Unmanned aerial vehicles (UAVs) have emerged as pivotal components in next-generation communication systems due to their broad coverage and flexible deployment capabilities, enabling efficient connectivity with multiple ground users. Although the integration of nonorthogonal multiple access (NOMA) and multiple-input multiple-output (MIMO) technologies in UAV communications has attracted growing research interest, existing studies remain insufficient for jointly optimizing beamforming and power allocation, particularly in terms of fully capturing the complex coupling among decision variables. This study investigates the joint optimization problem of beamforming, power allocation, and UAV position optimization in UAV systems, where the UAV communicates with multiple ground users using NOMA and MIMO technologies. The core objective is to maximize the achievable transmission rate of the system while complying with a total power budget constraint. Owing to the inherent nonconvexity of the formulated problem and the intricate coupling among decision variables, the original problem is decomposed into three subproblems: beamforming, power control, and UAV placement optimization. To address these subproblems efficiently, we propose an angle-sector-based iterative optimization framework by invoking the alternating optimization technique under the NOMA-MIMO system. This strategy not only enhances overall spectral efficiency but also ensures reliable connectivity for users at greater distances while maintaining high communication quality for those in proximity. The simulation results demonstrate that the adopted user grouping strategy, which incorporates a group matching mechanism, yields notable improvements in resource utilization. Compared with other solution strategies, the proposed alternating optimization algorithm exhibits superior performance in terms of achievable rate enhancement, thereby validating its effectiveness and practical value in complex UAV-enabled NOMA-MIMO systems.

Artificial intelligence-agent communication networks (ACNs) in the sixth-generation (6G) enable collaborative task execution among agents and butler. However, compared with traditional mobile edge computing (MEC), in ACNs, a large number of agents possess comparable computing capabilities and task proportions need to be jointly determined rather than being predefined, causing high optimization complexity in large-scale deployment scenarios. In this paper, we propose an effective framework to solve the large-scale coupled optimization problem under acceptable complexity. Specifically, we model the joint task allocation, resource allocation, task offloading and computation frequency adjustment problem as an NP-hard nonconvex mixed-integer nonlinear programming (MINLP) problem, and derive its lower bound through Lagrangian relaxation. We decompose the problem into resource allocation and task offloading subproblems, which are solved via proximal policy optimization (PPO) and minimum-cost models within a block coordinate descent (BCD) framework. Simulations demonstrate the tightness of the lower bound, achieving stable convergence for 30 agents while reducing latency from 150 ms to 50 ms and the energy consumption by 28.18%. Our algorithm has great potential for ACNs with many agents deployed in future 6G scenarios, such as autonomous vehicles, robotic swarms and precision telemedicine.

Satellite-based Internet of Things (S-IoT) faces a fundamental trilemma: propagation delay, dynamic fading, and bandwidth scarcity. While Layer-coded Hybrid ARQ (L-HARQ) enhances reliability, its backtracking decoding introduces age ambiguity, undermining the standard Age of Information (AoI) metric and obscuring the critical trade-off between data freshness and transmission efficiency. To bridge this gap, we propose a novel cross-layer optimization framework centered on a new metric, the Cross-layer Age of Error Information (C-AoEI). We derive a closed-form expression for C-AoEI, explicitly linking freshness to system parameters, establishing an explicit analytical connection between freshness degradation and channel dynamics. Building on this, we develop a packet-level encoded L-HARQ scheme for multi-GBS scenarios and an adaptive algorithm that jointly optimizes coding and decision thresholds. Extensive simulations demonstrate the effectiveness of our proposed framework: it achieves 31.8% higher transmission efficiency and 17.2% lower C-AoEI than conventional schemes. The framework also proves robust against inter-cell interference and varying channel conditions, providing a foundation for designing efficient, latency-aware next-generation S-IoT protocols.

Satellite systems have emerged as a key component of future 6G networks, where beam hopping is widely adopted to improve resource utilization under limited onboard power and bandwidth. However, due to the inherent timescale mismatch between beam scheduling and resource allocation, as well as the increased system complexity caused by wide-area coverage and flexible resource demands, efficient resource scheduling requires coordinated decision-making across multiple timescales while ensuring user-level QoS provisioning. This paper aims to address the three-dimensional coupling of resources in the beam, power, and frequency domains while satisfying heterogeneous user QoS requirements. The key idea is to establish a multitime-scale framework that decouples the original optimization problem in the temporal domain. Specifically, beam scheduling is performed over beam hopping time slots(BHTSs), while resource allocation are conducted over finer-grained user scheduling time slots. A verification approach is developed to provide theoretical justification for the proposed timescale decomposition and to ensure the consistency. Based on this framework, a weight-based search strategy is employed for beam hopping pattern (BHP) selection, and a block coordinate descent (BCD) method is adopted to iteratively optimize user-level resource allocation. The simulation results demonstrate that the proposed scheme significantly improves system throughput and service satisfaction quality compared to existing methods, while maintaining robust performance under heterogeneous traffic and delay-sensitive scenarios.

This paper proposes a novel adaptive dual-path framework for covert semantic communication (SemCom), which integrates covert information transmission with task-oriented semantic coding. Unlike conventional covert communication methods that embed hidden messages through power-domain signal superposition, our framework embeds covert data within task-specific features via semantic-level intrinsic encoding. This new architecture introduces dual encoding paths with adaptive block selection: an Explicit path for public task execution and a Stego path that jointly encodes both public and covert information through contrastive representation alignment. A Gumbel-Softmax enabled adaptive path selection mechanism dynamically activates network blocks based on task requirements. We formulate a multi-objective optimization framework that simultaneously ensures accurate semantic understanding and reliable covert transmission. We rigorously evaluate our framework’s security against a powerful, independently trained attacker. Experimental results on the Cityscapes dataset demonstrate a state-of-the-art level of covertness: our method suppresses the attacker’s detection accuracy to a near-random guessing level of 56.12%. This robust security is achieved while simultaneously maintaining superior performance on the primary semantic tasks compared to the baselines.

Orthogonal time frequency space (OTFS) modulation has emerged as a promising solution to mitigate the severe Doppler shift in low Earth orbit (LEO) satellite communications. However, the frequency-dependent Doppler shift induced by the high mobility of LEO satellites leads to the Doppler squint effect (DSE). This effect compromises the channel sparsity in the delay-Doppler (DD) domain, rendering existing channel estimation methods ineffective. To overcome this challenge, this paper proposes a DSE-resilient transmission scheme for cyclic prefix OTFS (CP-OTFS)-based LEO satellite systems. Specifically, we analyze the input-output relationship of the CPOTFS- based LEO satellite communication system and derive a DSE-aware representation of the satellite-terrestrial channel in the DD domain. To efficiently capture DSE-aware channel characteristics, we propose a novel OTFS frame structure that allows the energy distribution of the received signal to serve as prior information for channel estimation. Meanwhile, this frame structure strategically allocates pilot symbols to achieve uniform energy distribution and reduce the peak-to-average power ratio (PAPR), while imposing a time-domain waveform continuity constraint to suppress out-of-band emission (OOBE) caused by rectangular pulses. Based on the frame structure, we propose a prior-aided iterative channel reconstruction (PAICR) algorithm to mitigate the severe power leakage induced by DSE. The proposed algorithm iteratively extracts and removes dominant channel components using Doppler-domain received signal energy observations, with a convergence criterion ensuring reliable termination. Furthermore, a Cramer-Rao lower bound is derived to provide a theoretical benchmark for evaluating the algorithm's performance.

This paper presents a novel approach for wireless federated learning (WFL) that, for the first time, enables the aggregation of local models with mild to moderate errors under practical communication settings, which has to date been prevented by floating-point standards, e.g., IEEE binary32, and encryption. Specifically, we propose a new conversion from floating-point local models to fixed-point models on a layer basis, eliminating the need to transmit error-intolerant sign and exponent bits of floating-point numbers while accommodating variations in model layer widths and magnitudes. We also quantify how bit errors in the ciphertext affect the plaintext when symmetric encryption is employed for local model uploading, e.g., under Rayleigh, Rician, and Nakagami-m fading channels. Notably, these bit errors are leveraged to enhance the privacy of local models. We interpret the local model transmission process as a (λ, ϵ)-Rényi Differential Privacy (DP) mechanism, where bit errors induced by noisy channels, controlled via transmit powers, and exacerbated by decryption act as DP perturbations. Experiments show the superiority of the new WFL to the status quo with higher training accuracy and lower communication overhead.

This paper introduces an interference-free multi-stream transmission architecture leveraging stacked intelligent metasurfaces (SIMs), from a new perspective of interference exploitation. Unlike traditional interference exploitation precoding (IEP) which relies on computational hardware circuitry, we perform the precoding operations within the analog wave domain provided by SIMs. However, the benefits of SIM-enabled IEP are limited by the nonlinear distortion (NLD) caused by power amplifiers. A hardware-efficient interference-free transmitter architecture is developed to exploit SIM’s high and flexible degree of freedom (DoF), where the NLD on modulated symbols can be directly compensated in the wave domain. Moreover, we design a frame-level SIM configuration scheme and formulate a max-min problem on the safety margin function. With respect to the optimization of SIM phase shifts, we propose a recursive oblique manifold (ROM) algorithm to tackle the complex coupling among phase shifts across multiple layers. A flexible DoF-driven antenna selection (AS) scheme is explored in the SIM-enabled IEP system. Using an ROM-based alternating optimization (ROM-AO) framework, our approach jointly optimizes transmit AS, SIM phase shift design, and power allocation (PA), and develops a greedy safety margin-based AS algorithm. Simulations show that the proposed SIM-enabled frame-level IEP scheme significantly outperforms benchmarks. Specifically, the strategy with AS and PA can achieve a 20 dB performance gain compared to the case without any strategy under the 12 dB signal-to-noise ratio, which confirms the superiority of the NLD-aware IEP scheme and the effectiveness of the proposed algorithm.

To increase the positioning and tracking performance of dynamic user equipment (UE) in wideband millimeterwave (mmWave) systems, we propose a novel positioning error lower bound (PELB)-driven ping-pong positioning framework, where the base station (BS) and UE alternately transmit and receive adaptive beamforming signals for positioning. All beamformers are scheduled based on the locally evaluated PELB. In this framework, we exploit multi-dimensional information fusion to assist in positioning. Firstly, a multi-subcarrier collaborative positioning error lower bound (MSCPEB) is proposed to evaluate the positioning error limits of wideband mmWave systems, which quantifies the contribution of all subcarriers to positioning accuracy. Subsequently, we develop an alternating optimization (AO) algorithm to optimize the hybrid beamformers targeted for MSCPEB minimization. Finally, we develop a multipath collaborative positioning method that quantifies the impact of path reliability on positioning accuracy, with a closed-form solution for deriving the user position. The proposed method does not rely on path resolution or traditional triangular relationships. The Numerical results indicate that the proposed method improves the estimation accuracy by at least 16% compared to potential schemes without optimized beam configurations, while requiring only approximately one-quarter of the slot resources.

This paper investigates the challenges of user pairing and power allocation in unmanned aerial vehicle (UAV) systems that use nonorthogonal multiple access (NOMA) to communicate with multiple ground users. The main goal is to maximize the achievable transmission rate of the system while ensuring the quality of service (QoS) requirements of users, under a constrained total power budget. Considering the non-convexity of the original problem, a stepwise optimization approach is adopted. To improve resource utilization and solve the user pairing problem, a deep Q-network method assisted by group matching is proposed. Specifically, the group matching method is applied to allocate users, and an optimized deep Q-network (OP-DQN) is used to optimize power allocation strategies. The simulation results show that compared with other user pairing strategies, this method significantly improves resource utilization and fairness. In addition, the proposed scheme effectively enhances the system transmission rate and resource efficiency.

Specific emitter identification (SEI), known as radio frequency fingerprint (RFF) identification, is one of the key techniques to provide effective protection for the low-altitude security. However, most existing SEI methods cannot achieve satisfactory identification performance in low signal-to-noise ratio (SNR) environments. By fusing background information of the environment, this paper presents a new deep learning-based SEI method that can accurately identify emitters in the environments with severe noises. We first construct a dual convolutional neural network (DCNN) and a U-shaped Convolutional Network (UNet) to extract the RFFs and background information features, respectively. Then, a background-fingerprint attention fusion network (BFAFN) is designed to fuse the background information with RFF features. Using this network, we can obtain detailed emitters information through the fused signals, improving the identification accuracy. Experimental results show that our proposed SEI method outperforms other methods in performance on both open-source and collected unmanned aerial vehicles (UAVs) datasets, with an improvement in identification accuracy of 2% to 5%.

Reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) are two promising technologies for future wireless communication networks. This paper proposes a new transmission design for short-packet communications (SPCs) in a downlink RIS-assisted NOMA system, considering hardware impairments at the transceiver nodes, non-optimal continuous phase shifts (CPSs) and discrete phase shifts (DPSs) at the RIS, and imperfect successive interference cancellation (SIC) for a pair of NOMA users. Using the method of moment matching, we approximate the end-to-end channel gain with a Gamma distribution, and derive the closed-form approximate expressions for the average block error rates (BLERs) for NOMA users, along with the diversity order and the asymptotic expressions for the average BLERs at the high signal-to-noise ratio (SNR) region. Inspired by the asymptotic average BLER, the minimum blocklength is derived, and the optimal power allocation factor is obtained. Then, the system throughput is obtained to characterize the transmission efficiency of the RIS-assisted short-packet NOMA network. Analytical results of the proposed scheme indicate that: 1) The phase estimation error of the RIS has a significant impact on the system performance, and thus the non-optimal CPSs should be used in the theoretical analysis; 2) The DPSs with 3-bit quantization can effectively achieve a comparable performance to the optimal CPS scheme in practical applications. Numerical results validate our theoretical analysis, and clearly show the performance improvement of transmission reliability and effectiveness of the proposed scheme, as compared to existing transmission schemes.

With the advancement of vehicular networking technology, communication between vehicles, and between vehicles and cloudlets, is becoming increasingly frequent, leading to a growing demand for computing resources. This growing demand necessitates more robust and efficient computing solutions to handling the data exchange and processing requirements. Mobile edge computing (MEC) addresses computing demands by leveraging edge resources. In practice, numerous parameter constraints, such as task volumes and available resources, render optimal resource management challenging. This paper presents a vehicular networking communication scenario involving an MEC-enabled roadside unit and multiple vehicles. We propose a new method that jointly optimizes task offloading decisions along with power and bandwidth allocation, aiming to minimize system energy consumption. Given the non-convexity of the original problem, characterized by the complexity and interdependence of multiple optimization variables, we adopt a strategic approach to decouple it into two sub-problems. The problem can be solved using deep learning and subgradient methods separately. Finally, a refined solution can be obtained through iterative solving with the block coordinate descent (BCD) method. Simulations provide compelling evidence that our scheme significantly reduces system energy consumption, outperforming benchmarks and showcasing its superiority.

To enhance the positioning and tracking performance of dynamic user equipment (UE) in wideband millimeter-wave (mmWave) systems, we propose a novel positioning error lower bound (PELB)-driven ping-pong positioning framework, where the base station (BS) and UE alternately transmit and receive adaptive beamforming signals for positioning. All beamformers are scheduled based on the locally evaluated PELB. In this framework, we exploit multi-dimensional information fusion to assist in positioning. Firstly, a multi-subcarrier collaborative positioning error lower bound (MSCPEB) is proposed to evaluate the positioning error limits of wideband mmWave systems, which quantifies the contribution of all subcarriers to positioning accuracy. Moreover, we prove that the MSCPEB does not exceed the arithmetic mean of the PELBs of the individual subcarriers. Subsequently, we develop an alternating optimization (AO) algorithm to optimize the hybrid beamformers targeted for MSCPEB minimization. By convexifying this problem, closed-form solutions of beamformers are derived. Finally, we develop a multipath collaborative positioning method that quantifies the impact of path reliability on positioning accuracy, with a closed-form solution for user position derived. The proposed method does not rely on path resolution and traditional triangular relationships. Numerical results validate that the proposed method improves estimation accuracy by at least 16% compared to potential schemes without optimized beam configurations, while requiring only approximately one-quarter of the slot resources.

Inherent communication noises have the potential to preserve privacy for wireless federated learning (WFL) but have been overlooked in digital communication systems predominantly using floating-point number standards,e.g., IEEE 754, for data storage and transmission. This is due to the potentially catastrophic consequences of bit errors in floating-point numbers,e.g., on the sign or exponent bits. This paper presents a novel channel-native bit-flipping differential privacy (DP) mechanism tailored for WFL, where transmit bits are randomly flipped and communication noises are leveraged, to collectively preserve the privacy of WFL in digital communication systems. The key idea is to interpret the bit perturbation at the transmitter and bit errors caused by communication noises as a bit-flipping DP process. This is achieved by designing a new floating-point-to-fixed-point conversion method that only transmits the bits in the fraction part of model parameters, hence eliminating the need for transmitting the sign and exponent bits and preventing the catastrophic consequence of bit errors. We analyze a new metric to measure the bit-level distance of the model parameters and prove that the proposed mechanism satisfies (λ, ϵ)-Rényi DP and does not violate the WFL convergence. Experiments validate privacy and convergence analysis of the proposed mechanism and demonstrate its superiority to the state-of-the-art Gaussian mechanisms that are channel-agnostic and add Gaussian noise for privacy protection.

Multi-task semantic communication can serve multiple learning tasks using a shared encoder model. Existing models have overlooked the intricate relationships between features extracted during an encoding process of tasks. This paper presents a new graph attention inter-block (GAI) module to the encoder/ transmitter of a multi-task semantic communication system, which enriches the features for multiple tasks by embedding the intermediate outputs of encoding in the features, compared to the existing techniques. The key idea is that we interpret the outputs of the intermediate feature extraction blocks of the encoder as the nodes of a graph to capture the correlations of the intermediate features. Another important aspect is that we refine the node representation using a graph attention mechanism to extract the correlations and a multi-layer perceptron network to associate the node representations with different tasks. Consequently, the intermediate features are weighted and embedded into the features transmitted for executing multiple tasks at the receiver. Experiments demonstrate that the proposed model surpasses the most competitive and publicly available models by 11.4% on the CityScapes 2Task dataset and outperforms the established state-of-the-art by 3.97% on the NYU V2 3Task dataset, respectively, when the bandwidth ratio of the communication channel (i.e., compression level for transmission over the channel) is as constrained as$\frac{1}{12}$.

Decentralized federated learning (D-FL) allows clients to aggregate learning models locally, offering flexibility and scalability. Existing D-FL methods use gossip protocols, which are inefficient when not all nodes in the network are D-FL clients. This article puts forth a new D-FL strategy, termed route-and-aggregate (R&A) D-FL, where participating clients exchange models with their peers through established routes (as opposed to flooding) and adaptively normalize their aggregation coefficients to compensate for communication errors. The impact of routing and imperfect links on the convergence of R&A D-FL is analyzed, revealing that convergence is minimized when routes with the minimum end-to-end (E2E) packet error rates (PERs) are employed to deliver models. Our analysis is experimentally validated through three image classification tasks and two next-word prediction tasks, utilizing widely recognized datasets and models. R&A D-FL outperforms the flooding-based D-FL method in terms of training accuracy by 35% in our tested ten-client network, and shows strong synergy between D-FL and networking. In another test with ten D-FL clients, the training accuracy of R&A D-FL with communication errors approaches that of the ideal centralized federated learning (C-FL) without communication errors, as the number of routing nodes (i.e., nodes that do not participate in the training of D-FL) rises to 28.

In this work, we propose a reconfigurable subarray (RS) architecture for a millimeter-wave (mmWave) integrated sensing and communication (ISAC) system to improve itsintegration gain and cost-effectiveness. The RS-based hybrid beamforming (HBF) is optimized to maximize the sum-rate of the communication system while ensuring the sensing signal-to-clutter-plus-noise ratio (SCNR) requirement. The critical idea in solving the above non-convex coupling problem is that we transform the sum-rate maximizationproblem to an equivalent weighted minimum mean square error problem, then penalty dual decomposition (PDD) is applied to decouple the analog and digital beamformers and optimize the HBF in an alternating manner. Simulations corroborate that, with the RS architecture and HBF, ISAC offers effective communication and sensing performance and optimal energy efficiency (EE) compared to other hybrid architectures.

The development of Internet of Things (IoT) systems has given rise to security concerns stemming from the exposure of wireless channels and the exponential growth of connected devices. The security challenges can be severer in the next-generation IoT systems that can disperse over large areas under a cell-free (CF) network setting. In this article, we propose a novel covert downlink transmission scheme that jointly optimizes beamforming and artificial noise (AN) vectors to obscure critical transmissions at an eavesdropper in CF IoT Networks. We classify access points (APs) as information APs (IAPs) and noise APs (NAPs) based on their proximity to the IoT devices. IAPs transmit information while NAPs generate AN to prevent eavesdropping. We derive a closed-form solution for the detection error probability. By using the Lagrangian dual algorithm, the complex logarithmic problem is transformed into sum-of-ratios form. Then, we use semidefinite relaxation (SDR) to maximize the covert transmit rate. Numerical results show that the proposed scheme outperforms the state of the art, i.e., the suboptimal Rand- AP scheme, by increasing the transmission rate by more than 23% while maintaining covertness and is better than the rest of the benchmark schemes.

Achieving energy-efficient massive ultrareliable and low-latency communications (E2-mURLLC) is a promising application prospect for sixth-generation (6G) mobile communication networks. However, there are some insurmountable obstacles, such as a large number of potential users, complex and diverse small-scale and shadow fading, and stringent energy efficiency (EE), reliability, and latency requirements. Considering the above obstacles, we propose a cell-free massive multiple-input–multiple-output (MIMO) architecture based on the$\kappa $-$\mu $shadowed fading model, and maximum-ratio combining (MRC) multiuser detection with simple path-loss decoding (S-PLD) to achieve the simultaneous optimization of EE, latency, and reliability. Furthermore, the finite blocklength information theory is used to uncover the relationship among EE, reliability, latency, and achievable data rate when the packet size is small. Simulation results show that compared with the massive MIMO architecture, using our architecture with MRC multiuser detection and S-PLD can support a threefold increase in the number of access users, reduce transmit power by 90%, achieve a nearly 100 times reliability enhancement, and shorten transmission latency by 23.3%. Consequently, a cell-free massive MIMO system with MRC multiuser detection and S-PLD, as a considerable significant potential to facilitate the advancement from URLLC to E2-mURLLC, is promising to support some time-sensitive applications with massive access, such as unmanned aerial vehicles, the Industrial Internet of Things and vehicle-to-vehicle communications.

This paper analyzes the impact of imperfect communication channels on decentralized federated learning (D-FL) and subsequently determines the optimal number of local aggregations per training round, adapting to the network topology and imperfect channels. We start by deriving the bias of locally aggregated D-FL models under imperfect channels from the ideal global models requiring perfect channels and aggregations. The bias reveals that excessive local aggregations can accumulate communication errors and degrade convergence. Another important aspect is that we analyze a convergence upper bound of D-FL based on the bias. By minimizing the bound, the optimal number of local aggregations is identified to balance a trade-off with accumulation of communication errors in the absence of knowledge of the channels. With this knowledge, the impact of communication errors can be alleviated, allowing the convergence upper bound to decrease throughout aggregations. Experiments validate our convergence analysis and also identify the optimal number of local aggregations on two widely considered image classification tasks. It is seen that D-FL, with an optimal number of local aggregations, can outperform its potential alternatives by over 10% in training accuracy.

Dual-function-radar-communication (DFRC) is a promising candidate technology for next-generation networks. By integrating hybrid analog-digital (HAD) beamforming into a multi-user millimeter-wave (mmWave) DFRC system, we design a new reconfigurable subarray (RS) architecture and jointly optimize the HAD beamforming to maximize the communication sum-rate and ensure a prescribed signal-to-clutter-plus-noise ratio for radar sensing. Considering the non-convexity of this problem arising from multiplicative coupling of the analog and digital beamforming, we convert the sum-rate maximization into an equivalent weighted mean-square error minimization and apply penalty dual decomposition to decouple the analog and digital beamforming. Specifically, a second-order cone program is first constructed to optimize the fully digital counterpart of the HAD beamforming. Then, the sparsity of the RS architecture is exploited to obtain a low-complexity solution for the HAD beamforming. The convergence and complexity analyses of our algorithm are carried out under the RS architecture. Simulations corroborate that, with the RS architecture, DFRC offers effective communication and sensing and improves energy efficiency by 83.4% and 114.2% with a moderate number of radio frequency chains and phase shifters, compared to the persistently- and fully-connected architectures, respectively.

Intelligent resource allocation and power control schemes are regarded as important methods to alleviate the problems caused by the sharp increase in the number of users and operating costs. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based algorithm to jointly optimize resource block (RB) allocation and power control, which aims to maximize the average spectrum efficiency (SE) of the system while meeting quality of service (QoS) constraints. In view of the fact that centralized training distributed execution retains the advantages of centralized training while reducing the amount of computation and signaling overhead, the MADRL technique can be adopted. In the proposed MADRL model, the Q function of each agent is aggregated through the value decomposition network, which strengthens the cooperation of agents and improves the convergence of the algorithm. We add a reward discount network into the original MADRL framework to adaptively adjust the attention to future rewards according to the performance of agents in the training process. Simulation experiments show that the proposed algorithm has better performance and stability than the existing alternatives.

In recent years, the Internet of Things (IoT) has been studied in the context of the Metaverse to provide users with immersive cyber-virtual experiences in mixed-reality environments. This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), high-speed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoT-inspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence. In addition, this survey describes the open issues in the IoT-inspired Metaverse, which need to be addressed to eventually achieve the convergence of physical and cyber worlds.

This paper puts forth a new, reconfigurable intelligent surface (RIS)-assisted, uplink, user-centric cell-free (UCCF) system managed with the assistance of a digital twin (DT). Specifically, we propose a novel learning framework that maximizes the sum-rate by jointly optimizing the access point and user association (AUA), power control, and RIS beamforming. This problem is challenging and has never been addressed due to its prohibitively large and complex solution space. Our framework decouples the AUA from the power control and RIS beamforming (PCRB) based on the different natures of their variables, hence reducing the solution space. A new position-adaptive binary particle swarm optimization (PABPSO) method is designed for the AUA. Two twin-delayed deep deterministic policy gradient (TD3) models with new and refined state pre-processing layers are developed for the PCRB. Another important aspect is that a DT is leveraged to train the learning framework with its replay of channel estimates stored. The AUA, power control, and RIS beamforming are only tested in the physical environment at the end of selected epochs. Simulations show that using RISs contributes to considerable increases in the sum-rate of UCCF systems, and the DT dramatically reduces overhead with marginal performance loss. The proposed framework is superior to its alternatives in terms of sum-rate and convergence stability.

We investigate the performance of massive ultra-reliable and low-latency communications (mURLLC) under massive active users, and non-uniform small-scale and shadow fading in the uplink (UL) of a next-generation multiple access (NGMA) system that integrates massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) techniques. We first derive new closed-form expressions to accurately approximate the probability density function (PDF) and cumulative distribution function (CDF) of the channel gains in MIMO systems under the$\kappa $-$\mu $shadowed fading. Then, we derive the post-processing signal-to-noise ratio (SNR) and its closed-form PDFs and CDFs in the NGMA system, under both perfect and imperfect channel state information of the$\kappa $-$\mu $shadowed fading channel. Given the post-processing SNRs and their PDFs, the general expressions are established for the error probability (EP) to analyze the mURLLC of NGMA by applying finite blocklength information theory. Corroborated by extensive simulations, our analysis reveals that with the increasing reliability requirements of the users, the relative gaps in EPs enlarge between users experiencing different fading channels, and the feasible system configurations (i.e., the transmit powers of the users, and the numbers of antennas, active users, and subcarriers) also increasingly differ between the users. The impact of different fading on mURLLC implementations cannot be overlooked, and the research of mURLLC under the$\kappa $-$\mu $shadowed fading model is indispensable. The NGMA system considered in this paper is capable of achieving mURLLC under non-uniform small-scale and shadow fading.

Wireless federated learning (WFL) undergoes a communication bottleneck in uplink, limiting the number of users that can upload their local models in each global aggregation round. This paper presents a new multi-carrier non-orthogonal multiple-access (MC-NOMA)-empowered WFL system under an adaptive learning setting of Flexible Aggregation. Since a WFL round accommodates both local model training and uploading for each user, the use of Flexible Aggregation allows the users to train different numbers of iterations per round, adapting to their channel conditions and computing resources. The key idea is to use MC-NOMA to concurrently upload the local models of the users, thereby extending the local model training times of the users and increasing participating users. A new metric, namely, Weighted Global Proportion of Trained Mini-batches (WGPTM), is analytically established to measure the convergence of the new system. Another important aspect is that we maximize the WGPTM to harness the convergence of the new system by jointly optimizing the transmit powers and subchannel bandwidths. This nonconvex problem is converted equivalently to a tractable convex problem and solved efficiently using variable substitution and Cauchy’s inequality. As corroborated experimentally using a convolutional neural network and an 18-layer residential network, the proposed MC-NOMA WFL can efficiently reduce communication delay, increase local model training times, and accelerate the convergence by over 40%, compared to its existing alternative.

This work investigates the secrecy outage performance of the uplink transmission of a radio-frequency (RF)-free-space optical (FSO) hybrid cooperative satellite-aerial-terrestrial network (SATN). Specifically, in the considered cooperative SATN, a terrestrial source (S) transmits its information to a satellite receiver (D) via the help of a cache-enabled aerial relay (R) terminal with the most popular content caching scheme, while a group of eavesdropping aerial terminals (Eves) trying to overhear the transmitted confidential information. Moreover, RF and FSO transmissions are employed over S-R and R-D links, respectively. Considering the randomness of R, D, and Eves, and employing a stochastic geometry framework, the secrecy outage performance of the cooperative uplink transmission in the considered SATN is investigated and a closed-form analytical expression for the end-to-end secrecy outage probability is derived. Finally, Monte-Carlo simulations are shown to verify the accuracy of our analysis.

This paper presents new learning-based, energy-delay-aware power control strategies for the uplink of dynamic cell-free (CF) massive multiple-input multiple-output (MIMO) networks. We first propose a new algorithm to adaptively select an appropriate set of multi-antenna access points (APs) to serve each user at any time instant. A multi-level hidden Markov model (HMM) is constructed to predict the trajectory of a user and forecast the changing set of serving APs for the user over time. Given the selected, time-changing sets of serving APs, we also formulate a new multi-objective power control problem to minimize a weighted sum of the energy and delay costs of the users. By employing Bernoulli bandit learning (BBL) and Gaussian bandit learning (GBL), two new energy-delay-aware power control strategies are developed to adapt unpredictable channel dynamics, minimize the energy-delay costs online, and remain effective over a long time even when sharp changes occur in the channels. Numerical results demonstrate that the new predictive selection of serving APs and the new learning-based energy-delay-aware power control policies can significantly improve the reliability of uplink transmissions in dynamic CF massive MIMO networks, as compared to state-of-the-art static oracle solutions.

This paper proposes an effective and novel multi-agent deep reinforcement learning (MADRL)-based method for solving the joint virtual network function (VNF) placement and routing (P&R), where multiple service requests with differentiated demands are delivered at the same time. The differentiated demands of the service requests are reflected by their delay- and cost-sensitive factors. We first construct a VNF P&R problem to jointly minimize a weighted sum of service delay and resource consumption cost, which is NP-complete. Then, the joint VNF P&R problem is decoupled into two iterative subtasks: placement subtask and routing subtask. Each subtask consists of multiple concurrent parallel sequential decision processes. By invoking the deep deterministic policy gradient method and multi-agent technique, an MADRL-P&R framework is designed to perform the two subtasks. The newjoint reward and internal rewardsmechanism is proposed to match the goals and constraints of the placement and routing subtasks. We also propose the parameter migration-based model-retraining method to deal with changing network topologies. Corroborated by experiments, the proposed MADRL-P&R framework is superior to its alternatives in terms of service cost and delay, and offers higher flexibility for personalized service demands. The parameter migration-based model-retraining method can efficiently accelerate convergence under moderate network topology changes.

The application of intelligent reflecting surface (IRS) depends on the knowledge of channel state information (CSI), and has been hindered by the heavy overhead of channel training, estimation, and feedback in fast-changing channels. This paper presents a new two-timescale beamforming approach to maximizing the average achievable rate (AAR) of IRS-assisted MIMO systems, where the IRS is configured relatively infrequently based on statistical CSI (S-CSI) and the base station precoder and power allocation are updated frequently based on quickly outdated instantaneous CSI (I-CSI). The key idea is that we first reveal the optimal small-timescale power allocation based on outdated I-CSI yields a water-filling structure. Given the optimal power allocation, a new mini-batch sampling (mbs)-based particle swarm optimization (PSO) algorithm is developed to optimize the large-timescale IRS configuration with reduced channel samples. Another important aspect is that we develop a model-driven PSO algorithm to optimize the IRS configuration, which maximizes a lower bound of the AAR by only using the S-CSI and eliminates the need of channel samples. The model-driven PSO serves as a dependable lower bound for the mbs-PSO. Simulations corroborate the superiority of the new two-timescale beamforming strategy to its alternatives in terms of the AAR and efficiency, with the benefits of the IRS demonstrated.

This paper investigates a new downlink nonorthogonal multiple access (NOMA) system, where a multiantenna unmanned aerial vehicle (UAV) is powered by wireless power transfer (WPT) and serves as the base station for multiple pairs of ground users (GUs) running NOMA in each pair. An energy efficiency (EE) maximization problem is formulated to jointly optimize the WPT time and the placement for the UAV, and the allocation of the UAV’s transmit power between different NOMA user pairs and within each pair. To efficiently solve this nonconvex problem, we decompose the problem into three subproblems using block coordinate descent. For the subproblem of intra-pair power allocation within each NOMA user pair, we construct a supermodular game with confirmed convergence to a Nash equilibrium. Given the intra-pair power allocation, successive convex approximation is applied to convexify and solve the subproblem of WPT time allocation and inter-pair power allocation between the user pairs. Finally, we solve the subproblem of UAV placement by using the Lagrange multiplier method. Simulations show that our approach can substantially outperform its alternatives that do not use NOMA and WPT techniques or that do not optimize the UAV location.

As a promising 5G cutting-edge communication technology, mobile edge computing (MEC) can improve the quality of computing by offloading computation-intensive tasks to base stations (BSs) or adjacent users through device-to-device (D2D) links. When mobile users offload tasks through the D2D links, using this technology, both transmission energy consumption and transmission delay can be reduced. In this paper, we propose a D2D-assisted MEC system, which can process a large number of independent computing tasks to reduce energy consumption and delay. Different from conventional binary computing offloading, we use partial computing offloading in this paper, which can reduce delay and energy consumption. In particular, we first propose a Knapsack problem-based pre-allocation (PA) algorithm to reduce the amount of offloaded tasks with known transmission power. By using this algorithm, we can obtain the offloading decisions of the tasks. We also apply the variable substitution technique to recast the constructed non-convex problem to be convex, so that the optimal transmission power can be obtained. We finally propose a new alternate optimization algorithm to alternately optimize tasks offloading decisions and transmission power. Simulations show that the proposed algorithm can significantly reduce the delay and energy consumption compared with the traditional offloading schemes.

This paper investigates the energy-efficient resource allocation problem in massive multiple input and multiple output (MIMO) non-orthogonal multiple access (NOMA) networks with practical non-linear energy harvesting (NEH). In the considered system, a multi-antenna base station (BS) transfers power to the Internet-of-Things (IoT) devices via energy beamforming in the downlink, followed by the IoT devices sending their data simultaneously in the uplink by consuming the harvested energy. A time division protocol is designed to adequately allocation the energy harvesting (EH) time and wireless information transmission time. To improve the energy efficiency (EE), we propose a joint power, time and antenna selection allocation scheme under the NEH model. An EE maximization problem is formulated to effectively determine the optimal resource allocation strategies. As the formulated problem is non-trivial, a non-linear fraction programming method is applied to convert the problem into a convex optimization problem, and then solve it by Lagrange dual decomposition approach. Simulation results demonstrate the effectiveness of the proposed solution.

Reconfigurable intelligent surface (RIS) is highly promising to be applied to enable media-based modulation (MBM) transmissions, which is expected to greatly reduce the hardware cost at the transmitter. In this paper, we propose the two MBM schemes for RIS communications, which are non-differential and differential schemes. In the non-differential scheme, we utilize the phase variations of the RIS to achieve a higher transmission rate compared to the conventional MBM schemes with radio frequency (RF) mirrors. Particularly, in the differential scheme, we decompose the space time block (STB) matrix into a phase state space (PSS) matrix and a selection-permutation (SP) matrix for enhancing the flexibility of differential modulation. The proposed treatment breaks through two inherent design limitations in the traditional differential space time block code (DSTBC). Moreover, the upper bound on the average symbol error rate (SER) of both schemes are derived. Finally, the theory and simulation results show that our proposed schemes can achieve higher transmission rates than the benchmark schemes.

Abstract This paper presents a novel variational Bayesian learning‐based channel estimation scheme for hybrid pre‐coding‐employed wideband multiuser millimetre wave multiple‐input multiple‐output communication systems. We first propose a frequency variational Bayesian algorithm, which leverages common sparsity of different sub‐carriers in the frequency domain. The algorithm shares all the information of the support sets from the measurement matrices, significantly improving channel estimation accuracy. To enhance robustness of the frequency variational Bayesian algorithm, we develop a hierarchical Gaussian prior channel model, which employs an identify‐and‐reject strategy to deal with random outliers imposed by hardware impairments. A support selection frequency variational Bayesian channel estimation algorithm is also proposed, which adaptively selects support sets from the measurement matrices. As a result, the overall computational complexity can be reduced. Validated by the Bayesian Cramér‐Rao bound, simulation results show that, both frequency variational Bayesian and support selection‐frequency variational Bayesian algorithms can achieve higher channel estimation accuracy than existing methods. Furthermore, compared with frequency variational Bayesian, support selection‐frequency variational Bayesian requires significantly lower computational complexity, and hence, it is more practical for channel estimation applications.

Reducing cost and power consumption while maintaining high network access capability is a key physical-layer requirement of massive Internet of Things (mIoT) networks. Deploying a hybrid array is a cost- and energy-efficient way to meet the requirement, but would penalize system degree of freedom (DoF) and channel estimation accuracy. This is because signals from multiple antennas are combined by a radio frequency (RF) network of the hybrid array. This article presents a novel hybrid uniform circular cylindrical array (UCyA) for mIoT networks. We design a nested hybrid beamforming structure based on sparse array techniques and propose the corresponding channel estimation method based on the second-order channel statistics. As a result, only a small number of RF chains are required to preserve the DoF of the UCyA. We also propose a new tensor-based two-dimensional (2-D) direction-of-arrival (DoA) estimation algorithm tailored for the proposed hybrid array. The algorithm suppresses the noise components in all tensor modes and operates on the signal data model directly, hence improving estimation accuracy with an affordable computational complexity. Corroborated by a Cramér-Rao lower bound (CRLB) analysis, simulation results show that the proposed hybrid UCyA array and the DoA estimation algorithm can accurately estimate the 2-D DoAs of a large number of IoT devices.

This paper proposes to deploy multiple reconfigurable intelligent surfaces (RISs) in device-to-device (D2D)-underlaid cellular systems. The uplink sum-rate of the system is maximized by jointly optimizing the transmit powers of the users, the pairing of the cellular users (CUs) and D2D links, the receive beamforming of the base station (BS), and the configuration of the RISs, subject to the power limits and quality-of-service (QoS) of the users. To address the non-convexity of this problem, we develop a new block coordinate descent (BCD) framework which decouples the D2D-CU pairing, power allocation and receive beamforming, from the configuration of the RISs. Specifically, we derive closed-form expressions for the power allocation and receive beamforming under any D2D-CU pairing, which facilitates interpreting the D2D-CU pairing as a bipartite graph matching solved using the Hungarian algorithm. We transform the configuration of the RISs into a quadratically constrained quadratic program (QCQP) with multiple quadratic constraints. A low-complexity algorithm, named Riemannian manifold-based alternating direction method of multipliers (RM-ADMM), is developed to decompose the QCQP into simpler QCQPs with a single constraint each, and solve them efficiently in a decentralized manner. Simulations show that the proposed algorithm can significantly improve the sum-rate of the D2D-underlaid system with a reduced complexity, as compared to its alternative based on semidefinite relaxation (SDR).

Accurate channel parameter estimation is challenging for wideband millimeter-wave (mmWave) large-scale hybrid arrays, due to beam squint and much fewer radio frequency (RF) chains than antennas. This article presents a novel joint angle and delay estimation (JADE) approach for wideband mmWave fully-connected hybrid uniform cylindrical arrays. We first design a new hybrid beamformer to reduce the dimension of received signals on the horizontal plane by exploiting the convergence of the Bessel function, and to reduce the active beams in the vertical direction through preselection. The important recurrence relationship of the received signals needed for subspace-based angle and delay estimation is preserved, even with substantially fewer RF chains than antennas. Then, linear interpolation is generalized to reconstruct the received signals of the hybrid beamformer, so that the signals can be coherently combined across the whole band to suppress the beam squint. As a result, efficient subspace-based algorithm algorithms can be developed to estimate the angles and delays of multipath components. The estimated delays and angles are further matched and correctly associated with different paths in the presence of non-negligible noises, by putting forth perturbation operations. Simulations show that the proposed approach can approach the Cramér-Rao lower bound (CRLB) of the estimation with a significantly lower computational complexity than existing techniques.

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users.

Highly accurate localization is important for wire-less communications. In this paper, we propose a new tensor-based positioning method for 5G wideband mmWave massive MIMO systems. We first develop an extended multidimensional interpolation (E-MI)-based method as the preprocessing step to suppress the frequency-dependence of the array steering vectors. By using this method, the data across the whole frequency band can be processed jointly, and the high temporal resolution offered by wideband mmWave signals can be exploited. Then, we propose a parameter decoupling (PD)-based tensor multiparameter estimation algorithm. This algorithm can suppress the noises in all of temporal, spatial and frequency domains, and thus all the parameters can be precisely estimated. A simplified perturbation term (S-PT)-based method is also presented to match the estimated parameters at low complexity. Based on the quasi-optical property of mmWave signals, we propose a novel method to compute the 3D coordinates of the target. Simulation results demonstrate the effectiveness of the proposed positioning method in the end.

In 5G and beyond wireless communication systems, providing satisfactory service to users in high-mobility scenarios becomes very essential. Unstable service supply caused by frequent handover under high-mobility compromises the service experience of users. Moreover, the demanding requirements for processing delays and non-immediate information processing errors are particularly prominent. This paper proposes a novel learning-based approach to solving the dynamic multichannel access (DMCA) problem under fast time-varying channel arising from high-mobility. Specifically, we first propose the subjective experience-based quality of service, and formulate the corresponding optimization problem based on the designed access criterion. Invoking our proposed prediction-based deep deterministic policy gradient algorithm and incremental learning-based online channel prediction model, a novel DMCA scheme, which combines the recurrent neural network with deep reinforcement learning, is proposed. Corroborated by experiments built in real channel data, the performance of proposed learning-based DMCA scheme approaches that derived from the exhaustive search method when making a decision at each time-slot, and is superior to the exhaustive search method when making a decision for every few time-slots. Furthermore, our scheme significantly reduces processing delays and effectively alleviates errors caused by the non-immediacy of information acquisition and processing.

Intelligent reflecting surface (IRS) is envisioned as a promising solution for controlling radio propagation environments in future wireless systems. In this paper, we propose a distributed intelligent reflecting surface (IRS) assisted multi-user millimeter wave (mmWave) system, where IRSs are exploited to enhance the mmWave signal coverage when direct links between base station and users are unavailable. First, a joint active and passive beamforming problem is established for weighted sum-rate maximization. Then, an alternating iterative algorithm with closed-form expressions is proposed to tackle the challenging non-convex problem, thereby decoupling the active and passive beamforming variables. Moreover, we design a constraint relaxation technique to address the unit modulus constraints pertaining to the IRS. Numerical results demonstrate that the distributed IRS can potentially enhance the communication performance of existing wireless systems.

This paper investigates the energy-efficient resource allocation problem in multicell massive multiple input and multiple output (MIMO) non-orthogonal multiple access (NOMA) networks with wireless power transfer (WPT). In considered networks, Internet-of-Things (IoT) devices who have data packets to transmit in the uplink are charged by the wireless power harvested from the multi-antenna base stations (BSs) in the downlink. To adequately allocate the time of energy harvesting and data transmission, a time division protocol is proposed to divide downlink WPT and uplink wireless information transmission (WIT) time into two separate time slots. Aiming to obtain the optimal energy efficiency (EE) performance, we propose a joint power, time, subcarrier and antenna selection (AS) allocation scheme. An EE maximization problem is formulated to effectively determine the optimal resource allocation strategies. As the problem is nonconvex, we first invoke the nonlinear programming method to convert the problem into a convex optimization problem, and then solve it by taking an alternating direction method of multipliers (ADMM)-based distributed resource allocation algorithm. Simulation results demonstrate performance enhancement of the proposed algorithm over the benchmark schemes.

In this paper, we consider a user-centric massive multiple-input multiple-output (UC-MMIMO) system, wherein the optimal antenna selection (AS) is very complicated, because of the huge number of deployed antennas. Traditional AS algorithms rely heavily on full and perfect channel state information (CSI). Thus, we propose a novel AS algorithm to achieve low-complexity and less CSI reliance for UC-MMIMO. The proposed AS algorithm consists of the selection stage and the adjustment stage. In the selection stage, antennas are selected by a reinforcement learning (RL) based algorithm in which input data are the locations of users. In the adjustment stage, an adjustment mechanism is designed to further improve the performance. Numerical results show that our algorithm achieves better performance with lower complexity compared with related traditional algorithms.

Pattern division multiple access (PDMA) is a novel non-orthogonal multiple access (NOMA) solution to the problem of massive connection and higher spectral efficiency for the fifth generation (5G) wireless networks. The performance of PDMA is determined by the gain brought by the PDMA pattern and is generally related to the inner product of the pattern. This paper first sets up the system model of uplink (UL) and downlink (DL) PDMA, and formulates the pattern matrix design principles for enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable and low latency communications (URLLC). In this paper, the design criteria and examples of 5G application scenarios are given, and the performance of different PDMA pattern matrices is compared and analyzed by link-level simulation. The effectiveness of the proposed PDMA pattern matrix design principles is verified by Monte Carlo simulation.

To enable ultrareliable and low-latency communications (URLLCs) in the Internet of Things (IoT), a sparse-code multiple-access (SCMA)-enhanced full-duplex (FD) scheme (FD-SCMA) is proposed in this article. FD-SCMA can support short-packet transmissions of several SCMA users in the uplink (UL) and downlink (DL) simultaneously by an FD next generation node B (gNB). First, the gNB and UL users can generate and superpose signals according to the preconfigured SCMA codebooks, and simultaneously transmit the signals via occupied subcarriers in a joint SCMA pattern. The receivers at the gNB and DL users can demodulate and decode the signals with multiuser detection (MUD). With the imperfect self-interference suppression (SIS) of FD considered, the effective signal-to-noise ratio (SNR) of FD-SCMA at the gNB and DL users is formulated. The error probability of FD-SCMA in the UL and DL is also derived under a given transmission latency constraint of short-packet transmissions. In the stationary flat-fading channel, it is proved that FD-SCMA can achieve better reliability than the existing FD and SCMA schemes. In the time-invariant frequency-selective fading channel, the upper bounds for error probability of the UL and DL users in FD-SCMA are derived, respectively. Through the theoretical calculation and Monte Carlo simulation, it is verified that the superiority of FD-SCMA in supporting ultrareliable and low-latency short-packet transmissions in IoT.

It is challenging to satisfy the critical requirements of ultrareliable and low-latency communications (URLLCs) in the Internet of Things (IoT) under severe channel fading. The emerging massive multiuser multiple-input-multiple-output (MU-MIMO) concept is applied in IoT networks under shadow fading, enabling URLLC with pilot-assisted channel estimation (PACE) and zero-forcing (ZF) detection. Assuming users are uniformly and randomly deployed under log-normal shadow fading, the probability density function (pdf) of postprocessing signal-to-noise ratios (SNRs) is derived for the uplink (UL) of massive MU-MIMO with perfect channel state information (CSI) and imperfect CSI obtained by PACE. Then, finite blocklength (FBL) information theory is utilized to derive the error probability of accessing users with a given latency, thereby evaluating the reliability of massive MU-MIMO for short-packet transmissions. Further, the length of pilots to minimize the error probability can be decided by the golden section search method (GSSM), which can converge rapidly. Numerical results verify that massive MU-MIMO can support a large number of UL URLLC users even when users are randomly deployed under shadow fading.

Channel estimation is challenging for hybrid millimeter wave (mmWave) large-scale antenna arrays which are promising in 5G/B5G applications. The challenges are associated with angular resolution losses resulting from hybrid front-ends, beam squinting, and susceptibility to the receiver noises. Based on tensor signal processing, this paper presents a novel multi-dimensional approach to channel parameter estimation with large-scale mmWave hybrid uniform circular cylindrical arrays (UCyAs) which are compact in size and immune to mutual coupling but known to suffer from infinite-dimensional array responses and intractability. We design a new resolution-preserving hybrid beamformer and a low-complexity beam squinting suppression method, and reveal the existence of shift-invariance relations in the tensor models of received array signals at the UCyA. Exploiting these relations, we propose a new tensor-based subspace estimation algorithm to suppress the receiver noises in all dimensions (time, frequency, and space). The algorithm can accurately estimate the channel parameters from both coherent and incoherent signals. Corroborated by the Cramér-Rao lower bound (CRLB), simulation results show that the proposed algorithm is able to achieve substantially higher estimation accuracy than existing matrix-based techniques, with a comparable computational complexity.

This paper proposes a novel 3D localization method for wideband mmWave massive MIMO systems. A high dimensional linear interpolation (HDLI)-based preprocessing is first proposed to transform the frequency-associated dynamical array response vectors into the common counterparts at the reference frequency. Through this method, the received data in all frequency bands can be processed jointly, and thus the high temporal resolution provided by wideband mmWave systems can be fully exploited for position estimation. To reduce the computational complexity in the process of the parameter estimation, we then present a wideband beamspace (WBS)-based parameter estimation algorithm to estimate the angle and delay in the low-dimensional beamspace. By exploiting the quasi- optical propagation at the mmWave frequencies, a novel positioning scheme is also designed to determine the 3D location of the target. According to our analysis and simulation results, the proposed method is capable of achieving significantly reduced computational complexity, while maintaining high localization accuracy.

In this paper, we study a two-way relay network (TWRN), in which eavesdroppers' channel state information is imperfect, and all the eavesdroppers collude to form joint receive beamforming for enhanced receptions. To ensure the robustness of the TWRN, we design the transmit beamforming and establish two secure beamforming design formulations, which combines the physical layer network coding with artificial noise technique at the relay. One secure beamforming design formulation is to minimize the total transmit power in the TWRN, and the other one is to guarantee a maximum secrecy sum rate (SSR) in the worst-case. With the help of approximation techniques, the former optimization problem is converted into a convex problem, whose semidefinite relaxation solution is rank-one. However, due to the maximum available power limit of the system, the power optimization problem may be infeasible. To overcome this problem, the SSR optimization formulation is presented, and the optimization problem is decoupled into two subproblems that can be transformed into the convex forms. Finally, it is verified by the numerical results that the proposed schemes are effective.

The optimal economical caching strategy in cache-enabled heterogeneous networks is studied in this paper. In the meanwhile, the multimedia video services with personalized viewing qualities are delivered to mobile subscribers. We analytically derive the successful transmission probability and ergodic service rate, and then the closed-form EConomical Efficiency (ECE) is acquired. In order to improve the ECE performance, we formulate the ECE optimization problem. With equal cache size equipped at each serving small-cell base station (SBS), the layer caching indicator is determined. This problem is an integer programming problem, and is NP-hard in essence. After the l0-norm approximation, the original maximization problem is relaxed to be convex. Next, based on the optimal solution derived from the relaxed problem, we propose a heuristic algorithm based on the greedy strategy to acquire the layer caching indicators. Numerical results verify the correctness of our theoretical analysis. We also find that the proposed caching scheme is superior to the most popular layer placement strategy in terms of the ECE performance.

In this paper, a frequency-distributed variational Bayesian (F-DVB) channel estimation algorithm is proposed for wideband multiuser millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, where hybrid precoding architectures are adopted and frequency selective fading channels are assumed. First, a distributed compressed sensing-based method is employed by leveraging the joint sparsity of different subcarriers in the frequency domain, reducing the required pilot overhead significantly. Next, a hierarchical channel model, which adopts an identify-and-reject strategy to deal with hardware impairments, is designed to enhance the robustness of the proposed algorithm. Finally, the channel information is estimated by a modified variational Bayesian method, which improves the channel estimation accuracy dramatically. Simulation results verify that the proposed algorithm outperforms the state-of-the-art channel estimation strategies at low SNR and pilot overhead.

Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations. Finding those subtle traits that fully characterize the object is not straightforward. In this paper, we present a novel random part localization model, which first extracts the foreground object using the saliency map, and then localizes the discriminative parts through a set of potential regions in a random way based on their contribution to classification. We train three convolutional neural networks to capture the features that belong to different levels and average their classification results as our final prediction score. Experiments show that our approach achieves competitive performance compared with state-of-the-art methods on three publicly available fine-grained recognition datasets (CUB200-2011, Stanford Cars and FGVC-Aircraft).

In this paper, we investigate a power control of uplink connection in Heterogeneous Cloud-Radio Access Network (H-CRAN). Our main objective is to optimize Online Energy-Efficiency (OEE) from the users' perspective. Firstly, a realistic and new model of the Long-Term Evolution (LTE) user device's power consumption is proposed. This model includes the power used for operating modes and signal processing of mobile devices during the uplink data transmission. Secondly, the optimization problem is formulated by maximizing the OEE function subject to each user's quality-of-service (QoS) and a power constraint. Then, we allocate online power for the OEE by jointly optimizing the Macro Base Station (MBS) users and small cell Radio Remote Heads (RRHs) users. Furthermore, the Online Frank-Wolfe (OFW) method is adopted to obtain the optimal solution for the formulated OEE optimization problem. The regret metric is derived to describe the performance of the OFW. Finally, numerical results validate the accuracy of the proposed power model and demonstrate the superiority of the proposed method compared to the benchmark.

To cope with the rapidly growing demand for data traffic, heterogeneous network (HetNet), including different types of base stations (BSs), is advocated as a promising network architecture. Considering the different quality of service (QoS) requirements of the Internet of things (IoT) users and ordinary users, we propose a three-tier HetNet model with non-equal bandwidth. To reduce the power consumption caused by the dense deployment of BSs, we propose a novel reinforcement-learning (RL) based dynamic pico-cell base station (PBS) operation scheme. The proposed RL scheme is based on the asynchronous advantage actor-critic (A3C) algorithm, and can dynamically determine the on/off state of each PBS, aiming to achieve the minimal total power of the macro-cell without any prior information. Simulation results show that the proposed algorithm can achieve 92.1% performance gain of the optimal level in terms of the power consumption saving while needs less training time and lower running resource requirements compared to the benchmarks.

Physical layer security is a crucial technology to achieve secure information transmission. However, emerging technologies, such as machine learning, impose significant challenges on existing security systems. In this paper, we propose a new relay system to achieve physical layer secure transmission and construct a deep learning model on the relay to design secure beamforming vector. This design is able to adapt to the current channel of the legitimate user and resist the eavesdropping from eavesdroppers. The model takes the channel’s statistical characteristic into account and it can learn the channel state information (CSI) to design secure beamforming in order to against eavesdropping effectively. The performance of bit error rate (BER) is close to the Shannon limit when the receiver demodulates the received signals, and we also ensure that the eavesdroppers cannot demodulate the transmitted signals. In addition, we design a power allocation algorithm, which considers the eavesdropper’s CSI on the relay. Simulation results show that the proposed model is able to reach the pre-defined BER requirements. The model can maximize the system security rate by optimizing power allocation at the relay.

In this paper, a non-orthogonal multiple access (NOMA) based unmanned aerial vehicle (UAV) assisted communication network is investigated without the presence of base station to provide efficient wireless connections. As NOMA requires successive interference cancellation (SIC) technology at the receiver, the imperfect SIC with error propagation is considered to achieve the practical target. For the sake of evaluating the performance of the considered scenario, the analytical expressions of the outage probability are derived and the traditional orthogonal multiple access (OMA) technology is applied as the benchmark. Furthermore, in order to minimize the effect of error propagation, the range of the ideal target rate is derived. Under the ideal target rate, the imperfect SIC has no effect on the outage performance. Numerical results show that the performance of the NOMA-based UAV-assisted communication network with imperfect SIC is close to the performance of the network with perfect SIC under the non-ideal target data rate with small error propagation factor, and overlap with each other under the case of ideal target rate.

We consider a three-tier heterogeneous network (HetNet), consisting of macro-cell base stations (MBSs), small-cell base stations (SBSs) and pico-cell base stations (PBSs), serving both Internet of things (IoT) devices with low data rate requirements and general user equipments (UEs). In order to reduce the power consumption caused by dense deployment of base stations (BSs), we propose a novel machine-learning (ML) based dynamic PBS operation scheme. In contrast to conventional PBS on/off operation scheme, the proposed ML-based dynamic PBS operation scheme can dynamically change the PBS on/off status according to UEs' real-time location so as to reduce the total power of BSs. Specifically, we use the convolution neural network (CNN) algorithm to solve the proposed optimization problem. Simulation results show that CNN algorithm can achieve 68% performance of the optimum level in terms of power consumption saving while the calculate complexity is O(1).

This paper deals with the problem of the dynamic multichannel access (MCA) based on the LTE-WLAN aggregation in dynamic heterogeneous networks. To ensure the users' personalized requirements, the minimization after satisfied (MAS) criterion is firstly proposed. Then, a prediction-based deep deterministic policy gradient (P-DDPG) algorithm is presented, achieving continuous processing. Finally, a new reward function is designed according to the MAS criteria. Meanwhile, the virtual user approach and the base station-centric strategy are proposed to design the action spaces so that the number of actions is independent of the number of users. Simulation results demonstrate the effectiveness of the P-DDPG algorithm for solving the dynamic MCA problem and corroborate that the proposed MAS criterion is superior to the existing criteria.

A secure downlink transmission system which is exposed to multiple eavesdroppers and is appropriate for Internet of Things (IoT) applications is considered. A worst case scenario is assumed, in the sense that, in order to enhance their interception ability all eavesdroppers are located close to each other, near the controller and collude to form joint receive beamforming. For such a system, a novel cooperative nonorthogonal multiple access (NOMA) secure transmission scheme for which an IoT device with a stronger channel condition acts as an energy harvesting relay in order to assist a second IoT device operating under weaker channel conditions, is proposed and its performance is analyzed and evaluated. A secrecy sum rate (SSR) maximization problem is formulated and solved under three constraints: 1) transmit power; 2) successive interference cancellation; and 3) quality of service. By considering both passive and active eavesdroppers scenarios, two optimization schemes are proposed to improve the overall system SSR. On the one hand, for the passive eavesdropper scenario, an artificial noise-aided secure beamforming scheme is proposed. Since this optimization problem is nonconvex, instead of using traditional but highly complex, brute-force 2-D search, it is conveniently transformed into a convex one by using an epigraph reformulation. On the other hand, for the active multiantennas eavesdroppers’ scenario, the orthogonal-projection-based beamforming scheme is considered, and by employing the successive convex approximation method, a suboptimal solution is proposed. Furthermore, since for single antenna transmission the orthogonal-projection-based scheme may not be applicable a simple power control scheme is proposed. Various performance evaluation results obtained by means of computer simulations have verified that the proposed schemes outperform other benchmark schemes in terms of SSR performance.

With the emergence of the mission-critical Internet of Things applications, ultra-reliable low-latency communications are attracting a lot of attentions. Non-orthogonal multiple access (NOMA) with multiple-input multiple-output (MIMO) is one of the promising candidates to enhance connectivity, reliability, and latency performance of the emerging applications. In this paper, we derive a closed-form upper bound for the delay target violation probability in the downlink MIMO-NOMA, by applying stochastic network calculus to the Mellin transforms of service processes. A key contribution is that we prove that the infinite-length Mellin transforms resulting from the non-negligible interferences of NOMA are Cauchy convergent and can be asymptotically approached by a finite truncated binomial series in the closed form. By exploiting the asymptotically accurate truncated binomial series, another important contribution is that we identify the critical condition for the optimal power allocation of MIMO-NOMA to achieve consistent latency and reliability between the receivers. The condition is employed to minimize the total transmit power, given a latency and reliability requirement of the receivers. It is also used to prove that the minimal total transmit power needs to change linearly with the path losses, to maintain latency and reliability at the receivers. This enables the power allocation for mobile MIMO-NOMA receivers to be effectively tracked. The extensive simulations corroborate the accuracy and effectiveness of the proposed model and the identified critical condition.

We develop the optimal economical caching schemes in cache-enabled heterogeneous networks, while delivering multimedia video services with personalized viewing qualities to mobile users. By applying scalable video coding (SVC), each video file to be requested is divided into one base layer (BL) and several enhancement layers (ELs). In order to assign different transmission tasks, the serving small-cell base stations (SBSs) are grouped into $K$ clusters. The SBSs are able to cache and cooperatively transmit BL and EL contents to the user. We analytically derive expressions for successful transmission probability and ergodic service rate, and then the closed-form expression for EConomical Efficiency (ECE) is obtained. In order to enhance the ECE performance, we formulate the ECE optimization problems for two cases. In the first case, with equal cache size equipped at each SBS, the layer caching indicator is determined. Since this problem is NP-hard, after the l0-norm approximation, the discrete optimization variables are relaxed to be continuous, and this relaxed problem is convex. Next, based on the optimal solution derived from the relaxed problem, we devise a greedy-strategy based heuristic algorithm to achieve the near-optimal layer caching indicators. In the second case, the cache size for each SBS, the layer size, and the layer caching indicator are jointly optimized. This problem is a mixed integer programming problem, which is more challenging. To effectively solve this problem, the original ECE maximization problem is divided into two subproblems. These two subproblems are iteratively solved until the original optimization problem is convergent. Numerical results verify the correctness of the theoretical derivations. Additionally, compared to the most popular layer placement strategy, the performance superiority of the proposed SVC-based caching schemes is testified.

In this paper, we propose a high energy efficiency transmission scheme in multi-beam MIMO satellite systems. Satellite is regarded as a two-way decode-and- forward (DF) relay, where multiple pairs of users exchange information within pair. Zero-forcing transceivers are employed at the satellite. The challenge is that of deriving an accurate yet tractable expression of the system-level energy efficiency (EE) to be used as our objective function. To tackle this challenge, firstly, a closed-form expression of the EE is derived under the assumption of perfect satellite channel. Secondly, based on this analytical expression, we formulate a resource allocation optimization problem for the EE maximization by jointly optimizing satellite power and users power, subject to limited transmit power and minimum quality-of-service (QoS) constraints. Finally, the successive convex approximation technique is invoked to transform the original optimization problem into a concave fractional programming problem, which is then efficiently solved by the existed methods. Simulation results demonstrate the effectiveness of the proposed algorithms.

In this paper, we propose a fast channel estimation algorithm for wideband millimeter wave (mmWave) massive MIMO systems, where the hybrid precoding architectures are adopted. Stimulated by the joint sparsity of different subcarriers, a distributed compressed sensing-based strategy is presented to reduce the required pilot overhead. Based on a hierarchical channel model, a fast sparse Bayesian learning method, which can drastically release the relaxed evidence low bound, is designed to accelerate the convergence rate. Simulation results verify that the proposed algorithm is capable of achieving substantially higher estimation accuracy and convergence rate as compared to other existing Bayesian channel estimation strategies.

This paper investigates the artificial noise (AN)-aided robust secure beamforming design for multiple-input multiple-output (MIMO) two-way relaying (TWR) systems based on physical layer network coding (PNC). In terms of signal-to-interference-and-noise ratio (SINR), we propose two robust beamforming designs to optimize worst-case secrecy sum rate in the presence of an eavesdropper, where the eavesdropper's channel state information (ECSI) is imperfect. In low SINR regime, we give a robust joint beamforming design. Since the optimization problem is non-convex, we use a zero-forcing (ZF) constraint on AN beamforming, and after approximating the objective function, the non-convex problem is formulated into a semidefinite programming (SDP). On the other hand, in high SINR regime, we provide a quality-of-service (QoS)-based robust beamforming design, in which the optimal solution can be efficiently obtained by employing an iterative algorithm based on Taylor expansion and semidefinite relaxation (SDR) techniques. Numerical results show the efficiency of the proposed schemes.

Mobile edge computing (MEC) has the potential to enable computation-intensive applications in 5G networks. MEC can extend the computational capacity at the edge of wireless networks by migrating the computation-intensive tasks to the MEC server. In this paper, we consider a multi-user MEC system, where multiple user equipments (UEs) can perform computation offloading via wireless channels to an MEC server. We formulate the sum cost of delay and energy consumptions for all UEs as our optimization objective. In order to minimize the sum cost of the considered MEC system, we jointly optimize the offloading decision and computational resource allocation. However, it is challenging to obtain an optimal policy in such a dynamic system. Besides immediate reward, Reinforcement Learning (RL) also takes a long-term goal into consideration, which is very important to a time-variant dynamic systems, such as our considered multi-user wireless MEC system. To this end, we propose RL-based optimization framework to tackle the resource allocation in wireless MEC. Specifically, the Q-learning based and Deep Reinforcement Learning (DRL) based schemes are proposed, respectively. Simulation results show that the proposed scheme achieves significant reduction on the sum cost compared to other baselines.

To meet the requirements of high energy efficiency (EE) and large system capacity for the fifth-generation Internet of Things (IoT), the use of massive multiple-input multiple-output technology has been launched in the massive IoT (mIoT) network, where a large number of devices are connected and scheduled simultaneously. This paper considers the energy-efficient design of a multipair decode-and-forward relay-based IoT network, in which multiple sources simultaneously transmit their information to the corresponding destinations via a relay equipped with a large array. In order to obtain an accurate yet tractable expression of the EE, first, a closed-form expression of the EE is derived under an idealized simplifying assumption, in which the location of each device is known by the network. Then, an exact integral-based expression of the EE is derived under the assumption that the devices are randomly scattered following a uniform distribution and transmit power of the relay is equally shared among the destination devices. Furthermore, a simple yet efficient lower bound of the EE is obtained. Based on this, finally, a low-complexity energy-efficient resource allocation strategy of the mIoT network is proposed under the specific quality-of-service constraint. The proposed strategy determines the near-optimal number of relay antennas, the near-optimal transmit power at the relay, and near-optimal density of active mIoT device pairs in a given coverage area. Numerical results demonstrate the accuracy of the performance analysis and the efficiency of the proposed algorithms.

Massive connectivity and low latency are two important challenges for the Internet of Things (IoT) to achieve the quality of service provisions required by the numerous devices it is designed to service. Motivated by these challenges, in this paper we introduce a new millimeter-wave nonorthogonal multiple access (mmWave-NOMA) transmission scheme designed for cellular machine-to-machine (M2M) communication systems for IoT applications. It consists of one base station (BS) and numerous multiple machine type communication (MTC) devices operating in a cellular communication environment. We consider its down-link performance and assume that multiple MTC devices share the same communication resources offered by the proposed mmWave-NOMA transmission scheme, which can support massive connectivity. For this system, a novel MTC pairing scheme is introduced the design of which is based upon the distance between the BS and the MTC devices aiming at reducing the system overall overhead for massive connectivity and latency. In particular, we consider three different MTC device pairing schemes, namely: 1) random near and the random far MTC devices; 2) nearest near and the nearest far MTC devices (NNNF); and 3) nearest near and the farthest far MTC device. For all three pairing schemes, their performance is analyzed by deriving closed-form expressions of the outage probability and the sum rate. Furthermore, performance comparison studies of the three MTC device pairing schemes have been carried out. The validity of the analytical approach has been verified by means of extensive computer simulations. The obtained performance evaluation results have demonstrated that the proposed cellular M2M communication system employing the mmWave-NOMA transmission scheme improves outage probability as compared to equivalent systems using mmWave with orthogonal multiple access schemes.

We study the detection of misbehavior in a Gaussian relay system, where the source transmits information to the destination with the assistance of an amplify-and-forward relay node subject to unreliable channel state information (CSI). The relay node may be potentially malicious and corrupt the network by forwarding garbled information. In this situation, misleading feedback may take place, since reliable CSI is unavailable at the source and/or the destination. By classifying the action of the relay as detectable or undetectable, we propose a novel approach that is capable of coping with any malicious attack detected and continuing to work effectively in the presence of unreliable CSI. We demonstrate that the detectable class of attacks can be successfully detected with a high probability. Meanwhile, the undetectable class of attacks does not affect the performance improvements that are achievable by cooperative diversity, even though such an attack may fool the proposed detection approach. We also extend the method to deal with the case in which there is no direct link between the source and the destination. The effectiveness of the proposed approach has been validated by numerical results.

By suppressing repeated content deliveries, wireless caching has the potential to substantially improve the energy efficiency (EE) of the fifth-generation communication networks. In this paper, we propose two novel energy-efficient caching schemes in heterogeneous networks, namely, scalable video coding (SVC)-based fractional caching and SVC-based random caching, which can provide on-demand video services with different perceptual qualities. We derive the expressions for successful transmission probabilities and ergodic service rates. Based on the derivations and the established power consumption models, the EE maximization problems are formulated for the two proposed caching schemes. By taking logarithmic approximations of the l0-norm, the problems are efficiently solved by the standard gradient projection method. Numerical results validate the theoretical analysis and demonstrate the superiority of our proposed caching schemes, compared to three benchmark strategies.

In this paper, a novel three-dimensional (3-D) indoor positioning scheme is proposed for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Its operation is based upon a hybrid received signal strength and angle of arrival (RSS-AoA) positioning scheme, which employs only a single access point equipped with a large-scale uniform cylindrical array. To reduce the high computational complexity imposed by the large number of antennas used in mmWave massive MIMO (M3-MIMO) systems, we firstly propose a novel channel compression method. By proper quantization and selection of the received mmWave signals, which exhibit quasioptical and sparse multipath characteristics, the channel compression method reduces the dimension of the received signal space while maintaining the accuracy of the position estimation. Then, we propose a beamspace transformation approach to transform signal vectors in the element space to the beamspace, and thus the computational complexity of the angle estimation is significantly reduced. Finally, a novel hybrid RSS-AoA positioning scheme is designed for the computations of the 3-D coordinates of the target mobile terminal. Simulation results have shown that the proposed indoor positioning scheme is capable of achieving high accuracy as well as significantly lower computational complexity as compared to other previously known indoor positioning techniques.

Non-orthogonal multiple access (NOMA) has been investigated recently as a candidate radio access technology for the fifth generation (5G) networks due to its high spectrum efficiency (SE). As green radio which focuses on energy efficiency (EE) becomes an inevitable trend, energy efficient design is becoming more and more important. In this paper, we focus on energy efficient resource allocation problem in multi-user downlink NOMA system with the aim to optimize subchannel assignment and power allocation to maximize the system EE. We propose a novel low-complexity suboptimal subchannel assignment algorithm and obtain the optimal power allocation coefficients among subchannel multiplexed users. To further improve the system EE, unequal power allocation across subchannels (UPAAS) scheme including an optimal solution and a suboptimal Dinkelbach-like algorithm is studied. Simulation results show the effectiveness of our proposed resource allocation algorithms.

This paper deals with the detection of malicious attacks in a Gaussian two-hop relay network, in which the source transmits information to the destination with the assistance of an amplify and forward (AF) relay node with unreliable channel state information (CSI). We consider the case where the potentially malicious relay node attempts to corrupt the network by malicious forwarding. In addition, dishonest feedback results in reliable CSI is not available at the source and/or destination. By modeling the relay behavior with two kinds of operations, namely detectable and undetectable operations, we propose a novel detection method which can deal with any detectable malicious attacks and works effectively in the presence of unreliable CSI. For the detectable operation, it is shown that the malicious attacks can be, with high probability, successfully detected. Furthermore, our research has shown that, although for the undetectable operation each attack can fool the proposed detection method, these undetectable attacks do not affect the improvements cooperative diversity. The effectiveness of our method has been validated by numerical results obtained by means of computer simulations.

In this paper, we investigate the cache-enabled small cell networks to provide on-demand video services with differential perceptual qualities, i.e., standard definition video (SDV) and high definition video (HDV). As the extension technology of advanced video coding/H.264, scalable video coding is adopted in the considered networks and videos to be transmitted are divided into a base layer (BL) and N enhancement layers (ELs). In our proposed caching protocol, the n-th small cell base station (SBS) caches BLs and the n-th EL of the most popular videos. Depending on the distances between the typical user and SBSs in the observed cluster, the closest SBS is regarded as the serving node (SN) and the others are cooperative nodes (CNs). When SDV is required, the SN will transmit BL of the required video file to the typical user, while SN and CNs can cooperatively transmit BL and ELs to provide superior video quality if HDV is required. Based on the proposed caching and transmission protocol, we derive the expressions of the key performance indicators, i.e., local serving probability, ergodic service rate and service delay. Numerical results validate the theoretical analysis and show the superiority of our proposed scheme compared to the benchmarks.

In this paper we consider a two-tier heterogeneous network (HetNet) where pico base stations (BSs) can harvest energy from macro BSs. Three cases of special interests are investigated, i.e., pico BSs are deployed 1) without battery and power grid, 2) with power grid, and 3) with battery. In particular, a practical dual-slope path loss model is employed to facilitate the performance analysis. By means of stochastic geometry and Gamma second order moment matching, a compact expression of the distribution of harvested energy is derived. Then the network's energy efficiency (EE) is introduced and optimized with carefully designed system parameters. Finally, an important conclusion is obtained as follows: only when the intensity of pico BSs is high can HetNets with energy harvesting improve the network's EE compared with the conventional HetNets without energy harvesting.

A noncoherent multiuser transmission scheme is proposed for massive multiple-input multiple-output (M-MIMO) systems without explicit channel estimation. In particular, each user uses differential PSK modulation and the receiver employs differential detection. First, we propose a simple user selection scheme to optimize the distributions of power space profile (PSP) of individual users which alleviates the overlap of PSPs. Then, each output stream of the weighing filter is fed into a noncoherent successive interference cancellation (N-SIC) processor, and finally into a soft-input soft-output (SISO) multiple-symbol differential detector(MSDD). Employing the autocorrelation receiver (AcR) and the belief propagation(BP) message passing algorithm, the proposed SISO-MSDD framework can be easily integrated with advanced channel coding. The proposed scheme bears the potential to solve the high channel estimation overhead for conventional coherent M-MIMO systems. Simulation results show that the BER performance can be significantly improved within a few iterations of the proposed scheme.

In this paper, the carrier aggregation (CA) is applied to Heterogeneous Networks (HetNets) consisting of a macro base station (MBS) and low-power pico base stations (PBSs). The PBSs are distributed in the outer space of the disk centered at the MBS and the closed-form solution of the radius of the disk is analyzed. In the CA based HetNet, firstly, a practical user association scheme is proposed to classify the users into macrocell user (MUs) and pico-cell users (PUs). Secondly, a PU-centric cooperative transmission strategy is proposed to increase the data rate of PUs. Then, in order to eliminate the cross-tier interference in HetNet, the MUs and PUs schedule different component carriers (CCs) according to different algorithms. For the MUs, an improved simulated annealing (SA) scheduling algorithm is proposed to maximize the sum rate of the MUs. Compared with the existing SA, the inner loop of the proposed SA is redesigned. Consequently, the best value can be obtained more quickly. For PUs, a location information based low-complexity maximize minimum distance (MMD) algorithm is proposed to reduce the interference from PBSs to PUs as much as possible. Overall, an integrated scheme is proposed to improve the performance. At last, numerical results demonstrate the performance of the proposed schemes.

This paper investigates multicast beamforming for scalable videos in cache-enabled heterogeneous networks, where a macro base station (MBS) and multiple small base stations (SBSs) serve multicast group users on-demand video services. Inspired by the main idea of the scalable video coding, the extension technology of the H.264/advanced video coding, each video is divided into a base layer (BL) and an enhancement layer (EL). The BL can provide the fundamental viewing quality and adding EL to the received BL can guarantee superior perceptual experience. The MBS and multiple SBSs cache the BLs and ELs of the most popular videos, respectively. Employing the transmission schemes in coordinated multi-point, i.e., parallel transmission and joint transmission, the MBS and SBSs parallelly transmit BLs and ELs while the SBSs cooperatively transmit the ELs if higher video qualities are required. To improve the quality of the received videos for users whose requirements can be satisfied locally, our aim is to maximize the weighted sum rate of them and this problem can be converted into an iterative second-order cone programming problem by successive convex optimization with low complexity. Numerical results demonstrate the advantage of our proposed scheme compared to the benchmark scheme, i.e., only MBS serving the end users, even when the transmit power of each SBS is kept low.

We investigate power allocation optimization for global energy efficiency (GEE) maximization in the massive multiple-input multiple-output technique aided multi-pair one-way decode-and-forward relay systems. Assuming that the minimum mean-square error channel estimator and zero-forcing transceivers are employed at the relay, we first derive an accurate closed-form expression of the GEE of this complex system. Based on our analytical results, a non-convex power allocation optimization problem with the objective of GEE maximization is formulated under specific quality-of-service (QoS) and transmit power constraints. To solve this challenging problem, the successive convex approximation technique is invoked to transform the original optimization problem into a concave fractional programming problem, which is then efficiently solved by Dinkelbach's method and by the Charnes-Cooper transformation-based method. In addition, as a special case, the GEE maximization problem under the assumption of using the equal power allocation strategy at both the source users and the relay is also considered. Simulation results demonstrate the accuracy of our analytical results and the effectiveness of the proposed algorithms. Furthermore, the impact of several important system parameters (i.e., the QoS constraint, the transmit power constraints at both the source users and the relay, as well as the quality of channel estimation) on the maximum GEE achieved by the proposed algorithms is also illustrated.

This paper proposes a belief propagation (BP) message passing algorithm-based joint multiple symbol differential detection (MSDD) and channel decoding scheme for noncoherent differential ultra-wideband impulse radio (UWB-IR) systems. MSDD is an effective means to improving the performance of noncoherent differential UWB-IR systems. To optimize the overall detection and decoding performance, this paper proposes a novel soft-in soft-out (SISO) MSDD scheme for noncoherent differential UWB-IR. We first propose a new sampling mechanism for the noncoherent auto-correlation receiver to sample the received UWB-IR signal. The proposed sampling mechanism can exploit the dependences (imposed by the differential modulation) among data symbols throughout the whole packet. The signal probabilistic model has a hidden Markov chain structure. We use a factor graph to represent this hidden Markov chain. Then, we apply BP message passing algorithm on the factor graph to develop an SISO MSDD scheme, which is easy to integrate with SISO channel decoding to form a joint MSDD and channel decoding scheme. Performance results of bit error rate simulations and EXIT chart analyses indicate the performance advantages of our scheme over the previous MSDD scheme.

In this paper, a generalized low-complexity beamspace approach is proposed for two-dimensional localization of incoherently distributed sources with a uniform cylindrical array (UCyA) in large scale/massive multiple-input multiple-output (MIMO) systems. The received signal vectors in the antenna-element space are transformed into the beamspace by employing beamforming vectors. As a beneficial result, the total dimensions of the received signal vectors are significantly reduced. In addition, it is shown that the error introduced by the transformation decreases as the number of UCyA antennas increases. The UCyA is composed of multiple uniform circular arrays (UCAs), and the beamspace array response matrices of adjacent UCAs are linearly related. Then, the linear relation is exploited to estimate the nominal elevation direction-of-arrivals (DOAs) directly and the nominal azimuth DOAs based on a low-complexity search algorithm. In contrast, the linear relation in the traditional approach is based on approximations and the associated search algorithm is more complicated. Numerical results demonstrate that the proposed approach outperforms the existing approach in terms of both performance and complexity in the context of massive MIMO systems.

The relay-aided multicast network is considered, where an NT -antenna source multicasts confidential messages to N single-antenna legitimate users via a self-sustainable M-antenna regenerative relay. In particular, the relay is powered by the energy harvested from the radio signal of the source, and there are K unauthorized eavesdroppers wiretapping the channel. Assuming the knowledge of statistical channel state information of eavesdroppers, we aim to minimize the source transmission power via energy-efficient beamforming, subject to the signal-to-noise ratios of legitimate users/relay, the power constraint at the relay, and the outage constraints of the eavesdroppers. An efficient algorithm is developed by using the iterative first-order Taylor expansion and successive convex approximation, where the original nonconvex problem is transformed and solved.

We consider an amplify-and-forward relay network composed of a source, two relays, and a destination. In this network, the two relays are untrusted in the sense that they may perform Byzantine attacks by forwarding altered symbols to the destination. Note that every symbol received by the destination may be altered, and hence, no clean reference observation is available to the destination. For this network, we identify a large family of Byzantine attacks that can be detected in the physical layer. We further investigate how the channel conditions impact the detection against this family of attacks. In particular, we prove that all Byzantine attacks in this family can be detected with asymptotically small miss detection and false alarm probabilities by using a sufficiently large number of channel observations if and only if the network satisfies a non-manipulability condition. No pre-shared secret or secret transmission is needed for the detection of these attacks, demonstrating the value of this physical-layer security technique for counteracting Byzantine attacks.

Soft noncoherent detection, which relies on calculating the a posteriori probabilities (APPs) of the bits transmitted with no channel estimation, is imperative for achieving excellent detection performance in high-dimensional wireless communications. In this paper, a high-performance belief propagation (BP)-based soft multiple-symbol differential detection (MSDD) framework, dubbed BP-MSDD, is proposed with its illustrative application in differential space-time block-code(DSTBC)-aided ultra-wideband impulse radio (UWB-IR) systems. First, we revisit the signal sampling with the aid of a trellis structure and decompose the trellis into multiple subtrellises. Furthermore, we derive an APP calculation algorithm, in which the forward-and-backward message passing mechanism of BP operates on the subtrellises. The proposed BP-MSDD is capable of significantly outperforming the conventional hard-decision MSDDs. However, the computational complexity of the BP-MSDD increases exponentially with the number of MSDD trellis states. To circumvent this excessive complexity for practical implementations, we reformulate the BP-MSDD, and additionally propose a Viterbi algorithm-based hard-decision MSDD (VA-HMSDD) and a VA-based soft-decision MSDD (VA-SMSDD). Moreover, both the proposed BP-MSDD and VA-SMSDD can be exploited in conjunction with soft channel decoding to obtain powerful iterative detection and decoding-based receivers. Simulation results demonstrate the effectiveness of the proposed algorithms in DSTBC-aided UWB-IR systems.

In this paper, we consider low-complexity user scheduling schemes for the multi-pair two-way relay channel, where L pairs of single antenna users are selected from K pairs to perform pair-wise information exchange via an Nr-antenna amplify-and-forward (AF) relay (Nr≥ 2L - 1) with analogue network coding (ANC). We first propose a simple channel norm (CN) based scheme, which enables low-complexity implementation. Then, we propose a near-optimal user scheduling by jointly considering the pair-wise channels norms and orthogonality among all the users (CNO-A), which requires global channel state information (CSI) and centralized computation at the relay. It is noted that CNO-A scheme can achieve comparable performance as the optimal scheduling with reduced computational complexity. Finally, we propose a two-step selective CNO scheme (CNO-S), which strikes a flexible balance between complexity/CSI overhead and performance. CNO-S significantly reduces CSI overhead and computational complexity at the relay, while achieving comparable performance as the CNO-A scheme. Numerical results and complexity analysis not only show that the proposed schemes are feasible and effective, but also demonstrate their advantages over the existing schemes.

This paper proposes a linear precoding that aims at maximizing the sum-rate in a multiuser multiple-input multiple-output (MU-MIMO) simultaneous wireless information and power transfer (SWIPT) system. In this scenario some receivers harvest energy while the others decode information at the same time. The sum-rate for information decoding users is considered as the optimization policy where the transmit power constraint and power harvesting per user constraints are taken into account. Since the objective function of the sum-rate maximization problem is non-convex, it is difficult to find the optimal solution. Thus, we propose an iterative algorithm to find a locally optimal design based on a sequential convex approximation (SCA) method. In this way, the non-convex optimization problem is approximated by a convex program at each iteration. Finally, with the aid of the concept of the Rate-Energy (R-E) region simulation results show that the proposed scheme achieves significant improvement compared to existing works.

This paper considers to mitigate the uplink cross- tier interference (CI) in heterogeneous networks (HetNets) by the idea of opportunistic transmission. Firstly, we introduce the conventional opportunistic interference alignment (OIA) into HetNets and it indeed reduces the CI from the macrocell users (MUs) effectively. Interestingly, based on the unique characteristic in HetNets, the performance of OIA can be further improved. Therefore, the novel opportunistic interference mitigation (OIM) schemes based on adaptive reference signal spaces (A-RSSs) are proposed. In the novel OIM, an A-RSS is defined for each FBS and the A-RSS of a FBS constantly updates as the channel state information (CSI) of its FUs changes. According to the A-RSSs of all FBSs, each MU calculates its scheduling metric and the macrocell base station (MBS) selects MUs by the metrics. However, broadcasting the A-RSS constantly makes the system more complicated, so a complexity- performance tradeoff is considered. To decrease the complexity of the system, the quantization codebook is introduced and the quantized A-RSS based OIM is proposed. At last, extensive simulations are conducted. It is shown that, in terms of femtocells, the performance of novel OIM is better than that of the codebook based OIM (CB-OIM) and the performance of CB-OIM is better than that of conventional OIA.

In this paper, we study the secrecy communication in two-user MISO interference networks where an external eavesdropper is interested in the messages transmitted by both transmitters. We propose a beamforming design to maximize the achievable secrecy sum rate of the transmitters subject to the individual power constraint at each transmitter. To transform this complex non-convex problem into a convex one, we propose an iterative algorithm based on the constrained concave convex procedure (CCCP) and successive convex approximation (SCA). It is observed that the proposed algorithm converges fast to a stationary point within a few iterations. Furthermore, we also propose a low-complexity null-space beamforming design scheme in which the beamforming vectors have closed-form solutions. Simulation results show the effectiveness of the two proposed schemes in improving the secrecy sum rate performance.

Nonparametric belief propagation (NBP) algorithm can result in approximately optimal performance for probabilistic localization in wireless sensor networks without loops theoretically. However, in loopy networks the accuracy of NBP is doubtful and the computational complexity is high. In this paper, a novel approach running NBP on a minimum spanning tree (MST) is proposed, which mitigates the influence of loops and significantly reduces the computational cost as compared with the conventional NBP schemes. In addition, different from other spanning trees, the MST can confine more NBP particles into the bounding circle. Therefore, it shows better resistance to measurement errors. Numerical results show that the proposed method achieves better performance in terms of accuracy in highly connected networks, and the computational cost is much lower than the conventional NBP methods.

In this paper, we pioneer the study of physical-layer security in heterogeneous networks (HetNets). We investigate secure communications in a two-tier downlink HetNet, which comprises one macrocell and several femtocells. Each cell has multiple users and an eavesdropper attempts to wiretap the intended macrocell user. First, we consider an orthogonal spectrum allocation strategy to eliminate co-channel interference, and propose the secrecy transmit beamforming only operating in the macrocell (STB-OM) as a partial solution for secure communication in HetNet. Next, we consider a secrecy-oriented non-orthogonal spectrum allocation strategy and propose two cooperative STBs which rely on the collaboration amongst the macrocell base station (MBS) and the adjacent femtocell base stations (FBSs). Our first cooperative STB is the STB sequentially operating in the macrocell and femtocells (STB-SMF), where the cooperative FBSs individually design their STB matrices and then feed their performance metrics to the MBS for guiding the STB in the macrocell. Aiming to improve the performance of STB-SMF, we further propose the STB jointly designed in the macrocell and femtocells (STB-JMF), where all cooperative FBSs feed channel state information to the MBS for designing the joint STB. Unlike conventional STBs conceived for broadcasting or interference channels, the three proposed STB schemes all entail relatively sophisticated optimizations due to QoS constraints of the legitimate users. To efficiently use these STB schemes, the original optimization problems are reformulated and convex optimization techniques, such as second-order cone programming and semidefinite programming, are invoked to obtain the optimal solutions. Numerical results demonstrate that the proposed STB schemes are highly effective in improving the secrecy rate performance of HetNet.

In this letter, we jointly design relay antenna tilting with user scheduling so as to enhance the sum rate performance of a two-hop relay system, where the relay is equipped with a large-scale antenna array and performs zero-forcing processing. Building the fundamental of the joint design, a tight and tractable sum rate approximation is first derived by employing random matrix theory. Then the relay antenna downtilt and the number of active user pairs are jointly optimized to maximize the approximate sum rate. It is noted that the proposed scheme is independent of instantaneous channel state information. Therefore, it enjoys very low implementation complexity while improving the system performance.

In this paper, distributed user scheduling schemes are proposed for the multi-user MIMO-Y channel, where three NT-antenna users (NT= 2N, 3N) are selected from three clusters to exchange information via an NR-antenna amplify-and-forward (AF) relay (NR= 3N), and N ≥ 1 represents the number of data stream(s) of each unicast transmission within the MIMO-Y channel. The proposed schemes effectively harvest multi-user diversity (MuD) without the need of global channel state information (CSI) or centralized computations. In particular, a novel reference signal space (RSS) is proposed to enable the distributed scheduling for both cluster-wise (CS) and group-wise (GS) patterns. The minimum user-antenna (Min-UA) transmission with NT= 2N is first considered. Next, we consider an equal number of relay and user antenna (ER-UA) transmission with NT= 3N, with the aim of reducing CSI overhead as compared to Min-UA. For ER-UA transmission, the achievable MuD orders of the proposed distributed scheduling schemes are analytically derived, which proves the superiority and optimality of the proposed RSS-based distributed scheduling. These results reveal some fundamental behaviors of MuD and the performance-complexity tradeoff of user scheduling schemes in the MIMO-Y channel.

In this paper, bootstrap percolation is introduced to control the information propagation for efficient cooperative positioning in wireless networks. Particularly, we obtain a novel linear least square (LLS) estimator for the localization of agent nodes. Exploiting the idea of bootstrap percolation, agent nodes sequentially get activated and estimate their positions with an adaptive location updating rule. The rule is designed to first localize the more reliable agent nodes with at least three connections to the active nodes, and then gradually relax such connection constraints in each iteration so as to localize the agent nodes with fewer connections. Due to the activation characteristic, error propogation can be mitigated and energy is well managed. In addition, taking the uncertainty of the positional information into account, positioning errors can be further reduced. Simulations show that the proposed schemes improve the localization accuracy and use fewer links than traditional methods.

We investigate the secrecy beamforming in two-way relay channels (TWRC) with physical layer network coding (PNC). The multi-antenna relay broadcasts the superimposed signal of two user messages with secrecy beamforming after receiving the signals transmitted by the two legitimate users. We first propose a lower bound of the secrecy sum rate to quantify the secrecy performance of the TWRC with PNC. Because the maximization of the lower bound is non-convex under total power constraint, we propose a joint beamforming and power allocation scheme, in which the problem is successively approximated by several convex semidefinite programs. In order to reduce the complexity, we further propose an suboptimal scheme with closed-form solution. Numerical results indicate that the proposed schemes with PNC achieve much better secrecy sum-rate performance than the traditional AF schemes.

A simple distributed user scheduling (DUS) scheme is proposed for the MIMO multi-way relay channel (MWRC), where K (K ≥ 2) M-antenna (M ≥ 2) users are selected from K clusters of users to conduct all-cast information exchange via an M-antenna amplify-and-forward (AF) relay. In particular, the proposed DUS is based on the individual performance metric calculated by each user with its local channel state information (CSI). Therefore, DUS bypasses global CSI and complicated computations at the scheduling center, both are often inevitable with traditional centralized user scheduling (CUS). Furthermore, the outage performance of DUS is analyzed and the achievable multi-user diversity (MuD) order is derived. Numerical results validate the theoretical derivations and show that DUS achieves comparable performances to CUS in the considered scenarios.

In this paper, a novel scheme called blind interference neutralization is provided in the 3- cell interference channel with a shared instantaneous relay. The shared relay not only receives signals from sources, but also sends signals to destinations with a processing matrix. With the proposed scheme, each destination is able to pick up its own desired signal without encountering inter-user interference (IUI). In particular, the sources are blind in the sense that no channel state information (CSI) is required for transmit beamforming. Numerical simulation shows that the proposed scheme, compared with others, can increase the sum rate performance.

Precise ultra-wideband (UWB) ranging requires accurate estimation of time of arrival (TOA). In this paper,a novel TOA estimation approach using checking window is proposed for energy detection (ED) receivers in dense multipath channels. Unlike the traditional methods that treat the multipath components (MPCs) as interference sources, the proposed scheme exploits the MPCs to consolidate the detection of the first path (FP). By detecting dense threshold-crossing (TC) events over successive energy samples with the checking window, the MPCs are collected, and the false TC events that sparsely distributed in the noise region can be recognized and neglected for the FP detection. As a result, the early false detection is reduced and the leading edge detection is improved. Simulations demonstrate the effectiveness and robustness of the proposed scheme in IEEE 802.15.4a channels.

In this paper, a more accurate outage performance analysis is obtained by employing order statistics for multiple-input multiple-out two-way relay system with joint transmit/receive zero-forcing. Furthermore, closed-form upper and lower bounds are first derived for the overall outage probability when there exist spatial correlations at the relay. Analysis and simulation results indicate that the upper bound derived with order statistics is tight under various spatial correlations at the relay, and it is also tighter than that derived by eigenvalues of Wishart matrices over independent identically distributed Rayleigh fading channel. For example, when the relay is equipped with 4, 6 and 8 antennas, the upper bounds derived with order statistics are 2dB, 3dB and 4dB tighter than those with eigenvalues, respectively. In particular, when the numbers of antennas equipped at the users are greater than that equipped at the relay, the derived upper bound is nearly identical to the exact results.

We propose a low-complexity user selection scheme with zero-forcing precoding in multiuser MIMO downlink systems, where the base station (BS) is equipped with large-scale antenna arrays and the number of candidate-users is relatively small. The BS obtains the channel state information (CSI) of the user equipments (UEs) through the pilot-based minimum mean-square error channel estimation. Taking both the channel propagation and the UE location distribution into consideration, we first derive a deterministic approximation of the ergodic sum rate and investigate the optimal number of active UEs, K*, in the sense of sum rate maximization. Then, K* UEs are selected for simultaneous data transmission according to their large-scale channel fading. Small-scale channel fading is not taken into account in the selection procedure, thus reducing the computational complexity dramatically as well as improving the robustness of the proposed scheme in practice. Numerical simulations suggest that whether perfect CSI is available at the BS, our proposed scheme achieves high sum rate performance with very low complexity.

In this paper, two near-optimal detectors based on the convex optimization algorithm are proposed for multiuser detection (MUD) in the ultra-wide bandwidth (UWB) systems. The first detector performs semidefinite relaxation (SDR) to approximate the optimum multiuser detection (OMD) which is a nondeterministic polynomial time hard (NP-hard) problem. When the cutting planes generation algorithm is employed to strengthen the relaxation of the SDR, a tight MUD detector for combinatorial optimization is obtained by adding triangle inequalities to the well-known SDR in strict feasible region. Simulations demonstrate that the semidefinite programming (SDP) approaches can provide bit error rate (BER) performance close to the OMD efficiently using the interior point method, and the tight detector provides a better BER performance than the previous detector with a slightly higher complexity.

This paper reveals symbol extension is capable of improving secrecy performance in the multiple-input single-output (MISO) wiretap channel. We propose a symbol extension scheme jointly with the existing beamforming and artificial noise generation strategy to exploit the time variation of fading channel. After multiplying the data symbol vector by a proper designed square matrix, the data symbol can be extended to multiple time-slots. As a result, without any symbol rate loss, the proposed scheme enhances the secrecy performance in terms of secrecy outage probability. Furthermore, we also analyze the asymptotic secrecy outage probability and derive the achievable diversity order. Both analytical and numerical results show that the proposed scheme can bring more diversity gains into secrecy communication.

The key of time of arrival (TOA) estimation in ultra wideband (UWB) ranging is to detect the first path (FP). In this paper, we propose an optimized FP detector with the adaptive threshold in the absence of prior channel state information (CSI). In particular, the error information (EI) set is introduced to guide the threshold adjustment and determine the TOA estimate with a novel iterative algorithm. The EI set captures the characteristics of major errors regarding the inappropriate threshold. After the iterative process, the proposed scheme is shown to achieve the asymptotic optimal threshold without large number of repeated pulses. Therefore, the proposed scheme efficiently improves the TOA estimation accuracy as compare to the traditional schemes. Simulation results validate the effectiveness and superiority of the proposed scheme.

In this paper, we consider a K-cell multi-user interference network, where S single-antenna users are selected within each cell to carry out the uplink transmission with their M-antenna home base station, where 2 ≤ S ≤ M <; KS. For the considered scenario, a novel intra-cell performance aware opportunistic interference alignment (OIA) scheme is proposed to mitigate the inter-cell interference while reducing the intra-cell power loss caused by zero-forcing receiving. Unlike the traditional OIA schemes, the proposed scheme reuses the reference signal subspace (RSS) to balance not only the inter-cell interference but also the desired signal power and the intra-cell power leakage. It is shown that the refined selection further improves the achievable sum rate as compared to the existing schemes, and this observation is theoretically analyzed. Finally, numerical results validate that our scheme outperforms the existing uplink OIA schemes.

In this paper, semidefinite relaxation (SDR) technology is exploited for the multiple-symbol detection (MSD) over the multiple-input multiple-output (MIMO) ultra-wideband (UWB) systems. The existing scheme generalized likelihood ratio test (GLRT) MSD jointly detect multiple symbols, however, it entails a complexity of O(2M), where M is the observation window size. To this end, SDR is employed to reformulate the GLRT detection into a semidefinite programming (SDP) model, and two detectors, randomization-SDR (RSDR) and eigenvector-SDR (ESDR) are proposed on the order of O(M3.5) and O(M3), respectively. Complexity analysis validates that the SDR-MSD strategy is desirable owing to its reduced complexity, compared with the exponential-complexity sphere decoding (SD) MSD. Furthermore, Monte-Carlo simulations demonstrate that the proposed SDR detectors provide the bit error rate (BER) performance almost the same with that of the SD method, and the RSDR outperforms the ESDR at the price of slightly higher complexity.

The goal of this paper is to obtain the channel conditions (if exist) under which substitution attacks performed by relay node(s) in a relay network can be detected. The network model considered consists of a source node and a destination node. There are two independent transmission paths from the source to the destination, each via a potentially malicious relay which may perform substitution attacks by forwarding altered symbols to the destination. The destination attempts to detect any such malicious act of the relays by comparing the joint empirical distribution of the symbols received from the relays with known channel statistics along the two paths. Note that every symbol received by the destination may be altered, and hence no clean reference observation is available to the node. It is demonstrated that maliciousness of the relays can be asymptotically detected with sufficient channel observations if and only if the two relays do not collude and the network satisfies a non-manipulability condition.

In this paper, an undesired fact for multiuser (MU) MIMO (MU-MIMO) systems using block diagonalization is presented, i.e., the increment of user number (K) results in the degradation of sum rate. This fact is caused by the opposition between two main factors determining the sum rate, namely, MU gain and transmit diversity. The MU gain guarantees a high order of degrees of freedom (DoFs) on system level but decreases the rate per user, while, for the transmit diversity, it is exactly opposite. Therefore, the tradeoff between the two factors is established to quantify how the two factors impact on the sum rate. Based on the tradeoff, the optimal K that maximizes the sum rate is further obtained. Additionally, it is pointed out that the sum rate can be improved substantially by only adding a few antennas at the base station when the system is fully loaded. The derivations are under large-scale system assumption, and being implemented on different precoders. Numerical simulations verify the tightness and accuracy of our asymptotic results for both large-scale and conventional systems.

In this paper, a MIMO-Y channel based three-group information exchange problem is considered, where users from each group would like to exchange information with the other users in another two groups. In particular, a Max-Min user selection is proposed, which aims to harvest the multiuser diversity gain for reliable transmission in the MIMO-Y channel. The impacts of user configurations on the multiuser diversity gains are investigated by theoretical bounds as well as numerical results. It is shown that, by adding a few users in only one or two groups, the overall system performance can be improved significantly. This observation reveals an interesting behavior of MIMO-Y channel, i.e., the local configuration has a global impact. However, we prove that such asymmetrical multiuser diversity gain does not scale with the number of users. Finally, we show that multiuser diversity gain scales with the number of users if we add an equal number of users in all three groups.

Large-scale multi-cell multiuser multiple-input multiple-output (LS-MIMO) systems can greatly increase the spectral efficiency. But the performance of these systems is deteriorated by pilot contamination. In this paper, first, a pilot design criterion is proposed by exploiting the orthogonality of channel vectors of LS-MIMO systems. Second, following this criterion, Chu sequences based pilots are designed. Because of the proposed pilots, the channel estimate of most terminals of a cell is only interfered by the partial cells rather than all the other cells, where the latter is caused by traditional pilots. As a result, pilot contamination is mitigated. Numerical results verify the effectiveness of the proposed pilots.

In this paper, we study the amplify-and-forward (AF) relaying based signaling scheme for the Y channel consisting of three single-antenna users and a two-antenna relay. In such a particular scenario, traditional signal space alignment for network coding (SSA-NC) scheme is not feasible. Moreover, the time division based multi-user multiple-input multiple-output (MU-MIMO) scheme has to rely on time division mode, i.e., more than two time slots are required to complete the whole communication process, which results in throughput loss. We develop an asymmetric signal space alignment (ASSA) scheme to enable all the users to finish information exchange with each other via the relay within two time slots. Brief degrees of freedom (DOF) analysis and numerical simulations have been provided to demonstrate that the proposed signaling technique significantly outperforms the conventional time division based MU-MIMO scheme.

In this paper, we propose two kinds of interference alignment (IA) schemes with limited feedback for the two-cell interfering multi-user multiple-input multiple-output multiple access channel (MIMO-MAC). Since IA with limited feedback results in residual interference for the quantization error, more effective schemes are introduced to reduce the residual interference in this paper compared with the ever work. The first kind of schemes means that the precoding matrices at the transmitters are the quantization value after obtaining the IA close-form solution of the precoding and decoding matrices at the receivers. This kind of schemes has been generalized to K users in this paper, and decoding matrices design is considered to reduce the quantization error to improve the performance. For the second kind of schemes, the beamforming vectors are chosen in the codebooks directly which guarantee the inter-cell interference (ICI) are most aligned. Monte-Carlo simulations illustrate that the proposed schemes outperform the existing schemes.

In this paper, we propose a network-coded relay cooperative transmission scheme (NCRCT) to improve the outage performance of the primary system and the secondary system in a cognitive radio network. In conventional spectrum sharing scheme based on interference temperature (CSS-IT), the transmission power of the secondary user (SU) who only transmits its own data should satisfy peak power constraint and the interference temperature requirement perceived at the primary user (PU). In contrast to CSS-IT, the secondary user in NCRCT will aid the PU with its maximum allowable transmit power by employing network coding when the channel condition from the PU to the SU is good. The exact expressions for the outage probability of the primary system and the secondary system are derived. Theoretical and numerical results show that NCRCT can achieve better outage performance and higher diversity order of the primary system than the case without spectrum sharing and provide superior outage performance of the secondary system to CSS-IT scheme.

Large-scale multi-cell multiuser multiple-input multiple-output (LS-MIMO) systems have received much attention recently. But the performance of these systems is deteriorated by imperfect channel state information (CSI). Hence, in this paper, a subspace-based semi-blind channel estimator is proposed. Based on the approximate orthogonality of the channel vectors of LS-MIMO systems, singular value decomposition (SVD) is employed on the received signals to determine the channel matrix up to an ambiguity matrix. Then matrix inversion is avoided in resolving the ambiguity matrix, which is essential to traditional subspace-based estimators and loses the partial CSI. The properties of the estimators are analyzed, and the analysis shows that the estimation accuracy of the proposed estimator is improved. Simulations comparing the proposed approach with others illustrate improvement of the performance of the proposed approach.

The effective cross-tier interference (CTI) mitigation is a key technique for the macrocell and femtocell two-tier heterogeneous networks. In this paper, a cognitive radio relay selection (CRRS) scheme is proposed to improve the received power of the macrocell users served by the macrocell base station (MBS). The scheme is intended to ensure the macrocell users can endure much more interference from the femtocell users in order to improve the quality of service (QoS) of the femto-networks. In addition, the fairness of the femto-networks is analyzed, with the objective of accounting for the maximal density of femtocell base stations that located in a certain area. Simulation results demonstrate that, compared with the existing works, the proposed scheme can not only increase the number of the femtocell users whose Signal-to-Interference-plus-Noise-Ratio (SINR) requirements are guaranteed, but also improve the average SINR of the femtocell users.

In this paper, we analyze the non-cooperative power control algorithm based on game theory in the two-tier femtocell networks, and find that the outcome of the game in a Nash equilibrium (NE) is inefficient. That is to say, there are still many users that can not achieve the target Signal-to-Interference-Plus-Noise Ratio (SINR) at the NE point, especially femtocell user equipments (FUE). Therefore, we propose a novel power control scheme in heterogeneous Femto-Macro cell networks, which can guarantee the target SINR of the macrocell user (MUEs), and make as many as possible FUEs to achieve their target SINRs. The proposed power control algorithm introduces the user selection and channel re-allocation in the conventional non-cooperative power control game. In addition, to further optimize the proposed scheme, we propose a novel FUE-SINR based MUE link quality protection algorithm. The propose scheme is able to improve the efficiency of Nash equilibrium, i.e., ensure more users to attain the target SINRs. Numerical simulations verify the conclusions.

In this study, the problem of low complexity multiple‐input–multiple‐out signal detection based on belief propagation (BP) is addressed. The authors propose an edge selection approach that works on factor graph model to cut down the number of circles and high complexity of standard BP algorithm. The message passing from factor nodes to variable nodes is updated by only partial edges, and the mean feedback method is designed to compensate the information loss brought by the edge selection. Both binary and high‐order modulations are considered, and the scheme of mapping between bit soft output and modulation symbols when computing the feedback information is discussed. In addition, a minimum mean‐square error filter initialised algorithm is proposed, in which the initial message of BP detection is exploited. Both binary and high‐order modulations are discussed as well when the authors design this initial message. Simulation results along with convergence and complexity analyses verify that the proposed edge selection approach can achieve good performance with low complexity, and significantly outperform the existing methods with comparative complexity. Moreover, our approach has asymptotic optimality and is a self‐adapting scheme, which can achieve the trade‐off between performance and complexity by varying the number of selected edges.

It was conventionally regarded that the approximate Bayesian theorem based existing probabilistic data association (PDA) algorithms output the estimated symbol-wise a posteriori probabilities (APPs) as soft information. In our recent work, however, we demonstrated that these probabilities are not the true APPs in the rigorous mathematical sense, but a type of nominal APPs, which are unsuitable for the classic architecture of iterative detection and decoding (IDD) aided receivers. To circumvent this predicament, in this paper we propose an exact Bayesian theorem based logarithmic domain PDA (EB-Log-PDA) method, whose output has similar characteristics to the true APPs, and hence it is readily applicable to the classic IDD architecture of multiple-input-multiple-output (MIMO) systems using the general M-ary modulation. Furthermore, we investigate the impact of the EB-Log-PDA algorithm's inner iteration on the design of EB-Log-PDA aided IDD receiver. We demonstrate that introducing inner iterations into EB-Log-PDA, which is common practice in conventional-PDA aided uncoded MIMO systems, would actually degrade the IDD receiver's performance, despite significantly increasing the overall computational complexity of the IDD receiver. Finally, we investigate the relationship between the extrinsic log-likelihood ratios (LLRs) of the proposed EB-Log-PDA and of the approximate Bayesian theorem based logarithmic domain PDA (AB-Log-PDA) reported in our previous work. Despite their difference in extrinsic LLRs, we also show that the IDD schemes employing the EB-Log-PDA and the AB-Log-PDA without incorporating any inner PDA iterations have a similar achievable performance close to that of the optimal maximum a posteriori (MAP) detector based IDD receiver, while imposing a significantly lower computational complexity in the scenarios considered.

This paper considers physical-layer network coding (PNC) with M-ary phase-shift keying (MPSK) modulation in two-way relay channel (TWRC). A low complexity detection technique, termed symbol-based PNC (SPNC), is proposed for the relay. In particular, attributing to the outer product operation imposed on the superposed MPSK signals at the relay, SPNC obtains the network-coded symbol (NCS) straightforwardly without having to detect individual symbols separately. Unlike the optimal multi-user detector (MUD) which searches over the combinations of all users' modulation constellations, SPNC searches over only one modulation constellation, thus simplifies the NCS detection. Despite the reduced complexity, SPNC achieves full diversity in multi-antenna relay as the optimal MUD does. Specifically, antenna selection based SPNC (AS-SPNC) scheme and signal combining based SPNC (SC-SPNC) scheme are proposed. Our analysis of these two schemes not only confirms their full diversity performance, but also implies when SPNC is applied in multi-antenna relay, TWRC can be viewed as an effective single-input multiple-output (SIMO) system, in which AS-PNC and SC-PNC are equivalent to the general AS scheme and the maximal-ratio combining (MRC) scheme. Moreover, an asymptotic analysis of symbol error rate (SER) is provided for SC-PNC considering the case that the number of relay antennas is sufficiently large.

The combination of physical-layer network coding (PNC) and multiple-input multiple-output (MIMO) is expected to improve the throughput of two-way relay network. In this paper, we propose a zero-forcing based MIMO two-way relay scheme in conjunction with a simple Max-Min relay antenna selection. This scheme solves the unpractical constraint encountered by many existing MIMO two-way relay schemes for application, which requires the relay to equip fewer antennas than the end node. Our scheme, on the other hand, benefits from the dedicated relay that has more antennas than the end node. A notable diversity advantage is obtained from judicious relay antenna selection. The reliability of the simple ZF based MIMO two-way relay is therefore improved. Of particular note, this paper extends our previous study to 1) support the more general application with non-binary PNC and 2) give a complete analysis on the attained end-to-end diversity with explicit theoretical result under i.i.d. Rayleigh fading channel.

In this paper, we study the cognitive interference management (CIM) scheme in the coexistence networks of macro-cells and femtocells. A single-channel detection based spectrum allocation algorithm is proposed to enhance the performance of femtocells considering the co-tier interference and co-tier interference. The proposed scheme converts the complex interference environment into several specific channel categories and successfully recognizes these channel patterns, while the group-channel resource allocation algorithm does not distinguish the interference channels in detail and operates on the continuous group of channels. Therefore, femtocell base stations (FBSs) can recognize the spectral environment more completely and allocate more available channels. As a result, our scheme is able to significantly improve the femtocell spectral efficiency and the signal-to-interference-and-noise ratio (SINR) performance of femtocell users (FUEs), meanwhile, it avoids the strong interference on the existing macrocell. Numerical simulations verify the conclusions.

In this paper, we propose a new interference alignment scheme with limited feedback for the two-cell interfering multi-user multiple-input multiple-output multiple access channel (MIMO-MAC), which provides better performance compared with other schemes when the number of feedback bits is same. Then, we analyse the rate loss for the quantization error, and show that the rate loss is only impacted by the residual inter-cell interference. By characterizing the rate loss as a function of the number of feedback bits, a bits allocation algorithm is introduced to further improve the system throughput. Monte-Carlo simulations illustrate that our proposed scheme outperforms the existing schemes.

In this paper, we consider a multi-way relaying channel where 2K users are divided into two groups averagely and each of them exchanges messages with every user of the other group via an intermediate relay. We term it multi-user multi-way relaying X network. We design the beamforming vectors at the users and the relay to achieve an interference alignment (IA) solution. Meanwhile, we investigate the feasibility conditions on the required amount of antennas for each node. Brief theoretical analysis and numerical simulations have been provided to demonstrate that the degrees of freedom (DOF) of 2K2is obtained.

In this paper, we consider the resource allocation problem in the uplink transmission of an orthogonal frequency-division multiple access (OFDMA) based cognitive radio (CR) network. The resource allocation aims to maximize the uplink throughput of secondary users (SUs) in CR network under the constraints of the primary user (PU) interference and the transmit power limits of SUs. In general, the optimal joint power and subchannel allocation is known as NP-hard. To ease the computation complexity while maintain good performance, we propose a novel particle swarm optimization (PSO) based joint uplink power and subchannel allocation algorithm to solve this resource allocation problem. Due to the combinatorial nature of the resource allocation problem, our algorithm in which power continuously changes while the subchannel allocation strategy alters in the iteration process can obtain better performance than the existing decomposition based algorithm that subchannel assignment and power allocation are implemented separately. Simulation results show the effectiveness of the proposed algorithm.

Combining of physical-layer network coding (PNC) and multiple-input multiple-output (MIMO) can significantly improve the performance of the wireless two-way relay network (TWRN). This paper proposes novel Max-Min optimization based relay antenna selection (RAS) schemes for zero-forcing (ZF) based MIMO-PNC transmission. RAS relaxes ZF's constraints on the number of antennas and extends the applications of ZF based MIMO-PNC to more practical scenarios, where the dedicated relay has more antennas than the end node. Moreover, RAS also brings diversity advantages to TWRN and the achievable diversity gains of the proposed schemes are theoretically analyzed. In particular, an equivalence relation is carefully built for the diversity gains obtained by 1) RAS for ZF based MIMO-PNC and 2) transmit antenna selection (TAS) for MIMO broadcasting (BC) with ZF receivers. This equivalence transforms the original problem to a more tractable form which eventually allows explicit analytical results. It is interesting to see that Max-Min RAS keeps the network diversity gain of ZF based MIMO-PNC to be the same as the diversity gain of the point-to-point link within the TWRN. This insight extends the understanding on the behaviors of ZF transceivers with antenna selection (AS) to relatively complicated MIMO-TWRN/BC scenarios.

In this paper, the application of MPSK modulation to physical-layer network coding (PNC) with multiple antennas scheme is investigated. A linear complexity scheme with the capacity of achieving diversity is proposed assuming symbol- level synchronization only. In the proposed scheme, called symbol-based PNC, the constant amplitude of MPSK modulation symbols is exploited and the product of two end transmitted symbols, which is actually related to another modulation symbol, is considered as network code. The autocorrelation of received signal is calculated in the relay node equipped with multiple antennas, which contributes to compact the search space for the intended network code. Two detectors are designed for achieving diversity at the cost of linear complexity. We also investigate the power allocation for symbol-based PNC. The advantages of symbol- based PNC can be summarized as two points. First, it requires symbol-level time synchronization only rather than stringent carrier-phase synchronization. Second, it is linear in complexity with respect to the constellation size. Simulations show that symbol- based PNC can achieve diversity gain.

A physical-layer network coding (PNC) aided two-way relay scheme is proposed for Transmitted-Reference (TR) UWB networks. In particular, a novel noncoherent UWB-PNC detector is investigated for the TR UWB networks. Inheriting the simplicity of the TR-UWB receiver, the proposed PNC detector is based on the autocorrelation receiver (AcR) with simple structure, which effectively suppresses the multi-user interference and harvests the multipath energy. Equipped with the proposed TR UWB-PNC detector, the relay node first detects the bitwise XORed symbol directly from the overlapped information bearing waveforms transmitted from the source nodes, then broadcasts the estimate of the XORed symbol to achieve efficient two-way relay. Simulation results show that, compared with the non-relay, one-way relay and two-way relay with time division multiple access (TDMA) and network coded broadcasting (NCBC), the proposed PNC aided two-way scheme significantly improves both the energy and spectral efficiencies of the TR-UWB networks.

We consider a multiple-input multiple-output (MIMO) two-way relay network with two NT-antenna (NT≥ 2) end nodes and one dedicated NR-antenna (NR>; NT) relay node. A physical-layer network coding (PNC) based joint relay antenna selection and zero-forcing (ZF) spatial multiplexing scheme is proposed to support NTstreams of bidirectional data exchanging with the help of NTout of NRselected antennas at the relay node. The optimum relay antennas are selected by a Max-Min criterion with respect to the post-processing SNR of the whole system and then the linear ZF based MIMO two- way relay transmission is achieved with the help of the selected relay antennas. The optimum relay antenna selection not only fulfills the ZF based scheme's requirement on the number of effective relay antennas but also provides an end-to-end diversity advantage to the NTstreams of bidirectional data exchanging with linear transceiver at each end node. The diversity order of the proposed scheme is analyzed. Explicit diversity order d = NR- 1 is obtained theoretically for NT= 2 streams of bidirectional data exchanging. For NT≥ 2 cases, a conjecture that d = NR- NT+ 1 is obtained based on simulation results.

In this paper, a round-robin based relay protocol (R3P) is proposed to provide full cooperative diversity in a cooperative communication system. Distinct from traditional relay protocols which also yield full cooperative diversity, R3P is based on round-robin scheduling, thus avoids relay selection and requires no global channel statistic information (CSI) at the destination. R3P can therefore be realized with lower implementation complexity. Furthermore, in wireless network where there are more sources than relays, R3P requires much fewer time slots while maintaining reliability, and significantly improves the throughput of the network. Both theoretical analysis and simulation results verify the validity and superiority of R3P.

An ultra-high data rate Time Reversal (TR) Multiple-Input Multiple-Output (MIMO) Ultra-Wideband (UWB) communication system with space-time pre-equalizer is proposed. When the symbol duration is set to approach the duration of UWB monocycles, the data rate is close to the limit due to the extremely large bandwidth, resulting in the severe intersymbol interference (ISI). The zero-forcing (ZF)criterion based space-time pre-equalizer presented in this letter eliminates both ISI and multi-stream interference (MSI) caused by spatial multiplexing at the sampling time. With less demand for degree of freedom (the number of antenna) than other existing schemes, the proposed space-time pre-equalizer enables the data rate of TR-MIMO-UWB system to reach the order of Gbps without losing bit error rate (BER) performance.

In this paper, we investigate noncoherent Multiple-Input Multiple-Output (MIMO) ultra-wideband (UWB) systems where the signal is encoded by Differential Space-Time Block Code (DSTBC). Considering the specific signal format of DSTBC-UWB system and employing the property of DSTBC, a noncoherent multiple symbol detection (MSD) scheme is developed by generalized likelihood ratio testing (GLRT) approach. Although the proposed MSD scheme can enhance the performance of DSTBC-UWB system, the complexity of the exhaustive search based MSD exponentially increases with observation window size. To decrease the computational complexity, the original MSD metric is transformed into another equivalent form which can be implemented by sphere decoding (SD) for DSTBC-UWB system. Moreover, a suboptimal Decision-Feedback (DF) based MSD with lower complexity than SD based MSD is proposed to further reduce the computational complexity.

Inter-cell co-channel interference (CCI) mitigation is investigated in the context of cellular systems relying on dense frequency reuse. A distributed Base Station (BS) cooperation aided soft reception scheme using the Probabilistic Data Association (PDA) algorithm and Soft Combining (SC) is proposed for the uplink of multi-user multi-cell MIMO systems. The realistic hexagonal cellular model relying on unity Frequency Reuse (FR) is considered, where both the BSs and the Mobile Stations (MSs) are equipped with multiple antennas. Local cooperation based message passing is used instead of a global message passing chain for the sake of reducing the backhaul traffic. The PDA algorithm is employed as a low complexity solution for producing soft information, which facilitates the employment of SC at the individual BSs in order to generate the final soft decision metric. Our simulations and analysis demonstrate that despite its low additional complexity and backhaul traffic, the proposed distributed PDA-aided reception scheme significantly outperforms the conventional non-cooperative benchmarkers.

In this paper, a novel dual XOR hybrid automatic retransmission request scheme XOR2-HARQ is proposed for wireless broadcasting. Distinct from the traditional network coding (NC) based HARQ (NC-HARQ), an additional XOR operation is introduced to dynamically combine lost packets from the individual receiver instead of conducting XOR operation only across lost packets from different receivers. Furthermore, based on the linear block code's perspective, we optimize the retransmission strategy to yield optimal diversity gain. Analytical results show that conditioned on the same packet error ratio (PER), the retransmission rounds needed for XOR2-HARQ is strictly less than that of NC-HARQ, and the simulation results consolidate our analysis to show a significant reduction of required retransmissions.

By introducing the leakage concept, the maximum signal-to-leakage-and-noise ratio (SLNR) scheme has been served as a candidate precoding scheme in the advanced long term evolution (LTE-Advanced) communication system. However, the original scheme allocates every user the same transmit power and takes the matched filter to decode the receive signals, which results in the limited bit error rate (BER) performance. With the antenna correlation at BS and the channel estimation error for every user, we design a modified matrix by minimizing the system Mean Square Error (MMSE) after maximizing the SLNR under the total transmit power constraint. At each user, a linear decoder is calculated based on the MMSE criteria in the presence of imperfect channel state information (ICSI). Due to the dynamic power allocation during the parallel data streams and the linear MMSE (LMMSE) receiver, the proposed scheme can mitigate the residual interference induced by ICSI and improve the system BER performance efficiently.

In this paper, we propose a decode-and-forward (DF) two-way relay ultra-wideband (UWB) network with a joint-demodulated non-coherent receiver to boost the system throughput. We consider a three nodes relay network with two terminals exchanging information via a relay node during two time slots. After jointly detecting the simultaneously arrived signals, the relay node broadcasts the XORed signal to each terminal. A novel non-coherent receiver with energy detector (ED) is employed and the power allocation optimization is performed based on the result of the approximate effective signal to noise ratio (SNR). Simulation results show that the proposed technique achieves significant improvement over the existing nonrelay and relay schemes on system throughput.

A multi-layer orthogonal block coded transmission scheme is proposed in this paper to enhance the performance of the noncoherent Ultra-Wideband impulse radio (UWB-IR) system. The design employs a novel Multiple Orthogonal Block Coded Modulation (MOBCM), which transmits multiple information bearing orthogonal codewords with an ingenious layered structure. At the receiver side, a correspondent noncoherent multiple codeword detection technique is developed to jointly detect multiple codewords and exploit the high energy efficiency inherent in the MOBCM. Solid performance gain is achieved and our design reduces the general performance gap between noncoherent and coherent receiver for UWB-IR system.

In this paper, we proposed an effective and unified transmit power allocation (TPA) scheme for the Singular Value Decomposition (SVD)-Assisted multi-user multiple-input multiple-output (MU-MIMO) downlink (DL) and uplink (UL) transmissions in the presence of imperfect channel state information (ICSI). The existing power allocation policies for the SVD-Assisted MU-MIMO system, such as equal power allocation (EPA) in the DL and maximum signal-to-noise ratio (MSNR) policy in UL have been developed under the perfect CSI case. However, the EPA scheme doesn't exploit the CSI and ignores the Bit Error Rate (BER) difference among all singular values meanwhile the MSNR method neglects the noise enhancement caused by the decoding operation at Base Station (BS). Aiming at solving these problems, the proposed TPA scheme takes the smallest singular value and the noise enhancement into account and derives an unified expression for DL and UL. Under the ICSI, the proposed scheme can exploit the power allocation operation to mitigate the residual interference induced by ICSI and improve the system BER performance efficiently.

A unified Bit-based Probabilistic Data Association (B-PDA) detection approach is proposed for Multiple-Input Multiple-Output (MIMO) systems employing high-order Quadrature Amplitude Modulation (QAM). The new approach transforms the symbol detection process of QAM to a bit-based process by introducing a Unified Matrix Representation (UMR) of QAM. Both linear natural and nonlinear Gray bit-to-symbol mapping schemes are considered. Our analytical and simulation results demonstrate that the linear natural mapping based B-PDA approach attains an improved detection performance, despite dramatically reducing the computational complexity in contrast to the conventional symbol-based PDA aided MIMO detector. Furthermore, it is shown that the linear natural mapping based B-PDA method is capable of approaching the lower bound performance provided by the nonlinear Gray mapping based B-PDA MIMO detector. Since the linear natural mapping based scheme is simpler and more applicable in practice than its nonlinear Gray mapping based counterpart, we conclude that in the context of the uncoded B-PDA MIMO detector it is preferable to use the linear natural bit-to-symbol mapping, rather than the nonlinear Gray mapping.

Hybrid subcarrier exclusivity and sharing (HSEnS) scheme with optimized bit loading in uplink multicell OFDMA system is proposed in this paper. Optimizing bit loading under subcarrier exclusivity (SE) and subcarrier sharing (SS) schemes in two adjacent cells is analyzed. Bit loading criteria and boundary setting procedures for convex optimizing bit loading under SS scheme are proposed. And bit loading boundaries for convex optimizing bit loading under SE and SS scheme can be obtained. The HSEnS scheme is proposed to combine the advantages of SE and SS schemes for making tradeoff between throughput performance and algorithm failure rate when interference is strong, moderate or weak. Simulations support our proposals and HSEnS with optimized bit loading outperforms other schemes.

Existing inter-frame interference (IFI) and intersymbol interference (ISI) mitigation schemes for noncoherent UWB Impulse Radio (UWB-IR) mainly focus on signal processing after nonlinear autocorrelation receiver (AcR) or energy detector (ED). Steering the wheel, a simple but effective IFI and ISI pre-mitigation scheme is proposed in this paper, which realizes IFI and ISI mitigation before ED. Block coded modulation is adopted and the pre-mitigation scheme relies on optimized block code design. The optimization jointly considers the signal interference-patterns before ED and the properties of codewords. Thanks to the matching among codes, interference and detection scheme, leaked signal energy is partially used for detection. IFI and ISI mitigation is thus realized. It is showed in simulations that distinct performance improvement is achieved under moderate IFI and ISI.

Synchronization is a crucial task and big challenge for ultra-wideband (UWB) systems, especially in the presence of inter-symbol interference(ISI) and inter-frame interference (IFI). This paper proposes a blind synchronization and demodulation algorithm that is capable of mitigating ISI and IFI for noncoherent UWB system. Employing a series of codeword matching and averaging operations, the proposal can suppress interference and noise effectively. Moreover, it can acquire synchronization rapidly and improve the bit error rate (BER) performance significantly thanks to efficiently exploiting the observed signals. Simulation results demonstrate the substantial performance improvement compared to existing methods.

In this paper, we propose a novel transmit preprocessing scheme using Channel Selection (CS) to jointly design PreRake and Precoding for multiple-input multiple-output Ultra-Wideband (MIMO-UWB) communications. The PreRake technique is implemented to exploit the advantage of multipath diversity and can achieve spatial multiplexing based on our proposed CS. The combination of CS and PreRake transforms the UWB frequency selective fading channel into an equivalent flat fading channel and an equivalent channel matrix (ECM) is calculated. CS can construct the ECM as a well-conditioned matrix so that higher data transmission rate can be provided by spatial multiplexing. Then, Precoding method can be introduced to enhance system performance via diagonalizing the ECM. The simple but effective channel inversion (CI) precoding is employed in the proposed transmit preprocessing scheme which we denote as CS-CI-PreRake. Simulations results show the proposed scheme achieves a solid performance.

Channel allocation in cognitive radio networks completely determines the realizability and efficiency of cognitive radio since it is the final step before the cognitive subscribers can use the spectrum holes. In this paper, we consider channel allocation in cognitive radio networks as a resource allocation problem under the circumstance that the allocation of transmission rate, link and transmission power for secondary users are restricted. And considering the impact of primary user activity on available channels originally, we formulate such resource allocation problem as a binary integer optimal programming, and then we design two algorithms to solve this problem and compare their performances.Finally, numerical results show that the proposed channel allocation model and strategies are quite feasible.

In this paper, a joint blind synchronization and demodulation scheme is developed for ultra-wideband (UWB) impulse radio systems. Based on the prior knowledge of the direct-sequence (DS) spread codes, the proposed approach can achieve frame-level synchronization with the help of frame-rate samples. Taking advantage of the periodicity of the DS spread codes, the frame-level synchronization can be carried out even in one symbol interval. On the other hand, after timing acquisition, these frame-rate samples can be re-utilized also for the demodulation. Thus the acquisition time and the implementation complexity are reduced considerably. The performance improvement can be justified by simulation results, in terms of acquisition probability and bit error rate (BER).

One of the biggest challenges in ultra-wideband (UWB) communications is the accurate and rapid timing acquisition for the receiver. This paper presents a timing acquisition scheme dispensing with searching for UWB communications. Without training sequence involved, timing offset parameter is estimat

Directional antennas in ad hoc networks offer more benefits than the traditional antennas with omni-directional mode. With directional antennas, it can increase the spatial reuse of the wireless channel. A higher gain of directional antennas makes terminals a further transmission range and fewer hops to the destination. This paper presents the design, implementation and simulation results of a multi-channel Medium Access Control (MAC) protocols for dense Vehicular Ad hoc Networks using directional antennas with local beam tables. Numeric results show that our protocol performs better than the existing multichannel protocols in vehicular environment.

A new probabilistic data association (PDA) approach is proposed for symbol detection in spatial multiplexing multiple-input multiple-output (MIMO) systems. By designing a joint detection (JD) structure for consecutive symbol vectors in the same transmit burst, more a priori information is exploited when updating the estimated posterior marginal probabilities for each symbol per iteration. Therefore the proposed PDA detector (denoted as PDA-JD detector) outperforms the conventional PDA detectors in the context of correlated input bit streams. Moreover, the conventional PDA detectors are shown to be a special case of the PDA-JD detector. Simulations and analyses are given to demonstrate the effectiveness of the new method.

In this paper, a novel synchronization algorithm dispensing with searching is proposed for UWB communications. By adopting a stream of alternating orthogonal pulses, frame-level synchronization can be achieved without training sequence involved. Without any searching procedure, the timing offset estimator is obtained in a closed form, therefore considerably reducing the synchronization time. Simulations and comparisons confirm that the proposed scheme outperforms the existing alternative in terms of normalized mean square error (NMSE) and bit error rate (BER).

This paper proposes a novel bit-level combining scheme based on Dempster-Shafer (D-S) evidence theory, termed DS combining, for multiple-input multiple-output (MIMO) systems with hybrid-automatic-retransmission-request (HARQ) mechanism. The DS combining is assisted by the proposed DS detection for performance improvement. The focal-element-set (FES) characterizes the uncertainty contained in the decision statistics, and the corresponding basic-probability-assignment (BPA) of FES is the likelihood measure. The uncertainty can be counteracted by DS detection and further counteracted by DS combining, so more reliable decisions are achieved. Simulation results verify that the proposed combining scheme significantly outperforms its log-likelihood-ratio (LLR) combining counterpart with only moderate complexity increases.

In the whole working cycle of cognitive radio (CR), the primary job is to sense wireless communication environment of secondary users (SUs), therefore, spectrum sensing is the foundation and prerequisite for the application of CR. In order to get excellent detection performance, cooperative spectrum sensing has been applied widely. However, in cooperative spectrum sensing process, the detection results of the SUs have a great degree of uncertainty, leading to severe impact on the detection performances of CR. In this paper, we propose a novel cooperative spectrum sensing scheme based on fuzzy integral theory and optimization method, in which the reliability of local spectrum sensing is taken into account in the final decision whether the primary user (PU) is present or not and the optimization method is used to find the optimal fuzzy measures. Moreover, simulation results show that the proposed scheme performs better than other fusion strategies implementing in cooperative spectrum sensing scheme. Finally, almost all of the fusion strategies for cooperative spectrum sensing scheme in CR networks are discussed and analyzed.

Relying on judicious training symbols, we can construct a redundance-included demodulation template (RDT) from the received signal. When the RDT is available, there are two approaches to select. One is to demodulate transmitted symbols directly by the RDT, which does not require timing acquisition, and thereby has considerably lower complexity. The alternative is to first acquire the timing offset via simple energy detection, which is used to amend the RDT to obtain a non-redundance-included demodulation template (NRDT), and then demodulate transmitted symbols with the aid of the NRDT. Both the two approaches can avoid the effects of inter-frame interference (IFI) and unknown multipath channels, and suppress the intersymbol interference (ISI). Simulation results demonstrate that the proposed algorithms are sound and efficient.

In this paper, a blind two-step synchronization algorithm is proposed for direct-sequence (DS) ultra-wideband (UWB) communication systems. In the first step, the start of individual frames is estimated via simple square, overlap-add operation and energy detection technique. Then the first frame in each symbol is identified by exploiting the randomicity of DS codes and information symbols at the second step. Achieving synchronization with pulse-level resolution, the proposed algorithm considerably reduces synchronization time and complexity due to the two-step synchronization structure, compared with the single-step algorithm. Simulations and comparisons confirm that the proposed approach outperforms the existing alternative in terms of normalized mean square error (NMSE) and bit error rate (BER).

In this paper, two novel space-time coded modulation and corresponding noncoherent detection schemes for Ultra-Wideband (UWB) impulse radio system are proposed. An additional orthogonality is introduced to original Orthogonal Space-Time Block Code (OSTBC) for performance enhancement in the proposed two schemes, which is termed as Dual Orthogonal Space-Time Coded Modulation (DOSTCM). The specially designed signal structures from the DOSTCM exploit the advantage of multiantenna system and enable simple but effective noncoherent detection. Simulation results show our schemes achieve outstanding bit error rate (BER) performance.

This paper proposes a Dempster-Shafer theory (DST)-based iterative detection-decoding scheme in multiple-input multiple-output (MIMO) systems. On the one hand, the detection algorithm takes advantage of the characteristics of DST processing uncertainty, improving detection performance. On the other hand, during iterations the detection scheme allows for the soft information outputs from the decoder when combining multiple soft information sources, leading to the performance improvement. Moreover, compared with a related DST-based iterative detection scheme, the proposed algorithm has lower complexity, and its detection performance is closer to that of the optimal detection approach. Simulation results demonstrate the validity of the proposed scheme.

One of the biggest challenges in ultra-wideband impulse radio (UWB-IR) is timing acquisition. To accomplish synchronization, a novel modulation scheme is presented in this paper. Relying on the unique signal structure and the first order- statistics of the received signal, the timing offset can be acquired by energy detection even if in the presence of inter-frame interference (IFI) and inter-symbol interference (ISI). Simulations and comparisons confirm that the proposed approach outperforms the existing similar one in term of normalized mean square error (MSE) and bit error rate (BER).

This paper proposes a novel detection scheme based on Dempster-Shafer (D-S) evidence theory in multiple-input multiple-output (MIMO) systems. The focal-element-set (FES) characterizes the uncertainty contained in the decision statistics, and the corresponding basic-probability-assignment (BPA) of FES is the likelihood measure, acting as the soft-decisions of the transmit signals. The uncertainty can be counteracted during combining the soft information sources from all receive antennas, so more reliable decisions are achieved. Simulation results verify that the proposed algorithm significantly outperforms its conventional detection schemes counterparts with only moderate complexity increases.

In this paper, a novel rapid synchronization algorithm is proposed for UWB systems with noncoherent receivers based on an energy capture scheme, fully exploiting adequate multipath component. Only one symbol observation is required to acquire synchronization, which speeds up the overall synchronization process. And the lower complexity is achieved via not only the synchronization scheme but also the noncoherent receiver. Simulations are performed to demonstrate the promising performance of the presented algorithm.

In this paper, a novel scheme is developed for chip-level timing in UWB communications. To accomplish the proposed algorithm, the time hopping (TH) code is transformed to the spread time hopping (STH) code firstly, without changing ideas of UWB modulation. Invoking the cyclic autocorrelation of STH code, the timing parameter is acquired without training sequence. Desirable performance is brought out even if a few data is utilized for timing acquisition. Simulations are presented to validate the promising performance of the proposed scheme.

Adopting ultra-short impulses in ultra-wide bandwidth (UWB) transmission make systems vulnerable to timing-jitter. To overcome this challenge, we propose a timing-hopping high-order waveform modulation scheme in this letter. Central to our design is the adaptation of a high-order monocycle (HOM) that can provide timing-jitter robust UWB communications.

An important aspect of wireless networks is dynamic behavior. In this paper, we propose a joint cross-layer design for QoS (quality of service) content delivery. Central to our proposed cross-layer design is the concept of adaptation. We propose QoS-awareness scheduler and power adaptation scheme at both uplink and downlink medium access control (MAC) layer to coordinate the behavior of the lower layers for resource efficiency. The test results show that our cross-layer design provides a good scheme for wireless QoS content delivery.

An approach of joint blind estimation of the symbol timing offset and carrier frequency offset using multiple orthogonal frequency-division multiplexing (OFDM) symbols is proposed for OFDM systems in frequency selective fading environments. The proposed approach greatly improves the estimation accuracy of the synchronization parameters, more than the ML estimation of N. Lashkarian and S. Kiaei (see IEEE Trans. Commun., vol.48, p.2139-49, 2000). This conclusion is derived from analyzing their performances. Meanwhile, simulation shows that estimation of symbol timing is completely correct for signal-to-noise ratio (SNR) as low as 0 dB, and the variance of the frequency offset estimator is nearly close to its Cramer-Rao bound (CRB) with varying SNR.

We present a new algorithm for blind estimation of the symbol timing and frequency offset in a time-varying frequency-selective Rayleigh fading multipath channel for OFDM systems. It exploits the intrinsic structure of OFDM signals and only relies on a second-order moment without knowledge of the probability distribution function of the received signals. Under the minimum mean-square-error (MMSE) sense, the proposed estimators are totally optimum and easily implemented. Furthermore, we expand the estimation range of the frequency offset estimator and improve the timing estimator to be independent of the frequency offset. A more generalized channel model is considered. It is characterized by its power delay profile and time-varying scattering function. The channel has high reliability for a real mobile environment.