communication rate requirement, and other practical constraints. Since the formulated problem belongs to a non-convex problem, we develop an efficient one-dimensional search based optimization algorithm to obtain its suboptimal solutions. Finally, simulation results are presented to validate the correctness of the derived theoretical results and the effectiveness of the proposed allocation scheme.

This paper proposes a new hypothesis test to check the randomness and nonrandomness of the contaminated high dimensional signal. Specifically, for a signal plus noise model, we propose a statistic to distinguish whether the corresponding signal is random or not. In order to analyze the performance of the proposed method, we also prove two important results for signal plus noise models: 1) No eigenvalues outside the support of the limiting spectral distribution of the noncentered and centered sample covariance matrix; and 2) Exact separation of eigenvalues of the noncentered and centered sample covariance matrix. Simulation studies demonstrate that our proposed method works well under a variety of settings.

Channel acquisition is one of the main challenges for the deployment of reconfigurable intelligent surface (RIS) aided communication systems. This is because an RIS has a large number of reflective elements, which are passive devices with no active transmitting/receiving abilities. In this paper, we study the channel estimation problem for the RIS aided multi-user millimeter-wave (mmWave) multi-input multi-output (MIMO) system. Specifically, we propose a novel channel estimation protocol for the above system to estimate the cascaded channels, which are the products of the channels from the base station (BS) to the RIS and from the RIS to the users. Further, since the cascaded channels are typically sparse, this allows us to formulate the channel estimation problem as a sparse recovery problem using compressive sensing (CS) techniques, thereby allowing the channels to be estimated with less training overhead. Moreover, the sparse channel matrices of the cascaded channels of all users have a common block sparsity structure due to the common channel between the BS and the RIS. To take advantage of the common sparsity pattern, we propose a two-step multi-user joint channel estimation procedure. In the first step, we make use of the common column-block sparsity and project the received signals onto the common column subspace. In the second step, we make use of the row-block sparsity of the projected signals and propose a multi-user joint sparse matrix recovery algorithm that takes into account the common channel between the BS and the RIS.

In this paper, we investigate a novel uplink reconfigurable intelligent surface (RIS) based multi-user multi-input multi-output symbiotic radio system. It indicates that each RIS, as an Internet-of-Things (IoT) device, enhances the primary transmission from a nearby user to the base station (BS), and simultaneously transmits its own information to the BS by backscattering modulation. By embedding environmental sensors on the RISs, the proposed system enables the IoT transmission of locally collected environmental data to the BS while assisting the primary communications from the users to the BS. We consider both the case of perfect and imperfect channel state information (CSI), and design the active beamforming at the BS and the passive beamforming at the RISs jointly to maximize the weighted sum-rate of both the primary and IoT transmissions. For the perfect CSI case, we propose an algorithm based on the block coordinate descent (BCD) method to solve the problem. We also propose another algorithm with a similar framework to reduce the computational complexity. For the imperfect CSI case, an algorithm based on BCD and the online successive convex approximation technique is proposed. Simulation results show that the proposed system achieves significant performance gain over a number of baseline schemes for both the perfect and imperfect CSI cases. Furthermore, when the channel estimation error is small, the performance loss due to imperfect CSI is insignificant.

In this paper, we propose a robust secure transmission scheme for an active reconfigurable intelligent surface (RIS) enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). In the considered system, the active RIS is adopted to enable the secure transmission of primary signals from the primary transmitter to multiple primary users in a multicasting manner, and simultaneously achieve its own information delivery to the secondary user by riding over the primary signals. Taking into account the imperfect channel state information (CSI) related with Eves, we formulate the system power consumption minimization problem by optimizing the transmit beamforming and reflection beamforming for the bounded and statistical CSI error models, taking the worst-case SNR constraints and the SNR outage probability constraints at the Eves into considerations, respectively. Specifically, the S-Procedure and the Bernstein-Type Inequality are implemented to approximately transform the worst-case SNR and the SNR outage probability constraints into tractable forms, respectively. After that, the formulated problems can be solved by the proposed alternating optimization (AO) algorithm with the semi-definite relaxation and sequential rank-one constraint relaxation techniques. Numerical results show that the proposed active RIS scheme can reduce up to 27.0% system power consumption compared to the passive RIS.

Small cell networks (SCNs) have emerged as a promising solution to meet the demand for increasing data traffic for the sixth generation and beyond wireless networks. However, power consumption and two-tier interference issues are two bottlenecks that hinder further development. This paper proposes a novel reconfigurable intelligent surface (RIS)-based SCN in which an RIS serves multiple micro users as a small cell base station while assisting the macro user’s transmission. Compared to the conventional SCNs, the RIS-based SCN can achieve significant power reduction. Meanwhile, the reflected signal can be regarded as a multipath component instead of interference to the macro user. We propose two transmission schemes and formulate the design of the phase shift matrix at the RIS and the beamforming vector at the macro base station as an optimization problem. The alternating optimization algorithm is developed to optimize the phase shift matrix and the beamforming vector to minimize the total power consumption under the user rate and phase shift constraints. Simulation results show that the total power consumption can be reduced significantly by deploying the RIS in the SCN when the number of reflective elements is sufficiently large.

Symbiotic radio (SR) has emerged as a spectrum-and energy-efficient paradigm to support massive Internet of Things (IoT) connections. Two multiple access schemes are proposed in this paper to facilitate the massive IoT connections using the cellular network based on the SR technique, namely, the simultaneous access (SA) scheme and the selection diversity access (SDA) scheme. In the SA scheme, the base station (BS) transmits information to the receiver and multiple IoT devices simultaneously transmit their messages by passively backscat-tering the BS signal to the receiver, while in the SDA scheme, only the IoT device with the strongest backscatter link transmits information to the receiver. The receiver jointly decodes the information from the BS and the IoT devices. To evaluate the above two schemes, we derive the closed-form expressions of the ergodic rates for both schemes. Finally, numerical results are provided to verify the theoretical analysis and compare the proposed two multiple access schemes. When the number of IoT devices is small, the SDA scheme is more appealing since it can significantly reduce computational complexity while achieving equivalent performance to the SA scheme.

Symbiotic radio (SR) is a promising solution to achieve high spectrum- and energy-efficiency due to its spectrum sharing and low-power consumption properties, in which the secondary system achieves its data transmission by backscattering the signal originating from the primary system. In this paper, we are interested in the pilot design and signal detection when the primary transmission adopts orthogonal frequency division multiplexing (OFDM) scheme. In particular, in order to preserve the channel orthogonality among the OFDM sub-carriers, each secondary symbol is designed to span one OFDM symbol. The comb-type pilot is employed by the primary transmission, while the preamble pilot is used by the secondary transmission. With the designed pilot structures, the primary signal can be detected via the conventional methods by treating the secondary signal as a part of the composite channel. Furthermore, the secondary signal can be extracted from the estimated composite channel with the help of the detected primary signal. The bit error rate (BER) performance of the primary and secondary transmissions with both perfect and estimated CSI is analyzed. Simulation results show that the performance of the primary transmission is enhanced thanks to the backscatter link established by the secondary transmission. More importantly, even without the direct link, the primary and secondary transmissions can be supported via only the backscatter link.

Backscatter communication, which enables a pas-sive backscatter device to transmit information to a reader using incident radio-frequency signals, is a promising technology for low-power Internet of Things. To improve the backscatter communication range, we consider a multistatic backscatter communication system in which multiple dislocated readers illuminate a tag simultaneously, thus strengthening the received signals. However, this system suffers from strong direct co-channel interference between different readers. To tackle this challenge, we jointly design the tag's transmit waveform and the readers' optimal detectors to cancel out the interference first and then recover the tag information. Simulations results verify that the proposed transceiver achieves better bit-error-rate performance without error floor than the benchmark.

In symbiotic radio (SR), the secondary transmitter (STx) modulates its information over the RF signal from the primary transmitter (PTx). This modulation technology, also called “modulation in the air”, leads to the multiplication of the primary and secondary signals. Thus, SR can be modeled as a multiplicative multiple access channel (M-MAC). In this paper, we propose a modulation scheme for such an M-MAC, which consists of two additive parts: the symbol-invariant component used to aid the primary transmission and the symbol-varying component used to deliver STx information. By optimizing these two components, we can strike a balance between the primary and secondary transmissions. Particularly, due to the coupling between these two transmissions in the M-MAC, we focus on the composite signal formed by the primary and secondary signals. Then, we optimize the above two components by maximizing the minimum Euclidean distance as well as minimizing the Hamming distance between the adjacent constellations of the composite signal. Furthermore, we derive the closed-form solution of the optimal modulation scheme, which is related to the ratio of the direct link to the backscatter link. Finally, simulation results are provided to verify the effectiveness of our proposed scheme.

Symbiotic radio (SR) is a promising technology for Internet-of-Everything (IoE), which enables the IoE backscatter devices (BDs) to be integrated with an existing primary communication system with mutualistic benefits. In this paper, we consider an SR system where a two-way BD backscatters a portion of the incident signal for the backscatter transmission and receives the remaining part for the information decoding. Due to the nature of backscattering, there however exists a conflict between these two aims for the two-way backscatter communication. To reconcile the conflict and improve the overall performance, we particularly consider that the reconfigurable intelligent surface (RIS)-enabled two-way BD, with each reflecting element achieving the two-way backscatter communication. Based on the proposed system, we first investigate the achievable rates of the interested trans-missions when the RIS transmits its messages over the incident signal with the binary phase-shift keying (BPSK) scheme. We consider the joint design of the transmit beamforming at the primary transmitter and the reflection coefficients at the RIS to minimize the transmit power when these achievable rates meet their requirements. This transmit power minimization problem is then solved with an alternating optimization (AO) algorithm. Simulation results show that under the assistance from the RIS, the proposed two-way SR system is more energy-efficient.

Reconfigurable intelligent surface (RIS) has recently emerged as a promising technology for wireless communications, which intelligently controls the phase shift of each unit cell to form desired beams. Most prior works on RIS focus on a single frequency band, and thus for time-division duplexing (TDD) systems, the same phase shifts can be applied to both uplink and downlink. However, for the frequency-division duplexing (FDD) mode, if the same phase shifts are applied in both uplink and downlink, the directions of the uplink RIS beams will not be aligned with those of the downlink, which will in turn cause performance degradation. To address this issue, in this paper, we investigate the practical RIS design and optimization for FDD systems. By representing the reflection coefficients of RIS with the equivalent circuit model which includes the resistor, inductor and tunable capacitor, we first provide the guidelines on the circuit design to realize 2π phase control over the two frequency bands of the FDD system. In addition, we propose a low-resolution RIS configuration scheme with two tunable modes corresponding to two capacitances, and we formulate a max-min signal-to-noise ratio (SNR) problem to maximize the minimum SNR of uplink and downlink. To solve the non-convex problem, we propose an alternating optimization algorithm to obtain a suboptimal solution. Simulation results show that our proposed RIS design outperforms those benchmarks which design the circuits by only optimizing uplink or downlink.

In this paper, we analyse the achievable rate and capacity for ambient backscatter communications with dynamic sources under the binary input and signal output (BISO) channel. Dynamic sources are the sources that transmit signals to the air intermittently, rather than continuously transmitting signals like static sources. Instead of assuming static ambient sources, we investigate the dynamic sources. Due to the complexity of the expression of mutual information, we resort to the numerical simulation results for the BISO channel capacity and the capacity-achieving distribution is obtained by one-dimensional searching. We utilize inequality to show the relationship between static sources and dynamic sources in terms of the achievable rate and capacity. The numerical studies show that the maximal of the mutual information of the BISO channel is not achieved by a uniform input distribution, and the mutual information and capacity of the BISO channel with dynamic sources are close to that of the BISO channel with static sources, scaled by the probability that the dynamic source is in the on-state.

Reconfigurable intelligent surface (RIS) enables the wireless propagation environment to be reconstructed intelligently. In this paper, we consider a symbiotic cellular UAV communication network in which the base station communicates with both its cellular users and an UAV equipped with an aerial RIS. Our objective is to maximize the energy efficiency of this network by jointly optimizing the UAV trajectory, user access indicator factor, the base station’s active beamforming and the RIS’s passive beamforming. Simulation results show that the proposed dynamic time-division-multiple-access communication scheme can significantly improve the network’s energy efficiency compared with the communication-and-hover benchmark.

This paper investigates the challenging channel estimation problem for reconfigurable intelligent surface assisted wireless communications. By exploiting the channel’s structured-sparsity and two-timescale characteristics, a three-stage channel estimation method based on structured sparse Bayesian learning (SBL) is proposed to improve the estimation accuracy and reduce the pilot overhead. The row support set is first estimated at the large timescale to reduce the dimension of sparse channel matrix. A structured SBL algorithm is then designed to estimate the block sparse channel vector at the small timescale. The estimated support set and block sparse vector are finally used to reconstruct the channel. The computational complexity of the proposed method is analyzed. Numerical results show that the proposed method outperforms state-of-the-art benchmarks in terms of estimation accuracy, pilot overhead and robustness.

This article investigates a master unmanned aerial vehicle (MUAV)-powered Internet of Things (IoT) network, in which we propose using a rechargeable auxiliary UAV (AUAV) equipped with an intelligent reflecting surface (IRS) to enhance the communication signals from the MUAV and also leverage the MUAV as a recharging power source. Under the proposed model, we investigate the optimal collaboration strategy of these energy-limited UAVs to maximize the accumulated throughput of the IoT network. Depending on whether there is charging between the two UAVs, two optimization problems are formulated. To solve them, two multi-agent deep reinforcement learning (DRL) approaches are proposed, which are centralized training multi-agent deep deterministic policy gradient (CT-MADDPG) and multi-agent deep deterministic policy option critic (MADDPOC). It is shown that the CT-MADDPG can greatly reduce the complexity of optimization, and the proposed MADDPOC is able to support low-level multi-agent cooperative learning in the continuous action domains, which has great advantages over the existing option-based hierarchical DRL that only supports single-agent learning and discrete actions.

Blockchain is a promising technology for future dynamic spectrum access (DSA) management due to its decentralization, immutability, and traceability. However, many challenges need to be addressed to integrate the blockchain to DSA, such as the trustworthiness of participating nodes’ spectrum sensing results, privacy protection of sensing nodes’ identities, and affordable lightweight consensus algorithms for IoT devices. In this article, we propose a trust-centric privacy-preserving blockchain for DSA in IoT networks. To be specific, we propose a trust evaluation mechanism to evaluate the trustworthiness of sensing nodes and design a Proof-of-Trust (PoT) consensus mechanism to build a scalable blockchain with high transaction-per-second (TPS). Moreover, a privacy protection scheme is proposed to protect sensors’ real-time geolocation information when they upload sensing data to the blockchain. Two smart contracts are designed to make the whole procedure (spectrum sensing, spectrum auction, and spectrum allocation) run automatically. Simulation results demonstrate the expected computation cost of the PoT consensus algorithm for reliable nodes is low, and the cooperative sensing performance is improved with the help of the trust evaluation mechanism. In addition, incentivization and security are also analyzed, which show that our system can not only encourage nodes’ participation, but also resist many kinds of attacks which are frequently arise in the trust management mechanism and blockchain-based IoT systems.

In a backscatter communication system, the backscatter device (BD) transmits its messages to the backscatter receiver (BR) by reflecting the incident signal from an external radio frequency (RF) emitter, instead of using power-hungry active RF components themselves. Thus, backscatter communication has shown great potential for achieving low-power communication. The double-fading effect associated with the backscatter link, however, is a major limiting factor to achieve efficient backscatter communication. Reconfigurable intelligent surfaces (RISs), a recently developed technology, can be applied at the BD to enhance the backscatter link thanks to the fact that both RIS and backscatter communication share the same reflective principle. Such a design can also allow the backscatter communication system to capture the desired RF signal as a reflective carrier in a complex radio environment. In this article, a comprehensive overview of backscatter communication assisted by RIS is given. We first introduce the basics of backscatter communication, which covers the antenna scattering principle, backscatter modulation, and link budget calculation. Then, the details of RIS are discussed, which include antenna-based RIS and metamaterial-based RIS, followed by the discussion of the roles of RIS in backscatter communication. After that, we provide an overview of three types of backscatter communication systems assisted by RIS, including RIS-assisted unmodulated backscatter communication, RIS-assisted ambient backscatter communication, and RIS-assisted symbiotic radio. Emerging applications of these systems, technical challenges, and future opportunities in this emerging field are also presented.

Proactive reconfiguration of network slices according to uncertain traffic demands is essential to improve network resource utilization while ensuring service quality in 5G-and-beyond systems. Existing researches on network slice reconfiguration are either model-driven or data-driven methods. However, model-driven methods may cause resource over-provisioning due to a lack of prediction mechanism, while data-driven methods are unrealistic in inter-slice reconfiguration that involves costly and time-consuming operations such as VNF migration. To address these issues, in this paper, we propose a Hybrid Model-Data driven (HMD) framework that intelligently performs inter-slice reconfiguration by leveraging prediction interval and robust optimization. We design a Prediction Interval-oriented Predictor (PIP) to produce a prediction interval that can bracket the future traffic demand with a prespecified probability. Based on the prediction interval, we design an inter-slice reconfiguration scheme (named box optimizer) to perform fast inter-slice reconfigurations. To tackle the over-conservativeness of the box optimizer, we further design the ellipsoid optimizer with better optimality at a cost of increased complexity. Numerical results demonstrate that the proposed framework can provide high robustness with low power consumption. Meanwhile, the trade-off between the power consumption and the realized robustness can be flexibly adjusted according to the type of slice and the level of traffic demand fluctuations.

Targeting at implementing the next generation radio access networks (RANs) with virtualized network components, the open RAN (O-RAN) has been regarded as a novel paradigm towards fully open, virtualized and interoperable RANs. Through particularly introducing RAN intelligent controllers (RICs), machine learning (ML) can be unprecedentedly installed, adapting to various vertical applications and deployment environments without sophisticated planning efforts. However, the O-RAN also suffers two critical challenges of load balancing and frequent handovers in the massive base station (BS) deployment. In this paper, an intelligent user access control scheme with deep reinforcement learning (DRL) is proposed. To optimize the performance of distributed deep Q-networks (DQNs) trained by user equipments (UEs), a federated DRL-based scheme is proposed with a global model server installed in the RIC to update the DQN parameters. To further predictively train a global DQN with acceptable signaling overheads, the upper confidence bound (UCB) algorithm to select the optimal UE set and a dueling structure to decompose the DQN parameters are developed. With the proposed scheme, each UE effectively maximizes the long-term throughput and avoids frequent handovers. The simulation results well justify the outstanding performance of the proposed scheme over the-state-of-the-arts, to serve as references for the O-RAN standardization.

In this paper, we propose a reconfigurable intelligent surface (RIS) enhanced spectrum sensing system, in which the primary transmitter is equipped with a single antenna, the secondary transmitter is equipped with multiple antennas, and the RIS is employed to reduce the required signal samples while realizing a high detection probability. Without loss of generality, we adopt the maximum eigenvalue detection approach, and propose a corresponding analytical framework based on random matrix theory, to evaluate the detection probability in the asymptotic regime. Besides, the RIS is configured with only the statistical channel state information to avoid realtime channel estimation. With the statistical configuration, the asymptotic distributions of the equivalent channel gains are derived. Then, we provide the theoretical predictions about the number of reflecting elements required to achieve a detection probability close to 1. Finally, we present the Monte-Carlo simulation results to evaluate the accuracy of the proposed asymptotic analytical framework for the detection probability and the validity of the theoretical predictions about the number of REs required to achieve a detection probability close to 1. Moreover, the simulation results show that the proposed RIS-enhanced spectrum sensing system can substantially improve the detection performance.

This paper considers an adversarial scenario between a legitimate eavesdropper and a suspicious communication pair. All three nodes are equipped with multiple antennas. The eavesdropper, which operates in a full-duplex model, aims to wiretap the dubious communication pair via proactive jamming. On the other hand, the suspicious transmitter, which can send artificial noise (AN) to disturb the wiretap channel, aims to guarantee secrecy. More specifically, the eavesdropper adjusts jamming power to enhance the wiretap rate, while the suspicious transmitter jointly adapts the transmit power and noise power against the eavesdropping. Considering the partial observation and complicated interactions between the eavesdropper and the suspicious pair in unknown system dynamics, we model the problem as an imperfect-information stochastic game. To approach the Nash equilibrium solution of the eavesdropping game, we develop a multi-agent reinforcement learning (MARL) algorithm, termed neural fictitious self-play with soft actor-critic (NFSP-SAC), by combining the fictitious self-play (FSP) with a deep reinforcement learning algorithm, SAC. The introduction of SAC enables FSP to handle the problems with continuous and high dimension observation and action space. The simulation results demonstrate that the power allocation policies learned by our method empirically converge to a Nash equilibrium, while the compared reinforcement learning algorithms suffer from severe fluctuations during the learning process.

Symbiotic radio (SR) has emerged as a spectrum and energy-efficient communication paradigm for future passive Internet-of-Things (IoT). In this paper, we consider a multiuser multi- backscatter-device (BD) SR communication system to enhance the spectrum efficiency, by sharing a common time-frequency resource block. Due to the presence of inter-user and inter-BD interference, multiuser and multi-BD detection in the receiver design become much more challenging. Concretely, the detection problem involves several key components: direct-link channel estimation, backscatter-link channel estimation, user signal decoding, and BD symbol detection. A conventional way is to realise these components in two separate phases, in which a channel estimation phase is followed by a data decoding phase. However, channel state information (CSI) acquisition is very difficult for the multiuser multi-BD SR communication, as compared to the case of orthogonal multiple access. In addition, the backscatter-link is relatively weak, which further increases the difficulty of CSI acquisition. To address these issues, we propose a novel receiver design to perform joint channel estimation, user data decoding, and BD symbol detection. Based on the factor graph representation of the joint estimation problem, we design a message-passing receiver for the multiuser multi-BD SR system to iteratively refine the estimation outputs. Extensive simulation results demonstrate the effectiveness of the proposed receiver design.

In symbiotic radio (SR), a secondary transmitter (STx) transmits messages by modulating its information over the radio frequency (RF) signals received from a primary transmitter (PTx), and in return, the secondary transmission provides multipath gain to the primary transmission. In this paper, we are interested in the fundamental mutualistic mechanism between the primary and secondary transmissions, which describes the condition through which the two systems can benefit each other. Since the symbol period ratio$K$between secondary and primary transmissions is an important system parameter that affects the mutualistic symbiosis, we first derive the theoretical performance in terms of bit error rate (BER) for both primary and secondary transmissions for arbitrary$K$by using QPSK modulation scheme at the PTx and BPSK modulation scheme at the STx as an example setup. Then we the obtain closed-form expressions for the condition on$K$to enable mutualistic symbiosis in SR, which is not related to the specific channel realizations but determined by the average strengths of the direct and backscatter links when the number of receiving antennas is large. Meanwhile, we analyze the average BER performance and the diversity orders for both transmissions in the high signal-to-noise-ratio (SNR) regime. Extensive simulations and numerical results are provided to verify the accuracy of our theoretical analysis and demonstrate the interrelationship between the primary and secondary transmissions.

Mobile edge computing can effectively reduce service latency and improve service quality by offloading computation-intensive tasks to the edges of wireless networks. Due to the characteristic of flexible deployment, wide coverage and reliable wireless communication, unmanned aerial vehicles (UAVs) have been employed as assisted edge clouds (ECs) for large-scale sparely-distributed user equipment. Considering the limited computation and energy capacities of UAVs, a collaborative mobile edge computing system with multiple UAVs and multiple ECs is investigated in this paper. The task offloading issue is addressed to minimize the sum of execution delays and energy consumptions by jointly designing the trajectories, computation task allocation, and communication resource management of UAVs. Moreover, to solve the above non-convex optimization problem, a Markov decision process is formulated for the multi-UAV assisted mobile edge computing system. To obtain the joint strategy of trajectory design, task allocation, and power management, a cooperative multi-agent deep reinforcement learning framework is investigated. Considering the high-dimensional continuous action space, the twin delayed deep deterministic policy gradient algorithm is exploited. The evaluation results demonstrate that our multi-UAV multi-EC task offloading method can achieve better performance compared with the other optimization approaches.

Small cell network (SCN) is a promising solution to meet the demand for increasing data traffic for the sixth generation and beyond wireless networks. However, the power consumption and two-tier interference issues are two bottlenecks that hinder its further development. In this paper, we propose a novel intelligent reflecting communication (IRC) system in which a reconfigurable intelligent surface (RIS) is used to serve multiple micro users in an SCN while assisting the transmission from a macro base station (MBS) to a macro user. Compared to the conventional SCN, the RIS can achieve significant power reduction as it transmits the information by passively reflecting the incident signals. In addition, the reflected signal can be regarded as a multipath component instead of an interference to the macro user. We are interested in minimizing the total power consumption by jointly designing the phase shift matrix at the RIS and the beamforming vector at the MBS under the user rate constraints and the practical phase shift constraints. The solution is obtained by alternating optimization to iteratively solve two subproblems, one to optimize the phase shift matrix, and the other to optimize the beamforming vector. A TDMA transmission scheme is also proposed as an alternative to serve multiple users. Simulation results demonstrate that the total power consumption can be reduced significantly by deploying the RIS in the SCN when the number of reflecting elements is sufficiently large.

In this paper, we investigate a reconfigurable in-telligent surface (RIS)-based uplink massive multi-input multi-output symbiotic radio system, where each RIS, as an IoT device, enhances the primary transmission from a nearby user to the base station (BS) and simultaneously transmits its own infor-mation to the BS by backscattering modulation. By embedding environmental sensors on the RISs, the proposed system enables the IoT transmission of locally collected environmental data to the BS while assisting the primary transmission. Assuming imperfect channel state information (CSI), we jointly design the active beamforming at the BS and the passive beamforming at the RISs to maximize the weighted sum-rate of both the primary and IoT transmissions. An algorithm based on the block coordinate descent method is proposed to solve it. Simulation results show that the proposed system achieves significant performance gain compared with different baseline schemes. Besides, when the channel estimation error is small, the performance loss due to imperfect CSI is insignificant.

In this paper, we propose a novel Intelligent Reflected Surface (IRS)-aided Master-Auxiliary-Unmanned Aerial Vehicle (UAV)-powered Internet of Things (IoT) Network (IRS-MAIN). Compared to the conventional terrestrial or aerial IRS-assisted communication networks, the IRS-MAIN not only benefits from wide communication range due to the mobility of UAVs, but also enjoys enhanced channel condition brought by the IRS. To be specific, let the Auxiliary UAV (AUAV) carry an IRS to enhance signals from the Master UAV (MUAV) served as a radio frequency (RF) transmitter. We focus on the problem to maximize the total throughput by jointly optimizing the trajectories and transmit power of the MUAV. A modified multi-agent deep reinforcement learning (MADRL) based algorithm, named as Pre-activation Penalty Multi-agent Deep Deterministic Policy Gradient (PP-MADDPG), is proposed to solve the formulated problem in an accurate and efficient way. Simulation results are provided to demonstrate that PP-MADDPG outperforms the baseline method in terms of the throughput as well as the convergence rate.

In symbiotic radio (SR), also called cognitive backscatter communications, a secondary transmitter (STx) transmits messages by modulating its information over the RF signals from a primary transmitter (PTx), and in return, the secondary transmission provides multipath gain instead of interference to the primary transmission when the spreading factor$K$of SR is large enough. In this paper, we are interested in the fundamental mutualistic mechanism between the primary and secondary transmissions in SR, which describes the condition through which the two systems can benefit each other. We first derive the closed-form expressions for the bit error rates (BERs) for both primary and secondary transmissions for general$K$, then obtain the condition on$K$to enable mutualistic symbiosis in SR. It is observed that the critical point of$K$is related to the average strengths of the direct and backscatter links when the number of receiving antennas is large. Extensive simulation and numerical results are provided to verify the accuracy of theoretical analysis and demonstrate the interrelationship between primary and secondary transmissions.

The Open Radio Access Network (O-RAN) introducing a particular unit known as RAN Intelligent Controllers (RICs) has been regarded as revolutionary paradigms to support multiclass wireless services required in the fifth and sixth generation (5G/6G) networks. Through unprecedentedly installing various machine learning (ML) algorithms to RICs, a RAN is able to intelligently configure resources/communications to support any vertical applications over any operating scenarios. However, to practically deploy this RAN paradigm, the O-RAN still suffers two critical issues of load balance and handover control, and therefore the very first ML algorithm for the O-RAN should effectively address these issues. In this paper, inspired by the superior performance of deep reinforcement learning (DRL) in tackling sequential decision-making tasks, we therefore develop an intelligent user access control scheme with the facilitation of deep Q-networks (DQNs). A federated DRL-based scheme is further proposed to train the parameters of multiple DQNs in the O-RAN, so as to maximize the long-term throughput and meanwhile avoid frequent user handovers with a limited amount of signaling overheads in the O-RAN. The simulation results have fully demonstrated the outstanding performance over the state-of-the-arts, to service the urgent needs in the standardization of the O-RAN.

To support massive access for future wireless communications, we propose a novel intelligent reflecting surface (IRS) enhanced downlink multi-user multi-input single-output (MU-MISO) symbiotic radio (SR) system, where each IRS, acting as a reflecting Internet-of-Things (IoT) device, transmits its message to a nearby primary receiver (PR) by reflecting the RF signals from the primary transmitter (PT), and simultaneously enhances the transmission from the PT to the associated PR. Thus, each PR jointly decodes its own message as well as the one from the corresponding IRS. We are interested in maximizing the weighted sum-rate of both primary and IoT transmissions by jointly designing the active transmit beamforming at PT and the passive beamforming at each IRS, subject to the maximum transmit power constraint at PT. Besides, as the passive elements at IRS can only reflect the incident signal with discrete phase shifts in practice, the discrete reflection coefficient (RC) constraint is further considered at the IRSs. Due to the non-convexity of the formulated problems, we solve them with fractional programming (FP) technique and alternating optimization (AO) method. Simulation results have verified the effectiveness of the proposed algorithms compared to different benchmark schemes.

Symbiotic radio (SR) emerges as a spectrum and energy-efficient communication paradigm for future passive Internet-of-Things (IoT). In this paper, we consider a multiuser multi-backscatter-device (BD) SR communication system to enhance the spectrum efficiency, by sharing a common time and frequency resource block. However, the receiver design becomes much more complicated due to the presence of inter-user and inter-BD interference. To address these issues, we propose a novel receiver design to perform joint channel estimation, user data decoding, and BD symbol detection. Specifically, motivated by the idea of approximate message passing, we develop a computationally efficient iterative algorithm under the Bayesian inference framework to resolve the joint estimation problem. Simulation results demonstrate the effectiveness of the proposed receiver design.

With the objective of supporting next generation driving services, non-terrestrial networks (NTNs) with low earth orbit (LEO) satellites have been regarded as promising paradigms to implement global ubiquitous and high-capacity vehicular connections. However, due to the high moving speed, different satellites can only service a specific set of vehicles for few minutes. In such case, due to the limited computing capability of the satellite, machine learning (ML) based and non-ML based solutions cannot be performed within such a short duration. To address these issues, in this paper, we propose a multi-tier collaborative deep reinforcement learning (DRL) scheme for resource allocation in NTN empowered vehicular networks, in which ground vehicles and LEO satellites maintain DRL-based decision model to obtain resource allocation decisions cooperatively. Specifically, ground vehicles with powerful computing capabilities can assist the satellite to tackle resource allocation optimizations, and the satellite determines final decisions and model parameters by aggregating local calculated results of vehicles. Additionally, the parameters of DRL-based decision model can be transferred from the current satellite to its successor as the starting point for future resource allocation decision-makings. Comprehensive simulations have been conducted to show the effectiveness of our proposed scheme.

This paper studies semi-blind channel estimation for a reconfigurable intelligent surface (RIS) aided uplink massive multiple-input multiple-output (MIMO) system, in which the base station simultaneously estimates the channel coefficients and detects the partially unknown transmit symbols. We formulate the semi-blind channel estimation task as a trilinear inference problem. Based on the approximate message passing (AMP) principle, we develop a computationally efficient approach, called Trilinear AMP, to calculate the marginal posterior mean estimators of the trilinear inference problem. Simulation results demonstrate the effectiveness of the proposed Trilinear AMP approach.

This paper considers a reconfigurable intelligent surface (RIS) enhanced symbiotic radio (SR) multigroup multicast system, where the RIS passively transmits information to an Internet-of-Things receiver (IR) by modulating the incident multicasting signals, and also enhances the downlink multicast transmission from a base station (BS) to multiple primary receivers (PRs). We aim to minimize the BS's transmit power by jointly optimizing the active transmit beamforming at the BS and the passive phase shifts at the RIS, subject to the constraints on the PRs' signal-to-interference-plus-noise-ratio, the IR's successive-interference-cancellation and signal-to-noise-ratio, as well as the RIS's phase shifts. An efficient iterative algorithm is proposed to solve the non-convex problem, by employing alternating optimization, quadratic transform and semidefinite relaxation. The algorithm's convergence and complexity are also analysed. Numerical results show that the proposed scheme consumes less power than the traditional multicast scheme without RIS.

Reconfigurable intelligent surfaces (RIS) are a new and revolutionary technology to achieve spectrum-, energy- and cost-efficient wireless networks. This paper studies the resource allocation for RIS-empowered device-to-device (D2D) communication underlaying a cellular network, in which an RIS is employed to enhance desired signals and suppress interference between paired D2D and cellular links. We maximize the sum rate of D2D users and cellular users by jointly optimizing the resource reuse indicators, the transmit power and the RIS's passive beamforming. To solve the formulated non-convex problem, we first propose an efficient user-pairing scheme based on relative channel strength to determine the resource reuse indicators. Then, the transmit power and the RIS's passive beamforming are jointly optimized by an iterative algorithm, based on the techniques of alternating optimization, successive convex approximation, Lagrangian dual transform and quadratic transform. Numerical results show that the proposed design outperforms the traditional D2D network without RIS.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

The Internet-of-Things (IoT) paradigm holds the promise to revolutionize the way we live and work through connecting various machine-type communication terminals. In this work, we investigate a symbiotic network from a cross-layer perspective, where passive IoT devices coexist in symbiosis with an ambient network that uses carrier sense multiple access with collision notifications (CSMA/CNs) MAC protocol. In the ambient network, each full-duplex mobile user (MU) aims to transmit its own packets to the common access point (AP) while receiving the signal backscattered from its associated backscatter device (BD). Considering the imperfectness of carrier sensing of the MU, two key parameters, i.e., the probability of detection and the probability of false alarm, are quantified. Then we derive the PHY-layer outage probabilities and analyze the corresponding diversity orders for both the CSMA/CN and the BD system. By incorporating the outage probabilities and the carrier sensing metrics into the MAC-layer analysis, the cross-layer outage capacities of the CSMA/CN and the BD system are derived. Simulation results demonstrate that the system performance can be improved by appropriately setting the PHY-layer parameters, such as the BD reflection coefficient α and the number of samples for carrier sensing K, as well as the MAC-layer parameters, such as the sensing duration and the initial contention window. With the BD reflection coefficient being 0.05, the outage capacity of the overall system has improved by 41.85% compared with carrier sense multiple access with collision avoidance protocol.

This article investigates a full-duplex orthogonal-frequency-division multiple access (OFDMA)-based multiple unmanned-aerial-vehicles (UAVs)-enabled wireless-powered Internet-of-Things (IoT) networks. In this paper, a swarm of UAVs is first deployed in 3-D to simultaneously charge all devices, i.e., a downlink (DL) charging period, and then flies to new locations within this area to collect information from scheduled devices in several epochs via OFDMA due to potential limited number of channels available in IoT during an uplink (UL) communication period. To maximize the UL throughput of IoT devices, we jointly optimize the UL-and-DL 3-D deployment of the UAV swarm, including the device-UAV association, the scheduling order, and the UL-DL time allocation. In particular, the DL energy harvesting threshold of devices and the UL signal decoding threshold of UAVs are taken into consideration when studying the problem. Besides, both line-of-sight and non-line-of-sight channel models are studied depending on the position of sensors and UAVs. The influence of potential limited number of channels in IoT is also considered. Guidelines on the 3-D placement of UAVs in the DL charging and the UL communications are also given. Finally, simulation results show that the proposed optimal time allocation OFDMA-UAV scheme achieves significant throughput gains compared with conventional schemes.

Equivalent models of antennas are useful for the fast simulation of vehicle-to-everything (V2X) communication. The existing antenna-equivalent models are inflexible because they assume rectangular antenna contour. This article presents a hybrid-equivalent surface-edge current model to overcome the limitation of the existing equivalent models. Based on Huygens' principle, a V2X communication antenna is equivalent to a conducting plate excited by equivalent surface magnetic current (ESMC). The sources of fields reflected by the conducting plate and diffracted by its edges are modeled as the image of ESMC and equivalent edge current (EEC), respectively. A hybrid-equivalent surface-edge current model is thus established, and it consists of ESMC, the image of ESMC, and EEC. The unknown sources in the proposed model are solved from near fields of the antennas, and the proposed model is then used to simulate the radiation performance of antennas installed on vehicles. The transmission coefficient between V2X communication antennas can also be calculated by using the proposed model and the electromagnetic reaction theorem. Simulations and experiments are performed to demonstrate the effectiveness of the proposed method. It is shown that the proposed equivalent model not only accurately models antennas with arbitrarily shaped contour but also significantly accelerates the integrated simulation of antennas and vehicles.

In this article, we study the receiver design for the uplink transmission of a human-type communications (HTC) and machine-type communications (MTC) (H&M) coexisted massive MIMO system. We first establish a probability model to characterize the crucial system features including channel sparsity of massive MIMO and signal sparsity of MTC packets. With the probability model, we propose to conduct joint device activity identification, channel estimation, and signal detection. We develop a message-passing-based statistical interference framework to systematically and efficiently solve the joint estimation problem for the H&M coexisted massive MIMO system. Specifically, we propose two receiver schemes based on time-slotted and non-time-slotted grant-free random access for massive machine-type device connectivity. We show that, by exploiting the channel and signal sparsity, our proposed message-passing-based algorithms significantly outperform the conventional training-based approaches in which the device activity state and the channel are estimated by sending pilots prior to data transmission, and are able to approach the genie bound with known signal support in the relatively high signal-to-noise (SNR) regime. Last but not least, we show that there exists a significant gain in terms of the number of admissible devices in the system by allowing H&M coexistence, as compared to orthogonal transmission approaches in which different time/frequency slots are assigned to HTC and MTC services.

Non-Terrestrial Networks (NTNs) composed of space-borne (e.g., satellites) and airborne vehicles (e.g., drones and blimps) have recently been proposed by 3GPP as a new paradigm of infrastructures to enhance the capacity and coverage of existing terrestrial wireless networks. The mobility of non-terrestrial base stations (NT-BSs) however leads to a dynamic environment, which imposes unique challenges for handover and throughput optimization particularly in multi-user access control for NTNs. To achieve performance optimization, each terrestrial user equipment (UE) should autonomously estimate the dynamics of moving NT-BSs, which is different from the existing user access control schemes in terrestrial wireless networks. Consequently, new learning schemes for optimum multi-user access control are desired. In this article, we therefore propose a UE-driven deep reinforcement learning (DRL) based scheme, in which a centralized agent deployed at the backhaul side of NT-BSs is responsible for training the parameter of a deep Q-network (DQN), and each UE independently makes its own access decisions based on the parameter from the trained DQN. With the proposed scheme, each UE is able to access a proper NT-BS intelligently to enhance the long-term system throughput and avoid frequent handovers among NT-BSs. Through comprehensive simulation studies, we justify the performance of the proposed scheme, and show its effectiveness in addressing the fundamental issues in the NTNs deployment.

To support massive access for future wireless networks, we propose a novel reconfigurable intelligent surface (RIS)-enhanced downlink multi-user multi-input single-output (MU-MISO) symbiotic radio (SR) system. In the proposed system, each RIS not only enhances the primary transmission from the primary transmitter (PT) to the associated primary receiver (PR) nearby, but also acts as an Internet-of-Things (IoT) device to enable IoT transmissions to the same PR. Therefore, each PR needs to jointly decode the information from both the PT and its corresponding RIS. We are interested in maximizing the weighted sum-rate of both primary and IoT transmissions by jointly designing the active transmit beamforming at PT and the passive beamforming at each RIS under the maximum transmit power constraint at the PT and various constraints on the reflection coefficients (RCs), which include the ideal, continuous-phase and the discrete-phase cases. The formulated problem is non-convex, which cannot be solved directly. Thus, fractional programming (FP) method and alternating optimization (AO) technique are adopted to tackle the problem. In particular, three low-complexity algorithms are proposed to trade off between computational complexity and convergence rate. Compared to different benchmark schemes, simulation results demonstrate that with the aid of the RISs, the PRs can benefit from the enhanced primary transmission from the PT, and receive information from the associated RISs via IoT transmission.

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted two-way relay network, in which two users exchange information through the base station (BS) with the help of an RIS. By jointly designing the phase shifts at the RIS and beamforming matrix at the BS, our objective is to maximize the minimum signal-to-noise ratio (SNR) of the two users, under the transmit power constraint at the BS. We first consider the single-antenna BS case, and propose two algorithms to design the RIS phase shifts and the BS power amplification parameter, namely the SNR-upper-bound-maximization (SUM) method, and genetic-SNR-maximization (GSM) method. When there are multiple antennas at the BS, the optimization problem can be approximately addressed by successively solving two decoupled subproblems, one to optimize the RIS phase shifts, the other to optimize the BS beamforming matrix. The first subproblem can be solved by using SUM or GSM method, while the second subproblem can be solved by using optimized beamforming or maximum-ratio-beamforming method. The proposed algorithms have been verified through numerical results with computational complexity analysis.

Symbiotic radio (SR) is a promising technology for energy- and spectrum-efficient wireless communication, which exploits passive communication for Internet-of-Things (IoT) transmission and achieves a mutualistic spectrum sharing between the passive and active transmissions. In this paper, we study an reconfigurable intelligent surface (RIS) empowered symbiotic radio over a broadcasting system, i.e., a base station (BS) broadcasts signals to multiple primary receivers (PRs) under the assistance of an RIS, while the RIS also transmits information to an IoT receiver (IR) by riding over the broadcasting signals. We formulate a problem to minimize the BS’s transmit power by jointly optimizing the BS’s active precoding and the RIS’s passive beamforming, under the signal-to-noise-ratio constraints of the primary and IoT transmissions. However, the problem is challenging to be solved optimally, since the variables are coupled and the constraints are non-convex. An iterative algorithm based on block coordinated descent (BCD) and semidefinite relaxation (SDR) techniques is first proposed, and its convergence together with complexity are analyzed. Then, to tackle the problem of high computational complexity caused by SDR technique, we further propose an alternative algorithm based on generalized power method (GPM) technique. Simulation results validate that the proposed system outperforms the traditional broadcasting system without RIS. The GPM-based algorithm achieves nearly the same transmit power performance as SDR-based algorithm, with a significantly reduced computational complexity.

Reconfigurable intelligent surface (RIS) is a new and revolutionary technology to achieve spectrum-, energy- and cost-efficient wireless networks. This paper studies the resource allocation for RIS-empowered device-to-device (D2D) communication underlaying a cellular network, in which an RIS is employed to enhance desired signals and suppress interference between paired D2D and cellular links. We maximize the overall network’s spectrum efficiency (SE) and energy efficiency (EE), respectively, by jointly optimizing the spectrum reuse indicators, the transmit power, the RIS’s passive beamforming and the BS’s receive beamforming. To solve both mixed-integer non-linear programming problems, we first propose an efficient and low-complexity user-pairing scheme based on relative channel strength to determine the spectrum reuse indicators. Other variables are then optimized to maximize the SE by an iterative algorithm, based on the techniques of alternating optimization, successive convex approximation, Lagrangian dual transform and quadratic transform. The EE-maximization problem is solved by an alternating algorithm integrated with Dinkelbach’s method. Numerical results show that the proposed design achieves significant SE and EE enhancements compared to traditional underlay D2D network without RIS, relay-assisted D2D network and other benchmarks.

Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users (PUs). Meanwhile, intelligent reflecting surface (IRS), also known as reconfigurable intelligent surface (RIS), has been recently proposed as a promising approach to enhance energy efficiency (EE) of wireless communication systems through intelligently reconfiguring the channel environment. To improve both SE and EE, in this paper, we introduce multiple IRSs to a downlink multiple-input single-output (MISO) CR system, in which a single SU coexists with a primary network with multiple PU receivers (PU-RXs). Our design objective is to maximize the achievable rate of SU subject to a total transmit power constraint on the SU transmitter (SU-TX) and interference temperature constraints on the PU-RXs, by jointly optimizing the beamforming at SU-TX and the reflecting coefficients at each IRS. Both perfect and imperfect channel state information (CSI) cases are considered in the optimization. Numerical results demonstrate that IRS can significantly improve the achievable rate of SU under both perfect and imperfect CSI cases.

In this paper, a novel reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) symbiotic radio (SR) system is proposed, in which an RIS, operating as a secondary transmitter (STx), sends messages to a multi-antenna secondary receiver (SRx) by using cognitive backscattering communication, and simultaneously, it enhances the primary transmission from a multi-antenna primary transmitter (PTx) to a multi-antenna primary receiver (PRx) by intelligently reconfiguring the wireless environment. We are interested in the joint design of active transmit beamformer at the PTx and passive reflecting beamformer at the STx to minimize the total transmit power at the PTx, subject to the signal-to-noise-ratio (SNR) constraint for the secondary transmission and the rate constraint for the primary transmission. Due to the non-convexity of the formulated problem, we decouple the original problem into a series of subproblems using the alternating optimization method and then iteratively solve them. The convergence performance and computational complexity of the proposed algorithm are analyzed. Furthermore, we develop a low-complexity algorithm to design the reflecting beamformer by solving a backscatter link enhancement problem through the semi-definite relaxation (SDR) technique. Then, theoretical analysis is performed to reveal the insights of the proposed system. Finally, simulation results are presented to validate the effectiveness of the proposed algorithms and the superiority of the proposed system.

Efficient urban traffic monitoring is a key enabler for intelligent planning and management of modern cities. Network tomography can monitor the urban traffic with a comparably small number of traffic detectors like cameras, and has become an appealing technique for urban traffic management. However, previous work on network tomography based traffic monitoring focuses primarily on developing estimators using the given end-to-end travel time measurements, while the design of data collection for efficiently distributed collecting and processing the raw monitoring videos to such measurements is often neglected. We fill this gap by exploring the vision of edge intelligence for optimal urban traffic monitoring, and tackle the following two problems in regard of limited telecommunications resources: 1) when the total number of monitoring videos that are successfully processed into the end-to-end travel time measurements is pre-bounded, we employ a Fisher Information Matrix (FIM) to help determine the best quota scheme for the monitoring videos that each traffic detector need to generate and 2) when the centralised processing of monitoring videos alone is insufficient, we make use of the computation capabilities from these edge devices, i.e., traffic detectors, and employ a multi-agent reinforcement learning approach to help them conduct intelligent computation offloading individually. Extensive simulations demonstrate that our proposed scheme effectively reduces the estimation error of network tomography compared to common approaches with either uniform or random strategy.

With the recent advances in wireless communications, the automotive industry is leading to evolution. To succeed in this emerging era of technology, the Internet of Connected Vehicles (IoCV) has emerged as one of the potential applications of the Internet of Things (IoT). It refers to the dynamic mobile communication systems that communicate between vehicles and public networks to enhance the connectivity between cars via technology. By offering a wide variety of infotainment services, fleet operations, and in-vehicle applications, IoCV has gained the tremendous capacity to provide a safer and sustainable transportation system to the society. According to Gartner Inc., “the connected car is already a reality, and in-vehicle wireless connectivity is expanding rapidly.” As a result, the evolution of cars into the IoT will keep on accelerating the global market which is expected to grow by 270% by 2022. Furthermore, the increasing deployment of sensors and ever-evolving cognitive technology opens up new opportunities for IoCV. Due to these significant developments, connected vehicles are receiving widespread attention from the major automotive giants such as Tesla, BMW, Waymo (Google), Uber, Volvo, and so on. Despite all the opportunities offered by the IoCV, their highly dynamic topology and the increasing number of vehicles pose challenges regarding delivering low-latency vehicle-to-everything (V2X) communications.

Symbiotic radio (SR) is a new paradigm of spectrum sharing that allows passive Internet-of-Things (IoT) device to transmit messages over the ambient RF signal. Considering that the reader needs to collect information from multiple IoT devices in practice, and the traditional multiple access schemes that require coordination among users are unaffordable for the passive devices, we propose a random code assisted multiple access scheme, in which each backscatter device (BD) independently chooses a random code to spread its signal and the incident signal is backscattered. To avoid the need of the instantaneous information, including the specific codes and the instantaneous channel state information of the backscatter links, and to optimize the transmit power and the BD reflection coefficients, we derive the asymptotic BD signal to interference plus noise ratio (SINR) by employing the large-dimension random matrix theory. We formulate the joint optimization problem to maximize the minimum asymptotic SINR among BDs with guaranteeing the primary target rate. By investigating the property of the problem, the original problem is transformed and solved by the proposed iterative-based algorithm. Extensive simulations are executed to verify our theoretical analysis and demonstrate the outperformance of the proposed algorithm.

Tag signal detection is one of the key tasks in ambient backscatter communication (AmBC) systems. However, obtaining perfect channel state information (CSI) is challenging and costly, which makes AmBC systems suffer from a high bit error rate (BER). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols. To this end, we develop a DTL detection framework which consists of offline learning, transfer learning, and online detection. Specifically, a DTL-based likelihood ratio test (DTL-LRT) is derived based on the minimum error probability (MEP) criterion. As a realization of the developed framework, we then apply convolutional neural networks (CNN) to intelligently explore the features of the sample covariance matrix, which facilitates the design of a CNN-based algorithm for tag signal detection. Exploiting the powerful capability of CNN in extracting features of data in the matrix formation, the proposed method is able to further improve the system performance. In addition, an asymptotic explicit expression is also derived to characterize the properties of the proposed CNN-based method when the number of samples is sufficiently large. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI.

Reconfigurable Intelligent Surface (RIS) is a promising solution to reconfigure the wireless environment in a controllable way. To compensate for the double-fading attenuation in the RIS-aided link, a large number of passive reflecting elements (REs) are conventionally deployed at the RIS, resulting in large surface size and considerable circuit power consumption. In this paper, we propose a new type of RIS, called active RIS, where each RE is assisted by active loads (negative resistance), that reflect and amplify the incident signal instead of only reflecting it with the adjustable phase shift as in the case of a passive RIS. Therefore, for a given power budget at the RIS, a strengthened RIS-aided link can be achieved by increasing the number of active REs as well as amplifying the incident signal. We consider the use of an active RIS to a single input multiple output (SIMO) system. However, it would unintentionally amplify the RIS-correlated noise, and thus the proposed system has to balance the conflict between the received signal power maximization and the RIS-correlated noise minimization at the receiver. To achieve this goal, it has to optimize the reflecting coefficient matrix at the RIS and the receive beamforming at the receiver. An alternating optimization algorithm is proposed to solve the problem. Specifically, the receive beamforming is obtained with a closed-form solution based on linear minimum-mean-square-error (MMSE) criterion, while the reflecting coefficient matrix is obtained by solving a series of sequential convex approximation (SCA) problems. Simulation results show that the proposed active RIS-aided system could achieve better performance over the conventional passive RIS-aided system with the same power budget.

Traditional D2D power control methods require instantaneous interference information and so are difficult to implement in a real network due to the backhaul delay and high computational requirements. To overcome this challenge, we propose a distributed power allocation algorithm called interference feature extractor aided recurrent neural network (IFE-RNN). The core design ideas are described as follows. First, we design linear filters with various sizes termed IFEs to extract the different local interference patterns from outdated channel information. This feature extraction process enables our network to precisely learn the interference patterns around D2D links, so as to provide more effective power allocation strategies. Second, we propose to predict the real-time interference pattern based on the outputs of the IFEs and further make power decision. The prediction and decision can be modelled as a Markov decision problem (MDP) and solved by using a recurrent neural network. Third, an input reduction process is also designed to reduce the input size from O(N2) to O(1), which speeds up the operation time and reduces the system overhead. Finally, extensive simulation results show that the proposed algorithm achieves an encouraging performance compared to the state-of-the-art power allocation algorithm.

Device-to-device (D2D) technology, which allows direct communications between proximal devices, is widely acknowledged as a promising candidate to alleviate the mobile traffic explosion problem. In this paper, we consider an overlay D2D network, in which multiple D2D pairs coexist on several orthogonal spectrum bands, i.e., channels. Due to spectrum scarcity, the number of D2D pairs is typically more than that of available channels, and thus multiple D2D pairs may use a single channel simultaneously. This may lead to severe co-channel interference and degrade network performance. To deal with this issue, we formulate a joint channel selection and power control optimization problem, with the aim to maximize the weighted-sum-rate (WSR) of the D2D network. Unfortunately, this problem is non-convex and NP-hard. To solve this problem, we first adopt the state-of-art fractional programming (FP) technique and develop an FP-based algorithm to obtain a near-optimal solution. However, the FP-based algorithm requires instantaneous global channel state information (CSI) for centralized processing, resulting in poor scalability and prohibitively high signalling overheads. Therefore, we further propose a distributed deep reinforcement learning (DRL)-based scheme, with which D2D pairs can autonomously optimize channel selection and transmit power by only exploiting local information and outdated nonlocal information. Compared with the FP-based algorithm, the DRL-based scheme can achieve better scalability and reduce signalling overheads significantly. Simulation results demonstrate that even without instantaneous global CSI, the performance of the DRL-based scheme can approach closely to that of the FP-based algorithm.

In this article, a multiple signal classification (MUSIC) based algorithm is proposed for two-dimensional (2-D) direction-of-arrival (DOA) and polarization estimation of non-circular signals in three-dimensional (3-D) millimeter wave polarized massive multiple-input-multiple-output (MIMO) systems. The traditional MUSIC-based algorithms can estimate either the DOA and polarization for circular signals or the DOA for non-circular signals by using spectrum search. By contrast, based on the quaternion theory, a novel algorithm named quaternion non-circular MUSIC (QNC-MUSIC) is proposed for parameter estimation of non-circular signals with high estimation accuracy. Moreover, only the DOA estimation needs spectrum search, and the polarization estimation has a closed-form expression. First, the DOA estimation can be achieved based on the derivation principle. Then the closed-form expression of the polarization estimation can be obtained based on the chain rule of the derivation w.r.t. the polarization parameters. In addition, the computational complexity analysis shows that compared with the conventional DOA and polarization estimation algorithms, our proposed QNC-MUSIC has much lower computational complexity, especially when the source number is large. The stochastic Cramér-Rao Bound (CRB) for the estimation of the 2-D DOA and polarization parameters of the non-circular signals is derived as well. Finally, numerical examples are provided to demonstrate that the proposed algorithms can improve the parameter estimation performance when large-scale/massive MIMO systems are employed.

Backscatter communication which enables wireless-powered backscatter devices (BDs) to transmit information by reflecting incident signals, is an energy- and cost-efficient communication technology for Internet-of-Things. This paper considers an unmanned aerial vehicle (UAV)-assisted backscatter communication network (UBCN) consisting of multiple BDs and carrier emitters (CEs) on the ground as well as a UAV. A communicate-while-fly scheme is first designed, in which the BDs illuminated by their associated CEs transmit information to the flying UAV in a time-division-multiple-access manner. Considering the critical issue of the UAV's limited on-board energy and the CEs' transmission energy, we maximize the energy efficiency (EE) of the UBCN by jointly optimizing the UAV's trajectory, the BDs' scheduling, and the CEs' transmission power, subject to the BDs' throughput constraints and harvested energy constraints, as well as other practical constraints. Furthermore, we propose an iterative algorithm based on the block coordinated decent method to solve the formulated mixed-integer non-convex problem, in each iteration of which the variables are alternatively optimized by leveraging the cutting-plane technique, the Dinkelbach's method and the successive convex approximation technique. Also, the convergence and complexity of the proposed algorithm are analyzed. Finally, simulation results show that the proposed communicate-while-fly scheme achieves significant EE gains compared with the hover-and-fly scheme, the state-of-the-art scheme, and the CE-relay scheme. Useful insights on the optimal trajectory design and resource allocation are also obtained.

Reconfigurable intelligent surface (RIS) is a revolutionary technology to achieve spectrum-, energy-, and cost-efficient wireless networks. This paper considers an RIS-assisted downlink non-orthogonal-multiple-access (NOMA) system. To optimize the rate performance and ensure user fairness, we maximize the minimum decoding signal-to-interference-plus-noise-ratio (equivalently the rate) of all users, by jointly optimizing the (active) transmit beamforming at the base station (BS) and the phase shifts (i.e., passive beamforming) at the RIS. A combined-channel-strength based user-ordering scheme for NOMA decoding is first proposed to decouple the user-ordering design and the joint beamforming design. Efficient algorithms are further proposed to solve the non-convex problem, by leveraging the block coordinated descent and semidefinite relaxation (SDR) techniques. For the single-antenna BS setup, the optimal power allocation at the BS and the asymptotically optimal phase shifts at the RIS are obtained in closed forms. For the multiple-antenna BS setup, it is shown that the rank of the SDR solution of the transmit beamforming design is upper bounded by two. Also, the proposed algorithms are analyzed in terms of convergence and complexity. Simulation results show that the RIS-assisted NOMA system can enhance the rate performance significantly, compared to traditional NOMA without RIS and traditional orthogonal multiple access with/without RIS.

We consider a typical heterogeneous network (HetNet), in which multiple access points (APs) are deployed to serve users by reusing the same spectrum band. Since different APs and users may cause severe interference to each other, advanced power control techniques are needed to manage the interference and enhance the sum-rate of the whole network. Conventional power control techniques first collect instantaneous global channel state information (CSI) and then calculate sub-optimal solutions. Nevertheless, it is challenging to collect instantaneous global CSI in the HetNet, in which global CSI typically changes fast. In this article, we exploit deep reinforcement learning (DRL) to design a multi-agent power control algorithm, which has a centralized-training-distributed-execution framework. To be specific, each AP acts as an agent with a local deep neural network (DNN) and we propose a multiple-actor-shared-critic (MASC) method to train the local DNNs separately in an online trial-and-error manner. With the proposed algorithm, each AP can independently use the local DNN to control the transmit power with only local observations. Simulations results show that the proposed algorithm outperforms the conventional power control algorithms in terms of both the converged average sum-rate and the computational complexity.

Multiple-input multiple-output (MIMO) has been used in wireless communications to increase data rates via multiplexing or improve performance via diversity. Additionally, in radar systems, MIMO promises improved parameter identifiability and target resolution. Frequency diverse (FD)-MIMO radar can effectively distinguish targets that are closely spaced in the same angle cell by exploiting its range-angle-dependent transmit beamform. Therefore, in this paper, we first propose an integrated waveform design by embedding weighted, phase-modulated communication signals in the FD-MIMO radar waveform. Then, we derive the Cramer-Rao lower bound (CRLB) of the location estimation and analyze the communication performance achieved when the communication receiver extracts information from the integrated signals. Next, transmit beamforming is optimized to achieve the best tradeoff between the radar and communication performances of the system. Due to the nonconvexity of the optimization problem, we apply sequential parametric convex approximation (SPCA) and semidefinite relaxation (SDR) methods to solve the problem. The simulation results reveal the effects of some parameters, such as the frequency interval, symbol period, and communication symbol sequence, on radar and communication performance and demonstrate the tradeoff between radar and communication obtained by optimizing the transmit beamforming vector.

The performance of a device-to-device (D2D) underlay communication system is limited by the co-channel interference between cellular users (CUs) and D2D devices. To address this challenge, an intelligent reflecting surface (IRS) aided D2D underlay system is studied in this paper. A two-timescale optimization scheme is proposed to reduce the required channel training and feedback overhead, where transmit beamforming at the base station (BS) and power control at the D2D transmitter are adapted to instantaneous effective channel state information (CSI); and the IRS phase shifts are adapted to slow-varying channel mean. Based on the two-timescale optimization scheme, we aim to maximize the D2D ergodic rate subject to a given outage probability constrained signal-to-interference-plus-noise ratio (SINR) target for the CU. The two-timescale problem is decoupled into two sub-problems, and the two sub-problems are solved iteratively with closed-form expressions. Numerical results verify that the two-timescale based optimization performs better than several baselines, and also demonstrate a favorable trade-off between system performance and CSI overhead.

Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of communication channel and directly recover tag symbols. Inspired by the powerful capability of convolutional neural networks (CNN) in exploring the features of data in a matrix form, we design a novel covariance matrix aware neural network (CMNet)-based detection scheme to facilitate DTL for tag signal detection, which consists of offline learning, transfer learning, and online detection. Specifically, a CMNet-based likelihood ratio test (CMNet-LRT) is derived based on the minimum error probability (MEP) criterion. Taking advantage of the outstanding performance of DTL in transferring knowledge with only a few training data, the proposed scheme can adaptively fine-tune the detector for different channel environments to further improve the detection performance. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI.

Conventionally, a substantial number of reflecting elements (REs) is deployed at the intelligent reflecting surface (IRS) to mitigate the effect of the double-fading attenuation in the IRS-aided link, leading to a large surface size and considerable power consumption. In this paper, a new type of IRS, called active IRS, is proposed to solve this challenge by allowing each RE to amplify the incident signal with the assistance of the active loads (negative resistances). Thus, given a power budget at the IRS, the IRS-aided link can be enhanced by increasing the number of active REs as well as amplifying the incident signal. Specifically, we consider the use of an active IRS-aided single input multiple output (SIMO) system, in which the received signal-to-noise ratio (SNR) is maximized, by optimizing not only the reflecting coefficient matrix at the IRS but also the receive beamforming at the receiver. To solve this non-convex problem, we propose an alternating optimization algorithm, that iteratively optimizes the two design variables. In particular, the receive beamforming is founded to be in the form of a linear minimum mean square error (MMSE) detector, and the reflecting coefficient matrix is obtained via the Charnes-Cooper transformation and the semi-definite programming (SDP). Simulation results show that under a practical power consumption model, the proposed active IRS-aided system achieves better performance over the conventional passive IRS-aided system with the same power budget.

The accurate identification of congested links is crucial for network performance monitoring. Network boolean tomography uses end-to-end path measurements to identify congested links, and appears as a significant alternative when direct link monitoring is not available. However, most of existing tomographic methods assume no correlations between links, i.e., the congestion of one link is assumed to be independent from the congestion of any others, hindering their applications in practice because links could become correlated during a joint optimization procedure of many network operations like traffic routing and balancing. In this paper, we study practical network boolean tomography without such an assumption. We elaborate on the ill-posed nature of network boolean tomography to highlight the significance of integrating link correlations, and model the congested link identification from end-to-end congestion observations of paths as a problem of Maximum A-Posteriori (MAP) estimation. To avoid the explicit acquisition of any priori knowledge of link correlations, we then propose a learning-based algorithm with Long Short-term Memory (LSTM), a special recurrent neural network that is good at learning statistical dependencies of sequence elements from historical data. Numerical results over real network topologies validate our learning-based network boolean tomography.

In this paper, we study the cross-layer performance of a symbiotic system comprising passive internet-of-things (IoT) devices and the ambient system using carrier sense multiple access (CSMA) with collision notification (CSMA/CN) MAC protocol. Different from the CSMA with collision avoidance (CSMA/CA), the CSMA/CN protocol can enhance the throughput of the ambient system notably by using physical layer techniques to detect collisions. We first evaluate the physical-layer outage probabilities for the ambient and the backscatter device (BD) system respectively, and then introduce the outage probability of the ambient system into the MAC-layer performance analysis. Thereafter, the crosslayer outage capacities of the ambient system and the BD system are derived by considering the available transmission time for the BD system. We find that the overall system performance can be improved by appropriately setting the reflection coefficient of the BD and the parameters of the MAC protocol. The simulation results are provided to verify our theoretical analysis and demonstrate the system performance.

Reconfigurable Intelligent Surface (RIS) is a new and promising technique to solve the energy-efficiency, spectral-efficiency and hardware-cost problem faced by beyond-5G wireless networks. In this paper, we consider a RIS-assisted two-way relay network in which two users exchange information via the base station (BS) with the help of a RIS. By jointly designing the beamforming matrix at the BS and the phase shifts introduced by the RIS, the minimum SNR of the two users is maximized, under the transmit power constraint at the BS. The formulated problem is non-convex and difficult to solve in general. To start with, we first study the single BS antenna case. The design problem is reduced to the problem of how to choose the phase shifts for the RIS, in addition to optimizing the BS power amplification parameter. A Channel-Gain-Maximization (CGM) algorithm is proposed to solve the problem. For the multiple BS antenna case, we decouple the phase shifts and the beamforming matrix by taking an upper bound of the SNR. The way to obtain the RIS phase shifts is similar to the single BS antenna case while the beamforming matrix is obtained by utilizing an existing solution. A Channel-Gain-Maximization Maximal-Ratio-Beamforming (CGM-MRB) algorithm is thus developed. Finally, numerical results are presented to show the effectiveness of the proposed algorithms.

This paper studies reconfigurable intelligent surface (RIS) empowered symbiotic radio over broadcasting signals, i.e., a base station (BS) broadcasts to multiple primary receivers (PRs) under the assistance of a RIS, while the RIS also transmits information to an Internet-of-Things receiver (IR) by modulating the incident broadcasting signals. We formulate a problem to minimize the BS's transmit power by jointly optimizing the BS's active beamforming and the RIS's phase shifts (i.e., passive beamforming), under the signal-to-noise-ratio constraints of the primary and IoT transmission as well as the RIS's phase-shift constraints. However, the problem is challenging to be solved optimally, since the optimization variables are coupled and the constraints are non-convex. An efficient iterative algorithm based on the block coordinated descent and modified semidefinite relaxation techniques is proposed to solve this problem for both discrete and continuous phase shift scenarios. The convergency of the algorithm is proved and the complexity of the algorithm is analyzed. Numerical results validate that the proposed system outperforms the benchmark of traditional broadcasting system without RIS.

By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum efficiency of wireless networks. In this paper, we study a RIS aided multiuser multiple-input single-output (MISO) wireless power transfer (WPT) system, where the transmitter is equipped with a constant-envelope analog beamformer. We formulate a novel problem to maximize the total received power of all the users by jointly optimizing the beamformer at transmitter and the phase shifts at the RISs, subject to the individual minimum received power constraints of users. We further solve the problem iteratively with a closed-form expression for each step. Numerical results show the performance gain of deploying RIS and the effectiveness of the proposed algorithm.

To manage the interference and enhance the sum-rate of the heterogeneous network (HetNet), conventional power control algorithms first collect instantaneous global channel state information (CSI) and then design sub-optimal power control solutions. But, the global CSI in the HetNet typically changes fast and it is demanding to collect instantaneous global CSI. In this paper, we exploit deep reinforcement learning to design a multi-agent non-cooperative power control algorithm. Particularly, a deep neural networks (DNN) is established at each access point (AP) and a multiple-actor-shared-critic (MASC) method is developed to effectively train the DNNs. Then, each AP can use the DNN to independently optimize the transmit power by feeding only local information into the DNN. Simulation results show that, the proposed algorithm can rapidly converge to an average sum-rate higher than those of conventional power control algorithms.

In this paper, we investigate a large intelligent surface (LIS) assisted symbiotic radio (SR) system, in which a LIS device, operating as an Internet-of-Things (IoT) device, exploits the signal from a primary transmitter (PT) as its communication carrier to achieve its own information transmission, and concurrently serves as a desirable additional link to aid the primary transmission from the PT to a primary receiver (PR). A cooperative beamforming scheme (i.e., active transmit beamforming at the PT and passive reflecting beamforming at the LIS device) is proposed to minimize PT's transmit power under quality-of-service (QoS) constraints of both the primary and LIS device transmissions. Both continuous and discrete phase shift setups of the LIS device are considered. For the continuous phase shift setup, a closed-form solution is derived, analytically showing that by smartly configuring the phase shifts, the signals from primary link and backscatter link can add coherently at the PR; while for the discrete phase shift setup, a near-optimal solution for the 1-bit phase shifter is obtained via the semi-definite relaxation (SDR) technique, and a general successive refinement algorithm (SRA) is developed for any-bit phase shifter. Simulation results demonstrate that cooperative beamforming design can adaptively adjust beamformers to strike a balance between the primary and LIS device transmissions.

Unmanned aerial vehicle (UAV) is considered to be a key component in the next-generation cellular networks. Considering the non-convex characteristic of the trajectory design and power allocation problem, it is difficult to obtain the optimal joint strategy in UAV-assisted cellular networks. In this paper, a reinforcement learning-based approach is proposed to obtain the maximum long-term network utility while meeting with user equipments' quality of service requirement. The Markov decision process (MDP) is formulated with the design of state, action space, and reward function. In order to achieve the joint optimal policy of trajectory design and power allocation, deep reinforcement learning approach is investigated. Due to the continuous action space of the MDP model, deep deterministic policy gradient approach is presented. Simulation results show that the proposed algorithm outperforms other approaches on overall network utility performance with higher system capacity and faster processing speed.

Due to the explosive growth of data traffic and poor indoor coverage, ultra-dense network has been introduced as a fundamental architectural technology for the 5G-and-beyond systems. As the telecom operator is shifting to the plug-and-play manner in mobile networks, network planning and optimization become difficult, especially in residential small-cell base stations (SBSs) deployment. Under this circumstance, severe inter-cell interference becomes inevitable which deteriorates network performance and the quality of service (QoS) of user equipments (UEs). In this paper, we propose a fully distributed self-learning interference mitigation (SLIM) scheme for autonomous networks under a model-free multi-agent reinforcement learning (MARL) framework. In SLIM, SBSs autonomously perceive surrounding interferences and determine downlink transmit power without necessity of signaling interaction between SBSs for mitigating interferences. To tackle the dimensional disaster of joint action in MARL model, we employ the Mean Field Theory to approximate the action value function, thus to greatly decrease the computational complexity. Simulation results based on 3GPP dual-stripe urban model demonstrate that SLIM outperforms conventional interference coordination schemes in mitigating interference while guaranteeing UEs'QoS.

Intelligent reflecting surface (IRS), with a large number of reflective elements, is a promising technology to achieve both spectrum and energy efficient wireless communication. The IRS can reflect the incident electromagnetic wave passively and steer it to the desirable way before reaching the intended receiver by adjusting the phase shift on the reflective elements. In order to better improve communication quality, the beamforming vector at the base station (BS) and the phase shift induced by the IRS should be jointly designed carefully. However, thus far, previous works on IRS have assumed that the channel state information (CSI) is perfectly known at the BS, which is not available in the practical systems. In this paper, we study an IRS-enhanced multi-user multiple-input single-output (MISO) downlink communication system assuming imperfect CSI. An optimization problem is formulated to jointly optimize the beamforming vector at the BS and the phase shift at the IRS such that the total transmit power is minimized under the individual outage probability constraints. An algorithm based on alternating optimization (AO) and semi-definite relaxation (SDR) is proposed to solve this challenging non-convex problem. Finally, numerical results have validated the effectiveness of the proposed algorithm.

It is widely acknowledged that network slicing can tackle the diverse usage scenarios and connectivity services that the 5G-and-beyond systems need to support. To guarantee performance isolation while maximizing network resource utilization under traffic uncertainty, network slice needs to be reconfigured adaptively. However, it is commonly believed that the fine-grained resource reconfiguration problem is intractable due to the extremely high computational complexity caused by the numerous variables. In this paper, we investigate network slice reconfiguration with aim of minimizing long-term resource consumption by exploiting Deep Reinforcement Learning (DRL). To address the curse of dimensionality of the problem, we propose to incorporate the Branching Dueling Q-network (BDQ) into DRL, to avoid some unnecessary calculations of Q-value by separating the Q-network into a shared value branch and a number of distributed advantage branches. Furthermore, the value branch and the advantage branch of each dimension are aggregated to derive the corresponding dimension's sub-Q-value. Then the best reconfiguration action is composed of the subactions in individual dimensions which are selected by €-greedy policy. Finally, we design an intelligent online network slice reconfiguration policy based on BDQ and extensive simulation experiments are conducted to validate the effectiveness of the proposed slice reconfiguration policy.

Cognitive radio (CR) is an effective solution to increase the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share the spectrum with primary users (PUs). On the other hand, intelligent reflecting surface (IRS) is a promising approach to enhance the energy efficiency (EE) of wireless communication systems through passively reconfiguring the channel environments. In this paper, we propose an IRS enhanced downlink multiple-input single-output (MISO) CR systems to improve both SE and EE, where a single SU coexists with a primary network with multiple primary user receivers (PU-RXs). Specifically, for the MISO-CR system, we maximize the achievable rate of SU subject to a total power constraint on an SU transmitter (SU-TX) and an interference temperature (IT) constraint on PU-RXs, by jointly optimizing the beamforming vector at SU-TX and the phase shifts at the IRS. Furthermore, both perfect channel state information (CSI) and imperfect CSI are considered in the optimization. Numerical results demonstrate that the IRS can significantly improve the achievable rate of SU-RX under both the perfect and imperfect CSI conditions.

Intelligent reflecting surface (IRS) is a new and disruptive technology to achieve spectrum-, energy, and cost-efficient wireless networks. In this paper, we consider an IRS-assisted non-orthogonal-multiple-access (NOMA) system in which a base station (BS) transmits superposed downlink signals to multiple users. A combined-channel-strength (CCS) based user ordering scheme is first proposed. In order to optimize the rate performance and ensure user fairness, we further maximize the minimum decoding signal-to-interference-plus-noise-ratio (i.e., equivalently the rate) of all users, by jointly optimizing the power allocation at the BS and the phase shifts at the IRS. However, the formulated problem is non-convex and difficult to be solved optimally. By leveraging the block coordinate descent and semidefinite relaxation techniques, an efficient algorithm is then proposed to obtain a suboptimal solution. Simulation results show that the IRS-assisted downlink NOMA system can enhance the rate performance significantly, compared to traditional NOMA without IRS and traditional orthogonal multiple access with/without IRS, and the rate degradation due to the IRS's finite phase resolution is slight.

In the overlay D2D networks, multiple D2D pairs coexist with full frequency reuse resulting in complicated interference. Traditional centralized power control methods require instantaneous interference information and so are difficult to implement in a D2D network due to the backhaul delay and high computational requirements. To overcome this challenge, we propose a distributed power allocation algorithm called interference feature extractor aided recurrent neural network (IFE-RNN). The core idea of the scheme is described as follows. First, we design linear filters with various sizes termed IFEs to extract the local interference patterns from the outdated interference information. This feature extraction process enables our network to precisely learn the interference patterns around D2D links, and so as to provide more effective power allocation strategies. Then, we propose to predict the real-time interference pattern based on the outputs of the IFEs and further make power decision. The prediction and decision can be modelled as a Markov decision problem (MDP) and solved by using a recurrent neural network (RNN). The acquisition of the channel correlation can greatly improve the efficiency and the accuracy of our network according to our simulation results. It is worth noting that an input reduction process is also designed to reduce the space complexity from O(N2) to O(1) which speeds up the operation time and reduces the system overhead. Finally, extensive simulation results show that the proposed algorithm achieves an encouraging performance compared to the state-of-the-art power allocation algorithm.

Ambient Backscatter Communication (AmBC) is a promising technology for green IoT, which overcomes the energy, cost and spectrum resource shortage challenges. In AmBC, the weak backscatter link strength caused by the double-fading is a big challenge for IoT device symbol detection. This paper focuses on receiver design with the M PSK RF source symbol. Most previous works have not considered the issue of channel estimation that is very important for wireless communications. We focus on precisely estimating the channels at each of the AmBC receiving antennas, in particular on resolving the quadrant ambiguities in the channel estimates. The proposed channel estimator makes use of both the pilot and the data symbols of the RF source to eliminate quadrant ambiguity and enhance the estimation accuracy. We propose a novel distance detector (DD) that makes its decision in each IoT symbol interval based on which of the calibrated channel estimates the current channel estimate is closer to in Euclidean distance. What is more, we propose two modified distance detection methods to enhance the bit error rate (BER) performance. Finally, extensive numerical results show that the proposed distance detection method achieves performance comparable to that of the optimal detector with perfect channel state information (CSI).

To overcome the challenges in achieving extremely high throughput and super-massive access in future wireless communications, in this paper, we focus on a novel large intelligent surfacelantennas (LISA)-assisted symbiotic radio (SR) system. Specifically, in the proposed system, a LISA transmits messages to its destination by using backscatter communication, and at the same time, it assists the transmission from a base station (BS) to its user by intelligently reconfiguring the wireless environment. In this paper, we are interested in the joint active (BS) and passive (LISA) beamforming design problem to maximize the transmission rate of LISA subject to the BS transmission rate constraint. Due to the non-convexity of the problem, we first relax the rank-one constraint based on the technique of semi-definite relaxation (SDR) and then decouple that optimization problem into two subproblems based on the block coordinate descent (BCD) method, each of which is a convex problem. Due to the expectation terms in the constraints, we propose two algorithms called the Lagrangian algorithm and approximate algorithm to address it. Finally, simulation results are presented to validate the effectiveness of the proposed algorithms and the superiority of the proposed system.

In this article, a symbiotic radio (SR) system is proposed to support passive Internet of Things (IoT), in which a backscatter device (BD), also called IoT device, is parasitic in a primary transmission. The primary transmitter (PT) is designed to assist both the primary and BD transmissions, and the primary receiver (PR) is used to decode the information from the PT as well as the BD. The symbol period for BD transmission is assumed to be either equal to or much greater than that of the primary one, resulting in parasitic SR (PSR) or commensal SR (CSR) setup. We consider a basic SR system which consists of three nodes: 1) a multiantenna PT; 2) a single-antenna BD; and 3) a single-antenna PR. We first derive the achievable rates for the primary and BD transmissions for each setup. Then, we formulate two transmit beamforming optimization problems, i.e., the weighted sum-rate maximization (WSRM) problem and the transmit power minimization (TPM) problem, and solve these nonconvex problems by applying the semidefinite relaxation (SDR) technique. In addition, a novel transmit beamforming structure is proposed to reduce the computational complexity of the solutions. The simulation results show that for CSR setup, the proposed solution enables the opportunistic transmission for the BD via energy-efficient passive backscattering without any loss in spectral efficiency, by properly exploiting the additional signal path from the BD.

We consider a homogeneous cellular network where a multi-antenna base station (BS) in each cell transmits messages to its intended user over a common frequency band. To improve the system capacity of this multi-cell multi-input single-output (MISO) interference channel, one of the state-of-the-art algorithms, namely, downlink-beamforming coordination, allows all BSs to cooperate with one another to mitigate the effect of inter-cell interference. However, most existing algorithms are suboptimal and impractical in a dynamic wireless environment, due to the high computational complexity and the overhead involved in collecting global channel state information (CSI). In this study, we exploit deep reinforcement learning (DRL) and propose a distributed dynamic downlink-beamforming coordination (DDBC) method with partial observability of the CSI. Each BS is able to train its own deep Q-network and employs appropriate beamformer depending on its environment, which is observed through a designed limited-information exchange protocol. The simulation results show that the proposed DRL-based DDBC method, with a considerably lower system overhead, achieves a system capacity that is very close to that of the fractional programming algorithm with global and instantaneous CSI measurements. In addition, this work demonstrates the potential of utilizing DRL to solve DDBC problems in a more practical manner.

Operating LTE networks in unlicensed bands together with legacy WiFi systems is deemed as a promising technique to support explosively growing mobile traffic. In conventional LTE/WiFi spectrum sharing schemes, LTE systems need to know WiFi traffic demands for optimizing system parameters to protect WiFi systems, for which the two systems are required to cooperate with each other via signallings exchanges. However, it is difficult to establish a dedicated channel among the two independent systems for exchanging signallings. Hence, in this paper, we propose an intelligent duty-cycle medium access control protocol to realize the effective and fair spectrum sharing between LTE and WiFi systems without requiring signalling exchanges. Specifically, we first design a duty-cycle spectrum sharing framework, which allows an LTE system to share the spectrum with a WiFi system by using time sharing. After that, we develop deep reinforcement learning (DRL)-based algorithms to learn WiFi traffic demands by analyzing WiFi channel activity, e.g., the idleness/business of WiFi channels, which can be observed by the LTE system via monitoring WiFi channels. Based on the learnt knowledge, the LTE system can adaptively optimize LTE transmission time to maximize its own throughput and meanwhile to provide sufficient protection to the WiFi system. Simulation results show that, in terms of LTE throughput and WiFi protection, the performance of the proposed intelligent scheme can approach that of the genie-aided exhaustive search algorithm, which needs the perfect knowledge of WiFi traffic demands through massive signalling exchanges and is of high computational complexity.

Network slicing is identified as a fundamental architectural technology for future mobile networks since it can logically separate networks into multiple slices and provide tailored quality of service (QoS). However, the introduction of network slicing into radio access networks (RAN) can greatly increase user handover complexity in cellular networks. Specifically, both physical resource constraints on base stations (BSs) and logical connection constraints on network slices (NSs) should be considered when making a handover decision. Moreover, various service types call for an intelligent handover scheme to guarantee the diversified QoS requirements. As such, in this article, a multiagent reinforcement LEarning based Smart handover Scheme, named LESS, is proposed, with the purpose of minimizing handover cost while maintaining user QoS. Due to the large action space introduced by multiple users and the data sparsity caused by user mobility, conventional reinforcement learning algorithms cannot be applied directly. To solve these difficulties, LESS exploits the unique characteristics of slicing in designing two algorithms: 1) LESS-DL, a distributed Q-learning algorithm to make handover decisions with reduced action space but without compromising handover performance; 2) LESS-QVU, a modified Q-value update algorithm which exploits slice traffic similarity to improve the accuracy of Q-value evaluation with limited data. Thus, LESS uses LESS-DL to choose the target BS and NS when a handover occurs, while Q-values are updated by using LESS-QVU. The convergence of LESS is theoretically proved in this article. Simulation results show that LESS can significantly improve network performance. In more detail, the number of handovers, handover cost and outage probability are reduced by around 50%, 65%, and 45%, respectively, when compared with traditional methods.

It is widely acknowledged that network slicing can tackle the diverse usage scenarios and connectivity services that the 5G-and-beyond system needs to support. To guarantee performance isolation while maximizing network resource utilization under dynamic traffic load, network slice needs to be reconfigured adaptively. However, it is commonly believed that the fine-grained resource reconfiguration problem is intractable due to the extremely high computational complexity caused by numerous variables. In this article, we investigate the reconfiguration within a core network slice with aim of minimizing long-term resource consumption by exploiting Deep Reinforcement Learning (DRL). This problem is also intractable by using conventional Deep Q Network (DQN), as it has a multi-dimensional discrete action space which is difficult to explore efficiently. To address the curse of dimensionality, we propose to exploit Branching Dueling Q-network which incorporates the action branching architecture into DQN to drastically decrease the number of estimated actions. Based on the discrete BDQ network, we develop an intelligent network slice reconfiguration algorithm (INSRA). Extensive simulation experiments are conducted to evaluate the performance of INSRA and the numerical results reveal that INSRA can minimize the long-term resource consumption and achieve high resource efficiency compared with several benchmark algorithms.

Ambient backscatter technology utilizes ambient radio frequency (RF) signals to enable battery-free devices (tags and readers) to communicate. Most existing studies assume single-antenna tags. However, in this paper, we consider tags with multiple antennas, which are exploited to provide transmit diversity. Channel state information (CSI) estimation is then a fundamental challenge because the tags can transmit few or no training symbols. To overcome it, we require detectors that operate without CSI. Thus, we propose and design three detectors based on the chi-squared test, F-test and Bartlett's test. The latter two are blind detectors because they require neither CSI nor the knowledge of RF source power and noise variance. We derive the detection probability bounds for the first two detectors. We also propose optimal tag antenna selection schemes to maximize the detection probabilities. Finally, simulation results are provided to corroborate our theoretical studies.

Reconfigurable intelligent surfaces (RIS) is a promising solution to build a programmable wireless environment via steering the incident signal in fully customizable ways with reconfigurable passive elements. In this paper, we consider a RIS-aided multiuser multiple-input single-output (MISO) downlink communication system. Our objective is to maximize the weighted sum-rate (WSR) of all users by joint designing the beamforming at the access point (AP) and the phase vector of the RIS elements, while both the perfect channel state information (CSI) setup and the imperfect CSI setup are investigated. For perfect CSI setup, a low-complexity algorithm is proposed to obtain the stationary solution for the joint design problem by utilizing the fractional programming technique. Then, we resort to the stochastic successive convex approximation technique and extend the proposed algorithm to the scenario wherein the CSI is imperfect. The validity of the proposed methods is confirmed by numerical results. In particular, the proposed algorithm performs quite well when the channel uncertainty is smaller than 10%.

This paper proposes a peer-assisted power supply approach for a wireless powered Internet of Things (IoT) non-orthogonal multiple access (NOMA) network in which passive user equipments (UEs) without battery harvest energy from active UEs equipped with power supply. Specifically, passive UEs harvest energy from active UEs during their uplink transmission using NOMA. They then upload information along with the active UEs. Particularly, considering the combination of time division multiple access (TDMA) and NOMA, under the assumption that the power of active UEs is fixed, we study different transmission modes (non-stand-alone/stand-alone) and different operations (NOMA/NOMA-plus-TDMA). Taking into account the practical applications, we re-investigate the above schemes in the scenario where active UEs' energy is limited, i.e., the power of active UEs is not fixed and is affected by time allocation. We maximize the sum-throughput of each proposed model. We prove that the optimization problems for all cases are convex, and we obtain closed-form solutions for most cases. Finally, we show by simulations that, in all cases, the transmit power of active UEs and the number of UEs have a positive effect on the sum-throughput. Besides, in terms of maximizing the sum-throughput, the NOMA-plus-TDMA operation outperforms the NOMA operation. If active UEs' power is fixed, the non-stand-alone transmission outperforms the stand-alone transmission, and vice versa.

This paper investigates the rotary-wing unmanned aerial vehicle (UAV)-enabled full-duplex wireless-powered Internet-of-Things (IoT) networks, in which a rotary-wing UAV equipped with a full-duplex hybrid access point (HAP) serves multiple sparsely-distributed energy-constrained IoT sensors. The UAV broadcasts energy when flying and hovering, and collects information only when hovering. It is assumed that the transmission range of the UAV is limited and the sensors are sparsely distributed in the IoT network. Under these practical assumptions, we formulate three optimization problems: a sum-throughput maximization (STM) problem, a total-time minimization (TTM) problem, and a total-energy minimization (TEM) problem. For the TEM problem, we further take into consideration that the power needed for hovering, flying, and transmitting are different. For the STM, TTM and TEM problems, optimal solutions are obtained. Finally, numerical results show that the performance achieved by the proposed optimal time allocation schemes outperform existing time allocation schemes. It is also observed that i) the time allocation between hovering and flying time has different trends for different goals; ii) there is an optimal UAV transmit power range that minimizes the energy consumed by the UAV during the entire cycle.

In this paper, we are interested in symbiotic radio networks (SRNs), in which an Internet-of-Things (IoT) network parasitizes in a primary cellular network to achieve spectrum-, energy-, and infrastructure-efficient communications. Each IoT device transmits its own information by backscattering the signals from the primary network without using active radio-frequency (RF) transmitter chain. We consider the symbiosis between the cellular network and the IoT network and focus on the user association problem in SRN. Specifically, the base station (BS) in the primary network serves multiple cellular users using time division multiple access (TDMA) and each IoT device is associated with one cellular user for information transmission. The objective of user association is to link each IoT device to an appropriate cellular user by maximizing the sum rate of all IoT devices. However, the difficulty in obtaining the full real-time channel information makes it difficult to design an optimal policy for this problem. To overcome this issue, we propose two deep reinforcement learning (DRL) algorithms, both use historical information to infer the current information in order to make appropriate decisions. One algorithm, referred to as centralized DRL, makes decisions for all IoT devices at one time with globally available information. The other algorithm, referred to as distributed DRL, makes a decision only for one IoT device at one time using locally available information. Finally, simulation results show that the two proposed DRL algorithms achieve performance comparable to the optimal user association policy which requires perfect real-time information, and the distributed DRL algorithm has the advantage of scalability.

Unmanned aerial vehicles (UAVs) have recently been proposed as moving base stations to collect data from ground IoT nodes in remote areas. Since IoT nodes are normally battery-limited, energy efficiency is an important metric in IoT systems. In order to improve energy efficiency in UAV-enabled IoT systems, it is necessary to allocate both channels and transmit power properly for IoT nodes. Motivated by the superior performance of deep reinforcement learning (DRL) in decision-making tasks, we propose a DRL-based channel and power allocation framework in a UAV-enabled IoT system. With the proposed framework, the UAV-BS is able to intelligently allocate both channels and transmit power for uplink transmissions of IoT nodes to maximize the minimum energy-efficiency among all the IoT nodes. Simulation results validate the effectiveness of the proposed algorithm and show its superiority over the- state-of-the-arts.

In this paper, we study the resource allocation design for downlink multicarrier non-orthogonal multiple access systems with short-packet communications (MC-NOMA-SPC). In contrast to long- packet communications in conventional wireless systems, SPC suffers from a transmission rate degradation and a significant decoding error rate. Thus conventional resource allocation design based on the Shannon capacity assuming infinite blocklength is no longer optimal. In this paper, we employ the effective-throughput as the performance metric to evaluate the tradeoff between the transmission rate and the decoding error rate. Then, we jointly optimize the subcarrier assignment, transmission power allocation, and transmission rate adaptation of each user to maximize the total weighted effective-throughput subject to various practical constraints. Since the problem formulated belongs to a non-convex mixed integer non-linear programming (MINLP) problem, we develop an efficient algorithm based on the dynamic programming (DP) recursion framework to obtain its optimal solutions. In addition, we analyze the complexity of the proposed algorithm theoretically. Finally, simulation results show that the proposed optimal algorithm outperforms the suboptimal baseline schemes significantly.

Intelligent reflecting surface (IRS) is a promising solution to build a programmable wireless environment for future communication systems, in which the reflector elements steer the incident signal in fully customizable ways by passive beamforming. This work focuses on the downlink of an IRS-aided multiuser multiple-input single-output (MISO) system. A practical IRS assumption is considered, in which the incident signal can only be shifted with discrete phase levels. Then, the weighted sum-rate of all users is maximized by joint optimizing the active beamforming at the base-station (BS) and the passive beamforming at the IRS. This non-convex problem is firstly decomposed via Lagrangian dual transform, and then the active and passive beamforming can be optimized alternatingly. In addition, an efficient algorithm with closed-form solutions is proposed for the passive beamforming, which is applicable to both the discrete phase- shift IRS and the continuous phaseshift IRS. Simulation results have verified the effectiveness of the proposed algorithm as compared to different benchmark schemes.

In this paper, we consider a smart jammer that only attacks the channel if it detects activities of legitimate devices on that channel. To cope with smart jamming attacks, we propose an intelligent deception strategy in which the legitimate device will send fake transmissions to lure the jammer. Then, if the jammer launches attacks to the channel, the legitimate device can either backscatter the jamming signals to transmit data or harvest energy from the jamming signals for future active transmission. In this way, we can not only undermine the attack ability of the jammer, but also leverage jamming attacks as means to enhance system performance. In addition, to find an optimal defense strategy for the legitimate device under uncertainty of wireless environment as well as incomplete information from the jammer, we develop Q-learning and deep Q-learning algorithms based on the Markov decision process. Through simulation results, we demonstrate that our proposed solution is able to not only deal with smart jamming attacks, but also successfully leverage jamming attacks to improve the system performance.

This paper focuses on learning the representing dictionaries for sparse channel estimation in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. To overcome the energy leakage problem in traditional sparse channel estimation, we propose a geographical dictionary- based spatial channel model to efficiently represent the cell-specific geographical characteristics. Based on that, the properties, especially the block structure, of the expected dictionaries are analyzed, and we design a data-driven joint block-structured dictionary learning algorithm (JBSDL) to obtain the expected representing dictionaries. The simulation environment is generated according to 3GPP standard, and we systematically study the properties of the learned dictionaries, which reveals the physical meaning of the dictionary learning results in massive MIMO systems. The proposed method demonstrates superior downlink channel estimation performance through the simulations.

In this paper, we propose a Bayesian cooperative detection algorithm for ambient backscatter assisted generalized spatial modulation (AB-GSM) system, which recovers information from both the ambient backscatter sensor (ABS) and the generalized spatial modulation (GSM) source. To exploit the inherent sparsity of GSM, we adopt a two-layer hierarchical prior model for source symbol. Moreover, we derive linear detectors for comparison. Simulation results show that the proposed algorithm can achieve a superior detection accuracy than linear detectors in under-determined AB-GSM systems.

Traditionally, dynamic spectrum access (DSA) based on cooperative spectrum sensing relies on a centralized fusion centre to fuse and store the sensing results, which is vulnerable to single point of failure. In this paper, we propose a sensing-based DSA framework which is enabled by blockchain. The proposed DSA framework includes a protocol that specifies a time-slotted-based five-phase operations. In the proposed framework, each secondary user (SU) acts as both a sensing node for cooperatively sensing the spectrum and a node, i.e., a miner and a verifier, in the blockchain network for mining and updating the sensing and access results in a distributed and secure manner without the need for a fusion centre. In order to incentivize SUs for participating in such energy-consuming operations of the blockchain network, we reward them with tokens for sensing and mining, which can be used to bid for the access to the spectrum opportunities. The sensing and mining policies which they use to determine when to sense and mine affect the number of tokens they can obtain and subsequently how they bid for the spectrum. Hence, the performance of the system depends on their sensing-access-mining policy. Therefore, we consider a heuristic sensing-access-mining policy that determines whether to participate in sensing and mining in a probabilistic manner and that determines how much to bid based on its buffer occupancy and the number of available tokens. Simulation results show that although increasing sensing and mining probabilities can increase average transmission rate, it also leads to higher energy consumption. Moreover, there exists an optimal set of sensing and mining probabilities that maximize the system energy efficiency.

As a promising candidate to alleviate the mobile traffic explosion, device-to-device (D2D) technology enables the direct communications between proximal devices. To mitigate the mutual interference among D2D pairs and improve the spectrum efficiency, this paper investigates a weighted-sum-rate (WSR) maximization problem in multi-channel D2D networks. Particularly, we propose a deep reinforcement learning based scheme for each D2D pair to make decisions independently on the channel selection and power control. In contrast to the conventional methods that require instantaneous global network information, the proposed scheme only needs local information and outdated feedbacks. Simulation results demonstrate that, in terms of the WSR, the proposed scheme outperforms the conventional suboptimal fractional programming algorithm that requires the instantaneous global network information.

This study investigates time-division duplex (TDD) orthogonal-frequency-division multiple access (OFDMA) unmanned aerial vehicles (UAVs)-aided wireless-powered Internet-of-Things (IoT) networks. Here, a swarm of UAVs simultaneously charge all IoT devices with constant power during a downlink (DL) phase. Using the harvested energy, each IoT device transmits data to an UAV during an uplink (UL) phase via OFDMA. We propose a novel framework to maximize the UL throughput by formulating and solving a joint optimization problem to find the optimal DL and UL time portions, device-UAV association, and the 3D placement of the UAVs. Using our proposed framework, it is shown that the 3D position of the UAVs will have different trends during the UL communications and the DL charging. The proposed TDD-OFDMA UAVs- aided can significantly improve the sum throughput of the IoT devices compare to the fixed base stations schemes.

In this paper, we are interested in symbiotic radio networks (SRNs) and focus on the user association problem in SRNs. Specifically, in an SRN, the base station serves multiple cellular users using time division multiple access (TDMA) and each IoT device is associated with one cellular user for information transmission. The objective of user association is to link each IoT device to an appropriate cellular user by maximizing the sum rate of all IoT devices. However, the difficulty in obtaining the full real-time channel information makes it difficult to design an optimal policy for this problem.To overcome this issue, we propose a deep reinforcement learning (DRL) algorithm, which uses historical knowledge to infer the current information in order to make appropriate decisions for this user association problem. Finally, simulation results show that the proposed DRL algorithm achieves performance comparable to the optimal user association policy which requires perfect real-time information.

To ensure that the data aggregation, data storage, and data processing are all performed in a decentralized but trusted manner, we propose to use the blockchain with the mining pool to support IoT services based on cognitive radio networks. As such, the secondary user can send its sensing data, i.e., transactions, to the mining pools. After being verified by miners, the transactions are added to the blocks. However, under the dynamics of the primary channel and the uncertainty of the mempool state of the mining pool, it is challenging for the secondary user to determine an optimal transaction transmission policy. In this paper, we propose to use the deep reinforcement learning algorithm to derive an optimal transaction transmission policy for the secondary user. Specifically, we adopt a Double Deep-Q Network (DDQN) that allows the secondary user to learn the optimal policy. The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms the conventional Q-learning scheme in terms of reward and learning speed.

This paper considers a UAV-enabled backscatter communication network (UBCN) in which multiple backscatter devices (BDs) on the ground are illuminated by their associated ground carrier emitters (CEs) and transmit information to a flying UAV in a dynamic time-division-multiple-access manner. To tackle the critical issue of limited UAV on-board energy and CE transmission energy, we maximize the energy efficiency (EE) by jointly optimizing the BD scheduling, the BDs' power reflection coefficients, the CEs' transmission powers, and the UAV trajectory, subject to the BDs' throughput constraints and other practical constraints. Furthermore, we propose an iterative algorithm to solve the formulated non-convex problem, by leveraging the block coordinated decent and the successive convex approximation techniques. Finally, extensive simulation results show that the proposed communicate-while-fly scheme achieves significant EE gains compared to the benchmark hover-and-fly scheme.

Unmanned Aerial Vehicles (UAVs) have recently been proposed as flying base stations, called UAV-BSs, to provide reliable connections and extend the coverage of the existing wireless networks. The mobility of UAV-BSs leads to a dynamic network environment, in which the global network information is hard to be obtained. Since frequent information exchanges cause huge signaling overheads, it is difficult to deploy centralized algorithms in UAV networks. Hence, we propose a distributed deep reinforcement learning (DRL) framework for multi-user access control in UAV networks. In particular, each user makes its own access decisions independently based on the local network information, and maximizes the long-term throughput while avoiding frequent handovers. Simulation results have validated the effectiveness of the proposed algorithm and shown the superiority of the proposed DRL framework over the state of arts.

The ambient backscatter communication (AmBC) system enables radio-frequency (RF) powered devices (e.g., tags, sensors) to transmit their information bits to readers by backscattering and modulating the ambient RF signal. Different from traditional radio-frequency identification (RFID) systems, an AmBC system does not require a reader to transmit excitation signals to the tag and there is no additional carrier emitters required. Therefore, AmBC systems exhibit low-cost and high energy efficiency. The existing AmBC systems utilize an energy detector or a Minimum Mean Square Error (MMSE) detector to detect tag signals which suffers from high bit error rate (BER). In this paper, a machine learning based detection method is proposed to detect the tag signals for an AmBC system by transforming the detection problem into a classification problem. In more detail, the proposed method classifies the received signals into two groups based on the energy features of the received signals. Our simulation results show that the proposed machine learning based detection method outperforms the traditional detection methods, especially in the low SNR regime.

To address the drastic increase of multimedia traffic dominated by streaming videos, mobile edge computing (MEC) can be exploited to accelerate the development of intelligent caching at mobile network edges to reduce redundant data transmissions and improve content delivery performance. Under the MEC architecture, content providers (CPs) can access MEC servers to deploy popular content items to improve users' quality of experience. Designing an efficient caching policy is crucial for CPs due to the content dynamics, unknown spatial-temporal traffic demands and limited storage capacity. The knowledge of users' preference is important for efficient caching, but is also often unavailable in advance. Machine learning can be used to learn the users' preference based on historical demand information and decide the content items to be cached at the MEC servers. In this paper, we propose a learning based cooperative content caching policy for the MEC architecture, when the users' preference is unknown and only the historical content demands can be observed. We model the cooperative content caching problem as a multi-agent multi-armed bandit problem and propose a multiagent reinforcement learning (MARL)-based algorithm to solve the problem. Simulation experiments are conducted based on the real dataset from MovieLens and the numerical results show that the proposed MARL-based caching policy can significantly improve content cache hit rate and reduce content downloading latency in comparison with other popular caching strategies.

The existing spectrum sensing methods mostly make decisions using model-driven test statistics, such as energy and eigenvalues. A weakness of these model-driven methods is the difficulty in accurately modeling for practical environment. In contrast to the model-driven approach, in this paper, we use a deep neural network to automatically learn features from data itself, and develop a data-driven detection approach. Inspired by the powerful capability of convolutional neural network (CNN) in extracting features of matrix-shaped data, we use the sample covariance matrix as the input of CNN, proposing a novel covariance matrix-aware CNN-based detection scheme, which consists of offline training and online detection. Different from the existing deep learning-based detection methods which replace the whole detection system by an end-to-end neural network, in this work, we use CNN for offline test statistic design and develop a practical threshold-based online detection mechanism. Specially, according to the maximum a posteriori probability (MAP) criterion, we derive the cost function for offline training in the spectrum sensing model, which guarantees the optimality of the designed test statistic. Simulation results have shown that whether the PU signals are independent or correlated, the detection performance of the proposed method is close to the optimal bound of estimator-correlator detector. Particularly, when the PU signals are correlated with a correlation coefficient 0.7, the probability of detection of the proposed method outperforms the conventional maximum eigenvalue detection method by nearly 7.5 times at SNR = -14dB.

In this paper, we are interested in a symbiotic radio (SR) system, in which a passive full-duplex backscatter device (BD) is parasitic in an active primary transmission. The primary transmitter (PT) with multiple antennas is designed to broadcast common messages to the primary receiver (PR) and the BD, as well as to support passive information transmission from the BD to the PR. To do so, the full-duplex BD uses a fraction of the incident signal from the PT to decode the common messages, and simultaneously transmits its own information to the PR by backscattering the remaining part of the incident signal. We formulate a transmit power minimization problem by jointly designing the beamforming vector at the PT and the power splitting factor at the BD. This problem is first solved by the semi-definite relaxation technique together with a one-dimensional linear exhaustive search over the power splitting factor. Then, a suboptimal but low-complexity solution with closed-form expressions is proposed. Simulation results have shown that the proposed suboptimal solution achieves almost the same performance as the one obtained by the exhaustive search.

Network slicing (NS) has been identified as a fundamental technology for future mobile networks to meet extremely diverse communication requirements by providing tailored quality of service (QoS). However, due to the introduction of NS into radio access networks (RAN) forming a UE-BS-NS three-layer association, handoff becomes very complicated and cannot be resolved by conventional policies. In this paper, we propose a multi-agent reinforcement LEarning based Smart handoff policy with data Sharing, named LESS, to reduce handoff cost while maintaining user QoS requirements in RAN slicing. Considering the large action space introduced by multiple users and the data sparsity problem due to user mobility, LESS is designed to have two components: 1) LESS-DL, a modified distributed Q-learning algorithm with small action space to make handoff decisions; 2) LESS-DS, a data sharing mechanism using limited data to improve the accuracy of handoff decisions made by LESS-DL. The proposed LESS mechanism uses LESS-DL to choose both the target base station and NS when a handoff occurs, and then updates the Q-values of each user according to LESS-DS. Numerical results show that in typical scenarios, LESS can significantly reduce the handoff cost when compared with traditional handoff policies without learning.

As a promising technique to handle the conflict between the explosive mobile traffic and scare spectrum resource, license-assisted access (LAA) has been proposed to operate the LTE network on the unlicensed band. This paper considers the LAA-LTE system coexisting with an unsaturated WiFi system. Specifically, deep reinforcement learning (DRL) is adopted to enable the LAA-LTE system to learn the traffic pattern of the WiFi system and adaptively optimize its transmission time in each frame. Different from conventional coexistence schemes, which require massive signaling exchanges between the two systems to achieve fairness, the proposed DRL-based algorithm can maximize the spectrum usage while protecting the WiFi system without such signaling requirements. Simulation results demonstrate that the proposed scheme can achieve almost the same LAA-LTE throughput and protection to the WiFi system of the genie-aided exhaustive search algorithm, which has high complexity and requires to know the WiFi information perfectly.

This paper investigates the rotary-wing unmanned aerial vehicle (UAV)-aided full-duplex wireless powered Internet-of-Things (IoT) networks, in which a rotary-wing UAV equipped with a full-duplex hybrid access point (HAP) serves multiple sparsely distributed energy constrained IoT sensors. The UAV broadcasts energy while flying and hovering. On the other hand, the UAV collects information while hovering. It is assumed that the transmission range of the UAV is limited and the sensors are sparsely distributed in the IoT networks. Thus, the energy broadcasted from the UAV is only available for the adjacent sensor. Here, we propose a new line model for UAV-aided IoT networks. With the proposed line model, we investigate the optimal time allocation to maximize the network throughput subject to a total time constant and a UAV maximum flight speed. The formulated throughput maximization problem is proved to be a convex optimization problem and the optimal solution is obtained by the mutual coupling of the convex optimization conditions. We further propose a simple algorithm under a specific condition. Finally, the numerical results verify that the performance achieved by the proposed optimal time allocation scheme outperforms the existing time allocation schemes. The maximum communication distance of the UAV at different heights and different transmission powers can be obtained through the comparison of algorithms.

Non-orthogonal multiple access (NOMA) is envisioned to be a key technology to enhance the spectrum efficiency for 5G cellular networks. Meanwhile, ambient backscatter communication (AmBC) is considered as a promising solution to Internet-of-Things (IoT), due to its high spectrum- and power-efficiency. In this paper, we are interested in an integrated system of cellular and IoT networks, and propose a backscatter-NOMA system, which incorporates a downlink NOMA system with backscatter devices. In this system, the base station (BS) transmits information to two cellular users according to the NOMA protocol, while a backscatter device transmits its information over the BS signals to one cellular user using passive radio technology. We derive the closed-form expressions of the outage probabilities and analyze the diversity orders of all relevant transmissions in the proposed system. Finally, we provide numerical results to verify the theoretical analysis.

We study a cognitive heterogeneous network (HetNet), in which multiple pairs of secondary users coexist with a pair of primary users on a certain spectrum band. To protect primary transmissions, secondary transmitters (STs) adopt a sensing-based approach to access the spectrum band. Nevertheless, STs may cause uncertain interference to the primary receiver (PR) due to imperfect spectrum sensing, which is particularly significant when the wireless links between the primary transmitter (PT) and STs are extremely weak and the wireless links between STs and the PR are non-ignorable. This makes it difficult for the PR to select a proper modulation and/or coding scheme (MCS). To deal with the issue, we propose an intelligent deep reinforcement learning (DRL) based MCS selection algorithm for the primary transmission. With the proposed algorithm, the DRL agent at the PR is able to learn the pattern of the interference from the STs and predict the interference in the future. Simulation results show that the transmission rate of the proposed algorithm can converge to 90% ^ 100% transmission rate of the optimal MCS selection algorithm, which assumes that the interference from the STs is perfectly known at the PR as prior information. Meanwhile, the transmission rate of the proposed algorithm is around 100% higher than the transmission rate of the benchmark algorithm, which selects the MCS without the information about interference.

In an RF-powered backscatter cognitive radio network, multiple secondary users communicate with a secondary gateway by backscattering or harvesting energy and actively transmitting their data depending on the primary channel state. To coordinate the transmission of multiple secondary transmitters, the secondary gateway needs to schedule the backscattering time, energy harvesting time, and transmission time among them. However, under the dynamics of the primary channel and the uncertainty of the energy state of the secondary transmitters, it is challenging for the gateway to find a time scheduling mechanism which maximizes the total throughput. In this paper, we propose to use the deep reinforcement learning algorithm to derive an optimal time scheduling policy for the gateway. Specifically, to deal with the problem with large state and action spaces, we adopt a Double Deep-Q Network (DDQN) that enables the gateway to learn the optimal policy. The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms non-learning schemes in terms of network throughput.

In this paper, we investigate the dynamic access point and service selection in a backscatter-assisted radio-frequency-powered cognitive network, where many secondary transmitters (STs) can choose different transmission services provided by multiple access points. To analyze the access point and service selection of the STs, we formulate the problem as an evolutionary game. The STs act as the players and adjust their selections of the access points and services based on their utilities. Specifically, we model the access point and service adaptation of the STs by the replicator dynamics, and analytically prove the existence and uniqueness, and the stability of the evolutionary equilibrium. We also consider the delay of information used by the STs to adapt their selection and perform the analysis by using delayed replicator dynamics. In particular, the stability region of the delayed replicator dynamics in a special case is derived. Furthermore, we develop a low-complexity algorithm for the access point and service selection in the network based on evolutionary game. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed access point and service selection strategy in the network.

Cognitive ambient backscatter communication is a novel spectrum sharing paradigm, in which the backscatter system shares not only the same spectrum, but also the same radio-frequency source with the legacy system. Conventional energy detector (ED) suffers from severe error floor problem due to the existence of co-channel direct link interference (DLI) from the legacy system. In this paper, novel error-floor-free detectors are proposed to tackle the DLI using multiple receive antennas at the reader. First, beamforming-assisted ED and likelihood-ratio-based detector are proposed for backscatter symbol detection when the reader has perfect channel state information (CSI). Then a novel statistical clustering framework is proposed for joint CSI feature learning and backscatter symbol detection. Extensive simulation results have shown that the proposed methods can significantly outperform the conventional ED. In addition, the proposed clustering-based methods perform comparably as their counterparts with perfect CSI.

This paper is concerned with a machine learning approach to cancel the interference for cognitive Internet of Things (C-IoT) in the concurrent spectrum access (CSA) model, where the C-IoT system is noncooperative and has very limited knowledge on the interference. Our transceiver design uses an iterative processing structure, which consists of a linear estimator, a demodulation-and-decoding module, and a clustering module. In the clustering module, we employ modified expectation-maximization (EM)-based algorithms to estimate the interference under the knowledge of the modulation constraint (MC) of the interference. We show that this modified EM algorithm-based receiver outperforms the original EM-based receiver, since the former is able to generate a more accurate clustering result by reducing the dimension of the parameter space. We further improve the performance of the iterative receiver by introducing the extrinsic information technique, with the resulting algorithm referred to as the extrinsic modulation constrained EM (Ext-MC-EM) algorithm. We show that the Ext-MC-EM algorithm-based receiver considerably outperforms the counterpart iterative receivers, including the MC-EM algorithm.

This paper considers an ambient backscatter communication network in which a full-duplex access point (FAP) simultaneously transmits downlink orthogonal frequency division multiplexing signals to its legacy user (LU) and receives uplink signals backscattered from multiple backscatter devices (BDs) in a time-division-multiple-access manner. To maximize the system throughput and ensure fairness, we aim to maximize the minimum throughput among all BDs by jointly optimizing the backscatter time and reflection coefficients of the BDs, and the FAP's subcarrier power allocation, subject to the LU's throughput constraint, the BDs' harvested-energy constraints, and other practical constraints. For the case with a single BD, we obtain closed-form solutions and propose an efficient algorithm by using the Lagrange duality method. For the general case with multiple BDs, we propose an iterative algorithm by leveraging the block coordinated decent and successive convex optimization techniques. In addition, we study the throughput region which characterizes the Pareto-optimal throughput tradeoffs among all BDs. Finally, extensive simulation results show that the proposed joint design achieves significant throughput gain as compared to the benchmark schemes.

Massive wireless connections are emerging in Internet of Things (IoT) and will lead to a severe spectrum scarcity issue. To deal with this issue, we introduce the cognitive radio technology into the IoT, namely, cognitive IoT. Different from a conventional cognitive network, the cognitive IoT is dominated by short-packet transmissions, which suffer from a significant packet error rate even when the transmission rate is smaller than the Shannon capacity. In this paper, we jointly optimize the spectrum sensing time and packet error rate to maximize the cognitive effective-throughput, which is defined as the effective transmission rate by considering the packet error rate. First, we formulate an instantaneous effective-throughput maximization problem with the instantaneous channel state information (CSI) between cognitive transceivers, and develop a successive optimization algorithm. Second, we formulate an average effective-throughput maximization problem with the statistical CSI between cognitive transceivers. Due to the complicated expression of the average effective-throughput, we analyze its closed-form expression and adopt an exhaustive search method to obtain the optimal solution. Numerical and simulation results reveal that, the packet length has a significant impact on the optimal design. Meanwhile, the proposed algorithms can almost maximize the instantaneous/average effective-throughput.

One of the key problems in spectrum sensing is to design the test statistic. Existing methods generally exploit the model-based features as the test statistic, such as energies and eigenvalues. However, these features could not accurately characterize the real environment. Motivated by this, in this paper, we use a deep neural network (DNN) to intelligently explore the data-driven test statistic. Firstly, we introduce a DNN-based detection framework, where a DNN-based likelihood ratio test (DNN-LRT) is derived to guarantee the optimality of the designed test statistic. As a realization of the developed DNN-based framework, we use the sample covariance matrix as the input of a convolutional neural network (CNN), and propose a covariance matrix-aware CNN (CM-CNN)-based spectrum sensing algorithm, which further improves the performance. In addition, we also provide the theoretical analysis of the proposed method. To the best of our knowledge, it's the first time to analyze the theoretical performance of CNN-based methods. Finally, simulation results demonstrate that the performance of the proposed method is close to that of the optimal detector. Particularly, the proposed method could achieve a detection probability of 96.7% with a false alarm probability of 1.9% at SNR = -18dB, which significantly outperforms the conventional methods.

Ambient backscatter communication (AmBC) is a promising solution to energy-efficient and spectrum-efficient Internet of Things with stringent power and cost constraints. In an AmBC system, recovering the tag information at the reader, however, is a challenging task due to the difficulty in acquiring the relevant channel-state information (CSI). To eliminate the need to estimate the CSI, in this paper, we propose a label-assisted transmission framework, in which two known labels are transmitted from the tag before data transmission. By exploring the received signal constellation information, we propose modulation-constrained expectation maximization algorithm, based on which two detection methods are developed. One method, referred to as constellation learning with labeled signals, learns the parameters by clustering the labeled signals and recovers the unlabeled signals by the learnt parameters. The other method, referred to as constellation learning with labeled and unlabeled signals, uses all received signals in clustering. Efficient initialization techniques are provided for the two clustering algorithms. Finally, extensive simulation results show that the proposed constellation learning methods achieve comparable performance as the optimal detector with perfect CSI.

In conventional full-duplex communications, dedicated symbols are transmitted to estimate both the self-interference channel and the desired signal channel in order to perform self-interference cancellation (SIC) and to coherently detect the desired signal. However, inaccurate channel estimation will produce residual self-interference and degrade the detection performance. In this paper, we exploit a Gaussian mixture model (GMM) clustering to design a full-duplex transceiver (FDT), which is able to detect the desired signal without requiring digital-domain channel estimation and SIC. The frame structure of the designed FDT contains two successive phases: labeling phase and data transmission phase. In particular, the designed FDT performs cluster labeling in the labeling phase and performs GMM clustering based on an expectation-maximization (EM) algorithm in the data transmission phase. Furthermore, the theoretical analysis about the detection performance, computational complexity, and convergence performance for the designed FDT are studied. Finally, simulation results show that the bit error rate (BER) of the designed FDT is closed to the performance of the FDT with a maximum likelihood (ML) detector and perfect channel knowledge meanwhile is superior to the BER performance of the FDT with a ML detector and a least square (LS) or least mean square (LMS) channel estimator.

In this paper, we study time-varying orthogonal frequency division multiplexing (OFDM) systems, and propose a joint channel-and-signal estimation receiver based on turbo message passing (TMP) to efficiently suppress inter-carrier interference (ICI). We establish a factor graph representation of the problem and divide the whole factor graph into two parts, one for channel estimation and the other for signal detection. For the first part, we use Gaussian message passing (GMP) for channel estimation; for the second part, a discrete state space (DSS) model is employed to describe the transition of signal states, and a forward-backward algorithm is adopted for message passing over the transition trellis in signal detection. The resulting algorithm is referred to as DSS-GMP. The complexity of DSS-GMP quickly becomes the bottleneck as the increase of the signal constellation size and the ICI width. To address this issue, we further develop a continuous-state-space (CSS) model based turbo message passing algorithm, where the messages of modulated signals are approximated as continuous Gaussian messages. Numerical results demonstrate that the TMP based scheme significantly outperforms the state-of-the-art schemes.

Internet-of-Things (IoT) is a promising technology to connect massive machines and devices in the future communication networks. In this paper, we study a wireless-powered IoT network (WPIN) with short packet communication (SPC), in which a hybrid access point (HAP) first transmits power to the IoT devices wirelessly, then the devices in turn transmit their short data packets achieved by finite blocklength codes to the HAP using the harvested energy. Different from the long packet communication in conventional wireless network, SPC suffers from transmission rate degradation and a significant packet error rate. Thus, conventional resource allocation in the existing literature based on Shannon capacity achieved by the infinite blocklength codes is no longer optimal. In this paper, to enhance the transmission efficiency and reliability, we first define effective-throughput and effective-amount-of-information as the performance metrics to balance the transmission rate and the packet error rate, and then jointly optimize the transmission time and packet error rate of each user to maximize the total effective-throughput or minimize the total transmission time subject to the users' individual effective-amount-of-information requirements. To overcome the non-convexity of the formulated problems, we develop efficient algorithms to find high-quality suboptimal solutions for them. The simulation results show that the proposed algorithms can achieve similar performances as that of the optimal solution via exhaustive search, and outperform the benchmark schemes.

Recently proposed Bluetooth low energy (BLE)-backscatter allows interference-free transmission by locally generating a sub-carrier for frequency shifting (FS). Motivated by the BLE backscatter, in this paper, we investigate the price-based bandwidth allocation for multi-user backscatter communication (BackCom), where multiple BackCom users are allocated to different non-overlapping sub-channels and all users share the whole spectrum in a frequency division multiple access (FDMA) manner. Each BackCom user is charged by the primary user (PU) in the same sub-channel for a price, which is proportional to the allocated bandwidth and can be viewed as the cost for bandwidth sharing. A Stackelberg game is formulated to study joint maximization of the revenue of PUs and the utility function of each BackCom user subject to the total bandwidth constraint on the PUs and the individual bandwidth constraints on each BackCom user. Stackelberg Equilibriums (SEs) for two proposed schemes are investigated in closed-form expressions. Simulation results confirm the effectiveness of proposed schemes in improving the revenue performance of PUs and the sum capacity performance of BackCom users.

The licensed-assisted access-based long term evolution (LAA-LTE) is a promising solution to provide enhanced LTE services by sharing unlicensed bands with WiFi systems. However, the intense contention with the incumbent WiFi system makes it challenging for the LAA-LTE system to support guaranteed quality-of-service (QoS) for the users. This paper is interested in the QoS-aware LAA-LTE/WiFi coexistence system. We first propose a flexible coexistence framework using the listen-before-talk mechanism, based on which the QoS metrics of LAA-LTE and WiFi systems are quantified. Then, a joint user association and resource allocation problem is formulated, which aims to maximize the number of QoS-preferred users supported by LAA-LTE, while protecting the WiFi users. The considered optimization problem is equivalently decomposed into two subproblems, the sum-power minimization problem and the user association problem. For the first subproblem, the deep-cut ellipsoid method is adopted to optimize the LAA-LTE transmission time, subcarrier assignment, and power allocation. For the latter one, an efficient algorithm called successive user removal is proposed. The simulation results have demonstrated the effectiveness of the proposed scheme, based on which the tradeoff among different QoS metrics in the coexistence system is observed.

Ambient backscatter communications (AmBC) enable wireless communications riding on ambient radio frequency (RF) signals instead of self-generated RF signals. Therefore, it has been considered as a promising candidate for the future Internet-of-Things with stringent energy and spectrum constraints. In this paper, we investigate a full-duplex-enabled cognitive backscatter network, in which an AmBC system underlays a primary cellular system, and the primary access point can transmit primary signals and receive backscatter signals simultaneously via full-duplex communications. We aim to maximize the throughput of the AmBC system while guaranteeing the minimum rate requirements of the primary system via joint time scheduling, transmit power allocation, and reflection coefficient (RC) adjustment. To solve the problem, we propose an iterative method utilizing block coordinated decent to partition the variables into the time scheduling variable and the joint transmit power allocation and RC adjustment variable. For the time scheduling problem, we first prove its convexity and then utilize the interior-point method to solve it. For the joint power allocation and RC adjustment problem, we resort to the concave-convex procedure to transform it into a sequence of convex optimization problems, and then adopt Lagrange dual decomposition to tackle these convex optimization problems. The simulation results demonstrate that the proposed method can significantly increase the throughput of the AmBC system with a fast convergence speed.

We consider a cognitive heterogeneous network (HetNet), in which multiple pairs of secondary users adopt sensing-based approaches to coexist with a pair of primary users on a certain spectrum band. Due to imperfect spectrum sensing, secondary transmitters (STs) may cause interference to the primary receiver (PR) and make it difficult for the PR to select a proper modulation and/or coding scheme (MCS). To deal with this issue, we exploit deep reinforcement learning (DRL) and propose an intelligent MCS selection algorithm for the primary transmission. To reduce the system overhead caused by the MCS switchings, we further introduce a switching cost factor in the proposed algorithm. The simulation results show that the primary transmission rate of the proposed algorithm without the switching cost factor is 90% ~ 100% of the optimal MCS selection scheme, which assumes that the interference from the STs is perfectly known at the PR as prior information, is 30% higher than that of the upper confidence bandit (UCB) algorithm, and is 100% higher than that of the signal-to-noise ratio (SNR)-based algorithm. Meanwhile, the proposed algorithm with the switching cost factor can achieve a higher primary transmission rate than those of the benchmark algorithms without increasing system overheads.

Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment costs, which have been considered to be a promising technique in the next-generation wireless network. Due to the non-convex and combinatorial characteristics, it is challenging to obtain an optimal strategy for the joint user association and resource allocation issue. In this paper, a reinforcement learning (RL) approach is proposed to achieve the maximum long-term overall network utility while guaranteeing the quality of service requirements of user equipments (UEs) in the downlink of heterogeneous cellular networks. A distributed optimization method based on multi-agent RL is developed. Moreover, to solve the computationally expensive problem with the large action space, multi-agent deep RL method is proposed. Specifically, the state, action and reward function are defined for UEs, and dueling double deep Q-network (D3QN) strategy is introduced to obtain the nearly optimal policy. Through message passing, the distributed UEs can obtain the global state space with a small communication overhead. With the double-Q strategy and dueling architecture, D3QN can rapidly converge to a subgame perfect Nash equilibrium. Simulation results demonstrate that D3QN achieves the better performance than other RL approaches in solving large-scale learning problems.

Cognitive ambient backscatter communication (AmBC) is a novel spectrum sharing paradigm for green Internet of Things, in which the backscatter system shares not only the same spectrum, but also the same radio- frequency (RF) source with the legacy system. The conventional energy detector (ED) suffers from a severe error floor problem due to the existence of co-channel direct link interference (DLI) from the legacy system. In this paper, novel error-floor-free detectors are proposed to tackle the DLI through multi-antenna receive beamforming. A novel statistical clustering framework is proposed for joint channel state information (CSI) learning and backscatter symbol detection. Extensive simulation results have shown that the proposed methods can significantly outperform the conventional ED, and verified that the proposed clustering-based method achieves performance comparable to that of the perfect CSI cases.

Modulation classification using deep neural networks has recently received increasing attention due to its capability in learning rich features of data. In this paper, we propose a low- complexity blind data-driven modulation classifier. Our classifier operates robustly over Rayleigh fading channels under uncertain noise conditions modeled using a mixture of three types of noise, namely, white Gaussian noise, white non- Gaussian noise and correlated non-Gaussian noise. The proposed classifier consists of several layers of recurrent neural networks (RNN) which is well-suited for learning representations from time-correlated data. The classifier is trained using the labeled raw signal samples generated under different noise conditions. Simulation results show that the performance of our proposed classifier approaches that of maximum likelihood classifiers with perfect channel knowledge and outperforms existing expectation maximum (EM) and expectation conditional maximum (ECM) classifiers which iteratively estimate channel and noise parameters.

Deploying intelligent transportation systems (ITS) is a promising solution to on-road safety, transportation efficiency and high data rate services for traveling vehicles. In ITS, vehicular networks (VN) are expected to be deployed together with traditional cellular networks (CN) to enhance the network access quality. As even for the same network, the quality of network access varies from location to location, mobile data offloading (MDO), which dynamically selects access network for vehicles, should be jointly considered with vehicle route planning to further improve the network access quality for individual vehicles and to enhance the performance of the entire ITS. In this paper, we investigate joint route and access network selection for an individual vehicle in a metropolitan scenario. We aim to improve the wireless data throughput of the target vehicle while guaranteeing its transportation efficiency requirements in terms of traveling time and distance. To achieve this objective, we first formulate the joint route and access network selection problem as a semi-Markov decision process (SMDP). The optimal policy of the SMDP is then derived based on value iteration. Sufficient simulations are conducted and numerical results demonstrate that the derived optimal policy significantly outperforms the existing work in terms of the total throughput and the late arrival ratio.

This paper considers an ambient backscatter communication (AmBC) network in which a full-duplex access point (FAP) simultaneously transmits downlink orthogonal frequency division multiplexing (OFDM) signals to its legacy user (LU) and receives uplink signals backscattered from multiple wireless-powered backscatter devices (BDs) in a time-division-multiple-access manner. To maximize the system throughput and ensure fairness, we aim to maximize the minimum throughput among all BDs by jointly optimizing the backscatter time and reflection coefficients of the BDs, and the FAP's subcarrier power allocation, subject to the LU's throughput constraint, the BDs' harvested-energy constraints, and other practical constraints. However, the formulated problem is non-trivial to solve in general, since the variables are mutually coupled and result in non-convex constraints. We thus propose an iterative algorithm by leveraging the block coordinated decent and successive convex optimization techniques. We further show the convergence performances of the proposed algorithm and analyze its complexity. Finally, extensive simulation results show that the proposed joint design achieves significant throughput gain as compared to the benchmark schemes.

In ambient backscatter communication (AmBC), it is a challenging task to recover the tag information at the reader due to the difficulty in obtaining the relevant channel state information (CSI). In this paper, we translate the signal detection problem into a clustering problem, for which two known labels are transmitted from the tag as the prior knowledge to assist clustering initialization and signal detection. By exploiting the received signals directly, two clustering-inspired detection methods are proposed, one is called clustering with labeled signals (CLS), and the other is referred to as clustering with labeled and unlabeled signals (CLUS). Both methods are developed based on the proposed modulation-constrained (MC) Gaussian mixture model (GMM). Finally, extensive simulation results show that the proposed methods only have small gaps compared with the optimal detection with perfect CSI.

Heterogeneous networks (HetNets) can offload the traffic and reduce the deployment cost, which is regarded as a promising technique in next-generation cellular networks. Because of the non-convex and combinatorial features of the joint issue of user association and resource allocation, it is challenging to achieve an optimal solution. In this paper, a novel method is proposed to maximize the long-term overall network utility while ensuring the user equipments' quality of service requirements in the downlink of HetNets. Multi-agent reinforcement learning approach is developed to obtain the distributed optimal strategy. To solve the computationally expensive issue with the large action space, the multi-user deep reinforcement learning is presented. Double deep Q-network (DDQN) approach is introduced to achieve an optimal policy. Simulation results clearly indicate the better performance of DDQN than that of other reinforcement learning methods.

Inspired by the phenomenon that the received signals naturally form clusters, we propose a novel machine learning framework to design multi-input multi-output (MIMO) communication systems. In the proposed framework, the MIMO detection problem is converted into a clustering problem, and known labels are transmitted to assist the receiver for labeling the clusters. A modulation-constrained Gaussian mixture model (MC-GMM) and the associated optimization algorithm are developed to reduce the number of parameters to be learnt in the clustering algorithm. Furthermore, we propose a method called label reconstruction to minimize the overhead of label transmission, and the design of the optimal labels is studied. Simulation results are presented to verify the effectiveness of the proposed label-assisted clustering (LAC) receiver in approaching the optimal maximum likelihood detection (MLD) with perfectly known channel knowledge for typical MIMO systems.

In this paper, we propose an ambient backscatter multiple-access system, in which a receiver (Rx) simultaneously detects the information sent from an active transmitter (Tx) and a passive Tag. Specifically, the information-carrying signal sent by the Tx arrives at the Rx through two wireless channels: one is the direct Tx-Rx channel, and the other is the backscatter channel, i.e., the Tx- Tag-Rx channel, which further carries the Tag's information due to the multiplicative backscatter operation at the Tag. The proposed multiple-access scheme introduces a new channel model named as the multiplicative multiple-access channel (M-MAC), which has not been addressed before. We study the achievable rate region and the capacity region of the M-MAC, and prove that the achievable rate region of the M-MAC is strictly larger than that of the conventional time-sharing one (i.e., the M- MAC capacity region is strictly convex) in many cases, including the high SNR case and the typical case that the direct channel is much stronger than the backscatter channel. Moreover, the numerical results have also validated this phenomenon under a practical range of SNR and channel conditions. The proposed multiple-access scheme is an attractive technique to improve the throughput of ambient backscatter communication systems.

License-assisted access LTE (LAA-LTE) has been proposed to deal with the intense contradiction between tremendous mobile traffic demands and crowded licensed spectrums. In this paper, we investigate the coexistence mechanism for LAA-LTE based heterogenous networks (HetNets). A joint resource allocation and network access problem is considered to maximize the normalized throughput of the unlicensed band while guaranteeing the quality-of-service requirements of incumbent WiFi users. A two-level learning-based framework is proposed to solve the problem by decomposing it into two subproblems. In the master level, a Q-learning based method is developed for the LAA-LTE system to determine the proper transmission time. In the slave one, a game-theory based learning method is adopted by each user to autonomously perform network access. Simulation results demonstrate the effectiveness of the proposed solution.

Blind modulation classification is a fundamental step before signal detection for cognitive radio networks where the users may not have the complete knowledge of the modulation scheme due to the flexibility of operating dynamically in multiple frequency bands. In this paper, a modulation-constrained (MC) clustering classifier is proposed for recognizing the modulation scheme with unknown channel matrix and noise variance for MIMO systems. By recognizing the fact that the received signals within an observation interval form into clusters and exploiting the intrinsic relationships of different digital modulation schemes, the modulation classification problem is transformed into a clustering problem without direct channel estimation for each modulation scheme and the maximum likelihood criterion is applied for the final classification decision. A central component of the proposed classifier is a method called centroid reconstruction, which exploits the structural relationships in constellation diagrams to reconstruct cluster centroids with fewer number of parameters. Furthermore, a method to initialize the cluster centroids is also proposed. The proposed MC classifier together with centroid reconstruction and initialization methods not only reduce the number of parameters to be estimated, but also help to initialize the centroids for enhanced convergence of expectation- maximization (EM) algorithm. Simulation results show that our algorithm can perform excellently even at low SNR and with very short observation interval length.

Ambient backscatter communication (AmBC) enables a passive backscatter device to transmit information to a reader using ambient RF signals, and has emerged as a promising solution to green Internet-of-Things (IoT). Conventional AmBC receivers are interested in recovering the information from the ambient backscatter device (A-BD) only. In this paper, we propose a cooperative AmBC (CABC) system in which the reader recovers information not only from the A-BD, but also from the RF source. We first establish the system model for the CABC system from spread spectrum and spectrum sharing perspectives. Then, for flat fading channels, we derive the optimal maximum-likelihood (ML) detector, suboptimal linear detectors as well as successive interference-cancellation (SIC) based detectors. For frequency-selective fading channels, the system model for the CABC system over ambient orthogonal frequency division multiplexing carriers is proposed, upon which a low-complexity optimal ML detector is derived. For both kinds of channels, the bit-error-rate expressions for the proposed detectors are derived in closed forms. Finally, extensive numerical results have shown that, when the A-BD signal and the RF-source signal have equal symbol period, the proposed SIC-based detectors can achieve near-ML detection performance for typical application scenarios, and when the A-BD symbol period is longer than the RF-source symbol period, the existence of backscattered signal in the CABC system can enhance the ML detection performance of the RF-source signal, thanks to the beneficial effect of the backscatter link when the A-BD transmits at a lower rate than the RF source.

Ambient backscatter communication (AmBC) enables radio-frequency (RF) powered backscatter devices (BDs) (e.g., sensors and tags) to modulate their information bits over ambient RF carriers in an over-the-air manner. This technology, also called “modulation in the air,” has emerged as a promising solution to achieve green communication for future Internet of Things. This paper studies an AmBC system by leveraging the ambient orthogonal frequency division multiplexing (OFDM) modulated signals in the air. We first model such AmBC system from a spread-spectrum communication perspective, upon which a novel joint design for BD waveform and receiver detector is proposed. The BD symbol period is designed as an integer multiplication of the OFDM symbol period, and the waveform for BD bit “0” maintains the same state within the BD symbol period, while the waveform for BD bit “1” has a state transition in the middle of each OFDM symbol period within the BD symbol period. In the receiver detector design, we construct the test statistic that cancels out the direct-link interference by exploiting the repeating structure of the ambient OFDM signals due to the use of cyclic prefix. For the system with a single-antenna receiver, the maximum-likelihood detector is proposed to recover the BD bits, for which the optimal threshold is obtained in closed-form expression. For the system with a multi-antenna receiver, we propose a new test statistic which is a linear combination of the per-antenna test statistics and derive the corresponding optimal detector. The proposed optimal detectors require only knowing the strength of the backscatter channel, thus simplifying their implementation. Moreover, practical timing synchronization algorithms are proposed for the designed AmBC system, and we also analyze the effect of various system parameters on the transmission rate and detection performance. Finally, extensive numerical results are provided to verify that the proposed transceiver design can improve the system bit-error-rate performance and the operating range significantly and achieve much higher data rate, as compared with the conventional design.

We consider a broadcast channel with three receivers and three-degraded message sets, i.e., the transmitter has a common message intended for all three receivers, a message intended for receivers 2 and 3, and a private message intended only for receiver 3. The messages are transmitted over a family of parallel degraded broadcast channels. In the most general case, the broadcast channel consists of the product of six parallel degraded broadcast channels, each with a different order of degradedness. We first consider an achievable rate region of Nair and El Gamal, by appropriately choosing independent input random variables and auxiliary random variables for each subchannel. We then show that the achievable rate region attains the capacity region for two different classes of such broadcast channels, one consisting of the product of five parallel degraded broadcast channels and another consisting of the product of three parallel degraded broadcast channels. To accomplish this, we make use of an information-theoretic inequality that may be proven using the Csiszár-sum identity. Next, we extend the result to the Gaussian case. We consider the aligned Gaussian MIMO broadcast channel consisting of the product of six parallel degraded Gaussian broadcast channels, where the Gaussian noise vectors for each of the users in each of the subchannels follow a degradedness order, i.e., the noise covariance matrices may be ordered in a positive semi-definite sense. We show that the Nair-El Gamal achievable rate region considered in this paper is maximized by Gaussian inputs. To prove that Gaussian inputs are optimal, we prove an extremal entropy inequality employing a new method recently introduced by Geng and Nair to prove the capacity region of the two-user Gaussian MIMO broadcast channel with common and private messages.

The millimeter wave (mmWave) radio band is promising for the next-generation heterogeneous cellular networks (HetNets) due to its large bandwidth available for meeting the increasing demand of mobile traffic. However, the unique propagation characteristics at mmWave band cause huge redundant handoffs in mmWave HetNets that brings heavy signaling overhead, low energy efficiency and increased user equipment (UE) outage probability if conventional Reference Signal Received Power (RSRP) based handoff mechanism is used. In this paper, we propose a reinforcement learning based handoff policy named SMART to reduce the number of handoffs while maintaining user Quality of Service (QoS) requirements in mmWave HetNets. In SMART, we determine handoff trigger conditions by taking into account both mmWave channel characteristics and QoS requirements of UEs. Furthermore, we propose reinforcement-learning based BS selection algorithms for different UE densities. Numerical results show that in typical scenarios, SMART can significantly reduce the number of handoffs when compared with traditional handoff policies without learning.

In this paper, a new spectrum sharing model referred to as riding on the primary (ROP) is proposed for wireless-powered IoT devices with ambient backscatter communication capabilities. The key idea of ROP is that the secondary transmitter harvests energy from the primary signal, then modulates its information bits to the primary signal, and reflects the modulated signal to the secondary receiver without violating the primary system's interference requirement. Compared with the conventional spectrum sharing model, the secondary system in the proposed ROP not only utilizes the spectrum of the primary system but also takes advantage of the primary signal to harvest energy and to carry its information. In this paper, we investigate the performance of such a spectrum sharing system under fading channels. To be specific, we maximize the ergodic capacity of the secondary system by jointly optimizing the transmit power of the primary signal and the reflection coefficient of the secondary ambient backscatter. Different (ideal/practical) energy consumption models, different (peak/average) transmit power constraints, different types (fixed/dynamically adjustable) reflection coefficient, and different primary system's interference requirements (rate/outage) are considered. Optimal power allocation and reflection coefficient are obtained for each scenario.

Dynamic spectrum sharing will facilitate the interference coordination in device-to-device (D2D) communications. In the absence of network level coordination, the timing synchronization among D2D users will be unavailable, leading to inaccurate channel state estimation and device detection, especially in time-varying fading environments. In this paper, we design an asynchronous device detection/discovery framework for cognitive-D2D applications, which acquires timing drifts and dynamical fading channels when directly detecting the existence of a proximity D2D device (e.g. or primary user). To model and analyze this, a new dynamical system model is established, where the unknown timing deviation follows a random process, while the fading channel is governed by a discrete state Markov chain. To cope with the mixed estimation and detection problem, a novel sequential estimation scheme is proposed, using the conceptions of statistic Bayesian inference and random finite set. By tracking the unknown states (i.e. varying time deviations and fading gains) and suppressing the link uncertainty, the proposed scheme can effectively enhance the detection performance. The general framework, as a complimentary to a network-aided case with the coordinated signaling, provides the foundation for development of flexible D2D communications along with proximity-based spectrum sharing.

In this paper, we consider a novel ambient backscatter multiple-access system, where a receiver (Rx) simultaneously detects the signals transmitted from an active transmitter (Tx) and a backscatter tag. Specifically, the information-carrying signal sent by the Tx arrives at the Rx through two wireless channels: the direct channel from the Tx to the Rx and the backscatter channel from the Tx to the tag and then to the Rx. The received signal from the backscatter channel also carries the tag's information because of the multiplicative backscatter operation at the tag. This multiple-access system introduces a new channel model referred to as backscatter multiplicative multiple-access channel (BM-MAC). We analyze the achievable rate region of the BM-MAC and prove that its region is strictly larger than that of the conventional time-division multiple-access scheme in many cases, including, e.g., the high SNR regime and the case when the direct channel is much stronger than the backscatter channel. Hence, the multiplicative multiple-access scheme is an attractive technique to improve the throughput for ambient backscatter communication systems. Moreover, we analyze the detection error rates for coherent and noncoherent modulation schemes adopted by the Tx and the tag, respectively, in both synchronous and asynchronous scenarios, which further bring interesting insights for practical system design.

In this paper, we propose a new channel estimation scheme for TDD/FDD massive MIMO systems by reconstructing (sometimes also referred to as covariance computing or covariance fitting) uplink/downlink channel covariance matrices (CCMs) with the aid of array signal processing techniques. Specifically, the angle parameters and power angular spectrum (PAS) of channel are extracted from the instantaneous uplink channel state information (CSI). Then, the uplink CCM is reconstructed and can be used to improve the uplink channel estimation without any additional training cost. By virtue of angle reciprocity as well as PAS reciprocity between uplink and downlink channels, the downlink CCM could also be inferred with a similar approach even for the FDD massive MIMO systems. Then, the downlink instantaneous CSI can be obtained by training toward the dominant eigen-directions of each user. The proposed strategy is applicable to various PAS distributions. Numerical results are provided to demonstrate the superiority of the proposed methods over the existing ones.

With the ever-increasing spectrum requirements for transmitting explosively growing mobile data, spectrum-efficient solutions need to be integrated into future mobile networks. Spectrum sharing enables the primary system to share licensed spectrum with the secondary system. Thus, it is conceived as an appealing solution for improving spectrum usage to eliminate the spectrum supply-demand gap. In this paper, we develop an efficient transmit power control strategy for underlay spectrum sharing networks with spatially Poisson-distributed primary users. A distinguishing feature of the proposed strategy is that it only requires channel state information and location information of a few primary users close to the secondary transmitter, rather than those for all primary users. Furthermore, we evaluate the outage performance of the secondary system and the interference situation of the primary system under this kind of transmit power control strategy. Numerical results demonstrate that the proposed transmit power control strategy can achieve near-optimal outage performance compared to the perfect power control strategy, while reducing the control complexity and the feedback burden significantly at the same time.

The millimeter wave (mmWave) radio band is promising for the next-generation heterogeneous cellular networks (HetNets) due to its large bandwidth available for meeting the increasing demand of mobile traffic. However, the unique propagation characteristics at mmWave band cause huge redundant handoffs in mmWave HetNets if conventional Reference Signal Received Power (RSRP) based handoff mechanism is used. In this paper, we propose a reinforcement learning based handoff policy named LESH to reduce the number of handoffs while maintaining user Quality of Service (QoS) requirements in mmWave HetNets. In LESH, we determine handoff trigger conditions by taking into account both mmWave channel characteristics and QoS requirements of UEs. Furthermore, we propose reinforcement-learning based BS selection algorithms for different UE densities. Numerical results show that in typical scenarios, LESH can significantly reduce the number of handoffs when compared with traditional handoff policies.

Today multiple radio access technologies (RATs) coexist in wireless networks. Multi-mode mobile terminals (MMTs) which can switch between networks with different RATs can enjoy enhanced quality of service (QoS) by exploring the diversity among different radio access networks (RANs). Usually, it has been assumed in literature that an MMT can access only one network at a time. Multi-Radio Access (MRA) is a technology that allows an MMT to transmit and receive data via multiple RANs simultaneously. With MRA, users can combine the data streams from multiple networks to meet their throughput requirements and enjoy customized QoS. In this paper, we consider an MMT with MRA technology in a heterogeneous network environment. It is assumed that the connection session of the MMT will spread over multiple handover windows and that the MMT is allowed to switch from the present set of connected RANs to another set at each handover window. We are interested in designing an intelligent network switching strategy which maximizes the average cumulative utility function of the MMT. Taking into account the dynamics of the heterogeneous networks, we model the network selection problem as a Markov decision process (MDP). By using the value iteration algorithm, we obtain the optimal switching strategy. Simulation results show that the MDP method provides higher average cumulative utility function and higher average rate of minimum throughput satisfaction than greedy method.

Cooperative communication is a promising technique to mitigate channel impairment and improve spectrum efficiency. Due to the selfish nature of relay nodes, how to provide proper long-term incentives for relay nodes in dynamic communication environments is an essential issue. In this paper, a two-period dynamic contract is proposed under the dynamic asymmetric information scenario. Considering the relay nodes' types are independent in both periods with identical probability distribution, the contract-theoretic model for ability discrimination relay selection is formulated. And the necessary and sufficient conditions for the optimal contract are systematically characterized. To maximize the source's expected utility, a sequential optimization algorithm is proposed to obtain the optimal relay- reward strategy. Simulation results show that the optimal dynamic contract design scheme is effective in improving system performance for cooperative communication.

Incorporating the effect of residual self-interference, the spectral efficiency (SE) of full-duplex relay channel is analyzed for different relay schemes. The optimal relay power and the corresponding optimal SE have been derived in closed form for decode-forward, compress-forward, and amplify-forward (AF) schemes. In particular, backward decoding is introduced to the AF scheme in the presence of self-interference and it is shown to outperform forward decoding for high relay-destination SNR. Based on the obtained SE, bounds on the relay energy efficiency (REE) are also established. It evaluates how much the cooperation gain can be obtained from unit relay power consumption. We show that time sharing can improve the REE of AF scheme.

In this paper, a new spectrum sharing model referred to as riding on the primary (RoP) is proposed for wireless- powered IoT devices with ambient backscatter communication capabilities. The key idea of RoP is that the secondary transmitter harvests energy from the primary signal, then modulates its information bits to the primary signal, and reflects the modulated signal to the secondary receiver without violating the primary system's interference requirement. Compared with the conventional spectrum sharing model, the secondary system in the proposed RoP not only utilizes the spectrum of the primary system but also takes advantage of the primary signal to harvest energy and to carry its information. In this paper, we investigate the performance of such a spectrum sharing system under fading channels. To be specific, we maximize the ergodic capacity of the secondary system by jointly optimizing the transmit power of the primary signal and the reflection coefficient of the secondary ambient backscatter. Different (ideal/practical) energy consumption models, different (peak/average) transmit power constraints, different types (fixed/dynamically adjustable) reflection coefficient are considered. Optimal power allocation and reflection coefficient are obtained for each scenario.

In a multi-antenna time-division duplex (TDD) communication system, due to channel reciprocity, the downlink channel state information can be obtained by conducting uplink training. In a wire-tap channel, an active eavesdropper can perform active eavesdropping by pilot spoofing attack. In such an attack, the eavesdropper, during the uplink training phase, transmits the identical pilot sequence as that of the legitimate receiver to the transmitter. As a result, the estimated channel by the transmitter is a weighted sum of the legitimate channel and the eavesdropping channel. Motivated by the seriousness of pilot spoofing attack, in this paper, we propose a two-stage uplink training method for pilot spoofing attack detection and secure transmission. Using the new training method, the legitimate channel and the eavesdropping channel can be correctly estimated separately. Then we propose a pilot spoofing attack detector followed by a beamforming scheme for secure data transmission. Simulation results have shown that our proposed method achieves higher detection probability, and larger secrecy rate than previously proposed anti-pilot spoofing methods.

In ambient backscatter communications (Am-BC), a backscatter device can harvest power from ambient RF signals and modulate its information symbols over the ambient carriers without using complex RF transmitter. Conventional receiver design for AmBC focuses on tackling the direct link interference from the RF source. In this paper, a novel receiver, which is called cooperative receiver, is proposed to recover signals not only from the ambient backscatter device (A-BD), but also from the RF source. We first study the optimal maximum-likelihood (ML) detection for such system. Then, by exploiting the structural property of the system model, linear detectors and successive interference cancellation (SIC) based detectors are proposed. We also derive the closed-form bit error rate (BER) expressions for both ML detection and the proposed SIC algorithms. Finally, extensive numerical results show that the existence of backscattered signal in the considered system can significantly enhance the ML detection performance of the source signal, and the proposed SIC-based detectors can achieve near-ML detection performance for typical application scenarios.

Machine-to-Machine (M2M) communication plays a significant role in supporting Internet of Thing (IoT). This paper is concerned about multiuser detection (MUD) for massive M2M supported by Low-Activity Code Division Multiple Access (LA-CDMA). In previous work, maximum likelihood (ML) and maximum a posterior probability (MAP) detectors have been developed for such system. The ML detector has exponential complexity, while the MAP detector requires perfect knowledge of user activity factor. In practice, the user activity factor may not be known and could change from time to time. To design MUD detectors addressing these problems, in this paper, we formulate multiple measurement vector (MMV) model for uplink LA-CDMA system with time-varying user activities. Since the transmitted signals have block sparse structure, we introduce the pattern coupled spare Bayesian learning (PCSBL) by using the neighbour coherence of each transmitted signal, which effectively solves the user activity factor unknown problem. Furthermore, we embed the generalized approximate message passing (GAMP) to PCSBL and develop a novel algorithm, called generalized approximate message passing pattern coupled sparse Bayesian learning (GAMP-PCSBL). The GAMP-PCSBL does not require activity factor either, and greatly reduces the computational complexity. Simulation results have shown that the proposed algorithms have superior recovery performance than the conventional algorithms.

Dynamic spectrum access or cognitive radio is widely recognized as a major technology in the coming 5G communications to greatly increase spectral efficiency. In cognitive radio, different networks#x002F;systems may dynamically share some spectrum to achieve the maximum overall capacity. Each network#x002F;system may just use a few subcarriers or a few non-contiguous frequencies at a given time. It is very challenging to design the waveform to effectively use the fragmented spectrum with high flexibility for dynamical change. Filter bank multicarrier (FBMC) and generalized frequency division multiplexing (GFDM) are regarded as good candidates for the waveform at such situations. In this paper, we propose fast algorithms to implement the FBMC and GFDM when a number of cognitive radio users sharing the same spectrum. The major contributions are as follows. (1) We use the "sparse" property of the user's spectrum to reduce the sampling rate and complexity in the discrete time implementation; (2) We propose fast algorithms for computing the special Fourier transform, which reduce the complexity by nearly half.

We study a downlink non-orthogonal multiple access system with cooperative full-duplex relaying, where the near user in terms of the base station (BS) is enabled to act as a full-duplex relay for the far user. In particular, we first derive the outage probability and ergodic sum rate with closed-form expressions when the power allocations at the BS and relay (or the near user) are fixed. Then, we analytically obtain the optimal power allocations with closed-form expressions at the BS and relay to minimize the outage probability. Furthermore, by taking the fairness between the near user and far user into account, we characterize the optimal power allocations with closed-form expressions at the BS and relay to maximize the minimum achievable rate of users. Simulation results validate the correctness of the theoretical analysis and demonstrate the advantages of the proposed algorithms over the state of the art.

In this paper, we consider the sequential detection problem in a new cognitive radio scenario when the primary user (PU) works with more than one transmit power level. Different from most existing literature where PU is assumed to operate with a constant transmit power only, this new consideration well matches the practical standards, e.g., IEEE 802.11 Series, LTE, LTE-A, and so on, as well as the adaptive powering concept that a user would vary its transmit power under different situations. The targets of the secondary user here are not only to detect the presence of PU but also to recognize PU's transmit power levels. We first formulate a valid sequential detection approach via the modified Neyman-Pearson criterion and then derive the closed-form decision region for each PU's transmit power level. Many interesting discussions are raised. Moreover, the average sample number, a key metric for any sequential detection method, is derived in closed form to facilitate the performance evaluation. The performance comparison of the sequential detection and the fixed sample detection for this multiple primary transmit power levels scenario is analyzed. Finally, the simulation results are presented to verify the correctness of the proposed studies.

In cognitive radio networks, the channel gain between primary transceivers, namely, primary channel gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary channel gain is estimated in the primary system, and thus unavailable at the CT. To deal with the issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signal, an ML estimator is first developed. To reduce the computational complexity of the ML estimator, a median-based (MB) estimator is then proposed. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Simulation results show that the estimation errors of both estimators can be as small as 0.015. Meanwhile, the ML estimator outperforms the MB estimator in terms of the estimation accuracy if the sensed primary signal at the CT is weak. Otherwise, the MB estimator is superior to the ML estimator from the aspects of both the computational complexity and the estimation accuracy.

Full-duplex relaying has the potential to improve the spectral efficiency (SE) of cooperative communication systems. Due to residual self-interference (RSI), increase of the relay power does not always contribute to SE improvement. To fully utilize full-duplex relaying in cooperative communications, the effect of RSI on the SE achieved by different relay schemes need to be investigated. In this paper, we study bounds on the SE of full-duplex relay channel with decode-forward (DF) relaying, compress-forward (CF) relaying, and amplify-forward (AF) relaying in the presence of RSI. For respective schemes, optimal relay power and the corresponding maximal SE are derived in closed-form. Bounds on the relay energy-efficiency (REE) are presented for different schemes, which are useful for system design under per-node energy efficiency constraint. Based on the SE performance, the conditions of employing full-duplex relay, criteria for selecting relay scheme among DF, CF, and AF schemes, and the conditions of adopting hybrid full-duplex or half-duplex mode are elaborated regarding to RSI strength. In summary, this paper investigates the relationship among SE, REE, and system design by taking into account the effect of RSI for a general class of cooperation schemes.

This paper studies a green paradigm for the underlay coexistence of primary users (PUs) and secondary users (SUs) in energy harvesting cognitive radio networks (EH-CRNs), wherein battery-free SUs capture both the spectrum and the energy of PUs to enhance spectrum efficiency and green energy utilization. To lower the transmit powers of SUs, we employ multi-hop transmission with time division multiple access, by which SUs first harvest energy from the RF signals of PUs, and then, transmit data in the allocated time concurrently with PUs, all in the licensed spectrum. In this way, the available transmit energy of each SU mainly depends on the harvested energy before the turn to transmit, namely energy causality. Meanwhile, the transmit powers of SUs must be strictly controlled to protect PUs from harmful interference. Thus, subject to the energy causality constraint and the interference power constraint, we study the end-to-end throughput maximization problem for optimal time and power allocation. To solve this nonconvex problem, we first equivalently transform it into a convex optimization problem and then propose the joint optimal time and power allocation (JOTPA) algorithm that iteratively solves a series of feasibility problems until convergence. Extensive simulations evaluate the performance of EH-CRNs with JOTPA in three typical deployment scenarios and validate the superiority of JOTPA by making comparisons with two other resource allocation algorithms.

In cognitive radio networks, the performance of energy detection will be degraded significantly due to the cluster overlapping caused by noise uncertainty. To alleviate the noise uncertainty effect, a novel machine learning algorithm is proposed in this paper for cooperative spectrum sensing. The proposed algorithm incorporates fuzzy support vector machine and nonparallel hyperplane support vector machine. For membership assignment, kernel shadow c-means (KSCM) algorithm is utilized. Furthermore, the test statistics collected by the second users are arranged into a feature vector instead of being combined through weighted sum. Simulations results have shown that the proposed scheme, called NP-FSVM, is more robust to noise uncertainty than the existing methods.

Wideband spectrum sensing is a critical functionality in cognitive radio networks to enable dynamic spectrum sharing, but entails a major implementation challenge in compact commodity radios with restricted energy and computation capabilities. Exploiting jointly sparse nature of multiband signals, this paper proposes an efficient blind sub-Nyquist cooperative wideband spectrum sensing scheme, which reduces energy consumption in wideband signal acquisition, processing and transmission, with performance guarantee. In contrast to traditional sub-Nyquist approaches where a wideband signal or its power spectrum is first reconstructed from compressed samples, the proposed scheme locates occupied channels by recovering the signal support jointly from multiple secondary user (SU) measurements. Based on subspace decomposition, the low-dimensional measurement matrix computed at each SU from local sub-Nyquist samples can reduce transmission overhead while improving noise robustness. Numerical analysis and simulation results show that the proposed scheme can achieve good detection performance as well as reduce computation and implementation complexity in comparison with conventional cooperative wideband spectrum sensing schemes.

By offloading mobile traffic for macrocells, small cells can help to alleviate the pressure on conventional cellular networks from explosive data growth. On the other hand, the severe spectrum scarcity problem has prompted the research on spectrum sharing recently. In this paper, a spectrum sharing system of OFDMA small cells overlaying CDMA networks is considered, in which each OFDMA user in the small cells requests a specific transmission rate. We investigate the interferences among entities in the spectrum sharing system, from which a resource allocation problem is formulated along with the rate constraints, with the objective to meet the target rates of OFDMA users and to simultaneously protect CDMA system. This high- complexity and intractable problem is transformed into two separable problems by calculating the minimum required interference and selecting the activated users. We propose the on-demand resource allocation scheme to solve the resource allocation problem efficiently by deploying a semi-distributed algorithm. Simulation results are provided to evaluate the effectiveness and performance of the proposed scheme.

Ambient backscatter communications (AmBC) enables radio-frequency (RF) powered devices (e.g., tags, sensors) to modulate their information bits over ambient RF carriers in an over-the-air manner. This system, called “modulation in the air”, thus has emerged as a promising technology for green communications and future Internet-of-Things. This paper studies the AmBC system over ambient orthogonal frequency division multiplexing (OFDM) carriers in the air. We first establish the system model for such AmBC system from spread-spectrum perspective, from which a novel joint design for tag waveform and reader detector is proposed. We construct the test statistic that cancels out the direct-link interference by exploiting the repeating structure of the ambient OFDM signals due to the use of cyclic prefix. The maximum-likelihood detector is proposed to recover the tag bits, for which the optimal threshold is obtained with closed-form expression. Also, we analyze the effect of various system parameters on the transmission rate and detection performance. Finally, extensive numerical results show that the proposed transceiver design outperforms the conventional design.

Licensed-assisted access (LAA), which conveys control signal via licensed anchor carrier and data information via both licensed and unlicensed bands, is a promising solution to enhance the throughput of wireless communications. In view of the potential impact on the incumbent services in the unlicensed band, how to design the medium access control (MAC) protocol for LAA system to make fair and friendly coexistence with its neighboring incumbent users is one of the most critical and challenging issues. In this paper, a LAA using LTE (LAA-LTE) system in the WiFi unlicensed spectrum is investigated. The listen-before-talk (LBT) protocol is designed for the LAA-LTE system. By quantifying the WiFi throughput in the coexisting system, allowable LTE transmission time is determined by considering different targets of WiFi service protection. Then, the LTE transmission time is optimized for maximizing the overall normalized channel rate contributed by both LAA-LTE and WiFi system, with the protection to the WiFi system. Our work offers guidelines of designing the LAA-LTE system, paving the way to a controllable, not only harmonious, coexistence of LAA-LTE and WiFi systems in the unlicensed spectrum.

We investigate the energy efficiency (EE) in cognitive radio networks, where cognitive users are allowed to access the licensed frequency band opportunistically, provided that the licensed band is vacant. In particular, we study the impact of imperfect spectrum sensing and formulate the EE maximization as a joint optimization problem of the spectrum sensing duration and the transmit power of the cognitive transmitter. Specially, we consider the constraints of both the collision probability between the primary and cognitive transmission and the outage probability of the cognitive transmission. Since the joint optimization problem of the sensing duration and the transmit power is hard to be solved directly, we decompose it into two sub-problems with the spectrum sensing duration and the transmit power as variables, respectively. Based on the analytical solvers of the two sub-problems, we propose an iterative-based algorithm to solve the joint optimization problem. Finally, numerical results are provided to validate the analysis and performance of our proposed algorithms.

Machine-to-Machine (M2M) communications will be playing an important role in the development of 5th generation (5G) and future wireless communication systems. Due to the sporadic nature of massive access, Low-Activity Code Division Multiple Access (LA-CDMA) is one of possible multiple access schemes for M2M communications. In the literature, maximum a posterior (MAP) detector has been proposed to detect the active users when the user activity factor is known and small. However, the user activity factor is usually unknown and could be large in practice, which makes the multiuser detection (MUD) a challenging task for LA-CDMA. In this paper, we first introduce sparse Bayesian learning (SBL) method to recover the transmitted signals for LA- CDMA uplink access. The proposed method exploits the sparsity of the transmitted signals and does not require the knowledge of user activity. Furthermore, we add on the known finite-alphabet constraints and introduce Gaussian mixture model (GMM) method to obtain the transmitted signals. Simulation results have shown that the proposed methods outperform the conventional algorithms.

Licensed-assisted access (LAA), which conveys data information via both licensed and unlicensed bands through spectrum aggregation, becomes a promising solution to enhance the capacity of wireless systems. In view of the potential impact on the incumbent system operating in unlicensed bands, the medium access control (MAC) protocol design for LAA system to harmonically coexist with its neighboring incumbent users is one of the most critical and challenging issues. In this paper, we consider a long-term evolution-based LAA (LAA-LTE) system operating in the WiFi unlicensed spectrum, for which the listen-before-talk-based MAC protocol is carefully designed. By quantifying the WiFi throughput and packet delay in the coexisting system, we formulate the constraints of LAA-LTE transmission time to fairly maintain WiFi services. The conditions of known and unknown network size of incumbent WiFi system are each considered separately. Then, the feasible region of LAA-LTE transmission time is determined, and the LAA-LTE protocol is optimized for maximizing the LAA-LTE throughput or the overall throughput contributed by both LAA-LTE and WiFi system. The theoretical analysis is validated via simulation, which also illustrates important observations when LAA-LTE and WiFi systems coexist. This paper offers guidelines to design the LAA-LTE system, paving the way to a controllable, not only harmonious, coexistence of LAA-LTE and WiFi systems in the unlicensed spectrum.

The rising popularity of wireless services resulting in spectrum shortage has motivated dynamic spectrum sharing to facilitate efficient usage of the underutilized spectrum. Wideband spectrum sensing is a critical functionality to enable dynamic spectrum access by enhancing the opportunities of exploring spectral holes, but entails a major implementation challenge in compact commodity radios that only have limited energy and computation capabilities. In contrast to the traditional sub-Nyquist approaches where a wideband signal or its power spectrum is first reconstructed from compressed samples, this paper proposes a sub-Nyquist wideband spectrum sensing scheme that locates occupied channels blindly by recovering the signal support, based on the jointly sparse nature of multiband signals. Exploiting the common signal support shared among multiple secondary users (SUs), an efficient cooperative spectrum sensing scheme is developed, in which the energy consumption on wideband signal acquisition, processing, and transmission is reduced with detection performance guarantee. Based on subspace decomposition, the low-dimensional measurement matrix, computed at each SU from local sub-Nyquist samples, is deployed to reduce the transmission and computation overhead while improving noise robustness. The theoretical analysis of the proposed sub-Nyquist wideband sensing algorithm is derived and verified by numerical analysis and further tested on real-world TV white space signals. It shows that the proposed scheme can achieve good detection performance as well as reduce the computation and implementation complexity, in comparison with the conventional cooperative wideband spectrum sensing schemes.

We investigate the energy efficiency (EE) in cognitive radio networks, where cognitive users are allowed to access a licensed frequency band opportunistically, provided that the licensed band is vacant. In particular, we study the impact of imperfect spectrum sensing and formulate the average EE maximization problem in fading channels as a joint optimization problem of the spectrum sensing duration and the transmit power of cognitive users. Meanwhile, we consider the constraints of both the collision probability between the primary and cognitive transmissions and the outage probability of cognitive transmissions. However, the joint optimization problem subject to the constraints is complicated and it is computationally hard to obtain the optimal solution. Alternatively, we develop two algorithms, i.e., a linear search algorithm and an iterative-based algorithm, with considerable complexity to solve the problem. Numerical results verify the correctness of both algorithms and show that the proposed algorithms can achieve the performance close to that of the exhaustive search algorithm and outperform the state of arts.

In an underlay cognitive radio network, the cross-channel gain from a cognitive transmitter (CT) to a primary receiver (PR) is crucial for spectrum sharing. By exploiting the relaying capability of the CT, we propose a proactive estimation scheme for the cross-channel gain. Specifically, the CT proactively acts as a full-duplex amplify-and-forward (AF) relay for primary transceivers to trigger the power adaption of a primary transmitter (PT). By carefully designing the relay signal, the CT is able to obtain an estimation of the cross-channel gain by observing the power adaption. To demonstrate the accuracy of the estimation, we analytically characterize both an upper bound and a lower bound of the estimation performance. Furthermore, we study the impact of CT's relaying on the primary transmission and observe that the impact is related to the CT's location. By introducing a factor φ (0 ≤ φ ≤ 1) to denote the probability that the CT's relaying improves the primary transmission instead of causes interference, we design the CT location as a function of φ. Numerical results show that the estimation error of the proactive estimation scheme can be as small as 1.7% with success estimation probability around 91%. By comparing with the state of the art, we show the advantages of the proposed estimator.

In this paper, we investigate a spectrum refarming system where an OFDMA system shares the licensed band of a CDMA system. Both systems share the same cell site, but each has different base station (BS) antennas. Joint resource allocation problem is formulated to optimize the CDMA receive power, OFDMA transmit power, and subcarrier assignment. Conventional interference control to protect the primary CDMA system relies on the availability of cross-channel gains (CCGs) from each secondary OFDMA transmitter to the CDMA BS receiver. However, the CCGs are difficult to be obtained due to lack of intersystem cooperation. To address this problem, we first decouple the original problem into higher and lower-level problems via primal decomposition. Then, a robust lower-level resource allocation (R-LRA) scheme, which controls interference without using CCGs is proposed, with which CDMA users can be sufficiently protected. Thereafter, an enhanced R-LRA (ER-LRA) scheme is proposed to decrease the conservation of R-LRA scheme. Assisted by the CDMA inner power control and based on the ER-LRA, efficient algorithm is designed to solve the higher-level problem. Extensions of the ER-LRA for other scenarios are also studied. Simulation results are provided to validate the proposed schemes in facilitating and improving the spectrum sharing performance.

Increasing spectrum-efficiency (SE) as well as energy-efficiency (EE) has attracted much attention recently due to the fact that the future wireless networks need to address the issues of high throughput and low power consumption. However, the objective for optimizing SE sometimes conflicts with the one for optimizing EE, and the methods for improving EE may result in a decrease in SE. In this paper, we consider the SE-EE tradeoff for cognitive radio (CR) networks with co-operative spectrum sensing (CSS). First, we formulate the general problem, and analyze two special cases: the SE maximization problem and the EE maximization problem. The SE and EE are optimized separately via joint optimization of sensing duration and final decision threshold in CSS. Based on the solutions of the two special cases, the general problem for SE-EE tradeoff is solved. Then, we consider the tradeoff of SE and EE from two perspectives: (1) maximizing EE while satisfying SE requirement; and (2) maximizing SE while satisfying EE requirement. Efficient algorithms for sensing strategy design are proposed for each scenario. Finally, we demonstrate the effectiveness of the proposed sensing strategies and illustrate the tradeoff between SE and EE via simulations.

The pilot spoofing attack is one kind of active eavesdropping activities conducted by a malicious user during the channel training phase. By transmitting the identical pilot (training) signals as those of the legal users, such an attack is able to manipulate the channel estimation outcome, which may result in a larger channel rate for the adversary but a smaller channel rate for the legitimate receiver. With the intention of detecting the pilot spoofing attack and minimizing its damages, we design a two-way training-based scheme. The effective detector exploits the intrusive component created by the adversary, followed by a secure beamforming-assisted data transmission. In addition to the solid detection performance, this scheme is also capable of obtaining the estimations of both legitimate and illegitimate channels, which allows the users to achieve secure communication in the presence of pilot spoofing attack. The detection probability is evaluated based on the derived test threshold at a given requirement on the probability of false alarming. The achievable secrecy rate is utilized to measure the security level of the data transmission. Our analysis shows that even without any pre-assumed knowledge of eavesdropper, the proposed scheme is still able to achieve the maximal secrecy rate in certain cases. Numerical results are provided to show that our scheme could achieve a high detection probability as well as secure transmission.

Conventional spectrum refarming (SR) techniques assemble the legacy services to partial licensed spectrum, and free up the remained spectrum for operating other communication systems. In this paper, we propose a new broadband SR framework in which the orthogonal-frequency division multiple access (OFDMA) system dynamically shares the code division multiple access (CDMA) spectrum, which contains multiple bands in a concurrent manner. The interference margin provided by the downlink isometric random precoded CDMA system is first derived. To protect all the CDMA users, interference constraints that regulate the OFDMA transmission are then formulated. We formulate the joint CDMA load planning and OFDMA resource allocation problem for the SR system. By applying primal decomposition, it is shown that the higher-level problem is non-convex in general. We first propose an efficient algorithm to find the myopic optimal solution by investigating the derivative property of the higher-level problem. Through enhancing the original constrains, the higher-level problem is shown to be convex and solved with another efficient algorithm. Simulation results are provided to evaluate the SR performance, illustrating the significance of the CDMA load planning on the OFDMA throughput, the advantage of the proposed SR model over conventional SR, and the protection to the CDMA system.

Conventional spectrum refarming (SR) techniques assemble the legacy services to partial licensed spectrum, and free up the remained spectrum for operating other systems. In this paper, we propose a new broadband SR framework in which the OFDMA system can concurrently share partial or all the CDMA spectrum that contains multiple bands. The interference temperature provided by the downlink isometric random precoded CDMA system is derived, and the in-band and cross-band interferences introduced by the OFDMA system to CDMA system are quantified. With the derived interference temperature and interference power, resource allocation problems of the OFDMA system under full-concurrent and partial-concurrent broadband SR are formulated and solved. Numerical results are provided to evaluate the SR performance, illustrate the advantage of the full-concurrent broadband SR, and show the significance of the CDMA system load planning on the SR performance.

We study a spoofing attack happened in the physical layer of a multiple-antenna system, where an adversary tries to spoof the transmitter by sending the identical pilot (training) signal as that of a legitimate receiver in the uplink channel estimation phase. This attack, named as pilot spoofing attack, could lead to a secrecy information leakage to the adversary and information rate decrease at the legitimate receiver. Due to the serious results caused by the pilot spoofing attack, we propose an energy-ratio detector (ERD) to protect the legitimate components. The ERD makes the decision by exploiting the asymmetry of the received signal strength (RSS) between the transmitter and the legitimate receiver when the system is under the pilot spoofing attack. Numerical results are presented to illustrate the effectiveness of our proposed detector.

In a time-division duplex (TDD) based multi-input single-output (MISO) system, the channel state information (CSI) can be obtained during the channel estimation phase in which the receiver transmits the pilot symbols to the transmitter. This scheme may suffer from a so-called pilot spoofing attack, i.e., an adversary may send identical pilot signals as those of the legitimate receiver to spoof the transmitter. The resultant channel estimate may then contain the CSI of both legitimate and illegitimate receivers, and if such estimated channel is used for transmit beamforming, the information dedicated for legitimate receiver will leak to the illegitimate receiver. In this paper, we study the detection and defending strategies for such pilot spoofing attack. A two-way training based scheme is proposed. The durations of the training periods are also designed to optimize the secrecy rate. Finally, numerical results are presented to show the performance of our proposed method.

Spectrum refarming (SR) refers to a radio resource management technique which supports different generations of cellular networks to operate in the same radio spectrum. In this paper, an underlay SR model is proposed for an Orthogonal Frequency Division Multiple Access (OFDMA) system to share the spectrum of a Code Division Multiple Access (CDMA) system through intelligently exploiting the interference margin provided by the CDMA system when operating with a low system load. The asymptotic signal-to-interference-plus-noise ratio (SINR) of the CDMA system is used to quantify the interference margin, which interestingly does not depend on the instantaneous information (spreading codes and channel state information) of the CDMA system, thanks to its internal power control. By using the transmit power constraints together with the derived interference margin, the uplink resource allocation problem for OFDMA system is formulated and solved through dual decomposition method. The proposed SR system only requires high level system parameters from the CDMA system, hence, the upgrading of legacy CDMA system is not needed. Simulation results have verified our theoretical analysis, and validated the effectiveness of the proposed resource allocation algorithm and its capability to protect the legacy CDMA users.

The pilot spoofing attack is one kind of active eavesdropping conducted by a malicious user during the channel estimation phase of the legitimate transmission. In this attack, an intelligent adversary spoofs the transmitter on the estimation of channel state information (CSI) by sending the identical pilot signal as the legitimate receiver, in order to obtain a larger information rate in the data transmission phase. The pilot spoofing attack could also drastically weaken the strength of the received signal at the legitimate receiver if the adversary utilizes large enough power. Motivated by the serious problems the pilot spoofing attack could cause, we propose an efficient detector, named energy ratio detector (ERD), by exploring the asymmetry of received signal power levels at the transmitter and the legitimate receiver when there exists a pilot spoofing attack. Our analysis shows that by setting the ratio of received signal power levels at the transmitter and the legitimate receiver as the test statistic, the detecting threshold is derived without using the knowledge of the CSI of the legitimate channel as well as the illegitimate channel. Furthermore, we study the performance of the proposed ERD in various special cases in order to obtain useful insights. Numerical results are presented to further demonstrate the performance of our proposed ERD.

Spectrum refarming (SR) is a promising radio resource management technique which allows different generations of cellular networks to operate in the same radio spectrum. In this paper, an underlay SR model is proposed for an Orthogonal Frequency Division Multiple Access (OFDMA) system to refarm the spectrum assigned to a Code Division Multiple Access (CDMA) system through intelligently exploiting the interference margin provided by the CDMA system. We investigate the mutual effect of the two systems by evaluating the asymptotic signal-to-interference-plus-noise ratio (SINR) of CDMA users, based on which the interference margin tolerable by CDMA system is determined. With the interference margin together the transmit power constraints, the resource allocation problem of OFDMA system is formulated and solved through dual decomposition method. Simulation results have verified our theoretical analysis and validated the effectiveness of our proposed OFDMA resource allocation solution, as well as its protection to CDMA services.

The compound broadcast channel models the situation where each receiver has a number of possible realizations and its message is to be decoded regardless of the actual realization. Weingarten et al. established the capacity region for the two-user degraded case where the realizations exhibit a degradedness order defined through a fictitious receiver. In this paper, we consider a K-receiver degraded compound broadcast channel where, instead of specifying fictitious receivers, the receivers exhibit a pair-wise degradedness order, i.e., each realization from a weaker receiver is stochastically degraded with respect to each realization from a stronger receiver. There is a restriction on the number of possible realizations for all the receivers to two. We first prove the capacity region for this discrete memoryless class of broadcast channels. The achievability follows readily from superposition coding and successive decoding. To facilitate the proof of the converse, we give an alternative characterization of the achievable rate region. The main contribution in this paper is to bypass the use of the Csiszέr-sum lemma in order to prove the converse for an arbitrary number of receivers. We also make use of our converse proof as well as Geng and Nair's technique to prove an extremal entropy inequality. This is then finally used to prove the capacity region of the equivalent class of Kreceiver aligned Gaussian MIMO degraded compound broadcast channels.

In this paper, we investigate the physical-layer security of a multi-user multi-eavesdropper cognitive radio system, which is composed of multiple cognitive users (CUs) transmitting to a common cognitive base station (CBS), {while multiple eavesdroppers may collaborate with each other or perform independently in intercepting the CUs-CBS transmissions, which are called the coordinated and uncoordinated eavesdroppers, respectively}. Considering multiple CUs available, we propose the round-robin scheduling as well as the optimal and suboptimal user scheduling schemes for improving the security of CUs-CBS transmissions against eavesdropping attacks. Specifically, the optimal user scheduling is designed by assuming that the channel state information (CSI) of all links from CUs to CBS, to primary user (PU) and to eavesdroppers are available. By contrast, the suboptimal user scheduling only requires the CSI of CUs-CBS links without the PU's and eavesdroppers' CSI. We derive closed-form expressions of the secrecy outage probability of these three scheduling schemes in the presence of {the coordinated and uncoordinated eavesdroppers}. We also carry out the secrecy diversity analysis and show that the round-robin scheduling achieves the diversity order of only one, whereas the optimal and suboptimal scheduling schemes obtain the full secrecy diversity, {no matter whether the eavesdroppers collaborate or not. In addition, numerical secrecy outage results demonstrate that for both the coordinated and uncoordinated eavesdroppers, the optimal user scheduling achieves the best security performance and the round-robin scheduling performs the worst.} Finally, upon increasing the number of CUs, the secrecy outage probabilities of the optimal and suboptimal user scheduling schemes both improve significantly.

Nair and El Gamal established the capacity region of the three-receiver multilevel broadcast channel (MBC) with two-degraded message sets. For the three-receiver MBC with two-degraded message sets, the output at receiver 2 is a degraded version of the output at receiver 1. However, no order of degradedness is imposed on the output at receiver 3. The transmitter sends a common message to all three receivers and a private message to receiver 1. By considering a specific discrete-memoryless example, Nair and El Gamal showed that a direct extension of the Körner-Marton region (for the general two-receiver broadcast channel with degraded message sets) is strictly suboptimal. They also considered a three-receiver Gaussian product MBC and showed that, restricted to Gaussian inputs, the direct extension of the Körner-Marton region is again strictly suboptimal. However, whether Gaussian inputs are optimal remained unresolved. In this paper, we show that Gaussian inputs, along with time-sharing between rate points obtained with Gaussian inputs, achieve the capacity region of the three-receiver Gaussian multiple-input multiple-output MBC (this includes the three-receiver Gaussian product MBC considered by Nair and El Gamal) with two-degraded message sets. Our proof relies on the channel enhancement technique introduced by Weingarten as well as the perturbation approach employed by Liu and Viswanath.

The two-user compound Gaussian MIMO broadcast channel models the situation where each user has a finite set of possible realizations. The transmitter sends two messages, one for each user, such that each user must be able to decode its message regardless of the actual realization. This channel also models a broadcast channel (BC) with two groups of users and two messages, with one message intended for each group of users. Weingarten et al. established the capacity region for the degraded case where the realizations/users exhibit a degradedness order. The degradedness order is defined through an additional realization/user where the realizations/users from one set are degraded with respect to him and where he is degraded with respect to the realizations/users from the other set. To show that Gaussian inputs attain the capacity region, they proved a new extremal inequality and employed the use of the channel enhancement technique as well. In this paper, we extend the result to the N-user degraded compound Gaussian MIMO BC, where the N users exhibit a degradedness order similar to the two-user case. We first prove a generalization of the extremal inequality considered by Weingarten et al.; instead of considering the difference between the weighted sum of two sets of conditional differential entropies, we consider the summation of N - 1 sets of such differences, where the conditioning random variables of the N - 1 sets form a Markov chain. Our proof relies on the Gaussian perturbation approach, the necessary KKT conditions as well as a data processing inequality. Finally, we specialize the generalized extremal inequality to characterize the capacity region of the N-user degraded compound Gaussian MIMO BC. By making appropriate use of the necessary KKT conditions, we are able to do away with the use of the channel enhancement technique that was employed in the proof of the capacity region of the two-user case.

It is well known that broadband wireless communications (BWC) are necessary for high speed vehicles since passengers need many broadband wireless multimedia services. However, one design challenge is to identify sufficient spectrum resource to support BWC in high speed vehicles. Recently, cognitive radios (CRs) are being considered as a promising technology to solve scarcity problem of spectrum resource. Therefore, we investigate the spectrum resource allocation problem in the CR based high speed vehicle network (CR-HSVN) in this paper. Specifically, we propose a spectrum resource allocation framework, where high speed vehicles could effectively utilize the TV white spaces. Subsequently, we formulate the allocation of spectrum resource as an optimization problem to maximize the available spectrum resources (i.e., TV white spaces) utilized by the CR-HSVN, and investigate the property of the CR-HSVN to reduce the complexity by separation computing method without loss of optimality. Furthermore, we analyze the optimal solution based on branch and bound method, the suboptimal solutions based on single channel and linear programming, respectively. Simulation results show that the proposed framework could offer excellent performances of the spectrum utilization and fairness for the CR-HSVN. Meanwhile, the aggregated interference from vehicles to primary users is constrained.

Liu et al. proved an extremal entropy inequality using a vector generalization of the Costa entropy-power inequality (EPI). The generalized Costa EPI was proved, in turn, using a perturbation approach via a fundamental relationship between the derivative of mutual information and the minimum mean-square error (MMSE) estimate in linear vector Gaussian channels. In this paper, we consider two new variations of the (Liu et al.) extremal entropy inequality. Instead of employing perturbation approaches, we employ a new method recently introduced by Geng and Nair, which was used to resolve the capacity region of the Gaussian MIMO broadcast channel (BC) with common and private messages. As an application, we use one of the extremal entropy inequalities to prove the capacity region of a class of reversely degraded Gaussian MIMO BC with three users and three-degraded message sets.

Weingarten et al. established the capacity region for the two-user degraded compound broadcast channel where the realizations of each user exhibit a certain degradedness order. The degradedness order is defined through an additional fictitious user whose channel is stochastically degraded with respect to each realization from one set (the stronger receiver) while each realization from the other set (the weaker receiver) is stochastically degraded with respect to him. Rather than specifying a fictitious user, we consider a two-user degraded compound broadcast channel which is pair-wise degraded, i.e., each realization from the weaker receiver is stochastically degraded with respect to each realization from the stronger receiver. In this paper, we first prove the capacity region for a discrete memoryless class of this broadcast channels where the weaker receiver has only two possible realizations while there is an arbitrary number of possible realizations for the stronger receiver. Next, we consider the equivalent class of degraded compound MIMO Gaussian broadcast channels. To show that Gaussian inputs attain the capacity region, we prove a new extremal entropy inequality using a technique recently introduced by Geng and Nair that was used to resolve the capacity region of the two-user MIMO Gaussian broadcast channel with common and private information.

We consider a two-user broadcast channel (BC) with two-degraded message sets where a common message M2is intended for both receivers and a private message M1is intended for receiver one. There is an eavesdropper whose channel is stochastically degraded with respect to both receivers and both messages must be kept confidential from the eavesdropper. However, we do not assume any form of degradedness between the two receivers. We first characterize the capacity region of this class of discrete memoryless BC. Next, we consider the two-user Gaussian MIMO BC with two-degraded message sets and an eavesdropper. We characterize the capacity region for the case where the eavesdropper is only required to be stochastically degraded with respect to receiver one. We make use of the channel enhancement technique as well as an extremal entropy inequality of Liu et al. that was derived using the generalized Costa's entropy power inequality (EPI).

In recent years, filter bank multicarrier (FBMC) has recaptured widespread interests for its possible applications in cognitive radio and dynamic spectrum access. A distinctive feature for cognitive radio is its adaptivity to environment. When environment changes, a cognitive radio will change its parameters to optimize the transmission and receiving. Thus it is desirable to design a unified structure and algorithm for FBMC that needs little change for different parameters. In this paper, we propose a unified structure and parallel algorithms to implement the FBMC. The FBMC system and parallel algorithms are constructed based on the normalized prototype filter. The coefficients of the normalized prototype filter can be pre-computed and stored. The proposed parallel algorithms have the same structure for various choices of time duration, subcarrier spacing and bandwidth. Combined with known parallel algorithms for the fast Fourier transform (FFT), the proposed algorithms fully parallelize the computations for the transmitter and receiver, which can run much faster than conventional serial algorithms as modern processors usually have massive parallel capability.

The total out-of-band emission (OOBE) from the secondary users (SU) is determined not only by the baseband signaling waveforms, but also by the design of the transmitter chain, which includes the nonlinear power amplifier (PA). The increase in OOBE due to a nonlinear PA has been well-observed, but its characterization involves complicated analysis. In this paper, we present a closed-form approximation to the power spectral density (PSD) of a signal at the output of a nonlinear PA, or more specifically, the solid-state power amplifier (SSPA). The SSPA's response can be approximated using a third order nonlinear model (TONM) which has been proposed in literature. Using the TONM, we proposed a statistical approximation, together with a piecewise linear model, to determine a closed-form estimate to the OOBE of the PA output signal. Simulation results show a close fit between the theoretical expression for the derived PSD and the simulated model. Since the nonlinear PA increases the OOBE due to spectral regrowth, the ability to characterize this behavior is of particular importance to the application of TV band devices (in television broadcast frequency bands) that need to fulfill certain OOBE limits.

In this paper, a spectrum sharing protocol is proposed for device-to-device (D2D) communication overlaying cellular networks. Specifically, the protocol allows the D2D users to communicate bi-directionally with each other while assisting the two-way communication between the cellular base station (BS) and the cellular user (CU) over the same time and frequency resources. The achievable rate region of the sum-rate of the D2D transmissions versus that of the cellular transmissions is evaluated. The Pareto boundary of the region is found by optimizing the transmit power at BS and CU as well as the power splitting factor at the relay D2D node. We find through numerical results that the proposed two-way protocol with power control at the BS and CU is effective to improve the sum rate for both the D2D and cellular communication.

In this paper we consider multi-input multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM)-based systems under unknown narrow-band interference (NBI). We propose an iterative receiver to jointly estimate the channel information, which consists of channel coefficients and noise-plus-interference variances of each sub-carrier, and detect the transmitted signals. The simulation results show that our proposed receiver provides a close bit-error-rate (BER) to that of the case where perfect channel information is available at the receiver. Besides, Cramér-Rao lower bound (CRLB) of interested parameters are also derived. The mean-square-error (MSE) of the estimated parameters given by our proposed algorithm reaches the CRLB.

Physical layer security in wireless networks has received increasing attention in recent years. In this paper, we consider multiple-input single-output (MISO) fading wiretap channels, where the transmitter utilizes artificial noise-aided precoding (ANaP) transmission strategy to maximize the secrecy capacity of the channel. When the channel state information (CSI) of Eavesdropper's (Eve's) channel is known at Alice, we prove that the optimal ANaP strategy reduces to the conventional precoding strategy, i.e., all the transmit power should be allocated to the precoding of information signal. When the CSI of Eve's channel is unknown at Alice, we find that there exists an optimal power allocation ratio between the information signal and the artificial noise and that this power ratio depends on the number of antennas as well as the available transmit power at Alice. In particular, when the available transmit power at Alice increases or the number of antennas at Alice decreases, more power should be allocated to the artificial noise.

In this paper, the access strategy for spectrum-sharing based two-tier networks is investigated. By exploring the motivations of the home base station (HBS) and the macrocell user (MU), we propose a Stackelberg game based approach, which enables them to improve their performances by establishing direct links. The proposed approach copes with the distributed nature of the user-deployed small cell networks, and requires no inter-cell coordinations. Experimental results show that the proposed approach can guarantee the capacity gain of the small cells, while improving the energy efficiency of the macrocell users. Furthermore, by adjusting the objective function, the benefits of open access can be balanced between the capacity gain of small cells and the energy efficiency improvement of the MUs. Therefore it can be applied flexibly for various design purposes.

In this paper, we consider the medium access control (MAC) protocol design for random access cognitive radio network (CRN). Based on asynchronous spectrum sensing technique and RTS/CTS mechanism, a new MAC protocol, namely, cognitive-radio-based carrier sense medium access with collision avoidance (CR-CSMA/CA) is proposed to coordinate the channel access of secondary network as well as protect the operation of primary network, which applies to both single and multiple channel models. Using the G/G/1 queuing model with consideration of unsaturated and saturated network condition, we develop a framework to analyze the proposed MAC protocol and also derive closed-form expressions of specific performance metrics such as normalized throughput, average packet service time, etc. Performance evaluations illustrate and validate that the performance of CR-CSMA/CA varies with the offered traffic load of secondary network and the spectrum utilization rate of primary network, respectively, and also show that CR-CSMA/CA outperforms other relevant MAC protocols.

Orthogonal frequency division multiplex (OFDM) has been widely used in various wireless communications systems. Thus the detection of OFDM signals is of significant importance in cognitive radio and other spectrum sharing systems. A common feature of OFDM in many popular standards is that some pilot subcarriers repeat periodically after certain OFDM blocks. In this paper, sensing methods for OFDM signals are proposed by using such repetition structure of the pilots. Firstly, special properties for the auto-correlation (AC) of the received signals are identified, from which the optimal likelihood ratio test (LRT) is derived. However, this method requires the knowledge of channel information, carrier frequency offset (CFO) and noise power. To make the LRT method practical, we then propose an approximated LRT (ALRT) method that does not rely on the channel information and noise power, thus the CFO is the only remaining obstacle to the ALRT. To handle the problem, we propose a method to estimate the composite CFO and compensate its effect in the AC using multiple taps of ACs of the received signals. Computer simulations have shown that the proposed sensing methods are robust to frequency offset, noise power uncertainty, time delay uncertainty, and frequency selectiveness of the channel.

Device-to-device (D2D) communications has been proposed in the literature as an underlay approach to cellular networks to allow direct transmission between two cellular devices with local communication needs. In this paper, we consider a scenario of D2D communications overlaying a cellular network and propose a new spectrum sharing protocol, which allows the D2D users to communicate bi-directionally with each other while assisting the two-way communications between the cellular base station (BS) and the cellular user (CU). We derive the achievable rate region of the sum rate of the D2D transmissions versus that of the cellular transmissions. The Pareto boundary of the region is found by optimizing the transmit power at BS and CU as well as the power splitting factor at the relay D2D node. Since either of the two D2D users can be the relay and there can exist multiple pairs of D2D users, we also consider the relay selection from the potential D2D users. We find through numerical results that the proposed two-way protocol with power control at the BS and CU is effective to improve the sum rate for both the D2D and cellular users. In addition, relay selection can achieve further improvement in the sum rate of the cellular links.

With the prior knowledge that the primary user is highly likely idle and the primary signals are digitally modulated, we propose an optimal Bayesian detector for spectrum sensing to achieve higher spectrum utilization in cognitive radio networks. We derive the optimal detector structure for MPSK modulated primary signals with known order over AWGN channels and give its corresponding suboptimal detectors in both low and high SNR (Signal-to-Noise Ratio) regimes. Through approximations, it is found that, in low SNR regime, for MPSK (M > 2) signals, the suboptimal detector is the energy detector, while for BPSK signals the suboptimal detector is the energy detection on the real part. In high SNR regime, it is shown that, for BPSK signals, the test statistic is the sum of signal magnitudes, but uses the real part of the phase-shifted signals as the input. We provide the performance analysis of the suboptimal detectors in terms of probabilities of detection and false alarm, and selection of detection threshold and number of samples. The simulations have shown that Bayesian detector has a performance similar to the energy detector in low SNR regime, but has better performance in high SNR regime in terms of spectrum utilization and secondary users' throughput.

Multiple antennas at the transmitter and the receiver can increase the channel capacity significantly. However, this is at the expense of linearly increasing circuit power consumption due to the use of multiple radio frequency (RF) chains to support the antennas, which is quite significant for large-scale MIMO systems but is largely ignored in the literature. Hence, in this paper, we assess the performance of a point-to-point large-scale MIMO channel considering the overall power consumption on both the transmitter and the receiver, and study the optimal RF chain configurations to maximize the transmission rate under such a total power constraint. Both configurations with and without the channel state information (CSI) of the channel matrix are considered. While the former requires the design of optimal selection of RF chains, the latter only needs to determine the optimal number of RF chains. We find through numerical results that the gain of configuration with CSI over that without CSI diminishes as the dimension of the channel gets large. We also provide guidelines for selecting the optimal number RF chains for configuration without CSI. In particular, for MISO case, we find that it is near-optimal to choose half of the maximum number of transmit RF chains, the circuits of which are affordable to be powered on by the total power budget.

In this paper we consider two-way relaying systems consisting of two end users, N1and N2, which exchange their information with the help of a relay, R. The three terminals are equipped with multiple antennas. The information exchange process between N1and N2are divided into two phases. Both N1and N2send their information to R at the first phase. The relay will amplify its received signals and transmit the resultant signals back to N1and N2at the second phase. The end users estimate the necessary channel coefficients to decode the signals; therefore, the processing burden on the relay is reduced. In this paper, a least-squared based estimation method is proposed to estimate those coefficients. Optimal pilot vectors are also proposed to minimize the mean-square-error (MSE) of channel estimation. Furthermore, the proposed training signals has the peak-to-average-power ratio (PAPR) of 1.

This paper considers multi-sensor multi-antenna spectrum sensing. First, it is assumed that all users are able to send their raw data to the fusion center. In this case the global optimial solution is the likelihood ratio test (LRT) using all the raw data. A simple closed-form expression for the LRT is found. Although LRT is optimal, it is hardly useful in practice due to its reliance on the knowledge of primary user's channels and noise powers of all users. Thus a method using the estimated channels and noise powers is proposed, which is called generalized LRT (GLRT). Secondly, the optimal fusion scheme (OFS) is found if each user computes its test statistic based on an eigenvalue based detection and sends the test statistic to the fusion center. Both GLRT and OFS need the SNR information of all users. To make the detections more practical, two totally blind detections, namely, approximated OFS and approximated GLRT, are proposed. Simulations are provided to support the results.

We consider a broadcast channel with three receivers and three-degraded message sets, where the transmitter has a common message intended for all three receivers, a message intended for receivers 2 and 3, and a private message intended for receiver 3. The messages are transmitted over a family of parallel degraded broadcast channels. In the most general case, there are six parallel degraded broadcast channels with different orders of degradedness. We determine the capacity region for two classes of broadcast channels, one with five parallel degraded broadcast channels and the other with three parallel degraded broadcast channels. The main difficulty is in identifying the relevant auxiliary random variables in the proofs of the converse. To accomplish this, we make use of an information theoretic inequality that can be proven using the Csiszár-sum identity.

Weingarten et al. characterized the capacity region of a two-user compound MIMO broadcast channel when the two users exhibit a certain degradedness order. To show that Gaussian inputs attain the capacity region, they proved a new extremal inequality and made use of the channel enhancement technique. In this paper, we prove a generalization of the extremal inequality considered by Weingarten et al. We then apply the generalized extremal inequality to characterize the capacity region of the N-user compound MIMO Gaussian broadcast channel when the N users exhibit a degradedness order similar to the two-user case.

The essential tradeoff between sensing capability and achievable throughput of the secondary network is one of the active research topics for researchers working on cognitive radio. In this paper, noise uncertainty which has a great impact on sensing methods is taken into account in the maximization of throughput using eigenvalue based spectrum sensing schemes. This issue has not been tackled in the throughput associated studies before. First, the theoretical and empirical distributions of the decision statistics and the detection performances for eigenvalue based sensing techniques are studied in the presence of noise uncertainty. The computed detection probabilities of maximum-minimum eigenvalue (MME) detector and maximum eigenvalue detector (MED) are compared with the most widely used energy detector (ED). Then, in the light of the obtained results, the throughput of the secondary network is maximized in order to find out the sensing duration for each scheme using multiple receive antennas. It is shown that, under low signal to noise ratio (SNR) regime, the designed sensing slot duration achieves the best sensing throughput tradeoff.

In this paper we consider an orthogonal frequency-division multiplexing (OFDM)-based system under unknown narrow-band interference (NBI). We propose an iterative receiver to jointly estimate the channel information, which consists of channel coefficients and noise-plus-interference variances of each sub-carrier, and detect the transmitted signals. The simulation results show that our proposed receiver provides an extremely close bit-error-rate (BER) to that of the case where perfect channel information is available at the receiver. Besides, Cramer-Rao lower bound (CRLB) of interested parameters are also derived. The mean-square-error (MSE) of the estimated parameters given by our proposed algorithm reaches the CRLB.

Owing to the special structure of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC), the associated capacity region computation and beamforming optimization problems are typically non-convex, and thus cannot be solved directly. One feasible approach is to consider the respective dual multiple-access channel (MAC) problems, which are easier to deal with due to their convexity properties. The conventional BC-MAC duality has been established via BC-MAC signal transformation, and is applicable only for the case in which the MIMO BC is subject to a single transmit sum-power constraint. An alternative approach is based on minimax duality, which can be applied to the case of the sum-power constraint or per-antenna power constraint. In this paper, the conventional BC-MAC duality is extended to the general linear transmit covariance constraint (LTCC) case, which includes sum-power and per-antenna power constraints as special cases. The obtained general BC-MAC duality is applied to solve the capacity region computation for the MIMO BC and beamforming optimization for the multiple-input single-output (MISO) BC, respectively, with multiple LTCCs. The relationship between this new general BC-MAC duality and the minimax duality is also discussed, and it is shown that the general BC-MAC duality leads to simpler problem formulations. Moreover, the general BC-MAC duality is extended to deal with the case of nonlinear transmit covariance constraints in the MIMO BC.

When secondary users (SUs) in a cognitive radio network (CRN) are battery-powered wireless devices, energy resources become very precious. Therefore, it is important that their energies are used efficiently. In this paper, we define the energy efficiency as the ratio of the average throughput of the CRN over the average energy used by the CRN. In cooperative spectrum sensing, the fusion rule threshold, detector's thresholds at the SUs, length of the sensing time, and the number of cooperating SUs will affect both the average throughput and the average energy consumed by the CRN. Therefore, in this paper, we optimize and evaluate these parameters with the aim of maximizing the energy efficiency of the CRN.

In this paper, we consider the medium access control (MAC) protocol design for cognitive radio networks. An opportunistic spectrum access protocol named Slotted CR-ALOHA is proposed, and its performances in terms of normalized throughput and average packet delay are evaluated. Simulation results show that for various frame lengths and number of SUs, the optimal performance can be achieved at an appropriate spectrum sensing time, and there also exists a tradeoff between the achievable performance of secondary network and the protection effect on primary network.

In this paper, we investigate the distributed power allocation strategies for a spectrum-sharing femtocell network, where a central macrocell underlaid with several femtocells. Assuming that the macrocell protects itself by pricing the interference from the femtocells, a Stackelberg game is formulated to jointly consider the utility maximization of the macrocell and the femtocells. Then, the Stackelberg equilibrium for the proposed game is studied, and an effective distributed interference price bargaining algorithm with guaranteed convergency is proposed to achieve the equilibrium. Numerical examples are then presented to verify the proposed studies. It is shown that the algorithm is effective in distributed power allocation and macrocell protection requiring minimal network overhead for spectrum-sharing-based two-tier femtocell networks.

In an opportunistic spectrum access based cognitive radio network, it is usually assumed that the secondary user (SU) will transmit at peak power when it detects that the primary user (PU) is absent while it will not transmit when the PU is detected to be present. However, when the PU is detected to be inactive, the PU may not definitely be inactive as it may be a case of miss-detection. If it is a miss-detection, the SU's data rate for that particular data frame will be reduced due to interference from the PU. In this paper, power allocation strategies are designed for each data frame based on the spectrum sensing information (SSI) gathered during the sensing period. Using the SSI, the miss-detection probability for a data frame is taken into consideration when maximizing the average data rate and minimizing the outage probability of the SU. The probability of detecting the PU is required to be above a targeted threshold and the SU is required to satisfy its long-term transmit power budget. Our results showed that the transmit power of the SU should be a function of the SSI in order to maximize the average data rate and minimize the outage probability of the SU.

Analytical formulations of the throughput of cognitive multi-channel MACs with perfect and imperfect sensing are presented. Both imperfect concurrent sensing and imperfect sequential sensing schemes are considered. A discrete time Markov chain is used to model the number of communicating node pairs in the MAC protocols. The throughput of the MAC protocol with perfect sensing is expressed as a function of the number of available data channels, the channel transmission rate, the average utilization per channel, the steady state probability of having a number of available data channels, while the throughput of the MAC protocol with imperfect sensing is expressed as a function of the number of available data channels, the channel transmission rate, the average utilization per channel, the steady state probability of having a number of available data channels, probability of false alarm and probability of misdetection. The results also clearly demonstrate the advantage of our proposed MAC protocol with imperfect concurrent sensing having low probability of misdetection but high probability of false alarm.

Based on the Neyman-Pearson theorem, the optimal cooperative sensing for distributed sensors with time independent signals is derived. It is shown that the optimal scheme is simply a linearly combined energy detection and the combining coefficient is a simple function of the signal to noise ratio (SNR). To reduce the required information at the fusion center and simplify the decision-making process and threshold setting, an approximated optimal cooperative sensing is proposed and compared with some other sub-optimal methods. Finally the impact of decoding error in the reported results is analyzed. Based on the closed-form expression for the performance, it is proved that the impact of decoding error is equivalent to the reduction of sensing time. Simulations are provided to support the results.

In this paper, we establish the capacity region of a new class of deterministic K-receiver broadcast channels with degraded message sets, where the output Yi, i ∈ {1, ..., K}, is a deterministic function of the channel input X and user i requires messages (M1, ... Mi). In this class of deterministic broadcast channels, the outputs Y1→ Y2→ Y3... → YKform a Markov chain for a sufficient class of input probability distributions p (x) ∈ P. The main idea of the coding strategy is to allow user i to decode not only its own messages (M1, ..., Mi), but also part of the messages (Mi+1, ..., MK) intended for user j ∈ {i + 1, i + 2, ..., K}. We then show that the achievable rate region attains the capacity region of this new class of deterministic K-receiver broadcast channels.

A good approximate analytical formulation of the saturated throughput of a Cooperative MAC (CoopMAC) is presented via a two-dimensional discrete-time Markov chain rather than using an approximation. A helper node is used to relay packets from the source node to the destination node within a coverage via a faster two-hop link if possible. The analytical model is formulated with a number of data rates and their corresponding distances. The critical probabilities that at least a helper node exists in different regions to relay packets are explicitly derived through areas of the intersection of the coverages of the source and destination nodes using different data rates to the helper node. By using the weighted sum of the probabilities of the statistics of each of the stations, the saturated throughput of CoopMAC protocol is explicitly derived. Numerical results of the saturated throughput show that the agreement between the simulation and the new analytical results for CoopMAC is very good.

Analytical formulations of the throughput of multi-channel medium access control (MAC) protocols are presented. Both multi-channel multi-rate MAC and multi-channel CoopMAC protocols are considered. A discrete time Markov chain is used to model the number of nodes communicating with the AP in the multi-channel multi-rate MAC protocol, while another discrete time Markov chain is used to model the number of data channels used in the multi-channel CoopMAC protocol. The throughput of the multi-channel multi-rate MAC protocol is expressed as a function of the number of data channels, the channel transmission rate and the solutions to the first Markov chain, while the throughput of the multi-channel CoopMAC protocol is expressed as a function of the number of data channels, the channel transmission rates, and the solutions to the latter Markov chain. The first Markov chain only considers direct one-hop links communications in the multi-channel multi-rate MAC, while the latter Markov chain accounts not only the direct one-hop links communications but also for faster two-hop relaying links communications in the multi-channel CoopMAC as well. Numerical results of the throughput corresponding to typical values are presented. The results also clearly demonstrate the advantage of our proposed multi-channel CoopMAC protocol over a multi-channel multi-rate MAC protocol.

Based on the high probability that primary user is idle in cognitive radio networks, we propose an optimal Bayesian detector structure for spectrum sensing. Although the optimal detector by Neyman-Pearson theorem maximizes the detection probability for a given false alarm probability, Bayesian detector can achieve a higher overall spectrum utilization and SU throughput and at the same time the primary user is well protected from secondary user's interference. For BPSK modulated primary signals we show that the optimal Bayesian detector can be reduced to an energy detector in lower SNR regime, and it can be approximated to a detector employing the sum of received signal magnitudes in high SNR regime to detect primary signals. We give the analysis for optimal Bayesian detector and the corresponding suboptimal detector structure in both low and high SNR regimes, and verify the performance of the detector with simulation results.

In this paper, we consider a cognitive radio (CR) network where a secondary (cognitive) user shares the spectrum for transmission with a primary (non-cognitive) user over block-fading (BF) channels. It is assumed that the primary user has a constant-rate, constant-power transmission, while the secondary user is able to adapt transmit power and rate allocation over different fading states based on the channel state information (CSI) of the CR network. We study a new type of constraint imposed over the secondary transmission to protect the primary user by limiting the maximum transmission outage probability of the primary user to be below a desired target. We derive the optimal power allocation strategies for the secondary user to maximize its ergodic/outage capacity, under the average/peak transmit power constraint along with the proposed primary user outage probability constraint. It is shown by simulations that the derived new power allocation strategies can achieve substantial capacity gains for the secondary user over the conventional methods based on the interference temperature (IT) constraint to protect the primary transmission, with the same resultant primary user outage probability.

Energy-efficient design has become increasingly important to battery-powered wireless devices. In this paper, we focus on the energy efficiency of a cognitive radio network, in which a secondary user senses the channels licensed to some primary users sequentially before it decides to transmit. Energy is consumed in both the channel sensing and transmission processes. The energy-efficient design calls for a careful design in the sensing-access strategies and the sensing order, with the sensing strategy specifying when to stop sensing and start transmission, the access strategy specifying the power level to be used upon transmission, and the sensing order specifying the sequence of channel sensing. Hence, the objective of this paper is to identify the sensing-access strategies and the sensing order that achieve the maximum energy efficiency. We first investigate the design when the channel sensing order is given and formulate the above design problem as a stochastic sequential decision-making problem. To solve it, we study another parametric formulation of the original problem, which rewards transmission throughput and penalizes energy consumption. Dynamic programming can be applied to identify the optimal strategy for the parametric problem. Then, by exploring the relationship between the two formulations and making use of the monotonicity property of the parametric formulation, we develop an algorithm to find the optimal sensing-access strategies for the original problem. Furthermore, we study the joint design of the channel sensing order and the sensing-access strategies. Lastly, the performance of the proposed designs is evaluated through numerical results.

The joint linear transceiver design for Multiple-Input Multiple-Output (MIMO) \adhoc cognitive radio networks when the channel state information (CSI) is uncertain is discussed in this paper. The uncertainty in CSI is modeled using Stochastic Error (SE) and Norm Bounded Error (NBE) models. The Sum-Mean Square Error (SMSE) performance is used to formulate the design problem. To optimize the network, the transmit power budget for secondary user transmitters and the maximum allowed interference at primary user receivers are constrained. In the design methodology, the network parameters are optimized to best serve the users when the worst possible channel realizations occur. It is shown that for the SE model of uncertainty, this problem can be cast as a Second Order Cone Problem (SOCP), while for the NBE model, the problem becomes a Semi-Definite Program (SDP). These two problems are solved efficiently using the numerical solver packages YALMIP and SDPT3. Finally simulation results are presented to evaluate the performance of the proposed methods.

In an opportunistic spectrum access (OSA) based cognitive radio system, a secondary user (SU) is allowed to access the licensed spectrum of the primary user (PU) when it is inactive. Conventional power allocation strategies, which do not consider spectrum sensing information (SSI), may not be optimal in OSA based cognitive radio system because when the SU mis-detects the PU's presence, the interference from the PU will cause a lower data rate or a higher outage probability to the SU. In this paper, power allocation strategies for each frame are designed based on the SSI gathered by the SU during the sensing period of the frame. We consider both cooperative and non-cooperative spectrum sensing scenarios. In non-cooperative spectrum sensing, the SU transmitter (SU-Tx) has its SSI while in cooperative spectrum sensing, the SU-Tx has both its SSI and the SU receiver's (SU-Rx's) SSI. Using the SSIs, power allocation strategies are designed to either maximize the average data rate or minimize the outage probability of the SU. The proposed power allocation strategies have to ensure that the PU is sufficiently protected and the SU satisfies its long-term transmit power budget. Optimization of the sensing time is also considered to further enhance the performances of the system.

The problem of nonlinear transceiver optimization in a multi-tier Multiple-Input Multiple-Output (MIMO) network in Cognitive Radio Network (CR-Net) configuration is studied. The employed transmission schemes are based on the Matrix Decision Feedback Equalizer (DFE) and Tomlinson-Harashima Precoder (THP). It is assumed that the Channel State Information (CSI) is not known perfectly. The former type of uncertainty is modeled using Stochastic Error (SE) model while the latter one is modeled using Norm Bounded Error (NBE) model. The performance measure used for optimizing the network is based on the sum Mean Square Error (MSE) of symbol estimation in the system. The design problem is constrained by the transmit power of the Secondary Users (SU's) as well as the maximum allowed interfering power to the Primary Users (PU's). The design problem is not jointly convex in its design variables and has infinitely many constraints. To overcome this, a suboptimal iterative procedure is proposed. Based on the chosen model for uncertainty, the two resultant problems are Semidefinite Programs (SDP). These two problems are solved numerically. Finally simulations results are provided to assess the performance of the system.

In recent years, some spectrum sensing algorithms using multiple antennas, such as the eigenvalue based detection (EBD), have attracted a lot of attention. In this paper, we are interested in deriving the asymptotic distributions of the test statistics of the EBD algorithms. Two EBD algorithms using sample covariance matrices are considered: maximum eigenvalue detection (MED) and condition number detection (CND). The earlier studies usually assume that the number of antennas (K) and the number of samples (N) are both large, thus random matrix theory (RMT) can be used to derive the asymptotic distributions of the maximum and minimum eigenvalues of the sample covariance matrices. While assuming the number of antennas being large simplifies the derivations, in practice, the number of antennas equipped at a single secondary user is usually small, say 2 or 3, and once designed, this antenna number is fixed. Thus in this paper, our objective is to derive the asymptotic distributions of the eigenvalues and condition numbers of the sample covariance matrices for any fixed K but large N, from which the probability of detection and probability of false alarm can be obtained. The proposed methodology can also be used to analyze the performance of other EBD algorithms. Finally, computer simulations are presented to validate the accuracy of the derived results.

In this paper, we consider multi-input multi-output (MIMO) two-way relay networks with orthogonal frequency-division multiplexing (OFDM) modulation scheme. The networks consist of two end users exchanging their information via a relay. One information exchange between two end users is divided into two Phases. In Phase 1, two users send their information to the relay. The relay then amplifies and broadcasts its received signals to the two users in Phase 2, i.e., the relay works in the amplify-and-forward (AF) mode. By doing so, two-way relay networks require half as many time slots to accomplish one information exchange as the traditional one-way relay networks. We propose at each end user an iterative algorithm to jointly estimate the channel information and detect the data transmitted from the other user. The channel information consists of the \textit{composite} channels which are the combinations of individual channels in the networks and noise covariance matrices resulting from the AF working mode of the networks. We apply the Expectation Conditional Maximization (ECM) algorithm to estimate the necessary channel information for detection. Simulation results show that the performance of our proposed iterative algorithm is close to that given by the perfect channel information.

In this paper, we investigate the optimal transmitter design, under the restriction of Gaussian signalling without preprocessing of information, for a secure multiple-input single-output (MISO) cognitive radio network (CRN), which consists of four terminals: a pair of secondary user transmitter (SU-Tx) and receiver, an eavesdropper and a primary user (PU). It is assumed that all the channel state information is not perfectly known at the SU-Tx due to the channel estimation errors and the loose cooperation between the SU and the PU. The design involves a nonconvex semi-infinite optimization problem, which maximizes the rate of the secondary link while avoiding harmful interference to the PU and preventing the eavesdropper from decoding the messages sent regardless of the uncertainties in the CSI. For this challenging optimization problem, we relate it with a sequence of semi-infinite capacity-achieving transmitter design problems in an auxiliary CRN without any eavesdropper, which can then be solved through transformations and using convex semidefinite programs. Finally, numerical examples are presented to evaluate the performance of the proposed algorithm.

In this paper the linear beamforming design of a point to point relay system having multiple antennas at the transmitter, the relay and the receive side is addressed. The Channel State Information (CSI) is assumed to be perfectly known for the link from the source to the relay station. However, it is assumed that CSI from the relay station to the destination is imperfectly known. This uncertainty is described using stochastic and deterministic models known as Stochastic Error (SE) and Norm Bounded Error (NBE) models. The problem formulation is based on the Mean Square Error (MSE) of the signal at both ends of the transmission channel. This problem is constrained to satisfy the transmit power limitation of the relay station. It is shown that both the MSE of the system and the transmit power have a Second Order Cone (SOC) structure. The design problem for the SE model, is recast as a Second Order Cone Program (SOCP) while for the NBE model, the same design problem is a Semidefinite Program (SDP). Using the SE model, the average performance and using the NBE model, the worst-case related performance measure is guaranteed. Both problems are solved numerically and simulation results are provided.

Cyclostationary detection is regarded as a major method for spectrum sensing in cognitive radio and other applications as well. The rationale behind the detection is that the second order statistic of the interested signal is periodical. The period is therefore used as the critical feature for detection. In practice, due to clock error or oscillator error or other errors, the detector is hardly able to know the exact period of the signal. This causes a cyclic frequency mismatch in the detection. In this paper, the origin of the mismatch and the impact of it are analyzed. Theoretic analysis and simulations are presented to show that the cyclostationary detection is actually very sensitive to the mismatch. The theoretic analysis on the test statistics matches very well with simulations and can be used for predicting the detection performances and designing the detection parameters.

There is an increasing need for a cognitive radio (CR) that is capable of observing its environment and modifying its transmission characteristics to transmit on unused frequency bands in ways that cause no harmful interference to the primary users (PU). In this paper, we assume that the PU transmits using a conventional OFDM system. We present a non-contiguous OFDM (NC-OFDM) and a multi-block OFDM system as two possible modulation schemes for the SU. We formulate the theoretical expressions of the frequency spectrum for these two CR OFDM systems as a systematic way to study the side-lobe interference caused by the PU to the SU, and vice versa. Lastly, we discuss their implementation, complexity issues, and their BER performance.

The problem of robust linear transceiver design in Multiple-Input Multiple-Output (MIMO) ad hoc Cognitive Radio Networks (CR-Nets) is studied in this paper. In this problem multiple interfering MIMO links are active within the service range of a Primary Radio Network (PR-Net) for which, unlike in conventional design problems, the Channel State Information (CSI) is not known perfectly. The imperfection in CSI is modeled using norm-bounded uncertainty matrices. The design problem is formulated to provide the minimum Sum-Mean Square Error (SMSE) of all the links while the transmit power of Secondary Users (SUs) is limited and the amount of interfering power toward the Primary users (PUs) is controlled. This problem is not convex, because its objective function is non-convex in nature and it has semi-infinite robust constraints. To overcome these limitations an iterative solution, which is based on the relaxed version of this problem, is provided. The problem is then solved numerically. Finally simulation results are provided to demonstrate the performance of this method.

The problem of robust joint nonlinear transceiver design in a multiuser MIMO interfering Cognitive Radio Network (CR-Net) is studied in this paper. Because of the nonlinear nature of the precoding and equalizing schemes, the transmit or receive parties need to be fed back with some information from the other peers, which gives rise to the concept of the cooperation between the transmission peers. This network is a general multi-party network having all the links interfering with each others. It is also assumed that the Channel State Information (CSI) in this network for all the relevant channels is imperfectly known. The CSI is subject to a Stochastic Error (SE) model- based uncertainty. The design procedure is aiming to satisfy the average performance measures. The chosen performance measure is to minimize the sum Mean Square Error (MSE) of the symbol detection for all the cognitive links of the system while satisfying both Secondary Users' (SU) transmit power and Primary Users' (PU) interfering power constraints. This problem is not jointly convex in the design variables and also has infinitely many constraints, so to overcome these, a suboptimal iterative method is proposed. It is shown that for this SE model, the aforementioned problem is a Second Order Cone Program (SOCP). Finally numerical simulations are provided to show the performance of the proposed methods.

In cognitive radio networks, both the sensing time and the fusion schemes used for cooperative spectrum sensing affect the detection probabilities of the primary users and the throughput of the secondary users. Therefore, joint optimization of the sensing time and the cooperative fusion scheme has been studied before in terms of sensing-throughput tradeoff design. In this paper, different from the previous studies, we consider the case that each secondary user may have different detection signal- to-noise ratio (SNR), and requires different threshold for energy detection. Weightings are used to weigh the decisions from the secondary users before combining. A new algorithm is proposed to compute the thresholds for the secondary users and the optimal weightings for the decisions are shown. Computer simulations are presented to show the performance of the proposed algorithm.

In this paper, we are interested in a cognitive secondary wireless local area network (WLAN) which operates in the same or overlapping spectrum and coverage with a primary wireless network. The protection of primary network is quantified by the detection time, which defines the number of superframes required for the secondary network to recognize the re-appearance of the primary network. We obtain the mean and distribution of the detection time of the primary network using the cognitive WLAN, which has an access point (AP) and a number of stations, either sleeping or active. All of these stations are within the detection coverage of the base station (BS) of the primary network. If the BS of the primary network becomes active, the WLAN must be able to detect this activity and vacates the channel within certain amount of time. The mean probability of detection of the BS by each station is modeled as a function of the distance between the BS and the station within the coverage of the AP. Three fusion rules, including OR-fusion rule, AND-fusion rule and Majority rule, are used by the WLAN AP to fuse the sensing results obtained by the active WLAN stations. The mean and the distribution of the detection time of the primary network are derived by generically modeling the detection time by an absorbing discrete time Markov chain. The tradeoffs among different fusions rules and the distance between the BS and AP are investigated.

This paper is concerned with a spectrum sharing cognitive radio network. In particular, the individual outage capacity region for a M-user fading cognitive multiple access network sharing the same spectrum with an existing primary network is first characterized. The primary network's transmission is assumed to be protected by the interference power constraint. Then, under the interference power constraint and the individual transmit power constraint of each user, the individual outage capacity region is implicitly obtained by characterizing the boundary points on the individual usage probability region for a given rate vector. The optimal power allocation and decoding strategy is then derived by the Lagrange dual decomposition method. It is proved that the optimal decoding strategy is the successive decoding strategy, and the decoding order is determined by dual variables together with the channel fading gains of the primary and secondary links. Finally, several numerical examples are given to validate the proposed studies.

In this paper we consider a Multi-Input Multi-Output (MIMO) wireless relay network which comprises of a source terminal, a destination terminal and a relay. The transmission from the source terminal to the destination is divided into two phases. During Phase I, the source terminal sends its signals to the relay. The relay only amplifies and forwards its received signals to the destination terminal during Phase II, i.e., the network works in the Amplify-and-Forward (AF) mode. In this mode, the noise elements at the relay at Phase I are also amplified and forwarded through a link (from the relay to the destination terminal). It makes the overall noises across antennas of the destination terminal correlated. This correlation is characterized by a noise covariance matrix. Hence, for the purpose of data detection at the destination terminal, we need to estimate channel coefficients as well as the noise covariance matrix. In the considered network, an error correcting code (ECC) is used. We propose a doubly iterative receiver to estimate channel information and decode transmitted signals. The estimation part is an application of the Expectation Conditional Maximization (ECM) algorithm. Turbo-equalization is used to provide soft information of transmitted signals to the ECM-based algorithm. Simulation results show that our doubly iterative receiver provides an excellent BER performance.

We consider a cognitive radio (CR) network that makes opportunistic use of a set of channels licensed to a primary network. During operation, the CR network is required to carry out spectrum sensing to detect active primary users, thereby avoiding interfering with them. However, spectrum sensing may cause negative effect on the performance of the CR network, as all CR communications has to be postponed during channel sensing. This paper focuses on adaptively scheduling spectrum sensing and data transmission so that negative impacts to the performance of the CR network are minimized. We first consider the case when CR nodes always have data to transmit and experience time-varying channels. Based on knowledge of channel conditions, the sensing periods are adaptively scheduled to maximize the spectrum efficiency of the CR operation. We show how optimal sensing/transmission scheduling policies can be obtained and prove some important structural properties of such optimal policies. We then consider the case when CR nodes experience both stochastic data arrival and time-varying channels. By treating each sensing period as a 'virtual sensing packet'', we convert the problem of joint spectrum-sensing/datatransmission scheduling into a standard queueing model. Based on that, an efficient scheduling algorithm that takes into account channel and queue conditions of the CR network is proposed.

In this letter, we consider a bi-directional relay network in which two users, U1and U2, exchange their information via a relay station, RS. Multiple antennas are deployed at both users and at RS. Single carrier cyclic prefix (SCCP) is used to combat intersymbol interference (ISI) in frequency-selective fading channels. The transmission process is divided into two time slots. At the first time slot, both users send their information to RS concurrently. RS then amplifies and broadcasts its received signals at the second time slot. We propose an algorithm to estimate the channel information at end users based on the least square (LS) principle. To further minimize the mean square error (MSE) of the estimate, a method to design the optimal training sequences is also proposed. Simulation results show that the performance achieved by our optimal design is close to that with perfect channel information.

This paper studies the transmit strategy for a secondary link or the so-called cognitive radio (CR) link under opportunistic spectrum sharing with an existing primary radio (PR) link. It is assumed that the CR transmitter is equipped with multi-antennas, whereby transmit precoding and power control can be jointly deployed to balance between avoiding interference at the PR terminals and optimizing performance of the CR link. This operation is named as cognitive beamforming (CB). Unlike prior study on CB that assumes perfect knowledge of the channels over which the CR transmitter interferes with the PR terminals, this paper proposes a practical CB scheme utilizing a new idea of effective interference channel (EIC), which can be efficiently estimated at the CR transmitter from its observed PR signals. Somehow surprisingly, this paper shows that the learning-based CB scheme with the EIC improves the CR channel capacity against the conventional scheme even with the exact CRto- PR channel knowledge, when the PR link is equipped with multi-antennas but only communicates over a subspace of the total available spatial dimensions. Moreover, this paper presents algorithms for the CR to estimate the EIC over a finite learning time. Due to channel estimation errors, the proposed CB scheme causes leakage interference at the PR terminals, which leads to an interesting learning-throughput tradeoff phenomenon for the CR, pertinent to its time allocation between channel learning and data transmission. This paper derives the optimal channel learning time to maximize the effective throughput of the CR link, subject to the CR transmit power constraint and the interference power constraints for the PR terminals.

In this letter, we propose multi-antenna based spectrum sensing methods for cognitive radios (CRs) using the generalized likelihood ratio test (GLRT) paradigm. The proposed methods utilize the eigenvalues of the sample covariance matrix of the received signal vector from multiple antennas, taking advantage of the fact that in practice, the primary user signals to be detected will either occupy a subspace of dimension strictly smaller than the dimension of the observation space, or have a non-white spatial spectrum. These methods do not require prior knowledge of the primary user signals, or the channels from the primary users to the CR. By making different assumptions on the availability of the white noise power value at the CR receiver, we derive two algorithms that are shown to outperform the standard energy detector.

A new approach to spectrum sensing in cognitive radio systems based on the Kolmogorov-Smirnov (K-S) test is proposed. The K-S test is a non-parametric method to measure the goodness of fit. The basic procedure involves computing the empirical cumulative distribution function (ECDF) of some decision statistic obtained from the received signal, and comparing it with the ECDF of the channel noise samples. A sequential version of the K-S-based spectrum sensing technique is also proposed. Extensive simulation results demonstrate that compared with the existing spectrum detection methods, such as the energy detector and the eigenvalue-based detector, the proposed K-S detectors offer superior detection performance and faster detection, and is more robust to channel uncertainty and non-Gaussian noise.

This paper studies the achievable rates of the multi-antenna or multiple-input multiple-output (MIMO) secrecy channel with multiple single-/multi-antenna eavesdroppers. By assuming Gaussian input, the maximum achievable secrecy rate is obtained with the optimal transmit covariance matrix that maximizes the minimum difference between the channel mutual information of the secrecy user and those of the eavesdroppers. The maximum secrecy rate computation can thus be formulated as a non-convex max-min problem, which cannot be solved efficiently by existing methods. To handle this difficulty, this paper explores a new relationship between the secrecy channel and the recently developed cognitive radio (CR) channel, in which the secondary user transmits over the same spectrum simultaneously with multiple primary users, subject to the received interference power constraints at the primary users, or the so-called "interference temperature (IT)" constraints. By constructing an auxiliary multi-antenna CR channel that has the same channel responses as the secrecy channel, this paper shows that the optimal transmit covariance to achieve the maximum secrecy rate is the same as that to achieve the CR spectrum sharing capacity with properly selected IT constraints. Thereby, finding the optimal complex transmit covariance matrix for the secrecy channel becomes equivalent to searching over a set of real IT constraints in the auxiliary CR channel. Based on this relationship, efficient algorithms are proposed to solve the non-convex secrecy rate maximization problem by transforming it into a sequence of convex CR spectrum sharing capacity computation problems, under various setups of the secrecy channel.

We consider a point-to-multipoint cognitive radio network that shares a set of channels with a primary network. Within the cognitive radio network, a base station controls and supports a set of fixed-location wireless subscribers. The objective is to maximize the throughput of the cognitive network while not affecting the performance of primary users. Both downlink and uplink transmission scenarios in the cognitive network are considered. For both scenarios, we propose two-phase mixed distributed/centralized control algorithms that require minimal cooperation between cognitive and primary devices. In the first phase, a distributed power updating process is employed at the cognitive and primary nodes to maximize the coverage of the cognitive network while always maintaining the constrained signal to interference plus noise ratio of primary transmissions. In the second phase, centralized channel assignment is carried out within the cognitive network to maximize its throughput. Numerical results are obtained for the behaviors and performance of our proposed algorithms.

This paper considers a spectrum underlay network, where an OFDM-based cognitive radio (CR) system is allowed to share the subcarriers of an OFDMA-based primary system for simultaneous transmission. Instead of using the conventional interference power constraint (IPC) to protect the primary users (PUs) in the primary system, a new criterion referred to as rate loss constraint (RLC), in the form of an upper bound on the maximum rate loss of each PU due to the CR transmission, is proposed for primary transmission protection. Assuming the channel state information (CSI) of the PU link, the CR link, and their mutual interference links is available to the CR, the optimal power allocation strategy to maximize the achievable rate of the CR system is derived under RLC together with CR¿s transmit power constraint. It is shown that the CR system can achieve a significant rate gain under RLC as compared to IPC. Furthermore, the relationship between RLC and IPC is investigated, and it is shown that the rate gain is obtained by exploiting the additional CSI of the PU link. A more general case referred to as hybrid protection to PUs is then studied, by taking into account that some PU links¿ CSI is not available at CR.

This paper considers a spectrum sharing based cognitive radio network where a M-user fading multiple access network shares the same spectrum with an existing primary network. The primary network's transmission is assumed to be protected by the interference power constraint. Under this interference power constraint together with the individual transmit power constraint of each user, the outage capacity regions for the fading cognitive multiple access channel (C-MAC) are defined for two different scenarios, i.e., an outage must be declared simultaneously for all users (common outage) and outages are declared individually for each user (individual outage). Then, optimal power allocation strategies to achieve the boundary points of these outage capacity regions are derived by considering their equivalent problems, i.e., the common/individual usage probability maximization for given rate vectors. It is rigorously proved that the optimal decoding strategy is the successive decoding strategy, and the decoding order is determined by the dual variables and the channel power gains of the involved channels. Then, a modified ellipsoid method is proposed to obtain the optimal dual variables. Finally, several numerical examples are given to validate the proposed studies.

In cognitive radio networks, joint optimization of sensing time and cooperative fusion scheme has been studied in the past in terms of sensing-throughput tradeoff design. In this paper, different from previous studies, we consider the case that the secondary users have different detection signal-to-noise ratios (SNRs) and their decisions are weighted based on the likelihood-ratio test at the fusion center. We consider three scenarios. In Scenario I, we optimize individual secondary users' thresholds together with the fusion rule's threshold at the fusion center. In Scenario II, all the secondary users' thresholds are constrained to be the same and we seek the optimal threshold jointly with the fusion rule's threshold at the fusion center. In Scenario III, each secondary user computes its own threshold while the fusion center optimizes the fusion rule's threshold based on the secondary users' threshold results. Solutions are provided for the three different scenarios and computer simulations are presented to compare their performances.

In this paper, we address the physical-layer security issue of a secondary user (SU) in a spectrum-sharing cognitive radio network (CRN) from an information-theoretic perspective. Specially, we consider a secure multiple-input single-output (MISO) cognitive radio channel, where a multi-antenna SU transmitter (SU-Tx) sends confidential information to a legitimate SU receiver (SU-Rx) in the presence of an eavesdropper and on the licensed band of a primary user (PU). The secrecy capacity of the channel is characterized, which is a quasiconvex optimization problem of finding the capacity-achieving transmit covariance matrix under the joint transmit power and interference power constraints. Two numerical approaches are proposed to derive the optimal transmit covariance matrix. The first approach recasts the original quasiconvex problem into a single convex semidefinite program (SDP) by exploring its inherent convexity; while the second one explores the relationship between the secure CRN and the conventional CRN and transforms the original problem into a sequence of optimization problems associated with the conventional CRN, which helps to prove that beamforming is the optimal strategy for the secure MISO CR channel. In addition, to reduce the computational complexity, three suboptimal schemes are presented, namely, scaled secret beamforming (SSB), projected secret beamforming (PSB) and projected cognitive beamforming (PCB). Lastly, computer simulation results show that the three suboptimal schemes can approach the secrecy capacity well under certain conditions.

Abstract Networking cognitive radios and nodes from primary system (PS) results in a heterogeneous coexisting multi‐radio wireless network, so that significant network throughput gain can be achieved. However, by investigating cognitive radio network (CRN) architecture, the links in CRNs are unlikely to support complete security check due to link dynamics, opportunistic availability, and uni‐directional in available time window. We therefore introduce trusted cognitive radio networking (TCRN) concept to facilitate network functions such as association in dynamic spectrum access and routing. First of all, we explore the mathematical framework for trust in CRNs. We then show successful association of node to CRN based on the mathematical structure of trust from statistical decision theory. Furthermore, we modify the machine‐learning algorithm to update the trust measure for each node, and develop rules of thumbs to facilitate TCRN with learning capability, based on numerical simulations. Trusted CRN can greatly alleviate heterogeneous challenge for CRN operation. Copyright © 2009 John Wiley & Sons, Ltd.

This paper considers a spectrum sharing wireless environment, where an OFDM-based cognitive radio system is allowed to access the spectrum originally licensed to an OFDMA primary system. A new criterion referred to as the rate loss constraint, in the form of an upper bound on the maximum rate loss of the primary user due to the secondary transmission, is proposed for primary transmission protection. In addition, assuming that some PUs are protected by the rate loss constraint, and some PUs are protected by the interference power constraint, the optimal power allocation strategy to maximize the rate of the cognitive radio system under such a hybrid protection to PUs together with a transmit power constraint is derived. Then, the relationship between the rate loss constraint and the interference power constraint is investigated, and it is shown by simulation that the cognitive radio system can achieve a significant rate gain under the proposed constraint compared with that under the conventional interference power constraint.

This paper considers a cognitive radio network where a secondary user shares the same narrow band with a primary user for transmission. Instead of adopting the conventional interference-power/interference-temperature constraint, this paper proposes a new type of constraint for the secondary user to protect the primary transmission, which limits the maximum outage probability of the primary transmission subject to the secondary user's interference to be below a prescribed target. Under this newly proposed constraint along with the average/peak transmit power constraint, the paper derives the optimal power allocation strategies over block-fading channels for the secondary user to achieve its outage capacity. It is shown by simulations that the derived power allocation strategies achieve substantial outage capacity gains for the secondary user over the conventional power control policies based upon the interference-temperature constraint, given the same primary user's outage probability constraint.

Spectrum sensing is a fundamental problem in cognitive radio. As a result, it has been reborn as a very active research area in recent years despite its long history. Although various sensing methods have been proposed, their robustness at very low signal-to-noise ratio (SNR) and uncertain noise/interference environment is generally not satisfactory. In this paper, the concept of robust sensing is discussed first. Subsequently the cooperative covariance and eigenvalue based detections are proposed for robust spectrum sensing. It is proved mathematically that under some conditions the proposed methods are robust to uncertain and unpredictable noise and interference. The performances of the methods are also verified by simulations.

This paper studies the robust beamforming design problem for a multiple-input single-output (MISO) secrecy channel with a single-antenna eavesdropper. Due to the illegal nature, the eavesdropper may try to hide itself from being caught; thus, it could be difficult for the secrecy transmitter (S-Tx) to obtain accurate channel state information (CSI) of the eavesdropping link between S-Tx and the eavesdropper. Assuming that the CSI of the eavesdropping link belongs to a known uncertain set, this paper designs the optimal transmit strategy for the secrecy user to maximize the transmit rate under the condition that the eavesdropper cannot decode the secrecy message for all possible channel realizations of the eavesdropping link. This robust design problem is non-convex and cannot be solved by existing algorithms in the literature. By exploiting the relationship between the secrecy MISO channel and the cognitive radio (CR) MISO channel, this problem is transformed into a robust CR beamforming design problem, which can be solved efficiently by the interior point method. Numerical examples are provided to illustrate the effectiveness of the proposed algorithm.

This paper presents a multi-antenna cognitive radio (CR) system that is capable of operating concurrently with the primary radio (PR) link. The operation of the CR system consists of three stages: environmental learning, CR channel training and CR data transmission. In environmental learning stage, partial channel information between PR and CR are obtained blindly, based on which the transmit beamforming and the receive beamforming strategies are designed at CR to remove/reduce the interference to and from PR, respectively. We characterize all the interference values analytically and study the problem of learning/training tradeoff associated with the proposed scheme. The optimal balancing between learning and training is examined via the minimum mean square error (MSE) of the channel estimation. It is shown that for a given total learning/training time, there indeed exists a optimal learning time that minimizes the MSE of the channel estimation, yet the interference power to the PR is regulated.

We consider the channel estimation issues for two-way relay network (TWRN) that employs orthogonal frequency division multiplexing (OFDM) modulation. We propose a two-phase training protocol for channel estimation, which is compatible with two-phase data transmission scheme associated with TWRN. It will be seen that channel estimation in TWRN is quite different from that in the traditional point-to-point system or even that in the one-way relay network (OWRN). The identifiability issue of the channel estimation, which particularly exists for TWRN, is studied. Simulation results corroborate the effectiveness of the proposed method.

In this paper, we consider a secondary link sharing the spectrum with a primary link in a fading cognitive radio (CR) network. Instead of applying the conventional interference power constraint at the primary user (PU) receiver for the secondary user (SU) to protect the primary transmission, we propose a new constraint on the maximum tolerable outage probability for the PU due to the SU transmission. Under the assumption that perfect instantaneous channel state information (CSI) on the SU channel, the channel from the SU transmitter to PU receiver, and the PU channel is available at the SU transmitter, we derive the optimal power allocation strategies to achieve the ergodic capacity of the SU fading channel. It is shown by simulations that the proposed power allocation strategies can achieve substantial capacity gain for the SU over that based on the conventional interference power constraint, for the same PU outage probability loss.

In cognitive radio networks, protection to primary users can be quantified by the probability of detection. On the other hand, the probability of false alarm affects the achievable throughput of secondary users. Performances of these two probabilities depend heavily on the fusion scheme used when cooperative sensing is performed. In this paper, we consider the case where N secondary users sense the channel cooperatively using k-out-of-N fusion rule. A sensing-throughput tradeoff under cooperative sensing scenario is formulated to find a pair of sensing time and k value that maximize the secondary users' throughput subject to sufficient protection provided to the primary user. An iterative algorithm is proposed to obtain the optimal values of these two parameters. Computer simulations show that significant improvement of the secondary users' throughput can be achieved when the parameters from the fusion scheme and sensing time are jointly optimized.

In this paper, we propose an iterative receiver for a multi-input multi-output (MIMO) two-way wireless relay system. The two-way relay system requires two time slots to accomplish one information exchange. Compared to the traditional one-way relay system, which demands four time slots to complete one information exchange, our system only uses half as many time slots. Our proposed iterative receiver consists of two loops. One is the minimum mean square error (MMSE)-based iterative soft interference cancellation (SIC) algorithm which is extended from the algorithm in to cover correlated noise environment incurred from the nature of signalling. The other is the Expectation Conditional Maximization (ECM)-based estimation algorithm. The MMSE-SIC algorithm provides the soft information of the transmitted signals and this information is transferred to the ECM-based estimation algorithm to update the channel information. Simulation results show that the performance of the iterative receiver proposed here is close to that given by perfect channel information with small number of iterations.

This paper studies the wirelesstwo-wayrelaychannel(TWRC), where two source nodes, S1 and S2, exchange information through an assisting relay node, R. It is assumed that R receives the sum signal from S1 and S2 in one time-slot, and then amplifies and forwards the received signal to both S1 and S2 in the next time-slot. By applying the principle ofanaloguenetworkcoding(ANC), each of S1 and S2 cancels the so-called "self-interference" in the received signal from R and then decodes the desired message. Assuming that S1 and S2 are each equipped with a single antenna and R with multi-antennas, this paper analyzes thecapacityregionof an ANC-based TWRC with linear processing (beamforming) at R. The capacity region contains all the achievable bidirectional rate-pairs of S1 and S2 under the given transmit power constraints at S1, S2, and R. We present the optimal relay beamforming structure as well as an efficient algorithm to compute the optimal beamforming matrix based on convex optimization techniques.

This paper considers spectrum sharing between a cognitive radio (CR) and a primary radio (PR) where the CR protects the PR transmission by regulating the resultant interference power level at the PR receiver to be below some predefined threshold. The interference-power constraint at the PR receiver is usually one of the following two types: average interference power (AIP) constraint that regulates the average power level over different fading states and peak interference power (PIP) constraint that limits the peak power level at each fading state. From CR's perspective, AIP constraint is more favorable than PIP constraint because of its more flexibility for dynamic power allocations. On the contrary, from the perspective of protecting the PR, the more restrictive PIP constraint appears at a first glance to be a better option. Some surprisingly, this paper shows that in terms of various forms of capacity limits achievable for the PR fading channel, namely, ergodic and outage capacities, AIP constraint is also superior over PIP constraint. This result is based upon an interesting interference diversity phenomenon, i.e., variable interference power levels at the PR receiver in the AIP case are more advantageous over constant ones in the PIP case for minimizing the resulted PR capacity losses. Therefore, AIP constraint leads to larger fading channel capacities over PIP constraint for both CR and PR transmissions.

In this paper, we study a multi-antenna-based cognitive radio (CR) system that is able to operate concurrently with the primary radio (PR) system. We propose a novel CR transmission frame structure consisting of three stages, including a new environment learning stage in addition to the conventional channel training and data transmission stages. During the environment learning stage, the CR terminals blindly learn the spatial knowledge of the PR-CR channels, based on which cognitive beamforming is designed at CR transceivers to restrict the interference to and from the PR, respectively, in the subsequent channel training and data transmission stages. Considering the learning and training errors from the first two stages, we derive a lower bound on the ergodic capacity achievable for the CR link subject to a predefined interference-power constraint at the PR and the CR's own transmit power constraint. We then characterize a general learning/training/throughput (LTT) tradeoff associated with the proposed scheme, pertinent to transmit power allocation between training and transmission stages, as well as time allocation among learning, training, and transmission stages.

The conventional Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC)- multiple-access channel (MAC) duality has previously been applied to solve non-convex BC capacity computation problems. However, this conventional duality approach is applicable only to the case in which the base station (BS) of the BC is subject to a single sum-power constraint. An alternative approach is the minimax duality, established by Yu in the framework of Lagrange duality, which can be applied to solve the per-antenna power constraint case. This paper first extends the conventional BC-MAC duality to the general linear transmit covariance constraint (LTCC) case, and thereby establishes a general BC-MAC duality. This new duality is then applied to solve the BC capacity computation problem with multiple LTCCs. Moreover, the relationship between this new general BC-MAC duality and the minimax duality is also presented, and it is shown that the general BC-MAC duality has a simpler form. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.

In this paper, we consider the medium access control (MAC) protocol design problem for random access cognitive radio networks. A MAC protocol using a two-level opportunistic spectrum access strategy, called CR-CSMA, is proposed to efficiently schedule secondary users' packets, and yet to protect primary users' operations. We employ normalized throughput and average packet delay as network metrics, and derive closed-form expressions to evaluate our protocol. For various offered traffic rates of secondary users and frame lengths, the optimal performances of secondary users can be achieved at the same spectrum sensing time, and also there exists a tradeoff between performance and agility.

This paper is concerned with robust downlink beamforming in a multiuser Multi-Input Single-Output (MISO) Cognitive Radio Network (CR-Net) in which multiple Primary Users (PUs) coexist with multiple Secondary Users (SUs). It is assumed that the Channel State Information (CSI) for all relevant channels is not perfectly known. We design the linear precoder matrix to minimize the transmit power of the SU-Transmitter (SU-Tx) and simultaneously targeting a lower bound on the received Signal-to-Interference-plus-Noise-Ratio (SINR) for the SU's, while requiring an upper bound on the Interference-Power (IP) at the PUs. The method used is based on a worst case design scenario through which the performance metrics of the design are immune to variations of the channels. Three approaches based on convex programming are proposed. Finally, simulation results are provided to evaluate the robustness of the proposed approaches.

We consider a spectrum sharing based cognitive radio network where a secondary multiple-input multiple-output (MIMO) communication link coexists with a primary user by opportunistically utilizing the transmit spectrum originally allocated to the primary user (PU). The secondary link employs orthogonal space-time block coding (OSTBC) at the transmitter and maximum likelihood (ML) decoding at the receiver. For this system, we focus on designing linear precoders assuming the knowledge of the transmit antenna fading correlations at the secondary user transmitter (ST) for both the links between PU receiver (PR) and ST (ST-PR), and SU receiver (SR) and ST (ST-SR). Given a limited power budget of ST, the precoder is designed to minimize the minimum-distance average pair-wise error probability (PEP) at SR subject to the average interference-power constraint at PR. The original optimization problem is non-convex and we propose to solve this problem using a nonlinear programming method called the augmented Lagrangian algorithm. Simulation results show that this scheme performs better than previously designed suboptimal schemes at all signal to noise ratio (SNR) levels.

In this paper, we study the sensing-throughput tradeoff problem for a multiple-channel cognitive radio (CR) network. In particular, using the sensing-throughput tradeoff metric, we investigate the design of the optimal spectrum sensing time and power allocation schemes so as to maximize the aggregate ergodic throughput of the cognitive radio network to guarantee the quality of service (QoS) of the primary users (PUs) without exceeding the power limit of the secondary transmitter. The optimal sensing time and power allocation strategies are developed under the average power constraint. Finally, numerical results show that, for a CR network with 3 channels, whose signal-to-noise ratio of PUs are -12dB, -15dB and -20dB, respectively, there is an optimal sensing time, and the optimal sensing time is almost insensitive to the total transmit power.

To improve the spectrum utilization efficiency, cognitive radio (CR) has been proposed by allowing a cognitive radio network (CRN) to coexist with a licensed primary network. The security issues, although critical, have been less explored in the literature of CRN. In this paper, we consider a secure CRN in which a multi-antenna secondary user (SU) transmitter sends confidential information to a SU receiver on the same frequency band with a primary user (PU) in the presence of an eavesdropper. All receive terminals are equipped with a single antenna. The capacity-achieving transmitter design is formulated as a quasiconvex optimization problem to maximize the rate of the secondary link while avoiding harmful interference to the PU and preventing the eavesdropper from decoding the messages sent. By exploring the inherent convexity, the original problem is solved efficiently by a single semidefinite program (SDP). Besides, two suboptimal algorithms are proposed to reduce the computational complexity, namely, scaled secret beamforming (SSB) and projected secret beamforming (PSB). It is shown through computer simulations that the two suboptimal algorithms can achieve close-to-optimal secrecy capacity under certain conditions.

An analytical formulation of the mean detection time and the distribution of the detection time of a wireless microphone (primary user) in an IEEE 802.22 wireless regional area network (WRAN) is presented. The IEEE 802.22 WRAN has a number of customer-premises equipments (CPEs) in the coverage area of its base station. Out of this number of CPEs, a number of them are within the coverage area of the microphone. All of the CPEs can be inactive or active. This is modeled by a discrete time Markov chain. If the microphone turns on or becomes active, the WRAN must be able to detect the microphone and vacates the channel within a time threshold. It is assumed that as long as one of the active CPEs, within the coverage area of the microphone, detects the microphone as active, the microphone is detected. The mean and the distribution of the detection time of the microphone are derived by modeling the detection time by an absorbing discrete time Markov chain. Numerical results corresponding to typical parameter values are presented. The tradeoffs among the number of CPEs, coverage range of the wireless microphone (primary user) and probability of detection are investigated.

We propose a MAC for wireless ad hoc cognitive radio networks where secondary users employ cooperative spectrum sensing to mitigate the degradation of the channel between primary transmitter and secondary users. The sensing reports and fused decisions are transmitted based on random access of CSMA/CA and 802.11e EDCA on the control channel, whose access scheme determines the overall achievable throughput among the multi-channels. We propose several schemes and derive the upper bound of overall throughput. The saturation problem is also studied to address the optimization of the channel selection and the trade-off between cooperative sensing gain and channel reuse efficiency.

This paper considers a distributed opportunistic spectrum access (D-OSA) scenario in which multiple cognitive radio (CR) users attempt to access a channel licensed to some primary network. CR users operate on a frame-by-frame basis and need to carry out spectrum sensing at the beginning of each frame to determine if the primary network is active or idle. Upon detecting the primary network being idle, each CR user employs a modified 802.11 DCF protocol for contention-based channel access. Spectrum sensing is imperfect and introduces false alarms and mis-detections. To protect primary users, it is required that the combined probability of mis-detection of all CR users must be below a specified threshold. We provide concrete protocol design, performance analysis, and extensive simulation results for our D-OSA design. Our results highlight the importance of taking a cross-layer view and jointly designing PHY-layer spectrum sensing and MAC-layer channel access.

This paper considers a cooperative and cognitive radio (CCR) system where two secondary users (SUs) exchange their information through a "cognitive" relay station (RS) via the two-way relaying. The SUs share the same spectrum with a primary user (PU) while maintaining the interference power at the PU under a certain level. In order to enhance the achievable sum rate, the cognitive RS exploits the channel state information (CSI) of channel links from the RS to PU and from the RS to SUs to determine the relay beamforming (BF) and the transmit power levels for the RS and SUs. The structures of the optimal relay BF and suboptimal BF schemes based on the subspace projection are presented. Both orthogonal and non-orthogonal projection are considered. In addition, power control algorithms are proposed to satisfy the transmit power constraints as well as the interference power constraints. Numerical results are provided to compare the performance of the optimal relay BF with that of the suboptimal ones and also to justify the benefit of the optimal power allocation.

In cognitive radio networks, the secondary users (SUs) are allowed to use the spectrum originally allocated to primary users (PUs) as long as the PUs are not using it temporarily. This operation is called opportunistic spectrum access (OSA), and it is assisted through spectrum sensing. In distributed OSA, the SUs sense the channel independently; once the channel is available, they contend for channel access on a frame-by-frame basis. In this paper, we study the random medium access control (MAC) in conjunction with the sensing protocol design. In particular, we are interested in the design of frame duration, sensing time and MAC random access to maximize the secondary network throughput performance while protecting the PUs from the interference of secondary users' operations. We formulate the nonlinear constrained optimization problems for the described system model with cross-layered and layered approaches. Simulations show that the cross-layered approach performs much better than layered approach especially when the frame duration is small.

This paper studies the wireless two-way relay channel (TWRC), where two source nodes, S1 and S2, exchange information through an assisting relay node, R. It is assumed that R receives the sum signal from S1 and S2 in one timeslot, and then amplifies and forwards the received signal to both S1 and S2 in the next time-slot. By applying the principle of analogue network coding (ANC), each of S1 and S2 cancels the so-called "self-interference" in the received signal from R and then decodes the desired message. Assuming that S1 and S2 are each equipped with a single antenna and R with multi-antennas, this paper analyzes the capacity region of the ANC-based TWRC with linear processing (beamforming) at R. The capacity region contains all the achievable bidirectional rate-pairs of S1 and S2 under the given transmit power constraints at S1, S2, and R. We present the optimal relay beamforming structure as well as an efficient algorithm to compute the optimal beamforming matrix based on convex optimization techniques. Low-complexity suboptimal relay beamforming schemes are also presented, and their achievable rates are compared against the capacity with the optimal scheme.

In this letter, the problem of training sequence design in a bi-directional relay network is investigated. The two nodes, Tx1 and Tx2, communicate with each other via a relay (RL). The RL receives the signals that are transmitted simultaneously from both nodes and then broadcasts the superposition back to the two nodes. Single carrier cyclic prefix (SCCP) is deployed in the transmission from the two nodes and the RL as well for combating intersymbol interference (ISI). We are interested in the channel estimation problem in this scenario. We propose a design of training sequences from two nodes to minimize the mean-square error (MSE) of the channel estimation according to zero forcing (ZF) criterion. We do not separate the links between each node to RL but incorporate them into the composite channels. Simulation results show that our design performance suffers only 1-dB loss in terms of bit-error rate (BER) as compared to the case with perfect channel information.

In this work, we consider the two-way relay network (TWRN) where two terminals exchange their information through a relay node in a bi-directional manner and study the training-based channel estimation under the amplify-and-forward (AF) relay scheme. We propose a two-phase training protocol for channel estimation: in the first phase, the two terminals send their training signals concurrently to the relay; and in the second phase, the relay amplifies the received signal and broadcasts it to both terminals. Each terminal then estimates the channel parameters required for data detection. First, we assume the channel parameters to be deterministic and derive the maximum-likelihood (ML) -based estimator. It is seen that the newly derived ML estimator is nonlinear and differs from the conventional least-square (LS) estimator. Due to the difficulty in obtaining a closed-form expression of the mean square error (MSE) for the ML estimator, we resort to the Crameacuter-Rao lower bound (CRLB) on the estimation MSE for design of optimal training sequence. Secondly, we consider stochastic channels and focus on the class of linear estimators. In contrast to the conventional linear minimum-mean-square-error (LMMSE) -based estimator, we introduce a new type of estimator that aims at maximizing the effective receive signal-to-noise ratio (SNR) after taking into consideration the channel estimation errors, thus referred to as the linear maximum SNR (LMSNR) estimator. Furthermore, we prove that orthogonal training design is optimal for both the CRLB- and the LMSNR-based design criteria. Finally, simulations are conducted to corroborate the proposed studies.

In this paper, we are interested in the problem of joint channel estimation and data detection for multi-input multi-output (MIMO) interleaved frequency division multiple access (IFDMA) systems. Although IFDMA is free from the multiple access interference (MAI), it suffers from intersymbol interference (ISI). MIMO-IFDMA system suffers from both ISI and multi-stream interference (MSI). The block iterative generalized decision feedback equalizer (BI-GDFE) is an iterative and effective interference cancelation scheme which could provide near maximum likelihood (ML) performance with very low complexity. However, BI-GDFE needs the channel state information (CSI). In this paper, we utilize the soft estimates of the transmitted symbols provided by the BI-GDFE to estimate the channel via an Expectation Maximization (EM)-based algorithm. By doing so, a joint channel estimation and data detection receiver is developed. To evaluate the performance of the proposed channel estimation algorithm, we derive the Cramér-Rao Lower Bound (CRLB). Computer simulations show that the bit error rate (BER) performance of the proposed joint channel estimation and signal detection receiver can reach the performance of the BI-GDFE with perfect CSI.

Spectrum sensing is a fundamental component in a cognitive radio. In this paper, we propose new sensing methods based on the eigenvalues of the covariance matrix of signals received at the secondary users. In particular, two sensing algorithms are suggested, one is based on the ratio of the maximum eigenvalue to minimum eigenvalue; the other is based on the ratio of the average eigenvalue to minimum eigenvalue. Using some latest random matrix theories (RMT), we quantify the distributions of these ratios and derive the probabilities of false alarm and probabilities of detection for the proposed algorithms. We also find the thresholds of the methods for a given probability of false alarm. The proposed methods overcome the noise uncertainty problem, and can even perform better than the ideal energy detection when the signals to be detected are highly correlated. The methods can be used for various signal detection applications without requiring the knowledge of signal, channel and noise power. Simulations based on randomly generated signals, wireless microphone signals and captured ATSC DTV signals are presented to verify the effectiveness of the proposed methods.

Cognitive radio is an emerging technology that shows great promise to dramatically improve the efficiency of spectrum utilization. This paper considers a cognitive radio model, in which the secondary network is allowed to use the radio spectrum concurrently with primary users (PUs) provided that interference from the secondary users (SUs) to the PUs is constrained by certain thresholds. The weighted sum rate maximization problem is studied under interference power constraints and individual transmit power constraints, for a cognitive multiple access channel (C-MAC), in which each SU having a single transmit antenna communicates with the base station having multiple receive antennas. An iterative algorithm is developed to efficiently obtain the optimal solution of the weighted sum rate problem for the C-MAC. It is further shown that the proposed algorithm, although developed for single channel transmission, can be extended to the case of multiple channel transmission. Corroborating numerical examples illustrate the convergence behavior of the algorithm and present comparisons with other existing alternative algorithms.

This paper studies the information-theoretic limits of a secondary or cognitive radio (CR) network under spectrum sharing with an existing primary radio network. In particular, the fading cognitive multiple-access channel (C-MAC) is first studied, where multiple secondary users transmit to the secondary base station (BS) under both individual transmit-power constraints and a set of interference-power constraints each applied at one of the primary receivers. This paper considers the long-term (LT) or the short-term (ST) transmit-power constraint over the fading states at each secondary transmitter, combined with the LT or ST interference-power constraint at each primary receiver. In each case, the optimal power allocation scheme is derived for the secondary users to achieve the ergodic sum capacity of the fading C-MAC, as well as the conditions for the optimality of the dynamic time-division multiple-access (D-TDMA) scheme in the secondary network. The fading cognitive broadcast channel (C-BC) that models the downlink transmission in the secondary network is then studied under the LT/ST transmit-power constraint at the secondary BS jointly with the LT/ST interference-power constraint at each of the primary receivers. It is shown that D-TDMA is indeed optimal for achieving the ergodic sum capacity of the fading C-BC for all combinations of transmit-power and interference-power constraints.

This paper considers a scenario in which a secondary user (SU) opportunistically accesses a channel allocated to some primary network (PN) that switches between idle and active states in a time-slotted manner. At the beginning of each time slot, SU can choose to stay idle or to carry out spectrum sensing to detect the state of PN. If PN is detected to be idle, SU can carry out data transmission. Spectrum sensing consumes time and energy and introduces false alarms and mis-detections. The objective is to dynamically decide, for each time slot, whether SU should stay idle or carry out sensing, and if so, for how long, to maximize the expected reward. We formulate this as a partially observable Markov decision process and prove important properties of the optimal control policies. Heuristic control policies with low complexity and good performance are also proposed. Numerical results show the significant performance gain of our dynamic control approach for opportunistic spectrum access.

A cognitive radio network (CRN) is formed by either allowing the secondary users (SUs) in a secondary communication network (SCN) to opportunistically operate in the frequency bands originally allocated to a primary communication network (PCN) or by allowing SCN to coexist with the primary users (PUs) in PCN as long as the interference caused by SCN to each PU is properly regulated. In this paper, we consider the latter case, known as spectrum sharing, and study the optimal power allocation strategies to achieve the ergodic capacity and the outage capacity of the SU fading channel under different types of power constraints and fading channel models. In particular, besides the interference power constraint at PU, the transmit power constraint of SU is also considered. Since the transmit power and the interference power can be limited either by a peak or an average constraint, various combinations of power constraints are studied. It is shown that there is a capacity gain for SU under the average over the peak transmit/interference power constraint. It is also shown that fading for the channel between SU transmitter and PU receiver is usually a beneficial factor for enhancing the SU channel capacities.

Tomlinson-Harashima precoding (THP) can be combined with geometric mean decomposition (GMD) to decouple a multiple-input multiple-output (MIMO) channel into multiple single-input single-output (SISO) subchannels with identical signal-to-noise ratios (SNRs). The combined system is called GMD-THP MIMO system. As all subchannels for this system have identical SNRs, it is more convenient to design modulation/demodulation and coding/decoding schemes than other MIMO systems that have different SNRs among the subchannels. In this paper, we consider low-density parity-check (LDPC) coded GMD-THP MIMO systems. Modulo operation at the receiver is needed for THP decoding but it may generate modulo errors. These modulo errors cause the log likelihood ratio (LLR) values provided by conventional soft demapper to be very inaccurate. We propose an expanded soft demapper scheme to reduce the inaccuracy of the LLR values, by which significant performance improvement can be achieved for LDPC decoding. Furthermore, THP introduces power loss and modulo loss into the system. These two losses are related to the size of the modulo boundaries and therefore we derive the expressions for these two losses as functions of the modulo size factor. With these expressions, we find the optimal modulo size factor which achieves the minimum combined losses through doing a tradeoff between power loss and modulo loss. Computer simulations are presented to show that the tradeoff indeed improves the performance of the LDPC coded GMD-THP systems.

In this paper, we consider a wideband cognitive radio network (CRN) which can simultaneously sense multiple narrowband channels and thus aggregate the perceived available channels for transmission. We study the problem of designing the optimal spectrum sensing time and power allocation schemes so as to maximize the average achievable throughput of the CRN subject to the constraints of probability of detection and the total transmit power. The optimal sensing time and power allocation strategies are developed under two different total power constraints, namely, instantaneous power constraint and average power constraint. Finally, numerical results show that, under both cases, for a CRN with three 6 MHz channels, if the frame duration is 100 ms and the target probability of detection is 90% for the worst case signal-to-noise ratio of primary users being -12 dB, -15 dB and -20 dB, respectively, the optimal sensing time is around 6 ms and it is almost insensitive to the total transmit power.

Cyclic-prefix code division multiple access (CP-CDMA), multicarrier CDMA (MC-CDMA) and single carrier cyclic-prefix (SCCP) transmission are some schemes that could support the increasing demand of future high data rate applications. The linear and nonlinear equalizers used to detect the transmitted signal are always far from the maximum-likelihood (ML) detection bound. The block iterative generalized decision feedback equalizer (BI-GDFE) is an iterative and effective interference cancelation scheme which could provide near-ML performance yet with very low complexity. In order to deploy this scheme, the channel state information (CSI) must be available at the receiver. In practice, this information has to be estimated by using pilot and data symbols. This paper investigates the problem of channel estimation using the expectation maximization (EM) algorithm. The BI-GDFE provides the soft information of the transmitted signals to the EM-based algorithm in the form a combination of hard decision and a coefficient so-called the input-decision correlation (IDC). The resultant receiver becomes a doubly iterative scheme. To evaluate the performance of the proposed estimation algorithm, the Cramer-Rao lower bound (CRLB) is also derived. Computer simulations show that the bit error rate (BER) performance of the proposed receiver for joint channel estimation and signal detection can reach the performance of the BI-GDFE with perfect CSI.

This paper considers a spectrum sharing based cognitive radio (CR) communication system, which consists of a secondary user (SU) having multiple transmit antennas and a single receive antenna and a primary user (PU) having a single receive antenna. The channel state information (CSI) on the link of the SU is assumed to be perfectly known at the SU transmitter (SU-Tx). However, due to loose cooperation between the SU and the PU, only partial CSI of the link between the SU-Tx and the PU is available at the SU-Tx. With the partial CSI and a prescribed transmit power constraint, our design objective is to determine the transmit signal covariance matrix that maximizes the rate of the SU while keeping the interference power to the PU below a threshold for all the possible channel realizations within an uncertainty set. This problem, termed the robust cognitive beamforming problem, can be naturally formulated as a semi-infinite programming (SIP) problem with infinitely many constraints.We first transform this problem into a second order cone programming (SOCP) problem and then solve it via a standard interior point algorithm. Then, an analytical solution with significantly reduced complexity is developed from a geometric perspective. It is shown that both algorithms yield the same optimal solution. Simulation examples are presented to validate the effectiveness of the proposed algorithms.

In this paper, we consider a cognitive radio (CR) network, in which the unlicensed (secondary) users are allowed to concurrently access the spectrum allocated to the licensed (primary) users provided that their interference to the primary users (PUs) satisfies certain constraints. We study a weighted sum rate maximization problem for the secondary user (SU) multiple input multiple output (MIMO) broadcast channel (BC), in which the SUs are subject to not only a sum power constraint but also interference power constraints. We transform this multiconstraint maximization problem into its equivalent form, which involves a single constraint with multiple auxiliary variables. Fixing these multiple auxiliary variables, we propose a duality result for the equivalent problem. Exploiting the duality result, we develop an efficient subgradient based iterative algorithm to solve the equivalent problem and show that the developed algorithm converges to a globally optimal solution. Simulation results are provided to corroborate the effectiveness of the proposed algorithm.

In this paper, we study the channel estimation problem for the two-way wireless relay network (TWRN) where two terminals exchange their information through a relay node in a bi-directional manner. We derive the maximum likelihood (ML) channel estimator as well as a new estimator called the linear maximum signal-to-noise ratio (LMSNR) estimator. It is shown that our proposed methods give superior performance compared to the common channel estimators like the least-square (LS) and the linear minimum-mean-squared-error (LMMSE) in the TWRN scenario. The provided study is based on any given training sequence, while the optimal training sequence design will be presented in a separate work due to the lack of the space. Simulations are conducted to corroborate the proposed studies.

This paper studies the optimal resource allocation problem for the relay-assisted orthogonal frequency-division multiple-access (OFDMA) -based multiuser system. A new transmission protocol, named hierarchical OFDMA, is proposed to support two-way transmissions between the base station (BS) and each mobile user (MU) with or without an assisting relay station (RS) in "relay" or "direct" mode, respectively. In particular, two-way relaying is applied to MUs in relay mode with two relay-operations: decode-and-forward (DF) and amplify- and-forward (AF). Applying convex optimization techniques, efficient algorithms are developed for optimizing the resource allocation at the BS, RSs, and MUs. Simulation results show that substantial throughput gains are achievable by the proposed two- way relaying and optimal resource allocation schemes over the conventional one-way relaying and fixed resource allocation for relay-assisted OFDMA-based wireless networks.