The low-altitude economy (LAE), an emerging economic paradigm encompassing various flight activities in low-altitude airspace, has attracted widespread attention from academia and industry due to its appealing economic and social benefits. In this paper, we investigate the design of integrated sensing and communication (ISAC) waveforms for LAE applications. Specifically, we propose an adaptive ISAC waveform, which integrates the maximum distance separable (MDS) code and index modulation (IM) into the orthogonal frequency division multiplexing (OFDM) waveform, namely A-MDS-OFDM-IM. This design combines the hybrid benefits of MDS code, IM, and OFDM techniques, i.e., the error detection capability of MDS code, the high spectral efficiency (SE) of IM, and the high sensing resolution of OFDM, thereby enabling robust communication and sensing. A comprehensive performance analysis of A-MDS-OFDM-IM is provided, including its bit error rate (BER), peak-to-sidelobe level (PSL), and peak-to-average power ratio (PAPR). Moreover, to address the high PAPR issue of A-MDS-OFDM-IM, we develop an adaptive design criterion based on the alternating direction method of multipliers (ADMM), which is capable of jointly optimizing the communication, sensing, and PAPR performance of the proposed system. Simulation results demonstrate that the proposed waveform achieves better BER performance than conventional OFDM-based waveforms under a non-ideal high power amplifier (HPA), owing to its low-PAPR characteristic. Additionally, the proposed waveform ensures robust sensing with satisfactory PSL performance, making it a promising ISAC waveform for LAE applications.

In order to mitigate the distance reduction attack in Ultra-Wide Band (UWB) ranging, this paper proposes a secure ranging scheme based on a random time-hopping mechanism without redundant signaling overhead, which is also backward compatible with existing standards such as IEEE 802.15.4a/z. By randomly varying the reply interval between ranging messages while maintaining synchronization of the interval between legitimate devices, distance attacks can be effectively defended against. The effectiveness and feasibility of the proposed strategy are demonstrated through simulation and experiments in the case of the Ghost Peak attack, which can effectively reduce the ranging distance of existing commercial UWB chips. The random time-hopping mechanism is verified to be capable of reducing the success rate of distance reduction attacks to less than 0.01 %, thereby significantly enhancing the security of UWB ranging.

This paper proposes a framework for applying a novel multi-domain modulation scheme, orthogonal chirp division multiplexing with index modulation (OCDM-IM), in integrated sensing and communication (ISAC) systems by combining OCDM and index modulation (IM). To support simple SISO-ISAC applications, a low-complexity fast Fourier transform (FFT)-based sensing algorithm is developed for the sake of the superior performance of OCDM-IM over traditional OCDM. Building on this, the framework is further extended to more attractive MIMO-ISAC systems in order to achieve improved bit error rate (BER) and a lower peak-to-average power ratio (PAPR) compared to the conventional MIMO-OCDM scheme. For sensing, it enables distance, velocity, and angle estimation by formulating the OCDM-IM waveform within a compressed sensing framework, which is then efficiently solved using the proposed orthogonal matching pursuit (OMP) algorithm. Simulation results confirm that the OCDM-IM waveform enhances communication performance through IM while preserving the promising sensing performance.

As a promising candidate for high-mobility communications, orthogonal chirp division multiplexing (OCDM) has attracted growing attention owing to its robustness to Doppler shifts and efficient hardware implementation. In this paper, motivated by the urgent demand for seamless and reliable communications in high-speed railway (HSR) scenarios, we innovatively integrate OCDM into cell-free massive multiple-input multiple-output (CFmMIMO) systems and establish a novel transmission framework, termed CFmMIMO-OCDM. Within this framework, we conduct a comprehensive analysis of the doubly-dispersive HSR channel model and derive the input-output signal relation in HSR communications. Moreover, to address the challenges posed by high computational complexity and excessive data exchange inherent in centralized signal processing, we first reveal the quasi-sparsity of the Fresnel-domain channel matrix in HSR communications. Then, we develop a distributed baseband processing (DBP) architecture by leveraging the channel sparsity. Aimed at enhancing the signal detection efficiency and accuracy, we further design a distributed message passing (DMP)-based detection algorithm for CFmMIMO-OCDM in HSR communications, which achieves considerably reduced complexity and data exchange compared to the centralized detection. Numerical results confirm the superiority of CFmMIMO-OCDM over conventional orthogonal frequency division multiplexing (OFDM)-assisted CFmMIMO systems in HSR communications. Moreover, theoretical analysis and numerical results are provided to demonstrate that our proposed DMP detection can achieve attractive bit error rate (BER) and complexity performance compared to conventional centralized detection.

Semantic communications have the potential to improve transmission efficiency and support intelligent tasks. However, the commonly used global sampling-based pattern ignores the fact that the data processing ability of edge transmitters is strictly limited and only a small part of information is available for a single sample in some scenarios. This paper proposes a novel partial sampling-based semantic communication (PSSC) framework where an edge transmitter is guided by feedback from the receiver to locate and collect only part of content relevant to the target task. Taking the vision-based task as an example, the transmitter selectively samples a small patch of a large-size image until the intelligent task is successfully executed at the receiver. The selection of sampling location is modeled as a partially observable Markov decision process problem and an intelligent approach based on reinforcement learning is proposed to solve the problem. In addition, a recurrent neural network-based receiver is designed to fuse information received over multiple transmission rounds. Besides, we prove that the feedback does not increase the semantic channel capacity. Simulation results demonstrate that the proposed framework can locate the informative areas accurately and achieve competitive performance compared to the existing global sampling-based methods.

More and more intelligent tasks rely on network edge nodes to collect data, which brings inevitable communication problems such as limited bandwidth and high data volume. As-sisted by deep learning, semantic communication technology is an effective means to improve the efficiency of joint communication and intelligent task execution. However, the commonly used global sampling-based pattern ignores the fact that the data processing ability of edge devices is strictly limited and only a small part of information is available at one time in some applications. This paper proposes a novel partial sampling based semantic communication framework where an edge device is guided by feedback from the receiver to locate and collect only part of the semantic content relevant to the target task at one time. Taking the vision-based task as an example, the transmitter selectively samples a small patch of a large-size image until the intelligent task is successfully executed at the receiver. Simulation results demonstrate that the proposed partial sampling-based framework can locate the informative areas accurately and achieve com-petitive performance compared to existing global sampling-based methods.

In space-air-ground integrated networks (SAGINs), the global energy efficiency (GEE) is a crucial metric for balancing the network throughput and energy consumption, and the maximization of GEE requires the optimizations of both user association and power allocation. Most existing methods optimize user association and power allocation separately or successively, relying on instantaneous non-local channel state information (CSI) exchanges. Nevertheless, both the separate and successive methods may fail to find the jointly optimal solution, and acquiring the instantaneous non-local CSI across the SAGINs is challenging due to the long communication distances between the access points (APs) and users. To address these issues, we leverage cloud-edge collaborations and propose an online delayed-interaction collaborative-learning independent-decision multi-agent DRL (DICLID-MADRL) algorithm. With the proposed algorithm, each AP can independently select users and configure transmit power with only local information to enhance GEE. Simulation results demonstrate that the proposed algorithm achieves a higher GEE with reduced time complexity compared to the state of the arts.

The high reliability and low latency requirements of multimedia services necessitate the design of more efficient link adaptation methods. In this paper, we introduce instantaneous channel state information (CSI) reporting, specifically designed for 5G video conferencing, and enhance the outer loop link adaptation based on soft Acknowledgement (Soft-ACK). We also formulate a resource allocation problem in 5G physical downlink shared channel (PDSCH) to balance the uplink and downlink traffic in compliance with the specified latency constraints. Our proposed scheme operates in a relatively straightforward manner. It outperforms conventional link adaptation methods re-garding Block-Level Error Rate (BLER) and effectively adheres to stringent latency constraints in video transmission simulations.

Intelligent wireless networks have long been expected to have self-configuration and self-optimization capabilities to adapt to various environments and demands. In this paper, we develop a novel distributed hierarchical deep reinforcement learning (DHDRL) framework with two-tier control networks in different times cales to optimize the long-term spectrum efficiency (SE) of the downlink cell-free multiple-input single-output (MISO) network, consisting of multiple distributed access points (AP) and user terminals (UT). To realize the proposed two-tier control strategy, we decompose the optimization problem into two sub-problems, AP-UT association (AUA) as well as beamforming and power allocation (BPA), resulting in a Markov decision process (MDP) and Partially Observable MDP (POMDP). The proposed method consists of two neural networks. At the system level, a distributed high-level neural network is introduced to optimize wireless network structure on a large timescale. While at the link level, a distributed low-level neural network is proposed to mitigate inter-AP interference and improve the transmission performance on a small timescale. Numerical results show that our method is effective for high-dimensional problems, in terms of spectrum efficiency, signaling overhead as well as satisfaction probability, and generalize well to diverse multi-object problems.

In this paper, we adopt a three-stage based uplink channel estimation protocol with reduced pilot overhead for an reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) communication system, in which both the base station (BS) and the RIS are equipped with a uniform planar array (UPA). Specifically, in Stage I, the channel state information (CSI) of a typical user is estimated. To address the power leakage issue for the common angles-of-arrival (AoAs) estimation in this stage, we develop a low-complexity one-dimensional search method. In Stage II, a re-parameterized common BS-RIS channel is constructed with the estimated information from Stage I to estimate other users’ CSI. In Stage III, only the rapidly varying channel gains need to re-estimated. Furthermore, the proposed method can be extended to multi-antenna UPA-type users, by decomposing the estimation of a multi-antenna channel with$J$scatterers into estimating$J$single-scatterer channels for a virtual single-antenna user. An orthogonal matching pursuit (OMP)-based method is proposed to estimate the angles-of-departure (AoDs) at the users. Simulation results demonstrate that the proposed algorithm significantly achieves high channel estimation accuracy, which approaches the genie-aided upper bound in the high signal-to-noise ratio (SNR) regime.

In this paper, to obtain a higher information rate in the block fading channel, we propose an LDL-decomposition-based linear precoded faster-than-Nyquist (FTN) signaling with power allocation. We further propose an LDL-decomposition-based linear precoded FTN signaling with truncated power allocation (LDL-TPA-FTN) to extend the application range of the acceleration factor. Moreover, we derive the corresponding maximum average information rate, outage probability, and outage capacity of the proposed schemes. Also, the complexity of the proposed schemes is analyzed theoretically and compared with classic ones, which shows the proposed schemes have lower complexity. Numerical results show that the proposed schemes have higher outage capacity and achievable rate than Nyquist signaling in the block fading channel. Also, the proposed schemes can reduce the incurred inter-symbol interference (ISI) and have the same application range of the acceleration factors as the singular value decomposition-based FTN. Moreover, a trade-off should be made between the side-lobe suppression of the power spectral density of the transmit signal and threshold selection in LDL-TPA-FTN to obtain higher performance gain for small acceleration factors.

In the massive machine-type communications (mMTCs) scenario for Internet-of-Things (IoTs) applications, though a large number of devices are registered to an access point (AP), very few of them are active with uplink short packet transmission simultaneously, which requires a novel design of protocols and receivers to enable efficient data transmission and accurate multiuser detection (MUD). Aiming at this problem, grant-free nonorthogonal multiple access (GF-NOMA) is proposed, which enables active devices to directly transmit their preambles and data symbols altogether within one time frame, without grant from the AP. Compressive sensing (CS)-based receivers are adopted for nonorthogonal preambles (NOPs)-based MUD, and successive interference cancellation is exploited to decode the superimposed data signals. In this article, from an aspect of network deployment, we model, analyze, and optimize the CS-based GF-MONA mMTC system via stochastic geometry (SG). Based on the SG network model, we first analyze the success probability as well as the channel estimation error of the CS-based MUD in the preamble phase and then analyze the average aggregate data rate in the data phase. We optimize the energy efficiency and AP coverage efficiency for GF-NOMA via numerical methods to meet the low-energy-consumption and low-infrastructure-cost demands of IoT applications. Our analysis results are verified with Monte Carlo simulations, which show that CS-based GF-NOMA with NOP yields better MUD and data rate performances than contention-based GF-NOMA with orthogonal preambles and grant-free orthogonal multiple access.

In this paper, we study the resource allocation problem for a cooperative device-to-device (D2D)- enabled wireless caching network, where each user randomly caches popular contents to its memory and shares the contents with nearby users through D2D links. In order to enhance the throughput of spectrum-sharing D2D links, which may be severely limited by the interference among D2D links, we enable the cooperation among some of the D2D links to eliminate the interference among them. We formulate a joint link scheduling and power allocation problem to maximize the overall throughput of cooperative D2D links (CDLs) and non- cooperative D2D links (NDLs), which is NP-hard. To solve the problem, we decompose it into two sub- problems, which maximize the sum rates of the CDLs and the NDLs, respectively. For CDL optimization, we propose a semi-orthogonal-based algorithm for joint user scheduling and power allocation. For NDL optimization, we propose a novel low-complexity algorithm to perform link scheduling and develop a Difference of Convex functions (D.C.) programming method to solve the non-convex power allocation problem. Simulation results show that cooperative transmission can significantly improve both the number of served users and the overall system throughput.

In this paper, we investigate mobile association and power allocation in device-to-device (D2D)- enabled heterogeneous networks with non-orthogonal multiple access (NOMA) protocol. We formulate two optimization problems to maximize the minimum rate and the sum rate of the network, respectively. Each problem includes power allocation, access point selection and transmission mode switching. To solve the problems, we develop a two-step method, which can always reach the closed-form solution. Simulation results show that the proposed method can significantly improve the overall throughput of the system, compared with the traditional solutions without D2D communications. In addition, we also investigate the tradeoff between overall throughput and fairness.

In underlay heterogeneous networks (HetNets), the distance between a macro base station (MBS) and a macro user (MU) is crucial for a small-cell based station (SBS) to control the interference to the MU and achieve the coexistence. To obtain the distance between the MBS and the MU, the SBS needs a backhaul link from the macro system, such that the macro system is able to transmit the information of the distance to the SBS through the backhaul link. However, there may not exist any backhaul link from the macro system to the SBS in practical situations. Thus, it is challenging for the SBS to obtain the distance. To deal with this issue, we propose a median based (MB) estimator for the SBS to obtain the distance between the MBS and the MU without any backhaul link. Numerical results show that the estimation error of the MB estimator can be as small as $4\%$.

Full-duplex (FD) allows the exchange of data between nodes on the same temporal and spectrum resources, however, it introduces self interference (SI) and additional network interference compared to half-duplex (HD). Power control in the FD networks, which is seldom studied in the literature, is promising to mitigate the interference and improve the performance of the overall network. In this work, we investigate the random and deterministic power control strategies in the FD networks, namely, constant power control, uniform power control, fractional power control and ALOHA-like random on-off power control scheme. Based on the obtained coverage probabilities and their robust approximations, we show that power control provides remarkable gain in area spectrum efficiency (ASE) and energy efficiency (EE), and improves the fairness among the uplink (UL) and downlink (DL) transmissions with respect to the FD networks. Moreover, we evaluate the minimum SI cancellation capability to guarantee the performance of the cell-edge users in FD networks. Generally, power control is helpful to improve the performance of the transmission for long distance in the FD networks and reduce the requirement of SI cancellation capability.

The existing solutions for multi-user detection in uplink (UL) grant-free sparse code multiple access (SCMA) rely on the prior knowledge of user sparsity, i.e., the number of active users. An alternative solution, which sets the sparsity as a statistically empirical value to get a rough active user set and then eliminates the false detected inactive users with joint message passing algorithm (JMPA), leads to either increasing computation complexity of JMPA or high missed detection probability. In this paper, we propose a receiver for UL grant-free SCMA which relies on no prior knowledge of user sparsity. We propose a detection-based group orthogonal matching pursuit (DGOMP) active user detector to get an accurate active user set rather than a rough active user set. Then we modify the JMPA by taking the channel gain and noise power into consideration when calculating the prior information of the zero codeword. The modified JMPA helps to further eliminate the false detections caused by noise, channel fading and non-orthogonality of pilot sequences. Simulation results show that our proposed receiver without prior knowledge of user sparsity has acceptable performance degradation compared with currently existing solution with ideal, however unable to get in practice, prior knowledge of user sparsity.

In this paper, we investigate profit maximization problem in wireless powered communication (WPC) systems, where the profit is defined as the difference between the revenue earned from uplink data transmission and total energy cost of the system. Since the efficiencies of energy harvesting (EH) circuits and battery storage are both non-linear, conventional linear models will lead to suboptimal resource allocation. In this paper, we propose to maximize the profit of a WPC system under nonlinear energy harvesting and storage models. In order to solve the non-convex optimization problem, we develop a piecewise linear EH model, which is more accurate than conventional linear EH models and existing non-linear EH models. Numerical results show that the power and time allocation design based on the non-linear energy harvesting and storage models outperforms that based on existing linear models in terms of the profit.

We study a downlink non-orthogonal multiple access (NOMA) 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 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. Numerical results validate the correctness of the theoretical analysis and demonstrate the advantages of the proposed algorithms over 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. In particular, 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 instantaneous cross-channel gain by observing the power adaption. 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 art, we show the advantages of the proposed proactive estimator.

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 realize spectrum sharing. To obtain the primary channel gain, a backhaul between the primary system and the CT is needed. However, the backhaul is usually unavailable in practice. To deal with this issue, the CT is enabled to sense primary signals and estimate the primary channel gain in this paper. In particular, two estimators, namely, a high-complexity maximum likelihood (ML) estimator and a low-complexity median based (MB) estimator are proposed. Numerical results show that the ML estimator outperforms the MB estimator in terms of the accuracy if the signal to noise ratio (SNR) of the received primary signals at the CT is no smaller than 4 dB. Otherwise, the MB estimator is superior to the ML estimator from the aspects of both the computational complexity and accuracy.

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 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.

Control data separation architecture (CDSA) is a more efficient architecture to overcome the overhead issue than the conventional cellular networks, especially for the huge bursty traffic like Internet of Things, and over-the-top (OTT) content service. In this paper, we study the optimization issue of network energy efficiency of the CDSA-based heterogeneous cloud radio access networks (H-CRAN) networks, which has heterogeneous fronthaul between control base station (CBS) and data base stations (DBSs). We first present a modified power consumption model for the CDSA-based H-CRAN, and then formulate the optimization problem with constraint of overall capacity of wireless fronthaul. Then we work out the resource assignment and power allocation by the convex relaxation approach using fractional programming, norm approximation, and Lagrangian dual decomposition method, with a derived the close-form optimal solution. Finally, we verify the proposed method by system- level simulation. The comprehensive simulation results show that our proposed algorithm has 10% EE gain compared to the static algorithm, and the CDSA-based H-CRAN networks can achieve up to 14% EE gain compared to the conventional network even under strict fronthaul capacity limit.

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.

The problem of the centralized caching is studied in a two-layer network. The first layer of the network is constructed by a server and K1helpers, and the second layer consists of K1orthogonal sub-networks, each of which contains a helper and K2users. The pioneer caching design in the two-layer network is to directly apply the Maddah-Ali & Niesen (MAU) centralized caching [1] into individual layers, such that single-layer multicast opportunities (SMO) are deployed. In this paper, a joint caching (JC) algorithm is developed by exploiting both the SMO and the correlations of caching contents across two layers, namely, cross-layer storage correlations (CSC). Furthermore, cross-layer multicast opportunities (CMO) can also be created by applying the MAU scheme between the server and users. In order to simultaneously obtain the caching gains from SMO, CSC, and CMO, a hybrid caching scheme is proposed and demonstrated to be order-optimal when the storage sizes at both helpers and users are limited. In other words, the achievable rate region lies within a constant multiplicative and additive gap of the information-theoretic bounds. In particular, the multiplicative and additive factors can be carefully characterized to be 1/48 and 4, respectively.

Conventionally, fast frequency hopping (FFH) is regarded as a non‐coherent system, which has inevitable shortcomings in low spectral efficiency. To achieve a high spectral efficiency while maintaining a favourable bit error ratio (BER) performance in FFH systems, in this study, the authors extend their previously proposed subset‐based coherent FFH (S‐CFFH) scheme with the aid of spatial modulation (SM). Furthermore, they derive the closed‐form expressions of the pairwise error probability and asymptotically BER bound for the S‐CFFH/SM maximum‐likelihood receivers in the presence of multitone jamming and highly frequency‐selective Rayleigh fading channels, where the perfect and imperfect channel state information are both considered. The authors’ analysis and simulation results show that, the proposed S‐CFFH/SM scheme outperforms both the conventional non‐coherent FFH and the S‐CFFH schemes, in terms of spectral efficiency and BER, respectively.

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.

We study a one-hop device-to-device (D2D)-assisted wireless caching network, where popular files are randomly and independently cached in the memory of end users. Each user may obtain the requested files from its own memory without any transmission, or from a helper through a one-hop D2D transmission, or from the base station. We formulate a joint D2D link scheduling and power allocation problem to maximize the system throughput. However, the problem is non-convex, and obtaining an optimal solution is computationally hard. Alternatively, we decompose the problem into a D2D link-scheduling problem and an optimal power allocation problem. To solve the two subproblems, we first develop a D2D link-scheduling algorithm to select the largest number of D2D links satisfying both the signal to interference plus noise ratio and the transmit power constraints. Then, we develop an optimal power allocation algorithm to maximize the minimum transmission rate of the scheduled D2D links. Numerical results indicate that both the number of the scheduled D2D links and the system throughput can be improved simultaneously with the Zipf-distribution caching scheme, the proposed D2D link-scheduling algorithm, and the proposed optimal power allocation algorithm compared with the state of the arts.

Visible Light Communications (VLC) is emerging as an appealing technology to complement WiFi in indoor environments. Yet maintaining VLC performance under link dynamics remains a challenging problem. In this paper, we build a VLC software-radio testbed and examine VLC channel dynamics through comprehensive measurement. We find minor device movement or orientation change can cause the VLC link SNR to vary by tens of dB even within one packet duration, which renders existing WiFi rate adaptation protocols ineffective. We thus propose a new mechanism, DLit, that leverages two unique properties of VLC links (predictability and full-duplex) to realize fine-grained, in-frame rate adaptation. Our prototype implementation and experiments demonstrate that DLit achieves near-optimal performance for mobile VLC usage cases, and outperforms conventional packet-level adaptation schemes by multiple folds.

The use of low precision (e.g., 1 - 3 bits) analog-to-digital converters (ADCs) in very large multiple-input multiple-output (MIMO) systems is a technique to reduce cost and power consumption. In this context, nevertheless, it has been shown that the training duration is required to be very large just to obtain an acceptable channel state information (CSI) at the receiver. A possible solution to the MIMO system with low precision ADCs is joint channel-and-data (JCD) estimation. This paper first develops an analytical framework for studying the MIMO system using JCD estimation. In particular, we use the Bayes-optimal inference for the JCD estimation and realize this estimator utilizing a recent technique based on approximate message passing. Large-system analysis based on the replica method is then adopted to derive the asymptotic performances of the JCD estimator. Results from simulations confirm our theoretical findings and reveal that the JCD estimator can provide a significant gain over conventional pilot-only schemes in the MIMO system.

The subset-based coherent fast frequency hopping (S-CFFH), was recently proposed to enable reliable channel estimation and coherent schemes for FFH over fading channels. In this paper, we analyze the maximum-likelihood (ML) receiver for the S-CFFH/binary phase shift keying (BPSK), with partial band noise jamming (PBNJ), frequency selective Rayleigh fading, and imperfect channel state information (CSI). The expressions of closed-form bit error ratio (BER) are presented, and the results are validated by simulations. It is shown that the proposed ML receiver significantly outperforms some conventional diversity combining receivers in terms of BER. The ML receiver also greatly reduces the jammer's efficiency.

This paper studies the gain that cooperative multi-hop transmission provides when used to boost the data rate in Device-to-Device (D2D) communication. Both D2D transmitter and receiver are located in the coverage area of the same Macro Base Station, who is in charge of the control signaling to construct the cooperative cluster(s) of low-cost Relay Nodes (RNs) that Decode-and-Forward information non-coherently from source to destination. Communication resources are divided into two or three equal orthogonal parts for two- and three-hop cooperative transmissions, respectively. For the three-hop cooperative case, backward Interference Cancellation (IC) is also considered in the RNs of the first cluster to reduce multiplexing loss (using two orthogonal portions of communication resources instead of three). The end-to-end data rate of different multi-hop cooperative transmission strategies is studied for different clustering algorithms and measurement reports (i.e., SNR and SINR). Based on obtained performance results, it is possible to conclude that three-hop cooperative transmission with backward IC provides better performance than its two-hop counterpart.

Transform domain communication system (TDCS) is a multi- carrier cognitive radio (CR) technique which uses cyclic code shift keying (CCSK) for overlay opportunistic spectrum access. Specifically, at any given time, a TDCS system cyclically shifts a fundamental modulation waveform (i.e., a CR sequence satisfying a dynamic spectrum hole constraint) according to specific input data symbol. In practical TDCS system, an interesting research problem is how to design CR sequence with inter-carrier interference (ICI) resilience, where ICI is caused by carrier frequency offset (CFO) or Doppler spread. In this paper, we present a novel family of CR sequences which are able to achieve ICI self-cancellation in TDCS. Analysis and simulations validate that our proposed CR sequence is effective for ICI suppression in TDCS. A future work of this research is to optimize the peak-to-mean power ratio of such CR sequences.

Recently, a proactive estimation method has been proposed to obtain the cross-channel gain from cognitive transmitter (CT) to primary receiver (PR), where CT acts as a full-duplex amplified-and-forward (AF) relay for primary users. However, since the cognitive and primary users usually have no cooperation, it has no guarantee that the direct and relay signals are synchronized, i.e, the time delay between direct and relay signals may be randomly large or small. This may cause interference to PR. In this paper, we analyze the impacts of the small and large time delays on proactive estimation, and propose an interference-free method to estimate the cross-channel gain. Simulation results demonstrate that the proposed method obtains about 5% estimation error with about 99% interference-free probability.

In this paper, we develop a new proactive estimation method to obtain the cross-channel gain from cognitive transmitter to primary receiver without any backhaul between cognitive and primary users. In conventional proactive methods, the jamming signal is used for probing, which introduces the extra interference to primary receivers. In our method, the relayed primary signal is used for probing, which instead assists the primary transmission. Simulation results demonstrate that the proposed method with 2% estimation errors can obtain up to about 72% throughput improvement introduced by the cross-channel gain.

Cognitive radio (CR) technology has been developed to solve the spectrum-underutilization problem. In CR networks, the CR users have opportunities to access the licensed spectrum bands assigned to the primary users (PUs). Since the PUs have priorities to use the bands, the CR users are not allowed to generate unacceptable interference to them. In this paper, we investigate power allocation schemes for CR networks with both direct and relay-aided transmissions. We formulate an overall rate optimization problem with interference constraints to the PU and peak power constraints at each node and obtain solutions by theoretical analysis. Numerical results are provided to show the impact of the relay node and the PU locations on power allocation.

As a cognitive radio (CR) modulation technique, transform domain communication system (TDCS) has been proposed by utilizing white space spectrum for the network access and achieving low probability of interception (LPI). In the previously reported TDCS- based cognitive radio networks (CRN), however, the non-zero periodic correlation function between any pair of users results in multiuser interference (MUI). In this paper, a novel framework under the CR constraints is presented by employing a two- dimension spreading scheme (i.e., time and frequency domains). We first obtain a class of spreading sequences with almost perfect periodic correlation function for arbitrary spectrum utilization pattern. Then, an universal criteria is presented for synchronous MUI-free CRNs when employing TDCS. Simulation results demonstrate that the proposed TDCS-based CRN architecture is a preferable candidate for distribution wireless networks, such as CR Ad-hoc wireless sensor networks.

In this paper, we will propose a novel method to estimate the cross channel gain between cognitive transmitter to primary receiver as well as the primary channel gain between primary transceivers, where the cognitive user is acting as a relay to proactively trigger the primary link adaptation. But, this kind of estimator may obtain two possible estimations for each channel, which may confuse the cognitive user. Thus, we will further develop a selection method to pick the estimation with less errors. Simulation results show that the proposed method can effectively improve the estimation performance.

Energy efficiency is becoming more and more important for mobile devices in future green radio network. This paper addresses the tradeoff between energy-efficiency (EE) and spectral efficiency (SE) in uplink multiuser orthogonal frequency division multiple access (OFDMA) systems. In this paper, EE is measured by the sum of instantaneous bits-per-Joule of each user, and it is optimized with the restrictions on both the minimum rate requirements and available transmit power over frequencyselective channels. Based on this optimization model, a novel low-complexity energy-efficient resource allocation algorithm is proposed. Simulation results demonstrate that the proposed scheme provides a better tradeoff between SE and EE than the conventional schemes with reduced complexity.

Cognitive radio (CR) technology has been developed to solve the spectrum-underutilization problem. In CR networks, the CR users have opportunities to access the licensed spectrum bands assigned to the primary users (PUs). Since the PUs have priorities to use the bands, the CR users are not allowed to generate unacceptable interference to them. In this paper, we investigate power allocation schemes for CR networks with both direct and relay-aided transmissions. We first formulate an overall rate optimization problem with interference constraints to the PU and peak power constraints at each node and obtain solutions by theoretical analysis. To take the fairness among CR users into consideration, we further investigate the overall rate optimization problem with an additional sum power constraint and achieve fairness between two CR users by adjusting the sum power threshold. Numerical results are provided to show the impact of the relay node and the PU locations on power allocation solutions.

In cognitive radio systems, the channel gains between primary users (PUs) and cognitive users (CUs) and that between PUs are critical for the coexistence of CUs and PUs. In this paper, we propose a proactive channel gain estimation approach by using the received primary signal for probing, which obtains both kinds of channel gains without information exchange between CUs and PUs. In average, the probing in our proactive approach does not introduce interference to PUs while conventional ones usually do. Simulation results show that the relative estimation errors of the proposed approach are below 0.02 with a proper CU location, where the channels suffer path loss and shadowing, and their gains range from about -120 dB to about -50 dB.

In this paper, we develop a downlink dynamic soft frequency reuse (SFR) scheme with inter-cell coordination in orthogonal frequency division multiple access (OFDMA) cellular networks. A clustered base station (BS) coordination strategy is employed to facilitate frequency resource allocation among coordinated sectors. Compared to the traditional static SFR scheme, the proposed scheme can exploit frequency selectivity of wireless channels and multi-user diversity through inter-cell coordination and achieve better performance. Since the proposed approach performs resource allocation in a distributed way, only limited information needs to be shared or exchanged even within a cluster. As shown by computer simulation, compared with the traditional scheme, the proposed one can improve the throughput for those users at the edge of a sector by 41.9% while maintaining the same throughput for those users at the center of a sector.

One of the main advantages of cooperative communication systems is the use of information at the surrounding nodes in order to create spatial diversity and so far obtaining higher throughput and reliability. We propose in this paper a blind detector that involves the formulation of the system as a Blind Source Separation BSS problem. In the BSS framework, we do not have to estimate the channel using training data, removing the necessity of pilot symbols and the prior estimation of the channel. We analyze two kinds of distributed space-time codes for the single relay system, showing that they can be stated in terms of BSS as a linear instantaneous mixture of complex-valued sources. The BSS method applied is the complex version of the FastICA algorithm since it is very flexible, robust and the convergence is very fast so we can estimate the symbols accurately with a low-complexity algorithm that can adapt to changes in the channel with relative simplicity.

We investigate the design and use of systematic binary deterministic rateless (BDR) codes for information transmission over block-erasure broadcast channels. BDR codes are designed to obtain a level of maximal distance separable (MDS) properties, making these codes ideal for the considered broadcast scenario. For a certain number of encoded redundancy blocks, we derive an expression for the probability that the MDS properties are maintained. Moreover, if limited feedback is available, we extend the BDR coding protocol to further improve the system performance. Numerical results show that for a finite number of source blocks and as the number of users grows the proposed systematic BDR codes performs significantly better than LT codes. The proposed schemes with feedback have better performance than traditional ARQ schemes.

It has been shown in the literature that network coding can improve the transmission efficiency of wireless broadcasting as compared to traditional ARQ schemes. In this paper, we propose an improved network coding scheme that can asymptotically achieve the theoretical lower bound on transmission overhead for a sufficiently large number of information blocks. The proposed scheme makes use of an index allocation algorithm that distributes information blocks that have been erased during transmission into a minimum number of encoding sets, where each set represents the erased blocks to be jointly network encoded and retransmitted. Numerical results show that the proposed scheme enables higher transmission efficiencies than traditional ARQ, and previously proposed networks coding schemes for wireless broadcasting.

Cooperative diversity has recently emerged as novel communication scheme for future wireless communications because of diversity gain, which is achieved by sharing each cooperative user's antenna to form a virtual antenna array in the wireless network. Previous works on analysis of system performance of cooperative networks mainly focused on static scenarios. In this paper, we derive exact average symbol error rate (ASER) of a cooperative wireless transmission with adaptive decode-and-forward (DF) protocol [6], where the number of relays or cooperative user- nodes, n, which can correctly decode the received signal, is a random variable subject to the Poisson distribution, the normal distribution and the uniform distribution, respectively. Both the analytical and the numerical results corroborate that both diversity and coding gain will be affected by the relay- or user-node distribution. For the Poisson distribution and the normal distribution, the diversity gain is affected much than the coding gain. For the uniform distribution, the coding gain is affected whereas the diversity gain keeps fixed.

We explore in this paper one novel deterministic simulation model for downlink multiple-input multiple-output (MIMO) fading channel in wireless communication. The von Mises function, which characterizes accurately the distribution of non-isotropic scattering around mobile station, is used to deterministic modeling process for both space-time codes (STC) scenario and downlink beam-forming (DBF) scenario. Statistical fading characteristics of the simulated MIMO channel, including level-crossing rate, average duration of fades and envelope cross-correlation, are studied. The impact of non-isotropic scattering on MIMO-link capacity is also investigated. The numerical results show that existence of non-isotropic scattering degraded ergodic capacity of MIMO channel by 1 bit/Hz/s in the case of STC scenario. And the maximum achievable capacity in the case of DBF scenario is always lower than that of STC scenario because transmit steering vector affected the transmit antenna correlation. The proposed MIMO channel simulation model has a universal structure and requires less parameter measurements. Hence, it can be used effectively for link-level simulation and capacity evaluation in MIMO wireless communication systems.