Sliding network coding (SNC) has emerged as a promising solution for ultra-reliable and low-latency communication (URLLC) scenarios. The performance of SNC, particularly in terms of developing the encoding matrix and decoding strategy, is heavily influenced by the order h of the underlying Galois field,GF(2h). In this paper, we investigate high-order SNC, whereh> 1, to enhance transmission efficiency by employing a Vandermonde-based encoding matrix that ensures linear independence among coded packets. To maximize decoding efficiency, we design a structure-aware decoding strategy (SA-DS), which not only dynamically exploits the relationships between successfully decoded (SD) packets and the currently decoded (CD) packet, but also utilizes the first-packet deterministic decoding (FPDD) property of the Vandermonde matrix. Additionally, we develop a Markov chain-based performance analysis framework in terms of retransmission probability, packet error rate, and expected decoding delay. Numerical results demonstrate that in the evaluated settings, the proposed scheme outperforms several traditional schemes in the moderate-erasure region. In the low-erasure region, its advantage becomes particularly pronounced (reaching one to three orders of magnitude in both PER and retransmission probability while maintaining a comparable decoding delay), making it particularly suitable for URLLC applications.

Magnetic induction (MI) communication is an effective underground emergency communication technique after disasters such as landslides, mine collapses, and earthquakes, due to its advantages in mediums such as soil, concrete, and metals. However, the propagation mediums in practical MI based underground emergency communications are usually diverse and composed randomly due to the impact of disasters, which poses a challenge for MI communication in practical applications. In this paper, we formulate a statistical fading channel model, which reflects the random composition of diverse mediums and is shown to follow a lognormal distribution. To mitigate the impact of diverse medium fading, Multi-frequency Resonating Compensation (MuReC) based coils are used to achieve multi-band transmission. Then, we analyze the performance of MuReC based multi-band MI communication with diverse medium fading and derive the expressions of signal-to-noise ratio (SNR) probability density functions, ergodic capaciteis, average bit error rates (BERs), and outage probabilities for both multiplexing and diversity cases. Numerical results show that MuReC based multi-band transmission schemes can effectively reduce the impact of diverse medium fading and enhance the performance.

By enabling spectrum sharing between radar and communication operations, the cell-free dual-functional radar–communication (CF-DFRC) system is a promising candidate to significantly improve spectrum efficiency in future sixth-generation (6G) wireless networks. However, in wideband scenarios, synchronization errors caused by carrier frequency offset (CFO) can severely reduce both communication capacity and sensing accuracy, especially when multiple geographically distributed full-duplex (FD) access points (APs) are jointly coordinated. In this paper, we consider a wideband FD CF-DFRC system where each AP is equipped with movable antennas (MAs). This setting is fundamentally different from existing DFRC or MA-aided designs that typically assume fixed-position antennas, half-duplex operation, or perfect synchronization. First, we develop a field-response-based channel model and derive a worst-case weighted communication–sensing rate (WCSR) that explicitly captures the impact of inter-AP CFO on both the uplink communication signal-to-interference-plus-noise ratio (SINR) and the radar echo SINR. Our analysis reveals that CFO increases the Cramér–Rao lower bound (CRLB) of target position estimation, thereby degrading sensing accuracy. Based on this characterization, we formulate a robust worst-case WCSR maximization problem that jointly optimizes MA positions, transmit beamforming vectors, receive filters, and CFO-related parameters under transmit power and MA-position constraints. To tackle the resulting highly non-convex problem, we propose a two-stage robust optimization framework. In the first stage, we employ fractional programming together with manifold optimization (MO) and penalty dual decomposition (PDD) to solve the worst-case CFO subproblem on the complex unit-modulus manifold, thus obtaining a CFO-robust closed-form structure for the WCSR. In the second stage, we design a meta–reinforcement learning (MRL) based resource allocation scheme that jointly optimizes the MA positions and beamforming vectors in a data-driven manner for dynamic wireless environments. Unlike conventional deep reinforcement learning (DRL) methods, the proposed MRL framework learns a meta-policy that can rapidly adapt to varying channel and CFO realizations, substantially improving convergence speed and scalability. Simulation results show that the proposed robust MO–PDD–MRL framework significantly outperforms existing DRL-based and non-robust CF-DFRC schemes in terms of both communication and sensing performance under CFO impairments. Furthermore, compared to fixed-position antenna (FPA) architectures, the MA-aided CF-DFRC system exhibits markedly enhanced robustness and adaptability to CFO effects and target mobility.

In this paper, we propose a novel cooperative relay-based resource and learning optimization (CRRLO) scheme that extends device connectivity, balances learning contributions, and coordinates communication resources to mitigate the negative impact of intermittent participation on FL performance caused by unreliable communication in Industrial Internet of Things (IIoT) environments. After local training, a cooperative aggregation stage is proposed, where fully connected device-to-device (D2D) relaying enables devices with failed device-to-server (D2S) transmissions to still contribute to the global model, while avoiding the transmission burden and relay selection issues associated with single-relay strategies. To further ensure unbiased aggregation, we produce reliability-driven aggregation weights to calibrate each device’s contribution to the global update. We then formulate a joint optimization problem aimed at improving FL convergence rate under communication and resource constraints by co-optimizing blocklength, transmission power, and aggregation weights. An iterative algorithm is designed to determine blocklength bounds, a low-complexity method is developed for power optimization, and a convex relaxation approach is adopted for weight adjustment. These subproblems are alternately solved using a block coordinate descent (BCD) method. Simulation results demonstrate that the proposed CRRLO scheme significantly accelerates convergence and improves test accuracy by up to 24.33% compared to baseline schemes under high transmission error probability.

The rapid growth of Web3.0 is transforming the Internet from a centralized structure to decentralized, which empowers users with unprecedented self-sovereignty over their own data. However, in the context of decentralized data access within Web3.0, it is imperative to cope with efficiency concerns caused by the replication of redundant data, as well as security vulnerabilities caused by data inconsistency. To address these challenges, we develop a Trustworthy Decentralized Cooperative Caching (TDC-Cache) framework for Web3.0 to ensure efficient caching and enhance system resilience against adversarial threats. This framework features a two-layer architecture, wherein the Decentralized Oracle Network (DON) layer serves as a trusted intermediary platform for decentralized caching, bridging the contents from decentralized storage and the content requests from users. In light of the complexity of Web3.0 network topologies and data flows, we propose a Deep Reinforcement Learning-Based Decentralized Caching (DRL-DC) for TDC-Cache to dynamically optimize caching strategies of distributed oracles. Furthermore, we develop a Proof of Cooperative Learning (PoCL) consensus to maintain the consistency of decentralized caching decisions within DON. Experimental results show that, compared with existing approaches, the proposed framework reduces average access latency by 20%, increases the cache hit rate by at most 18%, and improves the average success consensus rate by 10%. Overall, this paper serves as a first foray into the investigation of decentralized caching framework and strategy for Web3.0.

In virtual reality (VR) applications, 360° images are crucial for delivering immersive and panoramic experiences. However, the substantial data volumes create significant challenges for network storage and bandwidth. Additionally, transmission channel noise can further degrade the user experience by introducing visual distortions. To address these challenges, in this paper we propose a condensed semantic communication framework, specifically the channel denoising-based latent consistency model (CDLCM), designed for efficient 360° image transmission. The CDLCM compresses transmission data by employing deep neural networks (DNNs) to extract multiscale semantic features, which are then condensed via vector quantization (VQ). While this approach reduces transmission overhead, it may result in the loss of crucial image details and increase vulnerability to noise. To counteract these effects, the CDLCM integrates a spherical attention mechanism to detect and correct VQ errors caused by noise during inverse quantization. Additionally, the latent consistency model (LCM) iteratively denoises and restores lost image details, ensuring high-quality reconstruction. The framework also adapts to estimated channel noise by dynamically adjusting the diffusion steps to improve computational efficiency. Numerical experiments verify that the CDLCM not only reduces transmission overhead but also achieves superior reconstruction quality for 360° images compared to state-of-the-art methods. Overall, CDLCM offers an effective solution that balances compression efficiency and reconstruction fidelity, making it well-suited for immersive 360° image transmission.

Leveraging the spatiotemporal continuous aware ness of low Earth orbit satellites, space-based spectrum monitoring systems can construct a spectrum situation map (SSM) to facilitate spectrum surveillance and management in mobile cognitive communication networks. However, due to limited on orbit processing capabilities and unfavorable ground-to-satellite transmission environments, spectrum measurements will inevitably be incomplete and corrupted by anomalies. Existing SSM construction methods assume stationary spectral environments and do not consider the dynamic changes in spectrum situation. Considering spectrum state is evolving constantly over time, this paper proposes a robust time-aware online streaming tensor (R TAST) completion algorithm by exploiting the time-frequency space correlation and temporal properties in real-world spectral measurements. Based on compressed wideband sampling, the proposed R-TAST algorithm integrates rank estimation, anomaly removal, and dynamic spectral tensor completion to achieve spectrum situation completion and evolutionary prediction. Numerical analyses conducted on simulated and realistic ray tracing based datasets demonstrate the effectiveness and efficiency of the proposed R-TAST in comparison with state-of-the-art streaming tensor completion and prediction algorithms.

The low-altitude economy (LAE) is inherently dependent on unmanned aerial vehicles (UAVs) as its core operational infrastructure, with applications spanning logistics, surveillance, and smart city development. Despite growing attention, LAE's practical deployment faces substantial obstacles, particularly in maintaining reliable UAV operations. These UAVs require secure and efficient wireless communication services delivered by base stations (BSs), yet their high mobility, combined with multipath effects, creates significant channel estimation challenges that threaten the seamless connectivity. According to the fixed airspace and planned routes characteristics of LAE scenarios, in this paper we construct the radio map to assist channel estimation using sensing information. First, a grid-based UAV channel measurement scheme is proposed to collect CSI data labeled with discrete locations and velocities. Then, a new generative adversarial network (GAN)-based model named continuous vector-conditioned GAN (CVCGAN) is developed to complete the discrete map by establishing the mapping from continuous sensing information to their channel space. After that, an integrator is designed to fuse the channel state information (CSI) provided by radio map with that estimated by pilots. Simulation results validate the superiority of the proposed method, which outperforms state-of-the-art (SOTA) approaches including ChannelNet, conditional GAN (CGAN), RadioUNet and long short-term memory (LSTM).

Reconfigurable Intelligent Surfaces (RIS) represent a transformative technology for sixth-generation (6G) wireless communications, but it suffers from a significant limitation, namely the double-fading attenuation. Active RIS has emerged as a promising solution, effectively mitigating the attenuation issues associated with conventional RIS-assisted systems. However, the current academic work on active RIS focuses on the system-level optimization of active RIS, often overlooking the development of models that are compatible with its electromagnetic (EM) and physical properties. The challenge of constructing realistic, EM-compliant models for active RIS-assisted communication, as well as understanding their implications on system-level optimization, remains an open research area. To tackle these problems, in this paper we develop a novel EM-compliant model with mutual coupling (MC) for active RIS-assisted wireless systems by integrating the developed scattering-parameter (S-parameter) based active RIS framework with multiport network theory, which facilitates system-level analysis and optimization. To evaluate the performance of the EM-compliant active RIS model, we design the joint optimization scheme based on the transmit beamforming at the transmitter and the reflection coefficient at the active RIS to maximize the achievable rate of EM-compliant active RIS-assisted MIMO system. To tackle the inherent non-convexity of this problem, we employ the Sherman-Morrison inversion and Neumann series (SMaN)-based alternating optimization (AO) algorithm. Simulation results verified that EM property (i.e., MC effect) is an indispensable factor in the optimization process of MIMO systems. Neglecting this effect introduces a substantial performance gap, highlighting its significance in the more pronounced the MC effect is, the greater the gap in achievable rates.

Although the emerging reconfigurable intelligent surface (RIS) paves a new way for next-generation wireless communications, it suffers from inherent flaws, i.e., double-fading attenuation effects and half-space coverage limitations. The state-of-the-art double-face active (DFA)-RIS architecture is proposed for significantly amplifying and transmitting incident signals in full-space. Despite the efficacy of DFA-RIS in mitigating the aforementioned flaws, its potential drawback is that the complex active hardware also incurs intolerable energy consumption. To overcome this drawback, in this paper we propose a novel dynamic energy-saving design for the DFA-RIS, called the sub-array based DFA-RIS architecture. This architecture divides the DFA-RIS into multiple sub-arrays, where the signal amplification function in each sub-array can be activated/deactivated dynamically and flexibly. Utilizing the above architecture, we develop the joint optimization scheme based on transmit beamforming, DFA-RIS configuration, and reflection amplifier (RA) operating pattern to maximize the energy efficiency (EE) of the DFA-RIS assisted multiuser multiple-input-single-output (MISO) system considering the imperfect channel state information (CSI) case. Then, the constrained stochastic majorization-minimization (CSMM) based AO algorithm address non-convex problems. Simulation results verified that our proposed sub-array based DFA-RIS architecture can benefit the EE of the system more than other RIS architectures.

Accurate acquisition of channel state information (CSI) is crucial for achieving high-rate communication, yet it introduces significant training overhead and latency, particularly in high-mobility cell-free massive multiple-input multipleoutput (CF-mMIMO) communication systems with unmanned aerial vehicles (UAVs). To address this challenge, we propose a predictive beamforming and resource allocation framework that significantly reduces training overhead while enhancing system throughput. Specifically, a novel frame structure is designed in which uplink training is performed only in the first time slot of each beam tracking frame, while distributed beam tracking and predictive beamforming are applied in all subsequent slots using the extended Kalman filter (EKF) at each access point (AP). Moreover, we develop a centralized information fusion algorithm that exploits cell-free multi-point cooperation to improve estimation accuracy with low fronthaul overhead. Then, we derive the theoretical posterior Cram´er-Rao bound (PCRB) for the fused estimation and show that, under the local linear-Gaussian approximation, the predicted PCRB coincides with the covariance of the fused estimate. We further establish an explicit analytical mapping between the predicted PCRB and the uplink pilot length. Leveraging this theoretical bridge, we formulate a prediction-aware joint optimization problem involving uplink pilot length, downlink AP-user association, and power allocation to actively adapt the training overhead and maximize the effective sum spectral efficiency (SE). A low-complexity iterative algorithm based on fractional programming is proposed to solve this problem. Numerical results demonstrate that the proposed framework achieves a favorable trade-off between signaling overhead and system throughput. Specifically, it reduces the training overhead by 95% with only a 3% decrease in positioning accuracy, incurs only modest additional fronthaul overhead, and improves the effective sum SE by 31% compared to traditional schemes. Furthermore, comprehensive evaluations show that the framework remains effective under multipath fading and higher UAV velocities, and continues to benefit from cooperative gains in expanded network deployments.

Task-Oriented Semantic Communication (TOSC) has been regarded as a promising communication framework, serving for various Artificial Intelligence (AI) task driven applications. The existing TOSC frameworks focus on extracting the full semantic features of source data and learning low-dimensional channel inputs to transmit them within limited bandwidth resources. Although transmitting full semantic features can preserve the integrity of data meaning, this approach does not attain the performance threshold of the TOSC. In this paper, we propose a Task-oriented Adaptive Semantic Communication (TasCom) framework to effectively facilitate the inference of different AI tasks. Based on the Generative AI (GAI) techniques, we first propose a Joint Source-Channel Coding (JSCC) that which only extracts and fuses task-related semantic features, and then transmits them to achieve efficient task-oriented semantic transmission. Then, we propose a generative training algorithm to train the proposed JSCC for optimal performance. Furthermore, an Adaptive Coding Controller (ACC) is proposed to find the optimal coding scheme for the proposed JSCC, which allows the semantic features with significant contributions to the task inference to preferentially occupy limited bandwidth resources for wireless transmission. The simulation results show that the proposed TasCom outperforms the existing TOSC and traditional codec schemes on the object detection and instance segmentation tasks under all considered channel conditions.

Reconfigurable distributed antenna and reflecting surface (RDARS) is a new architecture for the sixth-generation (6G) millimeter wave (mmWave) communications. In RDARS-aided mmWave systems, the active and passive beamforming design and working mode configuration for reconfigurable elements are crucial for system performance. In this paper, we aim to maximize the weighted sum rate (WSR) in the RDARS-aided mmWave system. To take advantage of RDARS, we first design a reconfigurable codebook (RCB) in which the number and dimension of the codeword can be flexibly adjusted. Then, a low overhead beam training scheme based on hierarchical search is proposed. Accordingly, the active and passive beamforming for data transmission is designed to achieve the maximum WSR for both space-division multiple access (SDMA) and time-division multiple access (TDMA) schemes. For the TDMA scheme, the optimal number of RDARS transmit elements and the allocated power budget for WSR maximization are derived in closed form. Besides, the superiority of the RDARS is verified and the conditions under which RDARS outperforms RIS and DAS are given. For the SDMA scheme, we characterize the relationship between the number of RDARS connected elements and the user distribution, followed by the derivation of the optimal placement positions of the RDARS transmit elements. High-quality beamforming design solutions are derived to minimize the inter-user interference (IUI) at the base station and RDARS side respectively, which nearly leads to the maximal WSR. Finally, simulation results confirm our theoretical findings and the superiority of the proposed schemes.

The spectrum sharing and open nature of wireless channels enable integrated sensing and communication (ISAC) susceptible to hostile jamming attacks, particularly in the context of partial band/broadband jamming attacks. How to significantly improve the anti-jamming performance of ISAC systems with unknown jamming channel state information (CSI) and limited bandwidth is an urgent problem to be exploxied. Due to the intrinsic orthogonality and rich angular information of orbital angular momentum (OAM), vortex electromagnetic waves with helical phase fronts have shown great potential to achieve high-precision position estimation of radar and strong anti-jamming capability of wireless communication. Focusing on solving the anti-jamming problem of ISAC mentioned above, in this paper we propose a novel ISAC for anti-jamming with OAM scheme, where the ISAC transmitter can simultaneously sense the position of jammers with dynamic behavior and send data to multiple OAM legitimate users. Specifically, we develop the enhanced multiple-signal-classification (EMUSIC) based three-dimensional (3D) position estimation scheme with continuous sensing in both two-dimensional (2D) frequency and angular domains to accurately estimate the position of the jammer, thus acquiring the jamming CSI. According to the estimated jamming CSI, we design the joint transmit-receive beamforming and power allocation alternating optimization scheme, where the transmit and receive beamforming matrices are dynamically adjusted to significantly mitigate inter-mode interference, inter-user interference, and jamming, thus maximizing the achievable sum rates (ASRs) of all users. Numerical results demonstrate that our proposed scheme can significantly increase the ASR under broadband jamming attacks and achieve high-precision estimation of targets as compared with the conventional multiple-input-multiple-output (MIMO)-based ISAC.

The emergence of extremely large-scale antenna arrays (ELAA) in millimeter-wave (mmWave) communications, particularly in high-mobility scenarios, highlights the importance of near-field beam prediction. Unlike the conventional far-field assumption, near-field beam prediction requires codebooks that jointly sample the angular and distance domains, which leads to a dramatic increase in pilot overhead. Moreover, unlike the farfield case where the optimal beam evolution is temporally smooth, the optimal near-field beam index exhibits abrupt and nonlinear dynamics due to its joint dependence on user angle and distance, posing significant challenges for temporal modeling. To address these challenges, we propose a novel Convolutional Neural Network– Generative Pre-trained Transformer 2 (CNN–GPT2) based near-field beam prediction framework. Specifically, an uplink pilot transmission strategy is designed to enable efficient channel probing through widebeam analog precoding and frequency-varying digital precoding. The received pilot signals are preprocessed and passed through a CNN-based feature extractor, followed by a GPT-2 model that captures temporal dependencies across multiple frames and directly predicts the near-field beam index in an end-to-end manner. A pretraining–finetuning strategy is further adopted, where the model is first pretrained via masked prediction and then finetuned for the downstream beam prediction task, significantly improving training efficiency and accuracy. Simulation results under the 3GPP TR 38.901 channel model demonstrate that the proposed method achieves higher beam prediction accuracy than conventional recurrent models, while maintaining competitive performance in normalized beam-forming gain. These results confirm the feasibility of employing large-model AI for robust and low-overhead near-field beam management in future 6G systems.

The multiple-input multiple-output dual functional radar communication (MIMO-DFRC) system is a promising platform for future integrated sensing and communication applications. Ensuring reliable performance of both radar and communication functions, the beam selection is a critical technology in MIMO-DFRC systems. However, the beam selection problem is known to be NP-hard, and efficiently addressing it remains an open issue, especially in distributed systems. In this paper, we address the beam selection problem for a MIMO-DFRC system by formulating it as a semi-Markov decision process and propose a novel hierarchical reinforcement learning (HRL) algorithm. In our approach, codebook-based beam selection for transmitting and receiving BS is controlled by an agent deployed in the cloud. Inspired by the mechanism of hierarchical codebook beam training, we employ an option-based policy that enables the agent to explore different layers of the codebook and extract context information across multiple discrete time steps. We utilize an invalid action masking technique to overcome the dynamic action space problem caused by the option-based policy. Simulation results demonstrate that the HRL-based algorithm outperforms existing beam selection methods and achieves remarkable performance even under conditions of a high probability of false alarm and low signal-to-noise ratio. Furthermore, we find that the proposed algorithm exhibits promising capabilities to learn a more efficient policy beyond the full hierarchical codebook training trajectory.

High-level synthesis (HLS) tools streamline FPGA design by enabling engineers to implement hardware using $\mathrm{C} / \mathrm{C}++$ languages. However, while clock management serves as a critical stage in the FPGA EDA flow that affects system-level performance, area, and especially power consumption, existing commercial HLS tools lack comprehensive solutions for clock management. Specifically, the diversity of clock resources creates a vast design space for finding the optimal configuration, and the insufficient analysis of multiple clock domain scenarios hinders effective clock-oriented optimizations in HLS. This work introduces AutoClock, an open-source integrated clock management framework that complements AMD Vitis HLS. AutoClock allocates resources for clock generation, assigns modules to appropriate clock domains, addresses metastability and time division multiplexing (TDM) malfunctioning introduced by multiclock domain architectures, and hierarchically gates the clock of modules in a design. Experimental results demonstrate that AutoClock can fully utilize clock resources on FPGAs and help reduce dynamic power consumption by up to 74.38%.

The exponential growth of wireless devices and stringent reliability requirements of emerging applications demand fundamental improvements in distributed channel access mechanisms for unlicensed bands. Current Wi-Fi systems, which rely on binary exponential backoff (BEB), suffer from suboptimal collision resolution in dense deployments and persistent fairness challenges due to inherent randomness. This paper introduces a multiagent reinforcement learning framework that integrates artificial intelligence (AI) optimization with legacy device coexistence. We first develop a dynamic backoff selection mechanism that adapts to real-time channel conditions through access deferral events while maintaining full compatibility with conventional CSMA/CA operations. Second, we introduce a fairness quantification metric aligned with enhanced distributed channel access (EDCA) principles to ensure equitable medium access opportunities. Finally, we propose a centralized training decentralized execution (CTDE) architecture incorporating neighborhood activity patterns as observational inputs, optimized via constrained multi-agent proximal policy optimization (MAPPO) to jointly minimize collisions and guarantee fairness. Experimental results demonstrate that our solution significantly reduces collision probability compared to conventional BEB while preserving backward compatibility with commercial Wi-Fi devices. The proposed fairness metric effectively eliminates starvation risks in heterogeneous scenarios.

IEEE 802.11be (Wi-Fi 7) introduces Multi-Link Operation (MLO) as a While MLO offers significant parallelism and capacity, realizing its full potential in guaranteeing strict delay bounds and optimizing Quality of Service (QoS) for diverse, heterogeneous traffic streams in complex multi-link scenarios remain a significant challenge. This is largely due to the limitations of static Enhanced Distributed Channel Access (EDCA) parameters and the complexity inherent in cross-link traffic management. To address this, this paper investigates the correlation between overall MLO QoS indicators and the configuration of EDCA parameters and Acess Catagory (AC) traffic allocation among links. Based on this analysis, we formulate a constrained optimization problem aiming to minimize the sum of overall packet loss rates for all access categories while satisfying their respective overall delay violation probability constraints. A Genetic Algorithm (GA)-based MLO EDCA QoS optimization algorithm is designed to efficiently search the complex configuration space of AC assignments and EDCA parameters. Experimental results demonstrate that the proposed approach’s efficacy in generating adaptive MLO configuration strategies that align with diverse service requirements. The proposed solution significantly improves delay distribution characteristics, and enhance QoS robustness and resource utilization efficiency in high-load MLO environments.

Modern processors feature complex architectures that necessitate the generation of extensive test programs to ensure functional correctness, making testing the most time-consuming stage of the processor design flow. Existing automated verification frameworks for agile design exhibit significant limitations, such as fixed program structures restricting flexibility, uncontrolled control flows leading to invalid instructions, and low coverage of the vast state space. To address these limitations, we propose CURE-Fuzz, a curiosity-driven reinforcement learning framework designed to enhance agile hardware testing. By integrating a hierarchical test generation model with a curiosity-driven exploration mechanism, CURE-Fuzz enables precise control over test program structure and dependencies while efficiently navigating unexplored processor states. Evaluations on Rocket and Boom core demonstrate that CURE-Fuzz achieves higher coverage and exhibits superior bug detection capabilities compared to state-of-the-art fuzzers.

Implementing LiDAR-based 3D object detection algorithms in practical autonomous driving situations presents a significant challenge. In current research algorithms, the inherent sparsity and randomness of point cloud data necessitate significant memory usage and frequent data read/write operations during preprocessing. Such demands are not well-suited for terminal devices with stringent real-time requirements and constrained resources. In this paper, we present a packetized processing Pillar Feature Net accelerator for LiDAR 3D object detection. By integrating voxelization and feature extraction into a pipelined architecture, the proposed accelerator significantly reduces the storage requirements for point cloud data and enhances the speed of feature extraction and pseudo-image generation. Experimental results indicate that the proposed method improves the computational throughput from point cloud data to pseudo-image generation by 1.2 times and eliminates the need for off-chip memory access during preprocessing.

Mixed-precision quantization CNNs have become crucial for edge vision algorithms. While model quantization techniques have improved inference efficiency, existing approaches have not fully addressed data coherence in coarse-grained parallelism or adaptation to various 2D data sizes. This study presents a novel CNN accelerator equipped with mixed-precision multiplier and feature linking mechanisms to enhance mixed-precision inference on edge NPUs. The accelerator incorporates reconfigurable multi-precision multiplication support in MAC units, enabling 8-bit/4-bit mixed-precision quantization. A feature linking mechanism is developed to overcome feature map size limitations, achieving scalable processing dimensions. The accelerator design, implemented on a Xilinx Zynq UltraScale+ MPSoC FPGA platform, demonstrates improved hardware performance and enhanced inference speeds for edge device CNN models such as Tiny-YOLOv3 and ResNet18. This work provides a significant advancement in deploying mixed-quantization CNNs on edge NPUs, enhancing the adaptability and performance of edge computing devices for complex neural network models.

The vortex electromagnetic wave carried by multiple orthogonal orbital angular momentum (OAM) modes in the same frequency band can be applied to the field of wireless communications, which greatly increases the spectrum efficiency. The uniform circular array (UCA) structure is widely used to generate or receive vortex electromagnetic waves with multiple OAM-modes. However, the maximum number of orthogonal OAM-modes based on UCA is usually limited to the number of array-elements of the UCA antenna, leaving how to utilize more OAM-modes to achieve higher spectrum efficiency given a fixed number of array-elements as an intriguing question. In this paper, we propose an N-dimensional quasi-fractal UCA (ND QF-UCA) antenna structure in different fractal geometry layouts to break through the limits of array-elements number on OAM-modes number. We develop the N-dimensional OAM modulation (NOM) and demodulation (NOD) schemes for OAM multiplexing transmission with the OAM-modes number exceeding the array-elements number, which is beyond the traditional concept of multiple antenna based wireless communications. Then, we investigate different dimensional multiplex transmission schemes based on the corresponding QF-UCA antenna structure with various array-elements layouts. Simulation results show that our proposed schemes can obtain a higher spectrum efficiency.

The vortex electromagnetic wave carrying multiple orthogonal orbital angular momentum (OAM) modes in the same frequency band can be applied to the field of wireless communications, which greatly increases the spectrum efficiency. The uniform circular array (UCA) is widely used to generate and receive vortex electromagnetic waves with multiple OAM-modes. However, the maximum number of orthogonal OAM-modes based on UCA is usually limited to the number of array-elements of the UCA antenna, leaving how to utilize more OAM-modes to achieve higher channel capacity with a fixed number of array-elements as an intriguing question. In this paper, we propose anN-dimensional quasi-fractal UCA (ND QF-UCA) antenna structure in different fractal geometry layouts to break through the limits of array-elements number on OAM-modes number. We develop theN-dimensional OAM modulation (NOM) and demodulation (NOD) schemes for OAM multiplexing transmission with the OAM-modes number exceeding the array-elements number, which is beyond the traditional concept of multiple antenna based wireless communications. Then, we investigate different dimensional multiplexing transmission schemes based on the corresponding QF-UCA antenna structure with various array-element layouts and evaluate the optimal layout type and dimension to obtain the highest channel capacity with a fixed number of array-elements. Simulation results show that our proposed schemes can obtain a higher spectrum efficiency, surpassing those of alternative array-element layouts of QF-UCA and the traditional multiple antenna systems.

Transmission at millimeter-wave (mmWave) frequencies is promising in the sixth generation (6G) and beyond systems due to abundant spectrum resources. Reconfigurable intelligent surface (RIS) assisted mmWave communications has been envisioned as an effective technique to effectively compensate for propagation loss and increase the capacity of line-of-sight (LOS) multiple-input-multiple-output (MIMO) wireless communication. To decrease the complexity of transceivers, the uniform circular array (UCA) is an effective antenna structure for RIS-assisted mmWave MIMO wireless communications when the transmit and receive UCAs are aligned. However, the complexity increases when the transceivers are not perfectly aligned and it is difficult to ensure the strict alignment in practice. To achieve low-complexity transceivers and increase the spectrum efficiency for RIS-assisted mmWave MIMO wireless communications, in this paper we investigate the concentric UCAs based spherical RIS-assisted mmWave MIMO wireless communications. The channel model is derived for the concentric UCAs based spherical RIS-assisted mmWave MIMO wireless communications. Then, a beamforming scheme at the transmit UCA, a phase compensation scheme at the spherical RIS, and a predetection scheme at the receive UCA, which can decrease the complexity of transceivers, are developed in the misaligned scenario. Our proposed fast symbol-wise maximum likelihood (ML) detection scheme can recover the signals with low complexity. Furthermore, we propose an algorithm to achieve the optimal spherical RIS design for increasing the spectrum efficiency of the concentric UCAs based spherical RIS-assisted mmWave MIMO wireless communications. Simulation results are presented to illustrate the theory for the concentric UCAs based spherical RIS-assisted mmWave MIMO wireless communications.

The advent of massive ultra-reliable and low-latency communications (mURLLC) has introduced a critical class of time- and reliability-sensitive services in next-generation wireless networks. This shift has attracted significant research attention, driven by the need to meet stringent quality-of-service (QoS) requirements. In this context, non-orthogonal multiple access (NOMA) systems have emerged as a promising solution to enhance mURLLC performance by providing substantial enhancements in both spectral efficiency and massive connectivity, particularly through finite blocklength coding (FBC) techniques. Nevertheless, owing to the dynamic nature of wireless network environments and the complex architecture of FBC-enhanced NOMA systems, the research on the efficient design of optimizing the system performance for maximizing system capacity while guaranteeing the tail distributions in terms of new statistical QoS constraints for delay and error-rate is still in its infancy. In an effort to address these challenges, we put forth the formulation and solution of$\epsilon $-effective capacity problems tailored for uplink FBC-enhanced NOMA systems, specifically catering to ensure statistical delay and error-rate bounded QoS requirements. In particular, we establish uplink two-user FBC-enhanced NOMA system models by applying the hybrid successive interference cancellation (SIC). We also develop the concept of the$\epsilon $-effective capacity and propose the optimal power allocation policies to maximize the$\epsilon $-effective capacity and$\epsilon $-effective energy efficiency while upper-bounding both delay and error-rate. We conduct a set of simulations to validate and evaluate our developed optimization schemes over FBC-enhanced NOMA systems.

In this paper, we propose a virtual full-duplex (VFD) technique with zero-interval modulation and sampling (ZIMS), where two half-duplex (HD) transceivers can simultaneously transmit signals and each transceiver can effectively receive the desired information. In ZIMS-VFD, the transceiver inserts a zero-interval for each symbol in the transmit signal and provides self-interference (SI)-free intervals for itself. Meanwhile, it samples the receive signal in the provided SI-free intervals and restores the desired symbols. Based on orthogonal frequency division multiplexing (OFDM), we formulate the system model and show the transmit signal structure. Then, we give the transceiver design for single input single output (SISO) ZIMS-VFD and extend it to multiple input multiple output (MIMO) communications. Numerical results verify our theoretical analyses and show that ZIMS-VFD can effectively increase the capacity and approach the FD without SI.

Thanks to the Internet of Things (IoT), there has been explosive growth in edge devices, which generate a tremendous amount of data that holds invaluable potential. However, conventional data mining and machine learning (ML) paradigms require transmitting raw data to data centers for further use, which puts a heavy burden on communication networks and is exposed to high privacy risks. Federated learning allows for the training of ML models using distributed datasets, which can be applied to protect data privacy and alleviate transmission burdens. Meanwhile, the technique of over-the-air (OTA) computation can be utilized to exploit the superposition property of wireless communication channels. Motivated by this, in this paper, we propose a co-phase OTA approach for communication-efficient uploading in multi-server federated learning, which does not require expansion of the uplink channel bandwidth when the numbers of users and models increase. Besides, the digital OTA with randomized transmission is proposed to overcome the disadvantages of analog OTA, where the performance analyses of analog OTA and digital OTA are deduced, respectively. Simulation results show that a lower cost function can be obtained by digital OTA while requiring fewer iterations for convergence than that in analog OTA as more users can upload.

In the Internet of Things (IoT), numerous devices and sensors are deployed to collect data sets. Although some IoT devices can process data locally, most devices may have limited power and computational capability. Since mobile edge computing (MEC) is a new paradigm to provide strong computing capability at the edge of networks close to users, these devices can offload their tasks to MEC servers. Therefore, designing an efficient computation offloading strategy to decide whether the tasks to be offloaded to MEC servers becomes crucial. In this paper, we study the computation offloading for IoT devices based on a non-cooperative game with one-shot random access, where users’ offloading decisions can be made independently to realize distributed offloading. In particular, we discuss the offloading game with and without sharing information among devices and find the Nash equilibrium (NE). Besides, we analyze the effective bandwidth as a performance metric from a device perspective, which considering the Quality of Service (QoS) of network layer while analyzing users’ offloading strategies. Simulation results show the effectiveness of proposed strategies and the impact of offloading tasks to users’ strategies in time-varying channel based on effective bandwidth.

In recent years, reconfigurable intelligent surfaces (RIS) have garnered significant attention for their ability to control the phase shifts in reflected signals. By intelligently adjusting these phases, RIS can establish seamless direct paths between communication devices obstructed by obstacles, eliminating the need for forwarding and significantly reducing system overhead associated with relaying. This capability is crucial in multi-hop ad hoc networks requiring multiple relay steps. Consequently, the concept of incorporating multi-hop RIS into wireless multi-hop relay networks has emerged. In this paper, we propose a novel network model where each UAV communication node is equipped with a RIS, facilitating seamless connections in multi-hop relay wireless networks. We analyze the performance of this model by integrating RIS-assisted physical layer modeling into the seamless connection network framework and conducting a detailed comparative analysis of RIS-assisted and conventional connections. At the medium access layer, we introduce a RIS-DCF MAC protocol based on the IEEE 802.11 distributed coordination function (DCF), modeling the medium access process as a two-hop access scenario. Our results demonstrate that the seamless connections and diversity gain provided by RIS significantly enhance the performance of multi-hop relay wireless networks.

Adaptive bitrate (ABR) streaming provides an effective way to improve the Quality of Experience (QoE) of video users and is now the de facto standard for video delivery. Meanwhile, mobile edge computing (MEC) has been applied to assist ABR streaming, improving the performance of mobile networks and enabling efficient video delivery. However, smooth ABR streaming relies on the bidirectional adaptation between bitrate selection and bandwidth allocation, as they operate on distinct timescales and have different optimization goals. Moreover, since the constrained wireless resources available within a cell are shared by multiple users, their QoE should be optimized not only jointly but fairly. To this end, we propose a QoE-fairness-aware bandwidth allocation (QFA-BA) method for MEC-assisted ABR video transmission. With a novel perspective on buffer occupancy modeling, the relationship between bitrate selection and bandwidth allocation is studied. An enhanced QoE evaluation model is then proposed to correlate bitrate selection with bandwidth allocation and facilitate QFA-BA. Finally, a soft actor-critic (SAC) framework improving both the QoE and QoE-fairness is presented for QFA-BA. Compared with the state-of-the-art methods, our QFA-BA can perceive fine-grained buffer occupancy and stabilize it near a preset value with relatively more and larger bitrate switchings, exhibiting smoother convergence, better QoE (50.29%) and QoE fairness (54.81%).

In multiple-input multiple-output (MIMO) communication systems, signal detection plays a crucial role in achieving reliable and high-performance wireless communication. However, the complexity of optimal detection methods, such as maximum likelihood (ML) detection, grows exponentially with the number of transmit antennas when exhaustive search is used, hindering practical implementation. To address this challenge, suboptimal algorithms such as successive interference cancellation (SIC)-based detection have been developed, but they suffer from error propagation. To mitigate error propagation in SIC detectors, a soft-decision based partial maximum a posteriori (MAP) method has been derived to enhance performance. Since the partial MAP method allows MIMO detection to be divided into multiple stages, detection of each layer can be approached as a regression problem, and can be carried out by deep learning (DL)-based method to reduce computational overhead. Therefore, in this paper, we propose PMAP-Net, which integrates deep neural networks (DNNs) into partial MAP method for MIMO systems. We derive the soft log-likelihood ratios (LLRs) for single and multiple signals and design the input sets of DNNs. To further reduce the number of inputs in DNNs, we decrease input dimensionality by deriving extended input sets, which alleviates computational burden to be linear with respect to the number of antennas. Simulation results demonstrate that our proposed DL-based detection algorithm can provide near-optimal performance with relatively low complexity and outperforms other DL-based detectors in various MIMO scenarios.

Due to the broadcast and open characteristics of wireless channels, near-field physical layer security has attracted much attention to facilitate wireless information security against illegitimate eavesdropping. However, highly correlated channels between the legitimate transceivers and eavesdroppers for existing near-field line-of-sight multiple-input multiple-output (MIMO) systems with low degrees of freedom make it difficult to efficiently distinguish eavesdropping channels and legitimate channels in the angular domain, thus resulting in low secrecy rates. Fortunately, orbital angular momentum (OAM) with rich phase information shows great potential to enhance the physical layer security. To significantly increase the secrecy rates of near-field wireless communications, in this paper we propose the double-reconfigurable-intelligent-surface (RIS) assisted OAM secure scheme, where RISs with few reflecting elements are easily deployed to reconstruct the direct links blocked by obstacles between the legitimate transceivers, mitigate the inter-mode interference caused by the misalignment of legitimate transceivers, and adjust the OAM beams direction to interfere with eavesdroppers. Meanwhile, due to the unique orthogonality among OAM modes, the OAM-based joint index modulation and artificial noise scheme is proposed to weaken the information acquisition by eavesdroppers while increasing the achievable rate with the low cost of legitimate communications. To maximize the secrecy rate of our proposed scheme, we develop the Riemannian manifold conjugate gradient (RMCG)-based alternative optimization (AO) algorithm to jointly optimize the transmit power allocation of OAM modes and phase shifts of double RISs. Numerical results show that our proposed double-RIS-assisted OAM near-field secure scheme outperforms the existing works in terms of the secrecy rate and the eavesdropper’s bit error rate.

Multilayer satellite networks (MLSNs) incorporating non-orthogonal multiple access (NOMA) offer a wide range of applications and enable seamless connectivity for various user demands. However, the effects of co-channel interference, complicated fading facts, and the dynamics of satellite networks limit further practical applications of NOMA in MLSNs. In this paper, we propose a NOMA-based MLSN system with an adaptive power allocation scheme. In order to accurately model the mobility of satellites, we derive a closed-form expression for the probability density function of the elevation angle observed by the terrestrial users for a low-Earth-orbit satellite, based on which detailed theoretical discussions are given about the statistical properties and channel capacities of satellite-terrestrial and inter-satellite links. Finally, we establish a novel adaptive power allocation scheme for the NOMA-based MLSNs to enhance user fairness by applying a particle swarm optimization (PSO) algorithm. The simulation results confirm the correctness of our theoretical results and the superiority of the adaptive power allocation scheme for user fairness of the NOMA-based dynamic MLSNs considered in this work.

In this paper, we investigate an unmanned aerial vehicle (UAV)-enabled secure communication scenario that a cluster of UAVs performs a virtual non-uniform linear array (NULA) to communicate with a base station (BS) in the presence of eavesdroppers (Eves). Our goal is to design the UAV topology, trajectory, and precoding to maximize the system channel capacity. To this end, we convert the original problem into equivalent two-stage problems. Specifically, we first try to maximize the channel gain by meticulously designing the UAV topology. We then study the joint optimization of the trajectory and precoding for total transmit power minimization while satisfying the constraints on providing quality of service (QoS) assurance to the BS, the leakage tolerance to Eves, the per-UAV transmit power, the initial/final locations, and the cylindrical no-fly zones. For the UAV topology design, we prove that the topology follows the Fekete-point distribution. The design of trajectory and precoding is formulated as a non-convex optimization problem which is generally intractable. Subsequently, the non-convex constraints are converted into convex terms, and a double-loop search algorithm is proposed to solve the transmit power minimization problem. Introduce random rotation offsets so as to perform a dynamic stochastic channel to enhance the security. Numerical results demonstrate the superiority of the proposed method in promoting capacity.

To enable cost-effective universal access and the enhancement of current communication services, the space-air-ground integrated networks (SAGINs) have recently been developed due to their exceptional 3D coverage and the ability to guarantee rigorous and multidimensional demands for quality-of-service (QoS) provisioning, including delay and reliability across vast distances. The integration of spatial, aerial, and terrestrial dimensions is thus regarded as a critical facilitator for accommodating massive Ultra-Reliable Low-Latency Communications (mURLLC) applications. In response to the complex, heterogeneous, and dynamic serving scenarios and stringent performance expectations for 6G SAGINs, it is crucial to undertake modeling, assurance, and analysis of the key technologies, aligned with the diverse demands for QoS provisioning in the non-asymptotic regime, i.e., when implementing finite blocklength coding (FBC) as a new dimension for error-rate bounded QoS metric. However, how to design new statistical QoS-driven performance modeling approaches that accurately delineate the complex and dynamic behaviors of networks, particularly in terms of constraining both delay and error rate, persists as a significant challenge for implementing mURLLC within 6G SAGINs in the finite blocklength regime. To overcome these difficulties, in this paper we propose to develop a set of analytical modeling frameworks for 6G SAGIN in supporting statistical delay and error-rate bounded QoS in the finite blocklength regime. First, we establish the SAGIN system architecture model. Second, the aggregate interference and decoding error probability functions are modeled and examined by using Laplace transform. Third, we introduce modeling techniques aimed at defining the$\epsilon $-effective capacity function as a crucial metric for facilitating statistical QoS standards with respect to delay and error-rate. To validate the effectiveness of the developed performance modeling schemes, we have executed a series of simulations over SAGINs.

Radio map is a promising technology that connects the user equipment (UE) location and its channel state information (CSI). By applying a radio map, the beamforming vector can be generated based solely on the location of the UE, thereby significantly saving pilot effort. However, the effectiveness of radio map-based beamforming is influenced by several factors, such as positioning errors and channel dynamics. To improve the adaptability and performance of radio map-based beamforming, in this paper we consider integrating location information with reduced pilots and examine the trade-off between pilot overhead and beamforming performance. In particular, an end-to-end method for joint extrapolation, denoising and performing beamforming with reduced pilots is proposed. Subsequently, the beamforming vector obtained from reduced pilots is integrated with that generated by the radio map. Considering the overhead caused by pilots, the support vector machine (SVM) is applied to determine whether it is worth introducing pilots for integration. According to the numerical simulations, the proposed end-to-end method with reduced pilots is superior to that with full pilots in terms of spectral efficiency (SE) and can be improved by integrating with the radio map. In addition, the application of SVM can effectively identify the integration needs, further reducing unnecessary pilot effort and thereby increasing SE.

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

Reconfigurable intelligent surface (RIS) has attracted much attention to assist millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) wireless communications over line-of-sight (LOS) channels for performance improvement due to low hardware cost and low power consumption. The uniform circular array (UCA) is an effective antenna structure with low-complexity transceivers for RIS-assisted mmWave MIMO wireless communications where the transceivers are aligned. However, it is difficult to achieve strict alignment between the transceivers in practice. To address the challenges, in this paper we investigate the UCA based sphere RIS-assisted mmWave MIMO wireless communications with misaligned transceivers and sphere RIS. The channel model is investigated for the UCA based sphere RIS-assisted mmWave MIMO wireless communications. Furthermore, a precoding scheme at the transmitter, a phase compensation scheme at the sphere RIS, and a predecoding scheme at the receiver, which can convert the channel matrix into an equivalent circulant matrix, are developed. Then, our proposed fast symbol-wise maximum likelihood (ML) detection scheme can recover the signals with low complexity. Simulation results are presented to illustrate the theory for the UCA based sphere RIS-assisted mmWave MIMO wireless communications.

In virtual reality (VR) applications, 360-degree images play a pivotal role in crafting immersive experiences and offering panoramic views, thus enhancing the visual experience of the user. However, the voluminous data generated by 360-degree images poses challenges in network storage and bandwidth. To address these challenges, we propose a novel Activation Map-based Vector Quantization (AM-VQ) framework, which is designed to reduce communication overhead for wireless transmission. The proposed AM-VQ scheme uses the Deep Neural Networks (DNNs) with vector quantization (VQ) to extract and compress semantic features. Particularly, the AM-VQ framework utilizes an activation map to adaptively quantize semantic features, thereby reducing data distortion caused by quantization. To further enhance the reconstruction quality of the 360-degree image, adversarial training with a Generative Adversarial Networks (GANs) discriminator is incorporated. Numerical results show that our proposed AM-VQ scheme achieves better performance than the existing Deep Learning (DL) based coding and the traditional coding schemes under the same transmission symbols.

Given the imperative of 6G networks’ ubiquitous connectivity, along with the inherent mobility and cost-effectiveness of unmanned aerial vehicles (UAVs), UAVs play a critical role within 6G wireless networks. Despite advancements in enhancing the UAV-enabled communication systems’ throughput in existing studies, there remains a notable gap in addressing issues concerning user fairness and quality-of-service (QoS) provisioning and lacks an effective scheme to depict the trade-off between system throughput and user fairness. To solve the above challenges, in this paper we introduce a novel fairness control scheme for UAV-enabled wireless communication systems based on a new weighted function. First, we propose a throughput combining model based on a new weighted function with fairness considering. Second, we formulate the optimization problem to maximize the weighted sum of all users’ throughput. Third, we decompose the optimization problem and propose an efficient iterative algorithm to solve it. Finally, simulation results are provided to demonstrate the considerable potential of our proposed scheme in fairness and QoS provisioning.

It is crucial to automatically detect anomalous patterns in system logs to protect computer systems from cyber attacks and malfunctions. However, as log data is becoming increasingly complex and labeled logs are difficult to obtain, it poses serious challenges to existing methods. To this end, this paper introduces the pre-training and fine-tuning paradigm to the log analysis domain and proposes a novel log anomaly detection framework. We propose the masked log reconstruction approach to pre-train a Transformer-based foundation model and fine-tune it for the event prediction task to obtain the anomaly detector. Our training methods exploit the sequential information within unlabeled logs with self-supervised learning. Experimental results on two public datasets demonstrate the performance superiority of our framework compared with existing state-of-the-art methods. More importantly, it is suitable for real-world scenarios where labeled logs are difficult to acquire.

Throughout various convolutional neural network (CNN) models, the sparsity increases as the network deepens, which poses significant potential to model compression and hardware acceleration. In this paper, a dual-factor hybrid-grained pruning method is introduced to make a good balance between model compression and accuracy preservation. The pro-posed pruning method combines hardware-friendly unstructured vector-level pruning with structured filter-level pruning to explore multiple grains of sparsity in CNNs. The architecture of the corresponding hardware accelerator is then proposed based on the row-based convolution dataflow, which could fully utilize the hybrid sparsity to accelerate CNN processing. Experimental results demonstrate that the proposed method increases the compression rate by 1.08× while causing 0.21% accuracy loss compared to the state-of-the-art filter pruning method in VGG16, and 2.39% hardware resource increase compared to the accelerator without sparsity optimization.

A reactive jamming attack, which performs spectrum jamming only during legal signal transmission based on the knowledge of user behaviors, poses a significant threat to wireless communications. In this paper, a novel deceiving approach is proposed for defending reactive jamming in unmanned aerial vehicle (UAV) assisted wireless communications. Specifically, the interaction of multiple legitimate users (LUs) and a malicious user (MU) is modelled as a Stackelberg game, where LUs are the leaders and MU is the follower. We first show that the equilibrium exists when the channel information is known. Then, we propose a multi-agent reinforcement learning (MARL) based method to address the reactive jamming. Finally, we perform simulations to demonstrate the superiority of our proposed method.

This paper studies a multi-user backscatter communication (BackCom) system for integrated sensing and communications (ISAC), where the ISAC transmitter sends excitation signals to power multiple passive backscatter devices (BD), and the ISAC receiver performs joint sensing (localization) and communication tasks based on the backscattered signals from all BDs. Specifically, the localization performance is characterized by the Cramér-Rao bound (CRB) on the transmission delay and direction of arrival (DoA) of the backscattered signals, whose closed-form expression is obtained by deriving the corresponding Fisher information matrix (FIM), and the communication performance is characterized by the sum transmission rate of all BDs. Then, a CRB minimization problem is formulated by considering the communication rate constraint, and is shown to be non-convex in general. To solve this problem, we propose an approach that combines fractional programming (FP) and Schur complement techniques to transform the original problem into an equivalent convex form. Finally, numerical results reveal the trade-off between the localization and communication performances.

Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become a pivotal issue in semantic communications. This article proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data-oriented semantic communications (JTD-SCs) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion. First, by analyzing the Bayesian model of the DSSCC framework, we derive a novel rate-distortion optimization problem via the Bayesian inference approach for general data distributions and semantic tasks. Next, for a typical application of joint image transmission and classification, we combine the variational autoencoder approach with a forward adaption scheme to effectively extract image features and adaptively learn the density information of the obtained features. Finally, an iterative training algorithm is proposed to tackle the overfitting issue of deep learning models. Simulation results reveal that the proposed scheme achieves better coding gain as well as data recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes.

The goal of this paper is to address the physical layer (PHY) security problem for multi-user multi-input single-output (MU-MISO) Internet of Things (IoT) systems in the presence of passive eavesdroppers (Eves). To this end, we propose an artificial noise (AN)-aided rate-splitting (RS)-based secure beamforming scheme. Our design considers the dual use of common messages and places the research emphasis on hiding the private messages for secure communication. In particular, leveraging AN-aided RS-based beamforming, we aim to maximize the focused secrecy sum-rate (F-SSR) by jointly designing transmit information and AN beamforming while satisfying the desired received constraints for the private messages at IoT devices (IoDs), and per-antenna transmit power constraint at base station. Then, we proposed a two-stage algorithm to iteratively find the optimal solution. By transforming non-convex terms into linear terms, we first reformulate the original problem as a convex program. Next, we recast the optimization problem to an unconstrained problem to obtain the global optimal solutions. Utilizing the duality framework, we further develop an efficient algorithm based on a barrier interior point method to solve the reformulated problem. Simulation results validate the superior performance of our proposed schemes.

Wireless sensor networks (WSNs) enable intelligent collaborative perceptions in the Internet of Things. However, devices in WSNs are battery-powered with limited energy resources. During transmission, routing policies significantly affect the energy efficiency in terms of both energy consumption and energy balance among nodes, and further impact the network lifetime. Previous works mostly used heuristic fixed strategies to make routing decisions based on incomplete information in a distributed manner for lower control costs and faster calculation when facing numerous devices in WSNs, which easily lead to performance limitations and routing loops. To this end, we model the network lifetime maximization problem as a decentralized partially observable Markov decision process and propose a new scheme MeFi based on Mean Field Reinforcement Learning to perform real-time energy-efficient routing policies for WSNs. The utilization of Mean Field Theory effectively simplifies the intractable interactions among numerous agents and guides the policy training. Additionally, a prioritized-sampling loop-free algorithm is developed to eliminate routing loops and avoid routing policies with significant energy consumption. Experimental results show that our scheme outperforms several algorithms by up to 50%, significantly enhancing energy efficiency and extending WSN lifetime under different circumstances.

The emergence of integrated positioning, communication, and computing (IPC2) technology has paved the way for advanced capabilities in physical-digital spatial positioning, intelligent communication, and computing. This article delves into an in-depth exploration of a federated learning-assisted multidimensionality fusion IPC2 system. Within this system, edge nodes collaboratively harness their locally distributed multidimensionality positioning and communication data to coordinate edge computing resources for model training. Throughout the process of fully distributed collaborative training, we focus on addressing two specific security concerns: 1) data tampering and 2) model tampering attacks. In pursuit of bolstering the system’s resilience against potential attacks, we introduce a novel federated-blockchain edge learning (FLBC) framework. This framework capitalizes on the inherent features of the blockchain, namely, its nontampering and traceability attributes. In addition, we present a meticulously designed verification algorithm tailored for the parameters aggregation process. Specifically, an aggregation algorithm is developed to enhance the efficiency and accuracy of the training model’s fitting. To assess the effectiveness of our proposed approach, comprehensive simulations are conducted using an openly accessible wireless artificial intelligence (AI) data set. The outcomes of these simulations clearly demonstrate that the proposed scheme adeptly combats data tampering attacks initiated by multiple malicious nodes and high-intensity model tampering attacks, all while maintaining minimal accuracy loss.

Orbital angular momentum (OAM) carried electromagnetic waves have the potential to improve spectrum efficiency in optical and radio-frequency communications due to the orthogonal wavefronts of different OAM modes. However, OAM beams are vortically hollow and divergent, which significantly decreases the capacity of OAM transmissions. In addition, unaligned transceivers in OAM transmissions can result in a high bit error rate (BER). The Talbot effect is a self-imaging phenomenon that can be used to generate optical or radio-frequency OAM beams with periodic repeating structures at multiples of a certain distance along the propagation direction. These periodic structures make it unnecessary for the transceiver antennas to be perfectly aligned and can also alleviate the hollow divergence of OAM beams. In this paper, we propose Talbot-effect-based fractal OAM generation and detection schemes using a uniform circular array (UCA) to significantly improve capacity and BER performance in unaligned OAM transmissions. We first provide a brief overview of fractal OAM. Then, we propose the fractal OAM beam generation and detection schemes. Numerical analysis and simulations verify the effectiveness of our proposed fractal OAM generation scheme and also demonstrate improved capacity and BER performance compared to normal OAM transmissions. We also analyze how the receive UCA radius and the distance between the UCAs impact the capacity and BER performances.

Metaverse is envisioned to shape a virtual digital world accommodating people to live, work, and interact with each other, which requires massive information exchange for frequent updates of panoramic information in the digital world and imposes unprecedented pressure on future networks, so it is desirable to only sample the information updating process covering objects of user’s main attention. Yet under the always-limited wireless capacity compared to the persistently-growing information load, a critically important but unanswered question remains, i.e., how often shall we sample information updating process and deliver it to users, while keeping information at users’ side as fresh as possible. To answer this question, we investigate the statistical age-of-information (AoI) minimization problems to catch varying wireless channels and attention of users. Unlike conventional average or maximum AoI optimization technologies, we concentrate on statistical feature of AoI to more accurately characterize the capability of supporting metaverse applications. The formulated problems are solved by fractional programming. Specifically, using the Dinkelbach’s and quadratic transforms, we derive the adaptive sampling and transmission schemes for cases with single and multiple users, respectively. The interaction among multiple users is also considered. Analyses reveal that the optimized sampling rate shall decrease as the information updating process covers more varying objects or the channel gets poorer. Moreover, when the AoI requirement becomes extremely stringent, the sampling rate approaches a constant. Numerical results validate that our proposals can achieve lower statistical AoI than baseline schemes, thus offering better experiences for metaverse users.

Communication infrastructure is often severely disrupted in post-disaster areas, which interrupts communications and impedes rescue. Recently, the technology of reconfigurable intelligent surface (RIS)-equipped-UAV has been investigated as a feasible approach to assist communication under such conditions. However, the channel characteristics in the post-disaster area rapidly change due to the topographical changes caused by secondary disasters and the high mobility of UAVs. In this paper we develop a new fading-shadowing model to fit the path loss caused by the debris. Following this, we derive the exact distribution of the new channel statistics for a small number of RIS elements and the approximate distribution for a large number of RIS elements, respectively. Then, we derive the closed-form expressions for performance analysis, including average capacity (AC), energy efficiency (EE), and outage probability (OP). Based on the above analytical derivations, we maximize the energy efficiency by optimizing the number of RIS elements and the coverage area by optimizing the altitude of the RIS-equipped UAV, respectively. Finally, simulation results validate the accuracy of derived expressions and show insights related to the optimal number of RIS elements and the optimal UAV altitude for emergency wireless communication (EWC).

Distributed Antenna Systems (DASs) employ multiple antenna arrays in remote radio units to achieve highly directional transmission and provide great coverage performance for future-generation networks. However, the utilization of fully digital or hybrid active antenna arrays results in a significant increase in hardware costs and power consumption for DAS. To address these issues, integrating DAS with Reconfigurable Intelligent Surfaces (RIS) offers a viable approach to ensure coverage and transmission performance while maintaining low hardware costs and power consumption. To incorporate the merits of RIS into the DAS from practical consideration, a novel architecture of “Reconfigurable Distributed Antennas and Reflecting Surfaces (RDARS)” is proposed in this paper. Specifically, based on the design of the additional direct-through state together with the existing high-quality fronthaul link, any element of the RDARS can be dynamically programmed to connect with the base station (BS) via fibers and perform theconnected modeas remote distributed antennas of the BS to receive or transmit signals. Additionally, RDARS also inherits the low-cost and low-energy-consumption benefits of fully passive RISs by default configuring the elements as passive to perform thereflection mode. As a result, RDARS encompasses both DAS and RIS as special cases, offering flexible control over the trade-off betweendistribution gainandreflection gainto enhance performance. To unveil the potential of such architecture, the ergodic achievable rate under the RDARS architecture is analyzed and closed-form expression with meaningful insights is derived. The theoretical analysis proves that the RDARS can achieve a higher achievable rate than both DAS and fully passive RIS with the passive beamforming gain provided by elements actingreflection modewhile combating the “multiplicative fading” suffered by RISs through theconnected modeperformed at the RDARS. Simulation results also demonstrate the superiority of the RDARS architecture over DAS and passive RIS-aided systems and its flexible trade-off between performance and cost. To further validate the feasibility and effectiveness, an RDARS prototype with 256 elements is built for real experiments. Experimental results show that the RDARS-aided system with only one element operating inconnected modecan achieve an additional 21% and 170% throughput improvement over DAS and RIS-aided systems, respectively.

Beam selection for joint transmission in cell-free massive multi-input multi-output systems faces the problem of extremely high training overhead and computational complexity. The traffic-aware quality of service additionally complicates the beam selection problem. To address this issue, we propose a traffic-aware hierarchical beam selection scheme performed in a dual timescale. In the long-timescale, the central processing unit collects wide beam responses from base stations (BSs) to predict the power profile in the narrow beam space with a convolutional neural network, based on which the cascaded multiple-BS beam space is carefully pruned. In the short-timescale, we introduce a centralized reinforcement learning (RL) algorithm to maximize the satisfaction rate of delay w.r.t. beam selection within multiple consecutive time slots. Moreover, we put forward three scalable distributed algorithms including hierarchical distributed Lyapunov optimization, fully distributed RL, and centralized training with decentralized execution of RL to achieve better scalability and better tradeoff between the performance and the execution signal overhead. Numerical results demonstrate that the proposed schemes significantly reduce both model training cost and beam training overhead and are easier to meet the user-specific delay requirement, compared to existing methods.

Recent learned image compression models surpass manually designed methods in rate-distortion performance by introducing nonlinear transforms and end-to-end optimization. However, there still lack quantitative measurements that efficiently evaluate the latent representations inferred by learned image compression models. To address this problem, we develop novel measurements on robustness and importance of the latent representations. We first propose an admissible range that can be efficiently estimated via gradient ascent and descent for establishing the empirical distribution of latent representations. Consequently, the in-distribution region within the admissible range is derived to measure the robustness and channel importance of latent representations of natural images. Visualization demonstrates the statistics of latent representations are significantly distinguishing in the properties of robustness and linearity within and outside the in-distribution region. To our best knowledge, this paper proposes the first statistically meaningful measurements for learned image compression and successfully applies the measurements in corruption alleviation during successive image compression and post-training pruning in a training-free fashion. Compared with existing methods, the shrunk in-distribution constraint derived from the in-distribution region achieves superior robustness and rate-distortion performance in successive compression. The channel importance allows post-training pruning to achieve comparable rate-distortion performance with a reduction of up to 60% entropy coding time.

Driven by the tremendous demand for real-time data processing in the Internet of Vehicles (IoV), edge computing is envisioned as a promising solution to alleviate the resource limitation on vehicles. Current works on edge task scheduling simply optimize the total system cost and ignore the various constraints of applications, which will result in the reduction of the task completion rate and even cause security accidents. Although few works studied the multi-task deadline-constrained scheduling problem, their complexity is too high, resulting in the explosive growth of the runtime. Spurred by the above issues, the multi-task scheduling problem is formulated to maximize the task completion rate. Further, a Multi-user Efficient Task Scheduling (METS) algorithm is proposed to solve the formulated problem, which consists of three key components: (1) the dominating set-based network clustering that aims to reduce the network scale, (2) the matching-based task assignment to assign tasks that are modeled by the Directed Acyclic Graph (DAG) to their proper clusters, and (3) the intra-cluster DAG scheduling to schedule DAGs to the proper network nodes. Simulation results show that the proposed METS algorithm can significantly improve the task completion rate and reduce the algorithm runtime in an IoV environment with thousand-level network scale and thousand-level task requests.

Broadcast plays a vital role in wireless ad hoc networks for information dissemination, while one of the key system parameters for maximizing broadcast performance is the transmission rate. However, due to the entangled impact of various factors on the broadcast performance such as node distribution, interference, transmission rate, and multi-hop links, theoretically modeling of the impact of different parameters on the broadcast performance is intractable, and hence the optimal transmission rate is still unknown. Inspired by the similarity between the process of packet broadcasting and the spreading of contagious diseases, we resort to epidemic theory to propose a tractable approach for modeling the dynamics of message broadcast process in wireless ad hoc networks. To characterize the impact of transmission rate on the broadcast performance, a novel utility function is proposed by jointly considering the packet delivery delay and packet size. Then, we derive the dynamics of packet broadcasting analytically and obtain the approximated expression for the proposed utility function in closed-form. Furthermore, the optimization of the transmission rate is carried out based on our proposed analytical model. Simulation results show that our analytical model can accurately characterize the process of broadcasting under a wide range of system parameters and that optimizing the transmission rate can improve broadcast performance significantly.

Jittering effect is a critical issue in the unmanned aerial vehicle (UAV) millimeter wave (mmWave) communications. In this paper, we identify and characterize the asymmetric impact of jitter on the angular domain information in the UAV mmWave channels, showing how it can lead to significant performance degradation if not properly handled. To address this issue, we propose an adaptive hybrid beamforming for the UAV mmWave communications to maximize the average transmission rate against the asymmetric jitter. The proposed adaptive hybrid beamforming consists of an optimal beam angular range design and an adaptive beamforming vector design. Specifically, we first analytically derive a compact expression of the average transmission rate of our systems. Based on the derived expression, we optimize the beam angular range to maximize the average transmission rate under arbitrary asymmetric jitter. Moreover, we derive the asymptotic expression of the optimal beam angular range in the high signal-to-noise ratio regime. We further develop a simple-yet-efficient algorithm to obtain an adaptive beamforming vector that delivers the optimal beam angular range. Through numerical results, we verify the destructive impacts of the asymmetric jitter, and demonstrate how our proposed scheme can be robust to it, compared to the existing methods without considering the asymmetric jitter.

The development of orbital angular momentum (OAM)-based radio vortex transmission presents a promising opportunity for increasing the capacity of wireless communication in correlated channels due to its inherent orthogonality among different OAM modes. One of the most popular schemes for high-efficient OAM transmission is the digital baseband associated with uniform circular array (UCA) based transceiver. However, the periodicity of complex-exponential feed makes the maximum number of orthogonal signals carried by multiple OAM modes generally restricted to the array-element number of UCA antenna, which poses an open question of how to employ more OAM modes given a fixed number of array elements. Furthermore, signals modulated with high-order OAM modes are difficult to be captured by the receiver due to their serious divergence as propagating in free space, thus severely limiting the capacity of radio vortex communications. To overcome the above challenges, in this paper based on the partly element-overlapped fractal geometry layout and effectively using low-order OAM modes, we propose the quasi-fractal UCA (QF-UCA) antenna based OAM multiplexing transmission. We perform the two-dimension OAM modulation (TOM) and demodulation (TOD) schemes with the orthogonal OAM mode number exceeding the array-element number, which is beyond the traditional concept of multiple antennas based wireless communications. Simulation results show that our proposed scheme can achieve more number of orthogonal multiplexing streams than the maximum number of orthogonal multiplexing corresponding to traditional multiple antenna systems.

Federated learning (FL) has been recognized as a promising distributed learning paradigm to support intelligent applications at the wireless edge, where a global model is trained iteratively through the collaboration of the edge devices without sharing their data. However, due to the relatively large communication cost between the devices and parameter server (PS), direct computing based on the information from the devices may not be resource efficient. This paper studies the joint communication and learning design for the over-the-air computation (AirComp)-based two-tier wireless FL scheme, where the lead devices first collect the local gradients from their nearby subordinate devices, and then send the merged results to the PS for the second round of aggregation. We establish a convergence result for the proposed scheme and derive the upper bound on the optimality gap between the expected and optimal global loss values. Next, based on the device distance and data importance, we propose a hierarchical clustering method to build the two-tier structure. Then, with only the instantaneous channel state information (CSI), we formulate the optimality gap minimization problem and solve it by using an efficient alternating minimization method. Numerical results show that the proposed scheme outperforms the baseline ones.

In this paper, we present a hybrid ambient backscatter communication (ABC) assisted framework for radio frequency (RF) powered cognitive radio networks (CRNs). In these CRNs, the secondary users (SUs) can actively transmit data when the primary user network (PUN) is idle, and harvest energy from the primary signal and transmit their own information over the primary signal when the PUN is busy. The proposed CRNs adopt non-orthogonal multiple access (NOMA) technique to further improve spectral efficiency. Considering two different spectrum sharing paradigms of the PUN and the SUs, we formulate two optimization problems by two Markov decision processes (MDPs) to maximize our long term throughputs for both underlay-interweave and overlay-interweave scenarios. We propose a deep reinforcement learning (DRL) based optimization algorithm, i.e., a deep deterministic policy gradient (DDPG)-based joint reflection coefficients adjustment and resource allocation (JCARA) algorithm, to solve these two non-convex problems under the two constructed MDPs without the non-causal and the statistical information about the dynamic environment a-priori. For the underlay-interweave scenario, the proposed JCARA algorithm jointly optimizes the transmit power and the reflection coefficients of the SUs, while for the overlay-interweave scenario it optimizes the above two variables plus time resource simultaneously. Simulation results clearly show the higher throughput performance of this proposed algorithm for the proposed CRNs in the comparison with other algorithms.

In physical layer for non-orthogonal multiple access (NOMA), most existing studies focus on the non-delay-sensitive metrics such as the spectral efficiency. In order to improve user’s quality of service (QoS) in delay-sensitive communications, however, effective capacity can be adopted to the NOMA system to consider the QoS metric while analyzing capacity. In this paper, we deduce the closed form expression for effective capacity in downlink NOMA and propose three optimization problems with delay and effective capacity constraints. Firstly, a joint rate and power allocation scheme is proposed to maximize the total effective capacity. Secondly, we deduce the optimal solution of the problem for maximizing the minimum delay QoS exponent. Thirdly, a minimum total transmit power allocation scheme is proposed with the effective capacity constraint. Since the problems of maximizing effective capacity and minimizing total transmit power are non-convex, the particle swarm optimization (PSO) algorithm is used to find global optimization solutions. Simulation results show our proposed power and rate allocation scheme maximizes the effective capacity, which is better than orthogonal multiple access (OMA). Meanwhile, the optimal minimum delay QoS exponent and minimum total transmit power with effective capacity constraint have been achieved.

As an emerging distributed learning framework, federated edge learning (FEEL) can efficaciously resolve the delay requirements and privacy concerns by enabling collaborative modeling among the edge devices under the premise of data localization. However, the communication bottlenecks, e.g., model damage and signal deviation, will critically diminish the convergence performance due to the restricted resources and the undesirable wireless fading. To overcome this challenge, one feasible way is to integrate the reconfigurable intelligent surface (RIS) into the FEEL system, to reinforce the communication quality by adaptively reconfiguring the signal propagation environment. However, the significant premise to effectively exploit the RIS in most of the prior works is the estimation of exact instantaneous channel state information (CSI), which is extremely thorny and potentially incurs additional communication overhead. To tackle this issue, we investigate in this paper the RIS-aided FEEL system under the realistic supposition where only the statistical CSI is available among devices. Specifically, considering the wireless outage caused by the uncertainty of non-line-of-sight components, we rigorously derive an explicit convergence upper bound of the RIS enabled FEEL framework with outage. Accordingly, a resource configuration problem with the goal of minimizing the sum of outage-probability is further formulated by jointly configuring the RIS configuration matrix and the bandwidth allocation. To seek the solutions, we carefully design a general Bernstein-Type inequality in this paper, and thus the probabilistic outage objective function can be effectively handled in an equivalent manner. Simulation experiments verify that our design can accomplish a significant promotion compared against state-of-the-art baselines.

In this paper, we investigate a novel reconfigurable distributed antennas and reflecting surface (RDARS) aided multi-user massive multiple-input multiple-output (MIMO) system with imperfect channel state information (CSI) and propose a practical two-timescale (TTS) transceiver design to reduce the communication overhead and computational complexity of the system. In the RDARS-aided system, not only distribution gain but also reflection gain can be obtained by a flexible combination of the distributed antennas and reflecting surface, which differentiates the system from the others and also makes the TTS design challenging. To enable the optimal TTS transceiver design, the achievable rate of the system is first derived in closed-form. The rate expression is general and covers that of the distributed antenna systems (DAS) and reconfigurable intelligent surface (RIS) aided systems as special cases. Then the TTS design aiming at the weighted sum rate maximization is considered. To solve the challenging non-convex optimization problem with high order design variables, i.e., the transmit powers and the phase shifts at the RDARS, a block coordinate descent based method is proposed to find the optimal solutions in semi-closed forms iteratively. Specifically, two efficient algorithms are proposed with provable convergence for the optimal phase shift design, i.e., Riemannian Gradient Ascent based algorithm by exploiting the unit-modulus constraints, and Two-Tier Majorization-Minimization based algorithm with closed-form optimal solutions in each iteration. Simulation results validate the effectiveness of the proposed algorithm and demonstrate the superiority of deploying RDARS in massive MIMO systems to provide substantial rate improvement with a significantly reduced total number of active antennas/RF chains and lower transmit power when compared to the DAS and RIS-aided systems.

Directional modulation and artificial noise (AN)-based methods have been widely employed to achieve physical-layer security (PLS). However, these approaches can only achieve angle-dependent secure transmission. This paper presents an AN-aided decomposed and distributed directional modulation (D3M) scheme for secure wireless communications, which takes advantage of the spatial signatures to achieve an extra range-dimension security apart from the angles. Leveraging decomposed and distributed structure, each of modulated signal is represented by mutually orthogonal in-phase and quadrature branches, which are transmitted by two distributed transmitters to enhance PLS. In particular, we first aim to minimize transmit message power by integrated design of the transmit beamformers, subject to prescribed received signal-to-noise ratio (SNR) for the legitimate user (LU) and no inter-branch interference. This guarantees reliable and accurate transmission for the LU with the minimum transmit message power. Considering the leakage power on the sidelobes, AN is superimposed on the messages to try to mask the confidential information transmission. Simulation results demonstrate the security enhancement of our proposed D3M system.

In this work, we investigate a reconfigurable intelligent surface (RIS) aided integrated sensing and communication scenario, where a base station (BS) communicates with multiple devices in a full-duplex mode, and senses the positions of these devices simultaneously. An RIS is assumed to be mounted on each device to enhance the reflected echoes. Meanwhile, the information of each device is passively transferred to the BS via reflection modulation. We aim to tackle the problem of joint localization and information retrieval at the BS. A grid based parametric model is constructed and the joint estimation problem is formulated as a compressive sensing problem. We propose a novel message-passing algorithm to solve the considered problem, and a progressive approximation method to reduce the computational complexity involved in the message passing. Moreover, an expectation-maximization (EM) algorithm is applied for tuning the grid parameters, hence mitigating the model mismatch problem. Finally, we analyze the efficacy of the proposed algorithm through the Bayesian Cramér-Rao bound. Numerical results demonstrate the feasibility of the proposed scheme and the superior performance of the proposed EM-based message-passing algorithm.

This paper studies the performance trade-off in a multi-user backscatter communication (BackCom) system for integrated sensing and communications (ISAC), where the multi-antenna ISAC transmitter sends excitation signals to power multiple single-antenna passive backscatter devices (BD), and the multi-antenna ISAC receiver performs joint sensing (localization) and communication tasks based on the backscattered signals from all BDs. Specifically, the localization performance is measured by the Cramér-Rao bound (CRB) on the transmission delay and direction of arrival (DoA) of the backscattered signals, whose closed-form expression is obtained by deriving the corresponding Fisher information matrix (FIM), and the communication performance is characterized by the sum transmission rate of all BDs. Then, to characterize the trade-off between the localization and communication performances, the CRB minimization problem with the communication rate constraint is formulated, and is shown to be non-convex in general. By exploiting the hidden convexity, we propose an approach that combines fractional programming (FP) and Schur complement techniques to transform the original problem into an equivalent convex form. Finally, numerical results reveal the trade-off between the CRB and sum transmission rate achieved by our proposed method.

Full-duplex (FD) is an attractive technology that can significantly boost the throughput of wireless communications. However, it is limited by the severe self-interference (SI) from the transmitter to the local receiver. In this paper, we propose a new SI cancellation (SIC) scheme based on reconfigurable intelligent surface (RIS), where small RISs are deployed inside FD devices to enhance SIC capability and system capacity under frequency-selective fading channels. The novel scheme can not only address the challenges associated with SIC but also improve the overall performance. We first analyze the near-field behavior of the RIS and then formulate an optimization problem to maximize the SIC capability by controlling the reflection coefficients (RCs) of the RIS and allocating the transmit power of the device. The problem is solved with alternate optimization (AO) algorithm in three cases: ideal case, where both the amplitude and phase of each RIS unit cell can be controlled independently and continuously, continuous phases, where the phase of each RIS unit cell can be controlled independently, while the amplitude is fixed to one, and discrete phases, where the RC of each RIS unit cell can only take discrete values and these discrete values are equally spaced on the unit circle. For the ideal case, the closed-form solution to RC is derived with Karush-Kuhn-Tucker (KKT) conditions. Based on Riemannian conjugate gradient (RCG) algorithm, we optimize the RC for the case of continuous phases and then extend the solution to the case of discrete phases by the nearest point projection (NPP) method. Simulation results are given to validate the performance of our proposed SIC scheme.

The integration of directed acyclic graph (DAG) blockchain and mobile edge computing (MEC) has emerged as a promising means to enable computation-intensive, delay-sensitive, and secure task execution in Internet of Things (IoT) applications. However, off-chain task execution results are not credible even if the results have been recorded on the chain, since blockchain cannot extend the trust of on-chain data to off-chain. To make the off-chain and on-chain trust consistent, we first develop a trusted MEC (T-MEC) framework by employing a DAG blockchain-aided decentralized trust management (DAG-DTM) mechanism. Specifically, DAG-DTM evaluates the off-chain trust of edge nodes according to the quality of task execution results, and the trust can be further verified off the chain by any edge node under the same trust management rule. Moreover, the approval time for recording the execution result of the edge node on the chain is positively correlated with the verified off-chain trust, which can further promote on-chain transaction security of trusted edge node. Second, we jointly optimize the bandwidth and computation resource allocation to minimize the system latency that consists of off-chain task execution delay and on-chain transaction confirmation delay. Numerical results compare system latency and security performance between the optimized T-MEC and the benchmark schemes. In particular, the optimized T-MEC can achieve a 33.12% gain of computation delay and a 10.19% gain of system latency at an affordable cost of transaction confirmation delay (i.e., 3.21%) over T-MEC, while meeting the requirements of off-chain task execution latency and on-chain transaction security simultaneously.

Facing the dramatic increase of real-time applications and time-sensitive services, large-scale ultra-low delay requirements are put forward for the sixth generation (6G) wireless networks. To support massive ultra-reliable and low-latency communications (mURLLC), in this paper we propose an adaptive finite blocklength framework to reduce the over-the-air delay for short packet transmissions with multiple-access and delay-bounded demands. In particular, we first give the specified over-the-air delay model. Then, we reveal the tradeoff relationship among queuing delay, transmission delay, and the number of retransmissions along with the change of finite blocklength, as well as formulate the adaptive blocklength framework. Based on the adaptive blocklength framework and associated with grant-free (GF) access protocol, we formulate the average over-the-air delay minimization problem, where the blocklength can be adaptively changed in terms of transmission time interval (TTI) design and bandwidth allocation to achieve the optimal tradeoff and obtain its minimum over-the-air delay. We develop the cooperative multi-agent deep Q-network (M-DQN) scheme with a grouping mechanism to efficiently solve the average over-the-air delay minimization problem. Numerical results validate our proposed adaptive blocklength scheme outperforms corresponding schemes in long-term evolution (LTE) and the fifth generation (5G) new radio (NR).

Intelligent reflecting surfaces (IRS) have been proposed in millimeter wave (mmWave) and terahertz (THz) systems to achieve both coverage and capacity enhancement, where the design of hybrid precoders, combiners, and the IRS typically relies on channel state information. In this paper, we address the problem of uplink wideband channel estimation for IRS aided multiuser multiple-input single-output (MISO) systems with hybrid architectures. Combining the structure of model driven and data driven deep learning approaches, a hybrid driven learning architecture is devised for joint estimation and learning the properties of the channels. For a passive IRS aided system, we propose a residual learned approximate message passing as a model driven network. A denoising and attention network in the data driven network is used to jointly learn spatial and frequency features. Furthermore, we design a flexible hybrid driven network in a hybrid passive and active IRS aided system. Specifically, the depthwise separable convolution is applied to the data driven network, leading to less network complexity and fewer parameters at the IRS side. Numerical results indicate that in both systems, the proposed hybrid driven channel estimation methods significantly outperform existing deep learning-based schemes and effectively reduce the pilot overhead by about 60% in IRS aided systems.

Rate splitting multiple access (RSMA) is one of the most promising techniques for ultra-reliable and low-latency communications (URLLC) with stringent requirements on delay and reliability of multiple access. To fully explore the delay performance enhancement brought by uplink RSMA to URLLC, in this paper, we evaluate the performance of two-user uplink RSMA and propose the corresponding blocklength minimization problem. We analyze the impact of finite blocklength (FBL) code on the achievable rate region and the effective throughput of uplink RSMA. On this basis, we propose the problem of minimizing the blocklength for uplink RSMA with power allocation under constrained reliability and effective throughput. Then, we present an alternating optimization method to solve this non-convex problem. Simulation results show that different from the infinite blocklength (IBL) regime, the achievable rate region of the uplink RSMA is not always larger than that of uplink non-orthogonal multiple access (NOMA) in the FBL regime. But with the help of our proposed blocklength minimization scheme, uplink RSMA can achieve the same achievable rate with a smaller blocklength compared to uplink NOMA, frequency division multiple access (FDMA), and time division multiple access (TDMA) without the need for time sharing in the FBL regime, showing the potential of uplink RSMA to achieve low delay for URLLC.

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

This paper proposes a deep separate source-channel coding (DSSCC) scheme for the semantic-aware image transmission, where image is lossily compressed and transmitted to receiver for recovery and processing certain semantic tasks. To improve the compression efficiency, the forward adaption (FA) method is incorporated into the DSSCC scheme to capture the density information of compressed features as side information. For a typical application of image classification task, we derive a novel rate-distortion optimization problem by analyzing the Bayesian model of the FA-based DSSCC framework. Then, a variational autoencoder approach is proposed to effectively compress image for semantic-aware transmission by minimizing the proposed rate-distortion problem. Simulation results reveal that the proposed FA-based DSSCC scheme achieves better image recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes.

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

In this paper, we propose a novel traffic prediction scheme for multiple radio access technology (multi-RAT) heterogeneous network. The scheme is named user-Cybertwin asynchronous learning (UCAL), which aims to extract meaningful patterns from noisy network traffic measurements and mitigate the impact of highly nonstationary measurements for ensuring the prediction accuracy. Specifically, we design a pattern extraction method that minimizes the Frobnius norm between the collected measurements and the expected k-rank approximation of the measurements in order to extract useful information. Then, by transforming the conventional long short term memory (LSTM) model into a nonlinear state space and incorporating Gaussian noise, we develop an online LSTM algorithm to adapt fast to changing environments. As a result, the parameter updating of the new online LSTM model can keep up with data changes while capturing complicated and nonlinear relationships among measurements. We consider both the surrounding environment conditions on the mobile user side and end-to-end link conditions on the Cybertwin side, and iteratively update the model parameters in both Cybertwin and MU. Simulation results demonstrate that the proposed UCAL scheme can achieve high traffic prediction accuracy in comparison to existing schemes. It can also significantly improve the efficiency in maintaining the prediction accuracy even when the dimension of traffic measurements increases.

With the widespread application of wireless networks, the importance of intelligent analysis of network behaviors is becoming increasingly prominent. In the analysis of networks behaviors, learning and reasoning about the connectivity of unknown networks is a fundamental problem. To obtain the topology information of a noncooperative wireless network that could not be accessed by the monitoring sensors, we propose a topology inference algorithm based on the network two-dimensional spatiotemporal features (TDSTFs). Specifically, the monitoring sensor network monitors the power of the noncooperative network and locates the nodes of the noncooperative network exploiting the neural network (NN)-based method. Then, the communication time and distance between the noncooperative nodes are used as characteristics to infer the topology of the noncooperative network based on$K$-nearest neighbors (KNNs). Simulation results validate that the proposed TDSTF topology inference algorithm outperforms other topology inference algorithms that do not consider both spatial and temporal features and can greatly improve the inference accuracy.

Mobile edge computing is a promising technique to provide timely edge service for the Internet of Things. With a continuously expanding network scale, the number and types of devices in the IoT network are growing rapidly. Moreover, the total number of devices that need edge service actually in the network is uncertain because the devices are online or offline frequently. It is very critical to design an efficient service selection strategy for every device to access edge service providers (ESPs) in such a huge number of devices situation. To address the problem, this article first formulates the access of IoT devices as a stochastic Stackelberg game model. Then, for the edge service selection of devices, a Poisson game is utilized to model the coordinated selection of edge services, we propose a distributed iterative algorithm to solve this game and give the optimal edge service selection strategy for each device group. Next, for the noncooperative competition among ESPs with incomplete information, the service prices of other ESPs are first estimated through a Bayesian neural network (BNN). Then we model this stochastic noncooperative game as a partially observable Markov decision process (POMDP) model, and finally obtain the optimal long-term pricing strategy through Bayesian deep$Q$-learning network (BDQN) algorithm. The experimental simulation results show that the proposed strategy can significantly improve the utilities of both ESPs and devices and effectively decrease the average queuing probability of all devices.

As a new degree of freedom (DOF), orbital angular momentum (OAM) has the potential of greatly enhancing the channel capacity of wireless communication systems. However, the OAM beams diverge with the increase of transmission distance and OAM mode, which makes the OAM beams modulated with information difficult to be received with a limited aperture array. Therefore, there is still a big challenge for achieving the expected extremely high data rate with orthogonal multiplexed OAM beams in a long-distance communication link. To reduce the divergence of OAM beams, the spherical Luneberg lens is proposed for the uniform circular array (UCA)-based OAM communication system to realize OAM beam convergence. However, due to the non-negligible UCA aperture relative to the Luneberg lens, we find through electromagnetism (EM) simulation that the 0-mode OAM beam becomes more divergent after passing through Luneberg lens. To solve this problem, we propose a hybrid circular array with a center element (CAC) and spherical Luneberg lens (HCCL) structure. When the HCCL structure is applied in the OAM wireless communication system, the divergence angles of generated OAM beams are significantly reduced, and the received signal-to-noise ratio (SNR) is expected to be significantly improved. Besides, the multiplexed OAM modes in the proposed HCCL-based OAM communication system keep orthogonal. Therefore, the channel capacities of the proposed HCCL-based OAM communication systems are obviously better than that of the existing UCA-based OAM system in the long-distance transmission. Furthermore, both numerical analysis and simulation results validate that the performance of the OAM system equipped with the HCCL structure both at transmitter and receiver is significantly better than that of the system equipped with the HCCL structure at one side

Distributed multi-task learning (MTL) can jointly learn multiple models and achieve better generalization performance by exploiting relevant information between the tasks. However, distributed MTL suffers from communication bottlenecks, in particular for large-scale learning with a massive number of tasks. This paper considers distributed MTL systems where distributed workers wish to learn different models orchestrated by a central server. To mitigate communication bottlenecks both in the uplink and downlink, we propose coded computing schemes for flexible and fixed data placements, respectively. Our schemes can significantly reduce communication loads by exploiting workers’ local information and creating multicast opportunities for both the server and workers. Moreover, we establish information-theoretic lower bounds on the optimal downlink and uplink communication loads, and prove the approximate optimality of the proposed schemes. For flexible data placement, our scheme achieves theoptimaldownlink communication load, and theorder optimaluplink communication load that is smaller than 2 times of the information-theoretic optimum. For fixed data placement, the gaps between our communication load and the optimum are within the minimum computation load among all workers, regardless of the number of workers. Experiments demonstrate that our schemes can significantly speed up the training process compared to the traditional approach.

Magnetic induction (MI) communications have gained substantial attention due to the advantages in special environments such as underground, underwater, and human body. MI wireless networks in such environments can enable important applications in future civil and public security fields. However, due to the particularity of application environments, it is challenging to guarantee the energy supply for MI devices. In this paper, we propose a backscatter based bidirectional full-duplex MI communication (BFMC), which can enhance the energy efficiency of MI communications. In BFMC, additional information is backscattered with the inherent backscatter property of MI channel and the backscatter device (BD) is free of self-interference (SI). For the BFMC based multi-user network, we formulate the joint magnetic beamforming and time allocation optimization problem, which aims to minimize the average energy consumption and guarantee the downlink signal to noise ratio (SNR), uplink signal to interference plus noise ratio (SINR), and energy harvesting requirements. The formulated problem is non-convex and transformed into magnetic beamforming and time allocation sub-problems. Based on block coordinate descent (BCD) method, the sub-problems are solved iteratively. Numerical results show that BFMC and the proposed joint beamforming and time allocation scheme can effectively reduce average energy consumption and enhance energy efficiency.

Millimeter wave (mmWave) cell-free MIMO achieves an extremely high rate while its beam alignment (BA) suffers from excessive overhead due to a large number of transceivers. Recently, user location and probing measurements are utilized for BA based on machine learning (ML) models, e.g., deep neural network (DNN). However, most of these ML models are centralized with high communication and computational overhead and give no specific consideration to practical issues, e.g., limited training data and real-time model updates. In this paper, we study the probing beam-based BA for mmWave cell-free MIMO downlink with the help of broad learning (BL). For channels without and with uplink-downlink reciprocity, we propose the user-side and base station (BS)-side BL-aided incremental collaborative BA approaches. Via transforming the centralized BL into a distributed learning with data and feature splitting respectively, the user-side and BS-side schemes realize implicit sharing of multiple user data and multiple BS features. Simulations confirm that the user-side scheme is applicable to fast time-varying and/or non-stationary channels, while the BS-side scheme is suitable for systems with low-bandwidth fronthaul links and a central unit with limited computing power. The advantages of proposed schemes are also demonstrated compared to traditional and DNN-aided BA schemes.

In this paper, we consider the physical layer (PHY) security problem for hybrid wiretapping wireless systems in millimeter wave transmission, where active eavesdroppers (AEs) and passive eavesdroppers (PEs) coexist to intercept the confidential messages and emit jamming signals. To achieve secure and reliable transmission, we propose an artificial noise (AN)-aided robust multi-beam array transceiver scheme. Leveraging beamforming, we aim to minimize transmit power by jointly designing the information and AN beamforming, while satisfying valid reception for legitimate users (LUs), per-antenna power constraints for transmitter, as well as all interception power constraints for eavesdroppers (Eves). In particular, the interception power formulation is taken into account for protecting the information against hybrid Eves with imperfect AE channel state information (CSI) and no PE CSI. In light of the intractability of the problem, we reformulate the considered problem by replacing non-convex constraints with tractable forms. Afterwards, a two-stage algorithm is developed to obtain the optimal solution. Additionally, we design the received beamforming weights by means of minimum variance distortionless response, such that the jamming caused by AEs can be effectively suppressed. Simulation results demonstrate the superiority of our proposed scheme in terms of energy efficiency and security.

Terahertz (THz) wireless communications are commonly regarded as one of the key technologies of 6G communication. Combined with THz, the newly exploited physical layer transmission dimension orbital angular momentum (OAM) that multiplexes a set of orthogonal modes on the same frequency channel, can unleash its potential in achieving high spectrum efficiency. Currently, most of the research on radio OAM communications focus on the point-to-point scenario in microwave and millimeter-wave (mmWave) bands. In this paper, we propose a uniform circular array (UCA)-based THz multi-user OAM (MU-OAM) communication system including downlink and uplink transmission schemes that simplifies the signal detection to the despiralization and amplitude detection (AD). A salient feature of the proposed MU-OAM communication scheme is lower computational complexity than the traditional MU-MIMO scheme without sacrifacing bit error rate (BER) and achievable sum rate performances, which is validated by mathematical analysis and numerical simulations.

Radio frequency-orbital angular momentum (RF-OAM) is a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectrum efficiencies. Since OAM requires precise alignment of the transmit and the receive antennas, the electronic beam steering approach has been proposed for the uniform circular array (UCA)-based OAM communication system to circumvent large performance degradation induced by small antenna misalignment in practical environment. However, in the case of large-angle misalignment, the OAM channel capacity cannot be effectively compensated only by the electronic beam steering. To solve this problem, we propose a hybrid mechanical and electronic beam steering scheme, in which mechanical rotating devices controlled by pulse width modulation (PWM) signals as the execution unit are utilized to eliminate the large misalignment angle, while electronic beam steering is in charge of the remaining small misalignment angle caused by perturbations. Furthermore, due to the interferometry, the receive signal-to-noise ratios (SNRs) are not uniform at the elements of the receive UCA. Therefore, a rotatable UCA structure is proposed for the OAM receiver to maximize the channel capacity, in which the simulated annealing algorithm is adopted to obtain the optimal rotation angle at first, then the servo system performs mechanical rotation, at last the electronic beam steering is adjusted accordingly. Both mathematical analysis and simulation results validate that the proposed hybrid mechanical and electronic beam steering scheme can effectively eliminate the effect of diverse misalignment errors of any practical OAM channel and maximize the OAM channel capacity.

In this paper, we study unmanned aerial vehicles (UAVs) assisted wireless data aggregation (WDA) in multi-cluster networks, where multiple UAVs simultaneously perform different WDA tasks via over-the-air computation (AirComp) without terrestrial base stations. This work focuses on maximizing the minimum amount of WDA tasks performed by each cluster by optimizing the UAV trajectory and transceiver design as well as cluster scheduling and association, while considering the WDA accuracy requirement. Such a joint design is critical for interference management in multi-cluster AirComp networks, via enhancing the signal quality between each UAV and its associated cluster for signal alignment while reducing the inter-cluster interference between each UAV and its non-associated clusters. Although it is generally challenging to optimally solve the formulated non-convex mixed-integer nonlinear programming, an efficient iterative algorithm as a compromise approach is developed by exploiting bisection and block coordinate descent methods, yielding an optimal transceiver solution in each iteration. The optimal binary variables and a suboptimal trajectory are obtained by using the dual method and successive convex approximation, respectively. Simulations show the considerable performance gains of the proposed design over benchmarks and the superiority of deploying multiple UAVs in increasing the number of performed tasks while reducing access delays.

Reconfigurable intelligent surface (RIS) can improve the communications between a source and a destination. Recently, continuous aperture RIS is proved to have better communication performance than discrete aperture RIS and has received much attention. However, the conventional continuous aperture RIS is designed to convert the incoming planar waves into the outgoing planar waves, which is not the optimal reflecting scheme when the receiver is not a planar array and is located in the near field of the RIS. In this paper, we consider two types of receivers in the radiating near field of the RIS: (1) when the receiver is equipped with a uniform linear array (ULA), we design RIS coefficient to convert planar waves into cylindrical waves; (2) when the receiver is equipped with a single antenna, we design RIS coefficient to convert planar waves into spherical waves. We then propose the maximum likelihood (ML) method and the focal scanning (FS) method to sense the location of the receiver based on the analytic expression of the reflection coefficient, and derive the corresponding position error bound (PEB). Simulation results demonstrate that the proposed scheme can reduce energy leakage and thus enlarge the channel capacity compared to the conventional scheme. Moreover, the location of the receiver could be accurately sensed by the ML method with large computation complexity or be roughly sensed by the FS method with small computation complexity.

As one of the most popular and attractive frameworks for model training, federated edge learning (FEEL) presents a new paradigm, which avoids direct data transmission by collaboratively training a global learning model across multiple distributed edge devices, thus overcoming the disadvantage of centralized machine learning in resource limitations, delay constraints, and privacy issues. However, due to the heavy cost of communicating gradient among edge devices, sharing the parameters of a large-scale neural network can still be time-intensive. To alleviate this bottleneck, an efficient scheme, called SignSGD has been recently proposed, where the one-bit gradient quantization with majority vote is featured at edge devices. Nevertheless, the performance of one-bit aggregation will inevitably deteriorate due to the undesirable propagation error introduced by wireless channels. To address this issue, we propose in this work a novel reconfigurable intelligent surface (RIS)-aided one-bit communication optimization scheme under orthogonal frequency division multiple access (OFDMA) to relieve the negative influence of communication error on the SignSGD-based FEEL. Specifically, a learning convergence analysis is firstly presented to quantitatively characterize the impact of wireless communication error measured by the union bound on pairwise bit error rate (BER) on the performance of SignSGD-based FEEL. Immediately, a unified communication-learning optimization problem is further formulated to jointly optimize the sub-band assignment strategy, the power allocation vector, and the RIS configuration matrix. Numerical experiments show that the proposed design achieves substantial performance improvement compared with the state-of-the-art approaches.

Orbital angular momentum (OAM) radars are able to estimate the azimuth angle and the rotation velocity of multiple targets without relative motion or beam scanning. Moreover, OAM wireless communications can achieve high spectral efficiency (SE) by utilizing a set of information-bearing modes on the same frequency channel. Benefitting from the above advantages, in this paper, we design a novel radar-centric joint OAM radar-communication (RadCom) scheme based on uniform circular arrays (UCAs), which modulates information signals on the existing OAM radar waveform. In details, we first propose an OAM-based three-dimensional (3-D) super-resolution position estimation and rotation velocity detection method, which can accurately estimate the 3-D position and rotation velocity of multiple targets without beam scanning. Then, we derive the posterior Cramér-Rao bound (PCRB) of the OAM-based estimates and, finally, we analyze SE of the integrated system. To achieve the best trade-off between imaging and communication, the transmitted integrated OAM beams are optimized by means of an exhaustive search method. Both mathematical analysis and simulation results show that the proposed radar-centric joint OAM RadCom scheme can accurately estimate the 3-D position and rotation velocity of multiple targets while ensuring the SE of the communication receiver, which can be regarded as an effective supplement to existing joint RadCom schemes.

Terrestrial-satellite networks (TSNs) can provide worldwide users with ubiquitous and seamless network services. Meanwhile, malicious eavesdropping is posing tremendous challenges on secure transmissions of TSNs due to their widescale wireless coverage. In this paper, we propose an aerial bridge scheme to establish secure tunnels for legitimate transmissions in TSNs. With the assistance of unmanned aerial vehicles (UAVs), massive transmission links in TSNs can be secured without impacts on legitimate communications. Owing to the stereo position of UAVs and the directivity of directional antennas, the constructed secure tunnel can significantly relieve confidential information leakage, resulting in the precaution of wiretapping. Moreover, we establish a theoretical model to evaluate the effectiveness of the aerial bridge scheme compared with the ground relay, non-protection, and UAV jammer schemes. Furthermore, we conduct extensive simulations to verify the accuracy of theoretical analysis and present useful insights into the practical deployment by revealing the relationship between the performance and other parameters, such as the antenna beamwidth, flight height and density of UAVs.

The large scale reflector array of programmable metasurfaces is capable of increasing the power efficiency of backscatter communications via passive beamforming and thus has the potential to revolutionize the low-data-rate nature of backscatter communications. In this paper, we propose to design the power-efficient higher-order constellation and reflection pattern under the amplitude constraint brought by backscatter communications. For the constellation design, we adopt the amplitude and phase-shift keying (APSK) constellation and optimize the parameters of APSK such as ring number, ring radius, and inter-ring phase difference. Specifically, we derive closed-form solutions to the optimal ring radius and inter-ring phase difference for an arbitrary modulation order in the decomposed subproblems. For the reflection pattern design, we propose to optimize the passive beamforming vector by solving a multi-objective optimization problem that maximizes reflection power and guarantees beam homogenization within the interested angle range. To solve the problem, we propose a constant-modulus power iteration method, which is proven to be monotonically increasing, to maximize the objective function in each iteration. Numerical results show that the proposed APSK constellation design and reflection pattern design outperform the existing modulation and beam pattern designs in programmable metasurface enabled backscatter communications.

Millimeter wave (mmWave) multi-user massive multi-input multi-output (MIMO) is a promising technique for the next generation communication systems. However, the hardware cost and power consumption grow significantly as the number of radio frequency (RF) components increases, which hampers the deployment of practical massive MIMO systems. To address this issue and further facilitate the commercialization of massive MIMO, mixed analog-to-digital converters (ADCs) architecture has been considered, where parts of conventionally assumed full-resolution ADCs are replaced by one-bit ADCs. In this paper, we first propose a deep learning-based (DL) joint pilot design and channel estimation method for mixed-ADCs mmWave massive MIMO. Specifically, we devise a pilot design neural network whose weights directly represent the optimized pilots, and develop a Runge-Kutta model-driven densely connected network as the channel estimator. Instead of randomly assigning the mixed-ADCs, we then design a novel antenna selection network for mixed-ADCs allocation to further improve the channel estimation accuracy. Moreover, we adopt an autoencoder-inspired end-to-end architecture to jointly optimize the pilot design, channel estimation and mixed-ADCs allocation networks. Simulation results show that the proposed DL-based methods have advantages over the traditional channel estimators as well as the state-of-the-art networks.

This paper studies the joint device selection and power control for wireless federated learning (FL), considering both the analog downlink and over-the-air computation (AirComp)-based uplink communications between the parameter server (PS) and the terminal devices. First, we propose an AirComp-based adaptive reweighing scheme for the aggregation of local updated models, where the model aggregation weights are directly determined by the uplink transmit power values of the selected devices. Furthermore, we provide a convergence analysis for the proposed wireless FL algorithm and derive the upper bound on the expected optimality gap between the expected and optimal global loss values with respect to (w.r.t.) the selected devices and downlink and uplink transmit power values. With instantaneous channel state information (CSI), we formulate the optimality gap minimization problem, which is solved by using the semidefinite programming (SDR) technique. Numerical results reveal that our proposed wireless FL algorithm achieves close to the best performance by using the ideal FedAvg scheme with error-free model exchange and full device participation.

In this paper, the problem of model aggregation for the federated edge learning (FEEL) over a realistic wireless network is investigated, where multiple distributed edge devices collaboratively train a global learning model using local data. In the considered model, the one-bit gradient quantization with majority vote is adopted at edge devices, which sends the local gradient sign to the edge server instead of transmitting high-dimensional stochastic gradients directly. After aggregating the quantified signs, the edge server sends back only the majority decision to significantly minimize the transmission overhead since all communications are compressed to one bit. Nevertheless, it turns out that the quality of training will be inevitably deteriorated by the undesirable propagation error introduced by wireless channels. To address this issue, we propose in this work a reconfigurable intelligent surface (RIS) assisted one-bit communication scheme under orthogonal frequency division multiplexing (OFDM) system to reduce the signal distortion during iterative model exchange of FEEL. Specifically, a learning convergence analysis with respect to wireless communication error is first established. After that, we further formulate a unified communication-learning design problem to jointly optimize the power allocation vector and the RIS configuration matrix. Numerical results demonstrate that the proposed design achieves substantial performance improvement compared with the state-of-the-art solutions.

In this work, we investigate a reconfigurable intelligent surface (RIS) aided integrated sensing and communication (ISAC) scenario, where a base station (BS) communicates with multiple devices in a full-duplex mode, and senses the positions of these devices simultaneously. An RIS is assumed to be mounted on each device to enhance the reflected echoes. Meanwhile, the information of each device is passively transferred to the BS via reflection modulation. We aim to tackle the problem of joint localization and information retrieval at the BS. A grid based parametric model is constructed and the joint estimation problem is formulated as a compressive sensing (CS) problem. Moreover, an expectation-maximization (EM) algorithm is applied for tuning the grid parameters to mitigate the model mismatch problem. Finally, we analyze the efficacy of various CS algorithms through the Bayesian Cramér-Rao bound (BCRB). Numerical results demonstrate the feasibility of the proposed scenario and the superior performance of the proposed EM-tuning method.

Current intelligent reflecting surface (IRS) configuration schemes, consisting of sub-channel estimation and passive beamforming in sequence, conform to the conventional model-based design philosophies and are difficult to be realized practically in the complex radio environment. To create smart radio environment, we propose a model-free design of IRS control that is independent of the sub-channel channel state information (CSI) and requires the minimum interaction between IRS and the wireless communication system. We firstly model the control of IRS as a Markov decision process (MDP) and then apply deep reinforcement learning (DRL) to perform real-time coarse phase control of IRS. Then, we apply extremum seeking control (ESC) as the fine phase control of IRS. Finally, by updating the frame structure, we integrate DRL and ESC in the model-free control of IRS to improve its adaptivity to different channel dynamics. Numerical results show the superiority of our proposed joint DRL and ESC scheme and verify its effectiveness in model-free IRS control without sub-channel CSI.

As the number of real-time applications with ultra-low delay requirements quickly grows, massive ultra-reliable and low-latency communication (mURLLC) has been proposed to provide a wide range of delay-sensitive services for the sixth generation (6G) wireless networks. However, it is difficult to meet the stringent delay demand of massive connectivity with existing grant-based (GB) random access and fixed frame structure in long-term evolution (LTE) and the fifth generation (5G) new radio (NR) systems. To solve this problem, in this paper we propose the new grant-free (GF) based adaptive blocklength scheme for short packet transmission to reduce the access delay. We develop the adaptive blocklength framework where the blocklength can be adaptively changed according to the real-time load, to revise the traditional non-flexible frame structure which impacts the delay performance. Taking the features of mURLLC into consideration, we analyze the GF random access procedure, packet arrival behavior, packet collision, and packet transmission error in the finite blocklength (FB) regime. On this basis, we derive the closed-form expression of successful access and transmission probability and give the GF-based status update model. Then, we propose the access delay minimization problem that jointly considers queuing delay and transmission delay to reduce the overall access delay. With the alternating optimization algorithm, we obtain the optimal blocklength of each packet, thus forming the corresponding adaptive blocklength scheme for mURLLC. Simulation results verify the correctness of theoretical results and show that our proposed adaptive blocklength scheme can significantly reduce the access delay compared with that of LTE and 5G NR systems.

In this paper, a fast optimization-based off-grid channel estimation method is proposed for millimeter wave (mmWave) or sub-terahertz(THz) cellular systems. Different from most existing works concerned with compressed sensing (CS) or off-grid methods, we proposed a novel first-order convex off-grid channel estimation approach to relieve the resolution loss from the angle quantization. Moreover, our proposed method is approximate inverse-free without costly matrix inversion. The numerical results demonstrate the effectiveness of the proposed FOG method, which achieves superior performance compared to CS-based approach and existing off-grid algorithm in terms of computational complexity and estimation accuracy.

Energy efficiency (EE) is one of the most important metrics for the beyond fifth generation (B5G) and the future sixth generation (6G) wireless networks. Reconfigurable intelligent surface (RIS) has been widely focused on EE enhancement for wireless networks because it is power-saving, programmable, and easy to be deployed. However, RIS is generally passive and thus difficult to obtain corresponding full channel state information (CSI), which severely impacts the EE enhancement of RIS-assisted wireless communications. To solve this problem, in this paper we propose the new single-active-antenna combined RIS transmitter structure, which can replace traditional multiple antennas to reduce hardware cost and power consumption. Based on the single-active-antenna combined RIS structure, we develop the Dumb RIS-Assisted Random Beamforming (Darb)-based Joint RIS-Elements and Transmit-power optimizAtion (Jeta) scheme, where dumb RIS randomly changes its phase shift according to isotropic distribution only depending on the CSI feedback from users to RIS-assisted transmitter. Then, we jointly design the number of RIS elements and optimize the transmit power to maximize the EE of RIS-assisted wireless communications. Simulation results show that compared with the traditional multi-antenna system, our developed Darb-based-Jeta scheme can significantly increase the EE without the full CSI.

This article presents an analytical model to analyze the communication performance of cluster backscatter communication networks (CBackCom Nets) by considering their unique interferences. In CBackCom Nets, interferences are from both backscatter transmitters (BTs) and carrier emitters (CEs), i.e., RF signal emitters. Because BTs are distributed in clusters around CEs, interferences from BTs and interferences from CEs constitute coupled interferences. In addition, since BTs conduct backscatter communications by reflecting RF signals from CEs, interfering signals from BTs, and interfering signals from CEs are power-correlated, leading to the particularity and complexity of coupled interferences of CBackCom Nets. In contrast to previous studies that analyze the performance of CBackCom Nets ignoring coupled interferences, this article develops a novel interference analysis approach to analyze their coupled interferences, and then analyze performance, including coverage probability and spatial throughput of a cluster. Our numerical results show that our analytical model can obtain more accurate results than prior analytical models. In addition, our results reveal the relationship between the communication performance and multiple factors, such as the node density, the energy harvesting model, the interferences from CEs, and the cluster size, offering insightful implications for constructing and configuring CBackCom Nets.

Nowadays, wireless communication is rapidly reshaping entire industry sectors. In particular, mobile-edge computing (MEC) as an enabling technology for the Industrial Internet of Things (IIoT) brings a powerful computing/storage infrastructure closer to the mobile terminals and, thereby, significantly lowers the response latency. To reap the benefit of proactive caching at the network edge, precise knowledge on the popularity pattern among the end devices is essential. However: 1) the spatiotemporal variability of content popularity; 2) the data deficiency in privacy-preserving system; 3) the costly manual labels in supervised learning; as well as 4) the not independent and identically distributed (non-i.i.d.) user behaviors pose tough challenges to the acquisition and prediction of content popularities. In this article, we propose an unsupervised and privacy-preserving popularity prediction framework for MEC-enabled IIoT to achieve a high popularity prediction accuracy while addressing the challenges. Specifically, the concepts of local and global popularities are introduced and the time-varying popularity of each user is modeled as a model-free Markov chain. On this basis, we derive and validate the essential relationship between the local and global popularities and then propose an unsupervised recurrent federated learning (URFL) algorithm to predict the distributed popularity while achieving privacy preservation and unsupervised training. Moreover, a federated loss-weighted averaging (FedLWA) scheme for the parameter aggregation is further designed to alleviate the problem of non-i.i.d. user behaviors. Simulations indicate that the proposed framework can enhance the prediction accuracy in terms of a reduced root-mean-squared error by up to 60.5%–68.7% compared to other baseline methods, i.e., recommendation algorithms, centralized learning algorithms, and other distributed learning algorithms. Additionally, manual labeling and violation of users’ data privacy are both avoided.

Intelligent reflecting surface (IRS) is envisioned to change the paradigm of wireless communications from “adapting to wireless channels” to “changing wireless channels”. However, current IRS configuration schemes, consisting of sub-channel estimation and passive beamforming in sequence, conform to the conventional model-based design philosophies and are difficult to be realized practically in the complex radio environment. To create the smart radio environment, we propose a model-free design of IRS control that is independent of the sub-channel channel state information (CSI) and requires the minimum interaction between IRS and the wireless communication system. We firstly model the control of IRS as a Markov decision process (MDP) and apply deep reinforcement learning (DRL) to perform real-time coarse phase control of IRS. Then, we apply extremum seeking control (ESC) as the fine phase control of IRS. Finally, by updating the frame structure, we integrate DRL and ESC in the model-free control of IRS to improve its adaptivity to different channel dynamics. Numerical results show the superiority of our proposed joint DRL and ESC scheme and verify its effectiveness in model-free IRS control without sub-channel CSI.

To extend the conventional wireless sensor networks (WSNs) to support wider applications such as intruder detection and border security monitoring, active radar sensors are introduced into WSNs to further enhance their capability, thus forming wireless radar sensor networks (WRSNs). To improve the network efficiency, the technology of integrated sensing and communication (ISAC) can be applied to co-design the sensing and communication functionalities of radar sensors. Since the cooperative operations of WRSNs require effective information interaction among radar sensors, data dissemination techniques need to be investigated, which become even more critical in desolate areas without the coverage of the base stations (BSs). Therefore, in this paper, a duty cycling mechanism is applied to the network to enhance the usage of WRSNs and support data dissemination, where a storage node is deployed to store the data spreading from radar sensors and a mobile data collector is employed to collect the data from the storage node periodically. Then, the epidemic theory, as an innovative tool for modeling data dissemination, is adopted to analyze the performance of WRSNs. After epidemiological modeling, the density of radar sensors is optimized by the epidemiological analytical method to maximize the throughput of the storage node by jointly considering the functions of radar detection and communication. Simulation results validate the accuracy of analysis, which also show the efficiency of the optimization for data dissemination.

This paper studies the joint device selection and power control scheme for wireless federated learning (FL), considering both the downlink and uplink communications between the parameter server (PS) and the terminal devices. In each round of model training, the PS first broadcasts the global model to the terminal devices in an analog fashion, and then the terminal devices perform local training and upload the updated model parameters to the PS via over-the-air computation (AirComp). First, we propose an AirComp-based adaptive reweighing scheme for the aggregation of local updated models, where the model aggregation weights are directly determined by the uplink transmit power values of the selected devices and which enables the joint learning and communication optimization simply by the device selection and power control. Furthermore, we provide a convergence analysis for the proposed wireless FL algorithm and the upper bound on the expected optimality gap between the expected and optimal global loss values is derived. With instantaneous channel state information (CSI), we formulate the optimality gap minimization problems under both the individual and sum uplink transmit power constraints, respectively, which are shown to be solved by the semidefinite programming (SDR) technique. Numerical results reveal that our proposed wireless FL algorithm achieves close to the best performance by using the idealFedAvgscheme with error-free model exchange and full device participation.

The next generation multiple access (NGMA) schemes are considered to support massive access for a large number of devices, which motivates us to develop a low-complexity approach for next generation systems. Since the generalized spatial modulation (SM) can be adopted to the system, a number of compressive sensing (CS) reconstruction algorithms are deployed for the detection of sparse signals, while the complexity of CS-based approaches is proportional to the number of antennas. In order to decrease the complexity, we propose a two-stage approach to detect sparse signals, where the received signals are divided into groups. Then, the activity variables of aggregated signals are decided and the sparse signal detection is carried out at the signals belonging to active groups. During the activity variable detection, the variational inference algorithm is applied to determine the activity variables. Moreover, in order to analyze the performance of activity variable detection, the$J$-divergence is proposed to measure the distance between the distributions, while the approximate expression of$J$-divergence is derived. Simulation results show that the proposed approach is able to provide good detection performance with low complexity. In addition, the$J$-divergence is confirmed to be useful as an evaluation metric to measure the detection performance.

Massive machine-type communications (mMTC) are expected to support a large amount of randomly deployed users for short package transmissions. Noncoherent random access provides an efficient and practical multi-access protocol for mMTC, and also poses new challenges for the receiver design. In this paper, we leverage two well-known methods, i.e., message passing and deep learning, to jointly detect the user activity and the desired data for the noncoherent mMTC. First, by exploiting the exact distribution information of the received signal, a generalized approximate message passing (GAMP)-based algorithm is proposed, which is shown to jointly detect the user activity and the desired data by two modules: inter-user interference elimination and data detection for each user. Inspired by the two-module GAMP-based algorithm, we then propose a model-driven deep learning method, which utilizes the deep neural networks (DNNs) to approximate both the two modules. The loss function for training the DNNs is derived by formulating the two-module detection as an unconstrained optimization problem. Simulation results reveal that the proposed GAMP-based algorithm outperforms the proposed deep learning method when the channel distribution is perfectly known, while it suffers from a significant performance degradation for the case with imperfect channel distribution information.

Magnetic Induction (MI) based transmissions, where the typical applications are wireless power transfer (WPT) and MI communications, have gained substantial attention in recent years. Existing works related to MI based transmissions mainly focus on the case with single resonant frequency. However, from the perspective of practical applications, it is highly demanded to achieve multi-frequency access for MI based transmissions. Although multiple coils can be used to generate additional resonant frequencies, each coil is often with one resonant frequency. In this paper, we develop the Multi-frequency Resonating Compensation (MuReC) coil based Multiple-band, Multiple-Input, and Multiple-Output (MbMIMO) simultaneous wireless information and power transfer (SWIPT), which can generate multiple resonant frequencies with one coil. The access point (AP) is equipped with multiple MuReC coils as well as delivers power and data to single-coil receivers (RXs) with different resonant frequencies. We propose the magnetic beamforming scheme for MuReC-MbMIMO-SWIPT, which aims to minimize the transmit power as well as guarantee the network throughput, total delivered power, and individual requirements of each channel. The beamforming schemes for two special cases of MuReC-MbMIMO-SWIPT, that is, MuReC-MbMIMO-communications and MuReC-MbMIMO-WPT, are also given. The optimal beamforming scheme in closed-form for MuReC-MbMIMO-communications is derived. In addition, the parameter design scheme for MuReC coils, which can support an arbitrary number of resonant frequencies, is developed. Numerical results verify our theoretical analyses and show that MuReC-MbMIMO transmissions and the proposed beamforming schemes can fully use antenna resources and effectively support multi-frequency access.

In this paper, we consider the problem of joint beam selection and link activation across a set of communication pairs to effectively control the interference between communication pairs via inactivating part communication pairs in ultra-dense device-to-device (D2D) mmWave communication networks. The resulting optimization problem is formulated as an integer programming problem that is nonconvex and NP-hard. Consequently, the global optimal solution, even the local optimal solution, cannot be generally obtained. To overcome this challenge, this paper resorts to design a deep learning architecture based on graph neural network to finish the joint beam selection and link activation, with taking the network topology information into account. Meanwhile, we present an unsupervised Lagrangian dual learning framework to train the parameters of the GBLinks model. Numerical results show that the proposed GBLinks model can converge to a stable point with the number of iterations increases, in terms of the weighted sum rate. Furthermore, the GBLinks model can reach near-optimal solutions through comparing with the exhaustive scheme in small-scale ultra-dense D2D mmWave communication networks and outperforms GreedyNoSched and the SCA-based method. It also shows that the GBLinks model can generalize to varying network densities and network coverage regions of ultra-dense D2D mmWave communication networks.

Mobile-Edge Computing (MEC) is an emerging paradigm in the Internet of Vehicles (IoV) to meet the ever-increasing computation demands of smart applications. To provide satisfactory computation performance, it is of significant importance to conduct computation offloading in IoV. In this paper, we investigate a multi-vehicle IoV system assisted by MECs with limited computation resources, where vehicles with complex applications can offload their subtasks to MEC servers. Applications are modeled as interdependent subtasks with general random task graphs, different from existing works with independent ones. To maximize the average logarithmic data processing rate (LDPR), the computation offloading problem is formulated as a time-average optimization with long-term constraints, which results from variable vehicle number, various applications and time-varying communication channels. To reduce the cooperation overhead, we propose a multi-agent Proximal Policy Optimization algorithm (Ly-MAPPO) which requires local observation only to solve the subproblems achieved by Lyapunov optimization technique in real time. In addition, to improve the performance of the Ly-MAPPO algorithm, Graph Convolutional Neural Network (GCN) is introduced to extract inter-dependencies between subtasks. Extensive simulations show that the GCN embedded Ly-MAPPO outperforms other baseline algorithms, e.g., greedy algorithm and gene algorithm, etc., for different traffic loads and computation resources in MEC servers.

In wireless networks, the optimization problems generally have complex constraints and are usually solved via utilizing the traditional optimization methods that have high computational complexity and need to be executed repeatedly with the change of network environments. In this paper, to overcome these shortcomings, an unsupervised deep unrolling framework based on projection gradient descent (PGD), i.e., unrolled PGD network (UPGDNet), is designed to solve a family of constrained optimization problems. The set of constraints is divided into two categories according to the coupling relations among optimization variables and the convexity of constraints. One category of constraints includes convex constraints with decoupling among optimization variables, and the other category of constraints includes non-convex or convex constraints with coupling among optimization variables. Then, the first category of constraints is directly projected onto the feasible region, while the second category of constraints is projected onto the feasible region using a neural network. Finally, an unrolled sum rate maximization network (USRMNet) is designed based on UPGDNet to solve the weighted SR maximization problem for the multiuser ultra-reliable low latency communication system. Numerical results show that USRMNet has a comparable performance with low computational complexity and an acceptable generalization ability in terms of the user distribution.

Massive machine-type communications (mMTC) for the Internet of Things (IoT) are expected to support a large number of devices/users for short packet transmissions with low complexity and low energy consumption. By utilizing the simple while efficient noncoherent energy-based transmission scheme, this work aims to jointly detect the user activity and the desired data for mMTC. First, by exploiting the sparse characteristics of the user activity, approximation message passing (AMP) algorithm is proposed to eliminate the multi-user interference, and a denoiser is designed to minimize the mean-squared error (MSE) of the transmitted signals. Then, maximum${a}$posteriori(MAP) criterion is adopted to approximately detect the user activity and the desired data. By minimizing the symbol error probability of the above two-step algorithm, the power constellation for each user is designed, and it is shown to be asymptotically optimal as the number of the receiver antennas goes to infinity. Finally, simulation results reveal that the proposed noncoherent scheme outperforms the coherent one in the low SNR regime and for short packet transmissions.

It is challenging to acquire accurate channel knowledge for sufficient beamforming gain because of the large number of antennas. In this paper, a deep learning aided channel estimation algorithm is proposed for mmWave cellular systems, in which both the base station (BS) and mobile station (MS) gain directional beamforming by employing large antenna arrays. We consider the off-grid (OG) mmWave channel estimation and propose a deep network architecture for solving this problem, which is different from most existing works concerning the compressed sensing (CS)-based channel estimation. Firstly, the off-grid channel model is used to relieve the basis mismatch issue by exploiting first-order Taylor-series approximation of the array manifold taken on a fixed grid of both BS and MS. Secondly, a new formulation of this off-grid channel estimation problem is solved by using a low computational complexity alternating direction method of multipliers (ADMM)-based algorithm, dubbed ADMM-OG algorithm. Thirdly, the idea of algorithm unrolling guides us to design a deep network architecture ADMM-OGChannelNet corresponding to the ADMM-OG algorithm for the mmWave channel estimation. Based on the interpretability of this deep network architecture, the optimal parameters of this network can be learned without tuning in a hand-crafted way. The Cramér-Rao bound (CRB) of the off-grid channel model is derived for performance comparison as well. Finally, from the simulation results, we can verify that the ADMM-OGChannelNet has better estimation accuracy and relatively low computational complexity compared with the state-of-the-art algorithms.

Intelligent reflecting surface (IRS) has emerged as a competitive solution to address blockage issues in millimeter wave (mmWave) and Terahertz (THz) communications due to its capability of reshaping wireless transmission environments. Nevertheless, obtaining the channel state information of IRS-assisted systems is quite challenging because of the passive characteristics of the IRS. In this paper, we consider the problem of beam training/alignment for IRS-assisted downlink mmWave/THz systems, where a multi-antenna base station (BS) with a hybrid structure serves a single-antenna user aided by IRS. By exploiting the inherent sparse structure of the BS-IRS-user cascade channel, the beam training problem is formulated as a joint sparse sensing and phaseless estimation problem, which involves devising a sparse sensing matrix and developing an efficient estimation algorithm to identify the best beam alignment from compressive phaseless measurements. Theoretical analysis reveals that the proposed method can identify the best alignment with only a modest amount of training overhead. Simulation results show that, for both line-of-sight (LOS) and NLOS scenarios, the proposed method obtains a significant performance improvement over existing state-of-art methods. Notably, it can achieve performance close to that of the exhaustive beam search scheme, while reducing the training overhead by 95%.

Jittering effects significantly degrade the performance of UAV millimeter-wave (mmWave) communications. To investigate the impacts of UAV jitter on mmWave communications, we firstly model UAV mmWave channel based on the geometric relationship between element antennas of the uniform planar arrays (UPAs). Then, we extract the relationship between (I) UAV attitude angles & position coordinates and (II) angle of arrival (AoA) & angle of departure (AoD) of mmWave channel, and we also derive the distribution of AoA/AoD at UAV side from the random fluctuations of UAV attitude angles, i.e., UAV jitter. In beam training design, with the relationship between attitude angles and AoA/AoD, we propose to generate a rough estimate of AoA and AoD from UAV navigation information. Finally, with the rough AoA/AoD estimate, we develop a compressed sensing (CS) based beam training scheme with constrained sensing range as the fine AoA/AoD estimation. Particularly, we construct a partially random sensing matrix to narrow down the sensing range of CS-based beam training. Numerical results show that our proposed UAV beam training scheme assisted by navigation information can achieve better accuracy with reduced training length in AoA/AoD estimation and is thus more suitable for UAV mmWave communications under jittering effects.

To boost the performance of wireless communication networks, unmanned aerial vehicles (UAVs) aided communications have drawn dramatically attention due to their flexibility in establishing the line of sight (LoS) communications. However, with the blockage in the complex urban environment, and due to the movement of UAVs and mobile users, the directional paths can be occasionally blocked by trees and high-rise buildings. Intelligent reflection surfaces (IRSs) that can reflect signals to generate virtual LoS paths are capable of providing stable communications and serving wider coverage. This is the first paper that exploits a three-dimensional geometry dynamic channel model in IRS- assisted UAV-enabled communication system. Moreover, we develop a novel deep learning based channel tracking algorithm consisting of two modules: channel pre-estimation and channel tracking. A deep neural network with off-line training is designed for denoising in the pre-estimation module. Moreover, for channel tracking, a stacked bi-directional long short term memory (Stacked Bi-LSTM) is developed based on a framework that can trace back historical time sequence together with bidirectional structure over multiple stacked layers. Simulations have shown that the proposed channel tracking algorithm requires fewer epochs to convergence compared to benchmark algorithms. It also demonstrates that the proposed algorithm is superior to different benchmarks with small pilot overheads and comparable computation complexity.

Owing to the spatial orthogonality, orbital angular momentum (OAM), which describes the helical phase fronts of electromagnetic waves, shows an extensive application prospect for increasing the spectrum efficiency and enhancing the physical layer security of wireless communications. However, efficient anti-jamming results of existing mode hopping (MH) schemes are achieved with pre-shared hopping sequences. Such MH schemes require the strict synchronization between the legitimate transmitter and receiver, thus leading to complex system design. Also, pre-shared hopping sequences greatly limit the MH application scenarios. To solve the problem of no pre-shared hopping sequences between the legitimate transmitter and receiver for anti-jamming, in this paper we propose the precoding-based mode hopping (PoM) scheme. Specifically, we design the transmitter by dividing the input information into the index information and signal information. Based on the index information, the activated OAM-modes for hopping and the phase shift for precoding are determined. OAM-mode and phase shift randomization make jammers difficult to disrupt the communication. Then, the receiver with OAM decomposition and phase shift decoding is designed to extract legitimate signals. Also, the lower bound of achievable rate for our proposed PoM scheme is derived. Numerical results show that our proposed PoM scheme is superior to the conventional index-modulation based mode division multiplexing (IM-MDM) schemes in terms of achievable rate under hostile jamming.

This paper studies a distributed scheme for a multi-input multi-output (MIMO) relay network, where the transmit nodes are subject to the nonlinear instantaneous power constraints. We introduce a novel perspective of regarding a relay network as a so-termed quasi-neural network by drawing its striking analogies with a (four-layer) artificial neural network (ANN). We propose a nonlinear amplify-and-forward (NAF) scheme inspired by the back-propagation (BP) algorithm, namely the NAF-BP, to optimize the transceivers to maximize the output signal-to-interference-plus-noise ratio (SINR) of the data streams. The NAF-BP algorithm can be implemented in a distributed manner with no channel state information (CSI) and no data exchange between the relay nodes. The NAF-BP can also coordinate the distributed relay nodes to form a virtual array to suppress interferences from unknown directions. Extensive simulations verify the effectiveness of the proposed scheme.

Current 3D single object tracking approaches track the target based on a feature comparison between the target template and the search area. However, due to the common occlusion in LiDAR scans, it is non-trivial to conduct accurate feature comparisons on severe sparse and incomplete shapes. In this work, we exploit the ground truth bounding box given in the first frame as a strong cue to enhance the feature description of the target object, enabling a more accurate feature comparison in a simple yet effective way. In particular, we first propose the BoxCloud, an informative and robust representation, to depict an object using the point-to-box relation. We further design an efficient box-aware feature fusion module, which leverages the aforementioned BoxCloud for reliable feature matching and embedding. Integrating the proposed general components into an existing model P2B [27], we construct a superior box-aware tracker (BAT)1. Experiments confirm that our proposed BAT outperforms the previous state-of-the-art by a large margin on both KITTI and NuScenes benchmarks, achieving a 12.8% improvement in terms of precision while running ∼20% faster.

Multi-input multi-output orthogonal frequency division multiplexing (MIMO OFDM) is a key technology for mobile communication systems. However, due to the issue of high peak-to-average power ratio (PAPR), the OFDM symbols may suffer from nonlinear distortions of the power amplifier (PA) at the transmitters, which degrades the channel estimation and detection performances of the receivers. To mitigate the clipping distortions at the receivers end, we leverage deep learning (DL) and devise a DL based receiver which is aided by the traditional least square (LS) channel estimation and the zero-forcing (ZF) equalization models. Moreover, a data driven DL based receiver without explicit channel estimation is proposed and combined with the model aided DL based receiver to further improve the performance. Simulation results showcase that the proposed model aided DL based receiver has superior performance of bit error rate and has robustness over different levels of clipping distortions.

In recent years, unmanned aerial vehicles (UAVs), especially swarming UAVs are widely deployed in a variety of Internet-of-Things (IoT) scenarios. Since UAVs' positions are essential for their collaboration, high-precision localization for swarming UAVs has attracted a lot of attention. Although the global positioning system (GPS) receiver has been widely integrated in UAV, it is not accurate enough and is prone to accidental or deliberate interferences. In this article, we propose a distributed and collaborative localization method for swarming UAVs that combines super multidimensional scaling (SMDS) and patch dividing/merging with GPS information. Specifically, the SMDS is first used to get the relative coordinates of the UAVs in each patch, then we merge relative map patches into a global map and transform the relative coordinates of the UAVs to their absolute coordinates. Furthermore, we propose a low-complexity algorithm that greatly reduces the computational complexity of SMDS with a large number of UAVs. Simulation results validate that with accurate enough angle measurements, the proposed SMDS localization algorithm outperforms the other MDS-based collaborative localization algorithms and can greatly improve the localization accuracy and robustness of swarming UAVs.

In this article, a hierarchical task offloading strategy is presented for delay-tolerant and delay-sensitive missions by integrating edge computing and artificial intelligence into Cybertwin-based network to guarantee user Quality of Experience (QoE), low latency, and ultrareliable services, which are huge challenges to the Internet of Things (IoT) due to diverse application requirements, heterogeneous multidimensional resources, and time-varying network environments. The novel scheme achieves faster task processing, dynamic real-time allocation, and lower overhead by taking advantages of a multiagent deep deterministic policy gradient (MADDPG). Moreover, federated learning is used to train the MADDPG model. Numerical results demonstrate that the proposed algorithm improves system processing efficiency and task completion ratio compared to the benchmark schemes.

Cybertwin is a promising technique in the 6G network to cope with an exponentially increasing demand for the mobile data traffic and growing number of required network service appliances. Multiple resources are required to fulfil various personalized services of mobile users while protecting their privacy. In this article, we propose a progressive adaptive user selection environment (PAUSE)-based combinatorial auction resource trading mechanism to allocate the resource efficiently and securely. Cybertwins can request the various resources as a bid according to its corresponding user and proceed the auction in a fully distributed manner. Not only the user’s privacy can be well protected but also its cost can be saved due to the transparency of this mechanism. Simulation results validate the effectiveness of our mechanism in comparison with the centralized benchmark.

In 5G and beyond wireless scenarios, it is expected to support tremendous amount of communicating machines. With an exponential increase of machine-to-machine (M2M) system deployments, efficient approaches to massive access by a large number of devices are to be studied. In this paper, we propose a massive multiple-input multiple-output (MIMO) based grant-free random access (RA) with resolution of preamble collision for massive access. Based on the channel hardening and favorable propagation characteristics of massive MIMO, collided signals processed at the base station (BS) can be viewed as a variation of superposition modulation, and are to be recovered by successive interference cancellation (SIC) techniques. Besides, taking into consideration the effect of pass loss and fractional power control (FPC), analytic expressions of success probability of the proposed collision resolution with conjugate beamforming (CB) and zero-forcing beamforming (ZFB) are derived. With simulation results, we verify the analyses and show that the proposed protocol can resolve most preamble collisions.

In 5G massive machine-type communication (mMTC), power-limited or battery-free parasite devices such as radio frequency identification (RFID) tags, can use the transmitted signals from host devices as ambient signals for backscatter communications to send information to a base station (BS). Unmanned aerial vehicles (UAVs) can be employed as host devices to help transmissions of parasite devices due to the advantages of high mobility and low operating cost. In this paper, we propose a signal detection approach based on the central limit theorem to detect the presence of parasite devices and separate parasite signals from host signals. Then, closed-form expressions for the probability of error detection and the bit error rate (BER) are derived. Moreover, the trajectory planning of multiple UAVs is optimized with the consideration of minimizing the energy consumption of UAV swarms to serve parasite devices. Theoretical and simulation results show that our proposed method provides good detection performance for parasite devices. It also shows that the trajectory planning of multiple UAVs is optimized.

For unforeseen natural disasters, such as earthquakes, hurricanes, and floods, etc., the traditional communication infrastructure is unavailable or seriously disrupted along with persistent secondary disasters. Under such circumstances, it is highly demanded to deploy emergency wireless communication (EWC) networks to restore connectivity in accident/ incident areas. The emerging fifth-generation (5G)/beyond-5G (B5G) wireless communication system, like unmanned aerial vehicle (UAV) assisted networks and intelligent reflecting surface (IRS) based communication systems, are expected to be designed or re-farmed for supporting temporary high quality communications in post-disaster areas. However, the channel characteristics of post-disaster areas quickly change as the secondary disaster resulted topographical changes, imposing new but critical challenges for EWC networks. In this paper, we propose a novel heterogeneous$\mathcal {F}$composite fading channel model for EWC networks which accurately models and characterizes the composite fading channel with reflectors, path-loss exponent, fading, and shadowing parameters in 5G-UAV based EWC networks. Based on the model, we develop the optimal power allocation scheme with the simple closed-form expression and the numerical results based optimal joint bandwidth-power allocation scheme. We derive the corresponding capacities and compare the energy efficiency between IRS and traditional relay based 5G-UAVs. Numerical results show that the new heterogeneous Fisher-Snedecor$\mathcal {F}$composite fading channel adapted resource allocation schemes can achieve higher capacity and energy efficiency than those of traditional channel model adapted resource allocation schemes, thus providing better communications service for post-disaster areas.

Due to its low energy consumption and simplicity, near field communication (NFC) has been extensively used in various short-range transmission scenarios, for example, proximity payment and NFC entrance guard. However, the low data rate of NFC limits its application in high rate demanded scenarios, such as high-resolution fingerprint identification and streaming media transmission as well as the future promising high rate indoor communications among pads, phones, and laptops. In this paper, we model and analyze the performance of the orbital angular momentum based NFC (OAM-NFC) system, which can significantly increase the capacity of NFC. We first give the OAM system model. With coils circularly equipped at the transmitter and receiver, OAM-NFC signals can be transmitted, received, and detected. Then, we develop the OAM-NFC generation and detection schemes for NFC multiplexing transmission. We also analyze the OAM-NFC channel capacity and compare it with those of single-input-single-output (SISO) as well as multi-input-multi-output (MIMO) NFC. Simulation results validate the feasibility and capacity enhancement of our proposed OAM-NFC system. How different variables, such as the transceiver misalignment, the numbers of transceiver coils, and transceiver distance, impact the OAM-NFC capacity are also analyzed.

In a wireless network that conveys status updates from sources (i.e., sensors) to destinations, one of the key issues studied by existing literature is how to design an optimal source sampling strategy on account of the communication constraints which are often modeled as queues. In this paper, an alternative perspective is presented—a novel status-aware communication scheme, namelyparallel communications, is proposed which allows sensors to be communication-agnostic. Specifically, the proposed scheme can determine, based on an online prediction functionality, whether a status packet is worth transmitting considering both the network condition and status prediction, such that sensors can generate status packets without communication constraints. We evaluate the proposed scheme on a Software-Defined-Radio (SDR) test platform, which is integrated with a collaborative autonomous driving simulator, i.e., Simulation-of-Urban-Mobility (SUMO), to produce realistic vehicle control models and road conditions. The results show that with online status predictions, the channel occupancy is significantly reduced, while guaranteeing low status recovery error. Then the framework is applied to two scenarios: a multi-density platooning scenario, and a flight formation control scenario. Simulation results show that the scheme achieves better performance on the network level, in terms of keeping the minimum safe distance in both vehicle platooning and flight control.

Intelligent reflecting surface (IRS) offers a cost-effective solution to link blockage problem in mmWave communications, and the prerequisite of which is the accurate estimation of (1) the optimal beams for base station/access point (BS/AP) and mobile terminal (MT), (2) the optimal reflection patterns for IRSs, and (3) link blockage. In this paper, we carry out beam training designs for IRSs assisted mmWave communications to estimate the aforementioned parameters. To acquire the optimal beams and reflection patterns, we firstly perform random beamforming and maximum likelihood estimation to estimate angle of arrival (AoA) and angle of departure (AoD) of the line of sight (LoS) path between BS/AP (or IRSs) and MT. Then, with the estimated AoDs, we propose an iterative positioning algorithm that achieves centimeter-level positioning accuracy. The obtained location information is not only a fringe benefit but also enables us to cross verify and enhance the estimation of AoA and AoD, and it also facilitates the estimation of blockage indicator. Numerical results show the superiority of our proposed beam training scheme and verify the performance gain brought by location information.

Multi-user millimeter wave (mmWave) massive multi-input multi-output (MIMO) is a promising technology for the next generation mobile communication systems. However, there are still unsolved problems before such commercial MIMO networks are rolled out. One main issue is the hardware cost and power consumption which grow significantly as the number of radio frequency (RF) components increases. To tackle this issue, we consider to deploy one-bit analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) at the base station (BS), and study uplink (UL)/downlink (DL) channel estimation and DL precoding techniques for the associated MIMO systems with one-bit ADCs/DACs. Specifically, we first formulate the UL channel estimation as an one-bit compressed sensing problem, and then devise an efficient gridless generalized approximate message passing-based (GL-GAMP) algorithm to handle it. Additionally, we develop an exhaustive search based proximal gradient descent method (PGM) for DL channel estimation. Note that with slight modifications, we show that PGM can also be applied to solve the DL precoding problem. Simulation results showcase that our methods have advantages over the state-of-the-art techniques and are able to offer good trade-offs between accuracy and computational complexity, which ultimately indicates their superiority in the application of mmWave MIMO systems with one-bit ADCs/DACs.

Massive machine-type communications (mMTC) are expected to support bunches of low cost devices, which are stochastically active. By utilizing the simple while efficient noncoherent energy-based transmission scheme, this work aims to jointly detect the user activity and the data in mMTC. First, by exploiting the sparse characteristics of the user activity, approximation message passing (AMP) algorithm is proposed to suppress the multi-user interference. Then, maximum a posteriori (MAP) criterion is adopted to approximately detect the user activity and the desired data. By minimizing the symbol error probability of the above two-step algorithm, the power constellations at each user are designed. Finally, the scaling behavior of the considered system is analyzed, and it is shown that to guarantee reliable communications, the number of the receiver antennas per user should vanish in the order of O([1/log(N)]) with N being the number of the total users.

Orbital angular momentum (OAM), which is considered as a novel way to achieve high capacity, has been attracted much attention recently. OAM signals emitted by uniform circular array (UCA) are widely treated as going through the Bessel-form channels. However, the channel gains corresponding to the Bessel-form channels are with low signal-to-noise-ratio (SNR) on OAM-modes and it is difficult to achieve high capacity using all OAM modes. To achieve maximum capacity offered by OAM multiplexing for wireless backhaul communications, in this paper we propose the optimal UCA design, which selects the optimal OAM-modes and radius of receive UCA. We formulate the capacity maximization problem and divide it into two subproblems for obtaining the corresponding optimal UCA design for OAM multiplexing based wireless backhaul communications. In particular, the optimal radius of the receive UCA is firstly derived. Then, we propose a mode selection scheme to choose appropriate OAM-modes for data transmission to maximize the capacity. Extensive simulations obtained validate that the capacity of OAM multiplexing can be significantly increased with our developed scheme.

Due to its low energy consumption and high security, near field communication (NFC) has been extensively used in various short-range non-contact transmission scenarios such as the proximity payment and NFC entrance guard. However, the low data rate of NFC limits its application in high rate demanded scenarios, such as high-resolution fingerprint identification and streaming media transmission. In this paper, we propose the orbital angular momentum (OAM) based NFC system to significantly increase the capacity for NFC systems. With coils circularly equipped at the transmitter and receiver, OAM signals can be transmitted, received, and detected. Then, we analyze the mutual inductances between the transmit and receive coils to derive the OAM-NFC magneto-inductive channel matrix. Based on the channel matrix, we develop the OAM-NFC transmission and detection schemes for NFC multiplexing transmission. We also compare the capacity of our proposed OAM-NFC system with those of SISO NFC system and MIMO NFC system. Simulation results validate the feasibility and capacity enhancement of our proposed OAM-NFC system. How the number of the transceiver coils impacts the capacity of OAM-NFC system is also evaluated.

Over the last decade, the Internet of Things (IoT) has been employed as an enabling technology for the smart ocean. As one of the key technologies in the IoT, machine-type communication (MTC) has been considered to support devices' connectivity. In the MTC, random access is introduced for devices to share a common access channel during the packet transmission with low signaling overhead. However, the collision caused by the presence of multiple devices is inevitable. Since maritime sensors have limited energy sources, in this article, we propose a relay-aided random access (RARA) scheme for the smart ocean, where retransmissions are carried out by maritime buoys with the relay function, to deal with collisions. In the RARA scheme, a base station (BS) is able to recover multiple collided signal packets simultaneously by using multiuser detection with multiple copies of collided signals forwarded by buoy nodes. As a result, our proposed scheme becomes energy efficient and reliable to be suitable for the smart ocean. Theoretical and simulation results show that a high throughput and a low outage probability can be achieved with a large number of buoy nodes.

Underwater exploration activities have grown significantly due to the proliferation of underwater Internet of Things (UIoT). However, to transmit sensor data from UIoT to remote onshore data processing center requires a huge cost of deploying and maintaining communication infrastructures. In this article, we propose an unmanned aerial vehicles (UAVs)-assisted underwater data acquisition scheme by placing multiple sink nodes on the water surface to serve as intermediate relays between underwater sensors (IoT nodes) and UAVs. In our scheme, the sensor data are first transmitted via an acoustic-signal link to a buoyant sink node, which then forwards the data to a UAV via an electromagnetic link. In particular, we adopt two sink-node-deployment methods, i.e., grid placement and random placement of sink nodes. Since the path connectivity from an underwater sensor node to the UAV is crucial to guarantee reliable data acquisition tasks, we establish a theoretical framework to analyze the path connectivity via the intermediate sink node for both grid and random sink-node-deployment methods. Extensive simulation results validate the accuracy of the proposed analytical model. Moreover, our results also reveal the relationship between the path connectivity and other factors, such as sink node placements, antenna beamwidth of UAVs, and wind speed. We also further extend our UAV-assisted data acquisition to other scenarios with the consideration of trajectories of UAVs, movements of sink nodes, interference of both underwater acoustic and terrestrial radio links, and integration with edge computing.

As unmanned aerial vehicles (UAVs) are becoming more popular and the demand for wireless links for UAVs is increasing, it is crucial to develop air-to-ground (A2G) wireless links for high-speed UAVs. Suffering from the high mobility and limitation of transmission power of UAVs, A2G wireless links become unstable to provide high quality communication services. In this paper, we design robust A2G wireless links for high speed UAVs, where conjunct power control is developed together with switched beamforming to maximize the power efficiency and minimize the fluctuation of A2G wireless links of millimeter wave (mmWave) signal transmission. We first present channel models for A2G wireless links of high-speed UAVs, which can be virtually seen as multiple-input multiple-output (MIMO) channels. To maximize the power efficiency, a conjunct power control problem is formulated to allocate powers for wireless links between antenna arrays on UAVs and access points (APs). For switched beamforming, beamformers are designed to provide a certain time-invariant signal-to-interference-plus-noise ratio (SINR) to minimize the SINR fluctuation of A2G wireless links. From theoretical analysis and numerical results, it is shown that the proposed architecture is able to provide robust and high quality A2G wireless links for high-speed UAV communication systems.

Spatial modulation is an efficient transmission scheme for RF-chain limited MIMO systems. In this paper, we design a beam-switching based spatial modulation for mmWave MIMO uplink systems. At user side, a power iteration based algorithm is proposed to generate spatial symbols under the constant modulus constraint. With the generated spatial codebook, the optimal transmission mode is determined. At base station side, a round-robin path selection algorithm is proposed to select paths that are separated in angle of arrival. Inter-user interference is then suppressed by constraining the power of the received spatial symbols over selected paths only. Finally, ordered successive interference cancelation is applied to further reduce interference among users. Numerical results show that the proposed beam-switching based spatial modulation is superior to transmission over the strongest propagation path and is able to yield a satisfactory error performance in mmWave MIMO uplink.

Multi-panel MIMO is a promising technology in millimeter wave communications. Due to its partially hybrid structure and non-uniform antenna array, existing channel estimation cannot be directly applied to multi-panel MIMO. In this paper, we study channel estimation for multi-panel mmWave MIMO. We firstly model channel estimation of multi-panel MIMO as a block-sparse signal recovery problem. Then, according to maximum likelihood criterion, we propose an orthogonal projection method to firstly detect the support of the sparse channel response vector and then perform least square estimation, based on which we also propose a low-complexity greedy support detection in order to reduce computational complexity. Finally, through analyzing pairwise error probability of support detection, we verify the performance gain of joint multi-panel channel estimation over single panel channel estimation, and we further find that joint multi-panel channel estimation with independently generated random combining matrices outperforms that with identically generated combining matrices. Numerical results verify the superiority of our proposed joint multi-panel orthogonal projection based channel estimation over existing schemes.

Orbital angular momentum (OAM) based mode hopping (MH) scheme is expected to be a potential anti-jamming technology in radio vortex wireless communications. However, it only uses one OAM-mode for hopping, thus resulting in low spectrum efficiency (SE). Index modulation offers a trade-off balance between the SE and performance reliability. In this paper, we propose an MH with OAM-based index modulation scheme, where several OAM-modes are activated for hopping, to achieve high SE at a given bit error rate in radio vortex wireless communications. Based on the proposed scheme, we derive the upper bound and lower bound of achievable SEs. Furthermore, in order to take advantage of index information, we derive the optimal hopped OAM-modes to achieve the maximum SE. Numerical results show that our proposed MH with index modulation scheme can achieve high SE while satisfying a certain reliability of radio vortex wireless communications.

Channel estimation is crucial to beamforming techniques in directional millimetre wave (mmWave) communications, which is generally designed based on channel state information with the assumption that the channel is static. However, due to the Doppler effect caused by the mobility of the users in highly mobile applications, the mmWave channel is changing rapidly. Spatial channel covariance, defined by long-term statistic information of channels, is a promising solution to reduce channel estimation frequency, and which can be used to design hybrid precoders. In this paper, we investigate compressive sensing based spatial channel covariance estimation for hybrid mmWave multiuser (MU) multiple input multiple output (MIMO) system. The updated sparse Bayesian learning (Updated-SBL) algorithm is proposed which is achieved by reducing the total squared mutual coherence of the sensing matrix in it. Simulations demonstrate that the total squared mutual coherence of the proposed Updated-SBL algorithm is dramatically reduced and the superiority of the proposed algorithm is validated by comparing to the other benchmark methods.

Orbital angular momentum (OAM) has attracted much attention for radio vortex wireless communications due to the orthogonality among different OAM-modes. To maintain the orthogonality among different OAM modes at the receiver, the strict alignment between transmit and receive antennas is highly demanded. However, it is not practical to guarantee the transceiver alignment in wireless communications. The phase turbulence, resulting from the misaligned transceivers, leads to serious intermode interference among different OAM modes and therefore fail for signals detection of multiple OAM modes at the receiver. To achieve practical OAM based wireless communications, in this paper we investigate the radio vortex wireless communications with misaligned transmit and receive antennas. We propose a joint Beamforming and Pre-detection (BePre) scheme, which uses two unitary matrices to convert the channel matrix into the equivalent circulant matrix for keeping the orthogonality among OAM-modes at the receiver. Then, the OAM signals can be detected with the mode-decomposition scheme at the misaligned receiver. Extensive simulations obtained validate and evaluate that our developed joint BePre scheme can efficiently detect the signals of multiple OAM-modes for the misaligned transceiver and can significantly increase the spectrum efficiency.

Orbital angular momentum (OAM), which is inherently possessed by the electromagnetic (EM) beams, bridges a new way for multiple access using multiple orthogonal OAM-modes in wireless communications. Uniform circular array (UCA), as a convenient antenna structure for supporting the transmission and reception of OAM based signals, has been paid much attention in recent years. However, the OAM beams generated by the UCAs are centrally hollow and divergent, limiting the efficient reception as well as the long-distance transmission for OAM based signals. To solve this problem, in this paper we propose the Zadoff-Chu (ZC) sequence based scrambling scheme to generate the non-hollow OAM beams while maintaining the orthogonality among different OAM-modes. Then, based on the properties of applying discrete Fourier transform (DFT) for circulant matrices and zero-free sequences, we develop the scrambling-tolerant detection scheme to efficiently obtain the transmit symbols. Performance evaluations show the non-hollow irregular OAM beams and validate that our developed ZC sequence based scrambling scheme works well for long-distance transmission in OAM based wireless communications.

In the past decade, more and more researchers have concentrated on orbital-angular-momentum (OAM) based radio vortex wireless communications, which is expected to provide orthogonality among different OAM-modes. The uniform circular array (UCA) is considered as one promising antenna structure for OAM based radio vortex wireless communications. However, most studies regarding UCA focus on the scenario where the transmit and receive UCAs are aligned with each other. In this paper, we investigate the OAM based radio vortex wireless communications with non-coaxial UCA, i.e., the UCA transceivers are parallel but non-coaxial. We study the channel model and develop the mode-decomposition scheme to decompose the OAM-modes. Then, we discuss the impact of included angles on the channel model under non-coaxial scenario. Numerical results are presented to evaluate our developed scheme and show that the spectrum efficiency of the non-coaxial UCA transceiver in some cases is larger than that of the aligned UCA transceiver based radio vortex wireless communications.

In the last few years, unmanned aerial vehicles (UAVs) have developed rapidly and the applications of UAVs have been expanded in wide areas, including photography, cargo delivery, inspection, and communications. Conventionally, UAVs are controlled and operated by a ground station using specific radio transmission modules, where the line-of-sight signal transmission is preferred and the operation range is limited, especially in urban areas. Using the Internet of Things (IoT) technologies, a UAV can be regarded as a terminal device connected in the ubiquitous network, where many other UAVs are communicated, navigated, controlled, and surveilled in real time and beyond line-of-sight.

Internet of Vehicle (IoV) is an important paradigm to realize the intelligent transportation system. However, with the increasing number of vehicular applications, how to satisfy the ubiquitous requirements of communication and computation is challenging. Fog computing provides the real-time transportation services to local users timely through close-proximity data processing, rather than routing data to a remote central data center in the cloud. More importantly, the fog computing will facilitate the autonomous driving (AD) revolutionarily. This paper investigates the problem of optimal deployment and dimensioning (ODD) of fog computing-based IoV infrastructure for AD. For the ODD problem, we present two diverse architecture modes, i.e., the coupling mode (CRF) and the decoupling mode (DRF), and formulate the ODD problem into two integer linear programming formulations with the objective of minimizing the deployment cost. A heuristic algorithm is also proposed to achieve the suboptimal deployment solution for large-scale fog computing-based IoV. Numerical results show that the DRF is more cost-effective and flexible than the CRF for deployment in practice.

Unmanned aerial vehicles (UAVs) can be deployed efficiently to provide high quality of service for Internet of Things (IoT). By using cooperative communication and relay technologies, a large swarm of UAVs can enlarge the effective coverage area of IoT services via multiple relay nodes. However, the low latency service requirement and the dynamic topology of UAV network bring in new challenges for the effective routing optimization among UAVs. In this paper, a layered UAV swarm network architecture is proposed and an optimal number of UAVs is analyzed. Furthermore, a low latency routing algorithm (LLRA) is designed based on the partial location information and the connectivity of the network architecture. Finally, the performance of the proposed LLRA is verified by numerical results, which can decrease the link average delay and improve the packet delivery ratio in contrast to traditional routing algorithms without layered architecture.

Beam tracking for unmanned aerial vehicle (UAV) communications is an enabling technology that maintains the connection between UAV and ground base stations. In this paper, we propose an efficient training-based beam tracking scheme for UAV system. To track the beam, we need to answer how frequently the training should be performed. The proposed scheme uses joint beam training and angular velocity estimation, based on which the beam coherence time can be obtained. Accordingly, the training frequency can be adjusted based on the variation of beam coherence time to save the training overhead. Then, we study the maximization of flying range and speed of UAV. To this end, we optimize the beamwidth by choosing different codebook. For flying range maximization, we first propose an efficient algorithm that can obtain the optimal beamwidth and the corresponding maximal flying range at a given speed of UAV. To gain more insight, we then show that in most practical scenarios, the maximal flying range is achieved with the narrowest beamwidth. Furthermore, we show that when the flying range is small, broad beam should be used to support high speed of UAV, and vice versa.

Smart home is an emerging technology for intelligently connecting a large variety of smart sensors and devices to facilitate automation of home appliances, lighting, heating and cooling systems, and security and safety systems. Our research revolves around Samsung SmartThings, a smart home platform with the largest number of apps among currently available smart home platforms. The previous research has revealed several security flaws in the design of SmartThings, which allow malicious smart home apps (or SmartApps) to possess more privileges than they were designed and to eavesdrop or spoof events in the SmartThings platform. To address these problems, this paper leverages side-channel inference capabilities to design and develop a system, dubbed HoMonit, to monitor SmartApps from encrypted wireless traffic. To detect anomaly, HoMonit compares the SmartApps activities inferred from the encrypted traffic with their expected behaviors dictated in their source code or UI interfaces. To evaluate the effectiveness of HoMonit, we analyzed 181 official SmartApps and performed evaluation on 60 malicious SmartApps, which either performed over-privileged accesses to smart devices or conducted event-spoofing attacks. The evaluation results suggest that HoMonit can effectively validate the working logic of SmartApps and achieve a high accuracy in the detection of SmartApp misbehaviors.

Orbital angular momentum (OAM) based radio vortex wireless communications have the potential to significantly increase the spectrum efficiency (SE) without increasing extra time and frequency resources. In this paper, we propose a hybrid orthogonal division multiplexing (HODM) scheme, which jointly uses the orthogonal mode division multiplexing and the conventional orthogonal frequency division multiplexing (OFD-M) to increase the SE of radio vortex wireless communications. In particular, we develop the mode and frequency dimensions based two-dimension inverse fast Fourier transform (2D-IFFT) algorithm at the transmitter to modulate transmit signal and two-dimension fast Fourier transform (2D-FFT) algorithm at the receiver to demodulate received signal, respectively. Then, we propose an optimal power allocation scheme to maximize the SE of our proposed HODM scheme. Numerical results show that the HODM scheme can significantly increase the SE in comparison with the conventional OFDM schemes.

We study the optimal degrees of freedom (DoF) region of three-user asymmetric multiple-input multiple- output (MIMO) Y channel by considering a hybrid data exchange model. In the hybrid data exchange, we consider both the pairwise data exchange and full data exchange. To derive the optimal DoF region, we analyze the DoF region from both the converse and the achievability aspects. For the converse part, the tight DoF region outer bound is derived using cut-set theorem and genie aided approach. Further, a novel and systematic approach is proposed to analyze the achievability of DoF region. In proving the optimality of achievability, we propose to design distinct patterns to pack the overall transmit data over the channel. We find that the obtained achievable DoF coincides with derived DoF region outer bound.

Multi-panel MIMO is a promising technology in millimeter wave communications. Due to its partially hybrid structure and non-uniform antenna array, existing channel estimation and hybrid precoding cannot be directly applied to multipanel MIMO. In this paper, we study channel estimation and hybrid precoding for multi-panel MIMO. We first transform the channel response vector into angular domain and then reconstruct channel state information (CSI) by using the estimated angular CSI. Moreover, by exploiting the sparse nature of mmWave channels, a sparsity-based channel estimation method is proposed to reduce training overhead and the computational complexity of hybrid precoding. Numerical results show that the proposed channel estimation and hybrid precoding scheme achieve satisfactory spectral efficiency performance with greatly reduced training overhead and low computational complexity.

With the prevalent of smart devices and home automations, voice command has become a popular User Interface (UI) channel in the IoT environment. Although Voice Control System (VCS) has the advantages of great convenience, it is extremely vulnerable to the spoofing attack (e.g., replay attack, hidden/inaudible command attack) due to its broadcast nature. In this study, we present WiVo, a device-free voice liveness detection system based on the prevalent wireless signals generated by IoT devices without any additional devices or sensors carried by the users. The basic motivation of WiVo is to distinguish the authentic voice command from a spoofed one via its corresponding mouth motions, which can be captured and recognized by wireless signals. To achieve this goal, WiVo builds a theoretical model to characterize the correlation between wireless signal dynamics and the user's voice syllables. WiVo extracts the unique features from both voice and wireless signals, and then calculates the consistency between these different types of signals in order to determine whether the voice command is generated by the authentic user of VCS or an adversary. To evaluate the effectiveness of WiVo, we build a testbed based on Samsung SmartThings framework and include WiVo as a new application, which is expected to significantly enhance the security of the existing VCS. We have evaluated WiVo with 6 participants and different voice commands. Experimental evaluation results demonstrate that WiVo achieves the overall 99% detection rate with 1% false accept rate and has a low latency.

Orthogonal frequency division multiplexing (OFDM), which has attracted much attention during the past few decades, can be used for not only anti-multipath but also high spectrum efficiency in wireless communications. However, as the spectrum becoming more and more crowded and the amount of traffic eventually increasing, it is very difficult to increase the spectrum efficiency in wireless communications. Recently, orbital angular momentum (OAM), which provides a novel mode dimension, offers the potential to achieve high spectrum efficiency for wireless communications. In this paper, we propose the orthogonal mode division multiplexing (OMDM) scheme for high capacity in wireless communications. In particular, we propose to transmit half-integer and integer OAM-modes signals within two narrowbands to avoid the inter-mode interference. Second, we propose the mode inverse fast Fourier transform (M-IFFT) algorithm at the transmitter to modulate signals and mode fast Fourier transform (M-FFT) algorithm at the receiver to demodulate signals, respectively. Finally, we develop the dynamic power allocation scheme to solve the problem of how to maximize the capacity of OMDM. Numerical results show that our developed OMDM scheme can achieve high capacity. Furthermore, we can jointly use our developed OMDM scheme with the conventional OFDM scheme to increase the capacity of wireless communications.

In this paper, we study transmit codebook designs for spatial modulation with analog phase shifters. The proposed spatial modulation with analog phase shifters can send more spatial symbols than conventional spatial modulation, while reserving the low complexity of single RF-chain MIMO structure. The main idea is to transmit Ntdifferent phase-shifted copies of a digitally modulated signal through Nttransmit antennas. In each time slot, the transmitted information bits are divided into two groups. The first group of the bits is mapped to the digitally modulated signal symbol. The second group of the bits is mapped to the spatial symbol, which determines the phase shift of each antenna. The transmit signal designs are carried out in open loop and closed loop scenarios, respectively. In open loop scenario, the transmit codebook is designed in two steps, i.e., generate the optimal spatial codebook according to Grassmannian line packing criterion, and find the optimal tradeoff between the cardinality of spatial codebook and the modulation order of signal symbol. In the closed loop scenario, the transmit codebook is designed according to the structure of Huffman tree. Extensive simulation results are given to validate the effectiveness of the proposed scheme in open loop scenario and close loop scenario, respectively.

In cognitive radio networks (CRNs), the transmissions of primary users (PUs) will cause interference to secondary users (SUs), consequently resulting in the less spectrum available at SUs. Instead of regarding the radio signals radiated by PUs as interference, these signals can be opportunistically harvested by SUs as energy. We name such cognitive radio networks with wireless energy harvesting technology as (WEH-CRNs). However, most of previous studies mainly consider using omni-directional antennas in WEH-CRNs (OMN-WEH-CRNs), consequently resulting in high interference. In this paper, we consider using directional antennas in WEH-CRNs (DIR-WEH-CRNs) to improve the performance of WEH-CRNs. We establish an analytical model to analyze the transmission probability of SUs in both DIR-WEH-CRNs and OMN-WEH-CRNs. Our simulation results show that our proposed model can accurately analyze the transmission probability of SUs in both DIR-WEH-CRNs and OMN-WEH-CRNs. Moreover, our results also show that the transmission probability of SUs depends on both the spectrum availability and the probability of SUs being charged. Compared with OMN-WEH-CRNs, DIR-WEH-CRNs can achieve higher transmission probability of SUs due to the reduced interference.

In this paper, optimal signal constellation for downlink 2-user NOMA is investigated from both information theoretical and system level perspectives. Specifically, utilizing the gradient relationship between mutual information and minimum mean squared error, we first propose the optimal superposition coded constellation that maximizes the sum weighted mutual information. In order to verify the effectiveness of the proposed design, we then implement the proposed design in a low-density parity-check code (LDPC) coded modulated NOMA system. Numerical results demonstrate that, in the considered system, the promised achievable rate of our optimized superposition coded constellation can be achieved with a negligible gap.

In this paper, antenna subset selection for downlink millimeter wave (mmWave) massive MIMO systems is studied. A low complexity method is proposed to select antenna subset on a uniform planar array (UPA) in base station. The antenna subset selection is reduced to find the optimal positions of the sub-arrays on the UPA. Numerical results show that our proposed antenna subset selections with two or four sub-arrays are superior to transmission using all antennas when the number of users is two in high SNR regime.

In this paper, we develop statistical quality of service (QoS)-driven power control policies to maximize the effective energy efficiency (EEE), which is defined as the spectrum efficiency under given specified QoS constraints per unit harvested energy, for energy harvesting based wireless networks. In particular, first, we analyze the long-term available energy constraints and formulate the EEE maximization problem. Then, we derive the closed-form solutions of optimal power control policies to the EEE maximization problem under the battery capacity dominated constraint, the average harvested energy constraint, and both the battery capacity and average harvested energy constraints, respectively. Furthermore, we derive the threshold to judge whether the optimal power control policy is limited by the battery capacity. The obtained numerical results validate our analyses and show that our developed optimal power control policies can optimize the EEE with statistical QoS provisioning over energy harvesting based wireless networks.

Recently, orbital angular momentum (OAM) based vorticose communication has attracted much attention because of its potential to significantly increase the spectrum efficiency (SE) of wireless communications. However, the multiple radio frequency (RF) chains used for multiple OAM modes lead to an unexpected cost for wireless vorticose communications. To reduce the high cost of RF chains for multiple OAM modes and the high complexity required for signal processing, we first propose the mode modulation (MM) based OAM system to allow multiple OAM modes sharing a common RF chain, which can not only reduce the hardware cost, but also boost the SE by introducing the mode as an additional dimension for data transmission. To solve the problem of how to maximize the SE of MM based OAM systems with the limited RF chains, we develop the equal-probability mode modulation (EMM) scheme, where the OAM modes are selected with equal probability and the signal is transmitted though the activated OAM modes. Moreover, we develop the Huffman coding based adaptive mode modulation (AMM) scheme, which can adaptively choose the OAM modes to further increase the SEs of OAM based vorticose communications. We also develop the OAM- water-filling power allocation policies for both EMM and AMM schemes to achieve the maximum SEs for OAM based vorticose communications. Numerical results are presented to show that the MM can offer the mode dimension for vorticose communications and the AMM scheme can achieve larger SE than the EMM scheme. Also, our developed power allocation policies can further increase the SEs for the MM based OAM communications.

In this paper, we propose an analog phase shifter aided spatial modulation scheme. The proposed scheme is able to send more spatial symbols than conventional spatial modulation (SM), while reserving the low complexity of single RF-chain MIMO structure. The main idea is to transmit Ntdifferent phase-shifted copies of the digitally modulated signal through Ntantennas. In each time slot, the transmitted information bits are divided into two groups. The first group of the bits are mapped to the analog phase modulated symbol. The second group of the bits are mapped to the spatial symbol, which determines the phase shift of each antenna. We further study the optimization of transmit codebook in two steps, i.e., generate the optimal spatial codebook according to Grassmannian line packing criterion, and find the optimal tradeoff between the size of signal codebook and the size of spatial codebook. Theoretical and numerical studies show that the proposed scheme has better performance than conventional SM with the same number of transmit antennas.

This paper studies the optimization of pilot training signals in Massive MIMO systems with insufficient pilot length. We aim at finding the optimal pilot design and pilot length that maximize the Spectral Efficiency (SE) of the system. To achieve that, we firstly derive an approximation of average achievable capacity, which is related to channel estimation error and pilot signals. Then some analysis and discussions on finding the optimal non-orthogonal pilot design are presented in order to maximize average sum SE. Since previous research on Massive MIMO systems with pilot training mainly focus on orthogonal pilots, in this paper we give a design criterion of non-orthogonal pilots, and relate the system SE as a function of pilot length. Simulation results show the gap between simulation results and our analytical results. Meanwhile, the performance of uplink SE with orthogonal and non-orthogonal pilots is shown, where for insufficient block length, non-orthogonal pilots could lead to higher SE than the orthogonal pilots. Our analysis and results suggest that choosing non-orthogonal pilots can get a better performance than orthogonal pilots in Massive MIMO systems.

In this paper we propose a new analog beamforming structure by switching on or off each of multiple transmit antennas according to channel state information. The proposed analogue beamforming can significantly reduce the high cost, high power and bulky analogue phase-shifters which are employed in analog massive MIMO systems. On one hand, the high performance low cost commercial switch devices make our architecture easy to implement, saving both system cost and space. On the other hand, our on-off analog beamforming (OABF) can achieve good performance with low complexity algorithms. Specifically, we first propose two SNR-maximization algorithms, which determines the on-off state of each switch under per-antenna power constraint. After that, we theoretically prove our on-off analog beamforming scheme can achieve full system diversity gain and array gain with polynomial complexity. The simple structure of OABF makes the massive MIMO much easier to implement, at a cost of small constant rate loss.

In this paper, we study spectrum overlay access to share spectrum between primary users (PUs) and secondary users (SUs). Our goal is to maximize the average throughput by optimal power allocation within finite time duration. To do this, we formulate this optimization problem as a Markov Decision Process with continuous state. An approximate value method with pre-allocation mechanism is proposed, which can effectively protect the PUs by obtaining a continuous closed-form solution rather than a discrete one. Numerical results show that the proposed algorithm exhibits better performance than traditional methods while guaranteeing non-interference to PUs.

In this paper, we study spectrum sharing of drone small cells (DSCs) network modeled by the 3-D Poisson point process. This paper also investigates an underlay spectrum sharing between the 3-D DSCs network and traditional cellular networks modeled by 2-D Poisson point processes. We take advantage of the tractability of the Poisson point process to derive the explicit expressions for the DSCs coverage probability and achievable throughput. To maximize the DSCs network throughput while satisfying the cellular network efficiency constraint, we find the optimal density of DSCs aerial base stations. Furthermore, we explore the scaling behavior of the optimal DSCs density with respect to the DSCs outage probability constraint under different heights of DSCs. Our analytical and numerical results show that the maximum throughput of the DSCs user increases almost linearly with the increase of the DSCs outage constraint. In order to protect the cellular user, the throughput of the DSCs user stops increasing when it meets the cellular network efficiency loss constraint. To further protect the cellular network in the spectrum underlay, we investigate the effect of primary exclusive regions (PERs) in a 3-D space. Unlike the circular PER in traditional cellular spectrum sharing in the 2-D space, the shape of the 3-D PER is found as a half sphere or a half sphere segment, depending on the radius of PER and the DSCs height limit. We show that the radius of PER should be restricted for small DSCs constraints and limited DSCs height.

Antenna switch enables multiple antennas to share a common RF chain. It also offers an additional spatial dimension, i.e., antenna index, that can be utilized for data transmission via both signal space and spatial dimension. In this paper, we propose a Huffman coding-based adaptive spatial modulation that generalizes both conventional spatial modulation and transmit antenna selection. Through the Huffman coding, i.e., designing variable length prefix codes, the transmit antennas can be activated with different probabilities. When the input signal is Gaussian distributed, the optimal antenna activation probability is derived through optimizing channel capacity. To make the optimization tractable, closed form upper bound and lower bound are derived as the effective approximations of channel capacity. When the input is discrete QAM signal, the optimal antenna activation probability is derived through minimizing symbol error rate. Numerical results show that the proposed adaptive transmission offers considerable performance improvement over the conventional spatial modulation and transmit antenna selection.

Antenna switch enables multiple antennas to share a common RF chain, thus an additional spatial dimension, i.e., antenna index, can be utilized in the design of single RF chain MIMO and information can be conveyed via both signal space and spatial dimension. In this paper, we propose a unified adaptive transmission scheme - adaptive spatial modulation that allocates information into signal space and spatial dimension in order to maximize the overall channel capacity for single RF chain MIMO. The proposed adaptive spatial modulation is realized by using Huffman coding, i.e., designing variable length codes to activate the transmit antenna with different probabilities. The optimal antenna activation probability is derived through optimizing channel capacity. To make the optimization tractable, closed form upper bound and lower bound are derived as the effective approximations for channel capacity. Numerical results show that the proposed adaptive spatial modulation offers considerable performance improvement over both spatial modulation and transmit antenna selection.

In this paper, we study drone small cells (DSCs) network modeled by 3D Poisson point process. We further study an underlay spectrum sharing between the 3D DSCs network and traditional cellular networks modeled by 2D Poisson Point Processes. We derive the explicit expressions for the downlink DSCs coverage probability and achievable throughput.We obtain the optimal density of DSCs aerial base stations that maximizes the DSCs network throughput while satisfying the cellular network efficiency constraint. Our analytical and numerical results show that the maximized throughput of the DSCs user increases almost linearly with the increase of the DSCs outage constraint. However, to protect the cellular user, the potential throughput of the DSCs user should stop increasing when it encounters the cellular network efficiency loss constraint.

Heterogeneous cellular networks have emerged as the primary solution for overwhelming traffic demands. This study addresses the architecture optimization of multitier open-access downlink heterogeneous cellular networks. A distinguishing feature of this work is that the transmission requirements of mobile users and the profit requirements of operators are all considered. Spatial throughput is maximized under practical constrains on the network deployment cost and traffic loads of individual tiers. By using stochastic geometry, the problems of optimizing the densities of base stations (BSs) in different tiers are shown to be linear-fractional programming that can be further transformed into linear programming by employing Charnes-Cooper transformation. The linear-fractional programming can then be solved efficiently. For a two-tier network, the optimal BS densities are derived in closed form by using the graphical method. Our results indicate that enlarging the densities of BSs may not always improve network throughput and that the optimal densities of each tier exist to satisfy the communication demands of mobile users and the profit requirements of operators. Numerical results demonstrate the significant throughput gain from the optimal deployment of multitier heterogeneous cellular networks.

We consider an asynchronous two-way relay network in which a few distributed relays assist in the communication between two single-antenna terminals through analog network coding. The asynchronous transmission between relays and terminals causes symbol misalignments and results in diversity loss in space-time codes (STCs). We propose a family of zero-padded time-reversal STC that can achieve full diversity with low-complexity maximum likelihood (ML) decoding, given a bound for the path delay difference. With ML decoding, the proposed code is decomposed into several independent parts, which greatly facilitate the decoding process. For two-relay scenarios, three code designs based on Alamouti code, quasi-orthogonal space-time block code, and multigroup decodable STC are provided for each relay with one, two, and four antennas, respectively. For three-relay scenarios, one code design based on quasi-orthogonal space-time block code is provided for each relay with one antenna. Proof of full diversity is established, and the decoding complexity order is analyzed for all four designs. Simulations confirm the full diversity gain of all designs. The bit-error-rate performance in asynchronous scenarios is almost similar to that in synchronous scenarios.

Relay-assisted access technique has been proposed as a promising solution to improve the energy efficiency and service quality of edge users for cellular networks. In this paper, we aim to find the optimal scheduling period, optimal power allocation, and optimal user scheduling and relay selection that minimizes the total power consumption under the constraints of minimum data rate requirements for the single-cell relay-assisted cellular network. Although we assume that every user in the network is interference-free with each other due to orthogonal resource allocation, such an optimization problem is in general a mixed-integer programming, and thus the optimal solution is difficult to achieve. To make the optimization problem tractable, we decompose the problem into the power allocation optimization subproblem and the joint user scheduling and relay selection optimization subproblem. First, we obtain the optimal scheduling period approximately equal to the ratio between the number of users and the number of relays by sequentially solving these two subproblems. Furthermore, we propose a distributed joint user scheduling and relay selection algorithm based on the duality theory and auction theory. The theoretical results show that the proposed algorithm can help every user select the optimal relay and transmission time slot in polynomial time. Simulation results further show that the proposed algorithm can guarantee the minimum scheduling duration without consuming more transmit power, in comparison with other existing algorithms.

In this paper, we study the max–min Euclidean distance-based multiple-input multiple-output (MIMO) transmit codebook design with channel state information at transmitter. Different from existing max–min precoder designs, a shaping-based codebook design method is proposed to construct the codebook from a structured signal lattice. Hyperrectangle shaping is found to be superior to the shaping region of the precoder-based design in terms of power efficiency. Based on the proposed hyper-rectangle shaping region, the number of available spatial dimensions used for codebook construction is first determined through maximizing the approximated constellation figure of merit. Then, the codebook is derived by lattice precoding and shifting the unshaped latticed points inside the hyperrectangle region. Numerical results illustrate that our proposed shaping-based codebook is strictly better than present precoder-based codebook designs and is more robust under different MIMO channel conditions.

Collaborative spectrum sensing is an effective method to improve the detection rate in cognitive radio. However, it is vulnerable to spectrum sensing data falsification attacks. In order to improve the robustness, numerous attack prevention schemes have been proposed to identify malicious secondary users (SUs). Nevertheless, most of them neglect to incentivize SUs to send truthful reports. Therefore, an incentive method based on Private-Prior Peer-Prediction with approximate subjective priors is proposed to identify malicious suspects and punish attackers when falsifying the sensing data simultaneously. The theoretical analysis and simulation results demonstrate that honest SUs are rewarded by accurate and truthful sensing results while malicious SUs receive heavy loss for making falsified sensing results. Moreover, a significant improvement of detection rates is demonstrated when there are a large number of malicious SUs conducting cooperative attacks compared to the pure majority rule scheme.

Heterogeneous cellular networks have emerged to be the primary solution for overwhelming traffic demands. This study addresses the architecture optimization of multi-tier open access downlink heterogeneous cellular networks to achieve the maximum network spatial throughput under practical constraints on the network deployment cost and traffic loads of individual tiers. In particular, with the use of a stochastic geometry-based network model, the problem of optimizing the densities of base stations (BSs) in different tiers is shown to be a linear-fractional programming that can be further transformed into a linear programming by employing Charnes-Cooper transformation. Such programming can, in turn, be solved efficiently. Furthermore, for the special case of a two-tier network, the optimal BS densities are derived in closed form using the graphical method. Simulation results demonstrate significant throughput gain from the optimal deployment of multi-tier heterogeneous cellular networks.

We consider an asynchronous two-way relay network, in which two double-antenna relays assist in the communication between two single-antenna terminals through analog network coding. The asynchronous transmission between relays and terminals causes symbol misalignments and results in diversity loss in space-time block code (STBC). We propose a zero-padded time-reversal quasi-orthogonal STBC that can achieve full diversity with low-complexity maximum likelihood (ML) decoding, given a bounded delay. With ML decoding, the proposed code is decomposed into several independent parts, which leads to single complex symbol decoding. Proof of full diversity is established, and the decoding complexity order is analyzed for the proposed design. Simulations confirm the full diversity gain. The bit error rate performance in asynchronous scenarios is almost the same as that in synchronous scenarios.

In this paper, we propose online discrete rate and power adaption policies based on 1-bit channel feedback for energy harvesting (EH) communications. The receiver (RX) periodically sends 1-bit feedback by comparing the channel power gain with a predetermined threshold. The transmitter (TX) correspondingly adjusts M-ary quadrature amplitude modulation (MQAM) level and transmission power based on the 1-bit feedback and available battery energy. To determine the optimal channel threshold, adaptive MQAM level and corresponding power allocation, we formulate a constrained optimization problem to maximize the throughput within a finite horizon. We show that this problem follows a Markov decision process and can be solved via backward induction method. To alleviate the complexity of the backward induction method, we further propose a suboptimal policy. Simulation results show that the optimal policy has higher throughput than the suboptimal policy, but the performance advantage is negligible.

In this paper, we propose diagonal precoder designs of spatial modulation (SM) by minimizing symbol error rate (SER) upper bound. A general precoder design method applicable for any signal-to-noise ratio (SNR) is first proposed. We then carry out asymptotic analysis of the precoder design in the regimes of high and low SNR. In high SNR regime, our proposed precoder design problem is found to be a non-convex quadratically constrained quadratic program (QCQP) problem that can be solved by semi-definite relaxation (SDR) technique. In low SNR regime, we find the closed form expression of the optimal solution that is equivalent to best antenna selection. Simulation results illustrate that our proposed algorithm can significantly enhance the SER performance and has a better performance than other algorithms.

We investigate the percolation connectivity of wireless ad hoc networks with directional antennas (called DIR networks). One of major concerns is to derive bounds on the number of edge-disjoint directed paths (or highways). However, it is non-trivial to obtain bounds on the number of directed highways in DIR networks since the conventional undirected percolation theory cannot be directly used in DIR networks. In this paper, we exploit the directed percolation theory to derive bounds on the number of directed highways. In particular, we make new constructions in bond directed percolation model. We show that with high probability there are at least Ω(√n/log log √n) directed highways in a network with n nodes, which is much tighter than the existing results in DIR networks.

In spectrum underlay cognitive radio networks, secondary users (SUs) are allowed to reuse the spectrum allocated to a primary system. The interference between SUs actually carries information and can potentially be exploited to improve the network performance through information-theoretic interference processing. In this paper, we design an optimal joint power and rate control algorithm that maximizes the secondary system utility subject to the interference temperature constraints of primary users based on the capacity-approaching interference processing scheme called as the Han-Kobayashi scheme. The optimal solution is difficult to achieve because the optimization problem is in general non-convex. To make the optimization problem tractable, this paper first transforms the problem into a monotonic optimization problem through exploiting its hidden monotonicity. We then devise an effective algorithm to obtain the global optimal solution to the joint power and rate control problem in the Han-Kobayashi scheme. The key idea behind the proposed algorithm is to construct a sequence of shrinking polyblocks that approximate the upper boundary of the feasible region with increasing precision. Numerical results further show that the achieved utility of our scheme significantly outperforms the utility of conventional schemes which treat the interference between SUs as the noise.

This paper develops a power allocation strategy for multiple networks of Poisson-distributed single-antenna nodes that share the available spectrum in a spectrum underlay scenario. This strategy aims to maximize the overall throughput obtained by sharing the spectrum while limiting the degradation of the successful transmission probability of each network. In its original form, this joint power allocation problem is difficult to solve. However, we demonstrate that the problem can be transformed into a convex optimization formulation, which can be efficiently solved. Furthermore, we obtain a quasi-closed-form solution that has a water-filling interpretation by analyzing the optimality conditions. Numerical results indicate that, when a spectrum-sharing scheme employs the proposed optimal strategy of power allocation, the throughput substantially improves over that obtained by exclusively allocating the spectrum to the primary network. Moreover, when the number of spectrum-sharing networks increases, the enhancement is significant, being up to the limit imposed by the maximum allowable degradation in the performance of each network.

In this paper, we propose online discrete-rate discrete-power adaptation policies based on 1-bit channel feedback for energy harvesting (EH) wireless communications. The receiver periodically sends 1-bit feedback by comparing the channel power gain with a predetermined threshold. The EH transmitter correspondingly adjusts M-ary modulation level and transmission power based on 1-bit feedback and battery energy. To determine the optimal channel threshold, adaptive modulation levels and corresponding power allocations, we formulate a constrained optimization problem to maximize the throughput within a finite horizon. We show that this problem follows a Markov decision process and can be solved via backward induction method. To alleviate the computational complexity of the backward induction method, we further propose a suboptimal policy that uses the best effort modulation level and the channel threshold that is optimized by maximizing the ergodic spectral efficiency over channel fading and EH processes. Simulation results show that the proposed policies have considerable throughput performance gain compared to other heuristic policies, and the suboptimal policy has negligible performance loss compared to the optimal policy.

This paper studies a device-to-device (D2D) LAN underlaying a cellular uplink, where multiple D2D users (DUs) intend to communicate with a D2D receiver. Two interference alignment (IA) based D2D communication schemes are proposed to manage the interference between two networks. The first scheme is referred to as “interference-free” IA scheme. In this scheme, the interference signals from DUs are designed to be aligned in the orthogonal space of cellular links at the evolved NodeB (eNB), so that the links of cellular users (CUs) are completely free from interference. The second scheme is referred to as “interferencelimiting” IA scheme. In this scheme, the DUs' signals are allowed to occupy some links of CUs, but the peak interference power on each of the “interfered” links is controlled under a certain threshold γ. The “interference-limiting” IA scheme is most efficient when there are a large number of DUs as the scheme can take advantage of multiuser diversity. Performance analysis shows that based on the proposed schemes, the interference generated on the cellular links is eliminated or well controlled, while the quality of service of the D2D LAN can also be guaranteed.

We consider downlink transmission in a two-cell network where each base station has multiple antennas and each user has a pattern reconfigurable antenna. Without channel state information at the transmitters, existing approaches do not fully exploit the degrees of freedom (DoF) of the network due to detrimental effects of inter-cell interference and intra-cell interference. In this work, we propose a blind interference alignment scheme which can cancel both types of interference. We show that the achievable DoF is asymptotically optimal for large antenna array.

Connectivity is one of the most fundamental properties of wireless multi-hop networks. This paper studies the connectivity of large wireless networks with secrecy constraint, i.e. a pair of nodes can communicate securely against eavesdropping. Specifically, we consider a network with a mixture of legitimate nodes and eavesdroppers that are distributed according to two independent Poisson point processes on a √n × √n square. Assuming that legitimate nodes can generate artificial noise, which is shown in the literature to be an effective way of suppressing eavesdropping, we provide sufficient conditions on the transmission power and the noise generation power required for a network with known intensity of eavesdroppers to be asymptotically almost surely connected with secrecy constraint as n → ∞, considering the cases of both non-colluding and colluding eavesdroppers.

This work studies achievable degrees of freedom (DoF) of two-cell two-hop interfering broadcast channels. In the two cells, base stations (BS) equipped with Mi and M2antennas intend to communicate with M1and M2single-antenna users, respectively. The messages sent from each BS are first received by two shared full-duplex multiantenna relays simultaneously in the first hop and then are forwarded to the intended users from the relays in the second hop. We study the achievable DoF of the network with various channel state information (CSI) available at the BSs and the users while the relays have CSI of the two-hop channels. We first consider the scenario when the BSs have the CSI of the first hop and the users have the CSI of the second hop, respectively. A linear scheme with precoding at the BSs and interference neutralization at the relays is designed to achieve the cut-set DoF of M1+ M2if both relays have M1+ M2- 1 antennas. We then consider the scenario when the BSs do not have any CSI but the users have the full CSI of both hops. A relay beamforming scheme is proposed to achieve the DoF of M1+ M2when both relays have M1+ M2- 1 antennas.

Mutual interference is the main bottleneck on the throughput of large random spectrum sharing networks. This work examines the extent to which the performance of such networks can be improved by employing multiple transmitting antennas, without degrading the average performance of individual users. By extending partial zero-forcing beamforming to spectrum sharing networks, the aggregate interference towards primary receivers is reduced, and the desired signals at both primary and secondary receivers are boosted. Considering randomly distributed users and spatially independent Rayleigh fading channels, this work provides upper and lower bounds on the maximum permissible density of secondary transmitters with respect to the numbers of primary and secondary transmitting antennas. The simulation results show that substantial increase in the density of secondary transmitters can be obtained while meeting the outage requirements of the spectrum sharing users.

This study designs space-time codes in analog network coding for asynchronous two-way relay networks, where asynchronous transmissions can cause diversity loss. We propose a novel code, called zero-padded interleave reversal Alamouti code (ZP-IR AC) to achieve full diversity with fast maximum likelihood decoding. Specifically, to combat symbol misalignment caused by asynchronous transmissions, the two terminals insert zero padding when transmitting to relays. Thereafter, the second relay performs an interleave reversal procedure to retain full diversity at each terminal. A salient feature of ZP-IR AC is that it can be decoupled into several independent parts that facilitate fast maximum likelihood decoding. Simulations of ZP-IR AC show full diversity gain. The bit error rate performance of ZP-IR AC is comparable to that of the synchronized Alamouti code and outperforms those of some recent schemes.

This paper studies multiuser diversity of uplink MIMO cognitive radio network and proposes a two-stage opportunistic user scheduling scheme. In the first stage, a cognitive beamforming design is proposed to ensure the interference caused by secondary signals is canceled or minimized on the spatial dimensions occupied by primary MIMO system. Then, some secondary users that cause minimal interference leakage at primary system are pre-selected as candidate users. In the second stage, some candidate users that produce maximum sum secondary rate are further selected for uplink scheduling. The proposed scheme enables the secondary link to take advantage of multiuser diversity while ensuring that the interference on primary link is within a certain threshold. Analytical results show that the sum rate of secondary uplink scales as Nslog logK for K secondary users and Nsantennas on secondary receiver for very large K.

In this paper, we propose a limited feedback based underlay spectrum sharing scheme in a Poisson cognitive network. Both primary and secondary transmitters are Poisson distributed nodes with limited feedback of channel quality information from their local receivers. The primary and secondary transmitters are elected to transmit if their local channel gains are above certain thresholds. Closed-form optimal node density of primary user is obtained analytically by maximizing the primary area spectral efficiency under the primary outage constraint. In addition, closed-form optimal node density of the secondary user is derived by maximizing the secondary area spectral efficiency under the secondary outage constraint and primary efficiency loss constraint, where the primary efficiency loss constraint guarantees the degradation of the area spectral efficiency of primary user is within a tolerable threshold. Numerical results show the maximized area spectral efficiency of secondary user increases as the increase of the secondary outage constraint before it is flattened out by the primary efficiency loss constraint.

In this paper, we propose a limited feedback based underlay spectrum sharing scheme where a secondary decode-and-forward opportunistic relay network shares the spectrum with a pair of primary users. The primary destination and each of the N secondary relays send 1-bit feedback of channel quality information to their corresponding transmitters. By overhearing the primary feedback and receiving the secondary feedback, one of the secondary relays is scheduled at the secondary transmitter in each fading block. By maximizing the average secondary rate under the average primary rate loss constraint and average secondary power constraint, the asymptotically optimal solutions of secondary thresholds and power allocation are derived analytically for large N. With the increase of the number of secondary relays, the average achievable rate of secondary user grows as 1/2 log log N.

This paper studies the achievable degrees of freedom (DoF) of 3-user MIMO interference channels based on linear spatial beamforming scheme. Previous work mainly focus on the scenario where each user must have the same DoF and the achievable sum DoF is 3⌊Doutover 3⌋, where Douterdenotes the outer bound of sum DoF. In this paper, we investigate the scenarios where three users are allowed to have different DoF. Three interference alignment based signal transmission strategies are proposed and the achievable DoF is derived. We show that the achievable DoF of the proposed interference alignment scheme achieves the DoF of ⌊Dout⌋ in some cases, which is equal to or higher than 3⌊Doutover 3⌋.

In this paper, we propose blind interference alignment (BIA) schemes using reconfigurable antenna technology to achieve high degree of freedom (DoF) or diversity gain in K-user interference channels. First, two DoF-oriented BIA schemes are proposed that fully and partially align the inter-user interference, respectively. We compare the achievable DoF of the two schemes and show that the partial BIA scheme has higher DoF than the full BIA scheme under some conditions, and vice versa. Then, three diversity-oriented BIA schemes with space-time coding are proposed to achieve both spatial diversity gain and reconfigurable antenna pattern diversity gain. With different tradeoffs among the diversity gain, the rate and the decoding complexity, the BIA with threaded algebraic space-time (TAST) codes, the BIA with orthogonal space-time block codes (OSTBCs), and the BIA with multiplexing Alamouti codes are proposed, respectively. The BIA with TAST codes can achieve the full diversity and high rate with high decoding complexity. The BIA with OSTBCs can achieve the full diversity and the linear decoding complexity but has low rate. The BIA with multiplexing Alamouti codes can achieve high rate with low decoding complexity at the expense of full diversity loss.

In this paper, we propose a limited feedback based underlay spectrum sharing scheme where a downlink secondary network shares the spectrum with a pair of primary users. The primary receiver and N secondary receivers each sends 1-bit feedback of channel quality information to their corresponding transmitters. By overhearing the primary feedback and receiving the secondary feedback, one of the secondary receivers is scheduled at the secondary transmitter in each fading block. The optimal channel quantization thresholds and power allocation are jointly determined by maximizing the average throughput of the secondary user under the average secondary power constraint and the average primary rate loss constraint. The average throughput of the secondary user grows as log log N.

It is known that with conventional spatial interference alignment, i.e., linear interference alignment without symbol extension, the achievable Degrees of freedom (DoF) of MIMO X channels is no more than ⌊Douter⌋, where Douterdenotes the outer bound of DoF of MIMO X Channels. In this paper, we propose a spatial interference alignment and zero-forcing framework in combination with asymmetric complex signaling, which can achieve the DoF of 1/2⌊2Douter⌋ for arbitrary number of antennas on each terminal. Note that 1/2⌊2Douter⌋ is equal to or larger than ⌊Douter⌋. The result shows that the technique of asymmetric complex signaling, which was originally proposed for single-antenna systems, can be useful for MIMO channels, in particular when symbol extensions are not allowed.

Distributed space-time codes (STCs) can achieve cooperative diversity in distributed antenna systems. But the path delay difference may cause diversity loss. In this paper, a family of asynchronous STC is proposed to achieve full diversity, given that the path delay difference is within a tolerance bound. The proposed code structure allows the overall decoding process to be decomposed into several independent sub-processes, which can be tackled by low-complexity maximum-likelihood decoders. Two code designs based on the Alamouti code and the Golden code are given for a system with two distributed antennas. Moreover, two designs based on orthogonal STC and fast group decodable STC are introduced for a system with four distributed antennas, with each transmitter having two antennas. Full diversity gain is proved for all code designs and their associated decoding complexities are analyzed. Simulations of the proposed codes confirm our theoretical results on full diversity gain.

In this paper, we propose two limited feedback based underlay spectrum sharing schemes in Poisson cognitive radio networks without and with primary exclusive regions, respectively. Both primary and secondary transmitters are Poisson distributed nodes with limited feedback of channel quality information from their local receivers. The primary transmitters are active if their local primary channels are above a certain threshold. In the opportunistic spectrum sharing scheme without primary exclusive region, the secondary transmitters are elected to transmit if their local channels are above the required threshold. In the opportunistic spectrum sharing scheme with a primary exclusive region, the secondary transmitters transmit if their local channels are above the threshold and they are outside the exclusive regions of the active primary receivers. For both schemes, the optimal secondary node density is analytically derived by maximizing the secondary area spectral efficiency subject to the secondary outage constraint and the primary efficiency loss constraint. Numerical results show that, for a tight secondary outage constraint, it is more beneficial to use the scheme with no primary exclusive region. For relatively loose secondary outage constraint, the scheme with primary exclusive regions is recommended.

In this paper, we study the transport capacity of large multi-hop wireless CSMA networks. Different from previous studies that rely on the use of a centralized scheduling algorithm and/or a centralized routing algorithm to achieve the optimal capacity scaling law, we show that the optimal capacity scaling law can be achieved using entirely distributed routing and scheduling algorithms. Specifically, we consider a network with nodes Poissonly distributed with unit intensity on a$\sqrt{n}\times\sqrt{n}$square$B_{n}\subset\Re^{2}$. Furthermore, each node chooses its destination randomly and independently and transmits following a CSMA protocol. By resorting to the percolation theory and by carefully tuning the three controllable parameters in CSMA protocols, i.e., transmission power, carrier-sensing threshold, and countdown timer, we show that a throughput of$\Theta(1/\sqrt{n})$is achievable in distributed CSMA networks. Furthermore, we derive the pre-constant preceding the order of the transport capacity by giving an upper and a lower bound of the transport capacity. The tightness of the bounds is validated using simulations.

Spectrum sharing between cellular and ad-hoc networks is studied in this work. Weak signals and strong interferences at the cell-edge area usually cause severe performance degradation. To improve the cell-edge users' performance quality while keeping high spectrum efficiency, in this paper, we propose a cooperative spectrum sharing scheme. In the proposed scheme, the ad-hoc users can actively employ cooperative diversity techniques to improve the cellular network downlink throughput. As a reward, a fraction of the cellular network spectrum is released to the ad-hoc network for its own data transmission. To determine the optimal spectrum allocation, we maximize the ad-hoc transmission capacity subject to the constraints on the outage probability of the ad-hoc network and on the throughput improvement ratio of the cellular network. Both the transmission capacity of the ad-hoc network and the average throughput of the cellular network are analyzed using the stochastic geometry theory. Numerical and simulation results are provided to validate our analytical results. They demonstrate that our proposed scheme can effectively facilitate ad-hoc transmissions while moderately improving the cellular network performance.

This paper studies a two-way relay channel where two single-antenna users exchange messages via a relay with$K$antennas. A new physical-layer network coding (PNC) method is proposed, referred to as channel-quantized PNC (CQ-PNC), that can achieve full diversity gain of$K$. The proposed method converts$K$received signals at the relay into two signals by a QR decomposition. The first one is a weighted summation of the two users' messages and the second one is a scaled version of one user's message. Then, the first signal is quantized at the relay by using the channel coefficient and the quantization error is cancelled by using the side information of the second signal. Finally, a Gaussian-integer weighted summation of the two users' messages is obtained, mapped to network codeword, and then broadcast to the two users. It is proved that the proposed CQ-PNC can achieve the full diversity gain of$K$with receiver side channel information, low computational complexity and symbol level synchronization. Simulation results demonstrate that the proposed method obtains optimum performance results with negligible gap.

We consider the K-user MISO interference channel where each transmitter has multiple antennas and each receiver has a reconfigurable antenna capable of switching among several pre-set modes. The channels in different modes are assumed to be independent. We propose an interference alignment scheme without channel information at transmitters. We show that the achievable degree of freedom of the proposed scheme outperforms that in the traditional K-user MISO interference channel without the reconfigurable antenna.

Since the seminal work of Gupta and Kumar, extensive research has been done on studying the capacity of large wireless networks under various scenarios. Most of the existing work focuses on studying the capacity of networks with uniformly or Poissonly distributed nodes. While uniform and Poisson distribution form an important class of spatial distributions, their capability in capturing the spatial distribution of users in various scenarios and application settings is limited. Therefore it is critical to investigate to what extent, the aforementioned results on capacity of networks with uniformly or Poissonly distributed nodes depend on the underlying node distribution being uniform or Poisson. In this paper, we study the capacity of networks under a general node distribution. A capacity upper bound on networks with generally distributed nodes is obtained, which is valid for both finite networks and asymptotically infinite networks. By imposing some mild conditions on the transmission range, we further simplify the result and show that the asymptotic capacity upper bound can be expressed as a product of four factors, which represents respectively the impact of node distribution, link capacity, number of source destination pairs and the transmission range. The upper bound is shown to be tight in the sense that for the special case of networks with uniformly distributed nodes, the bound is in the same order as known results in the literature.

While the outer-bound of sum degrees of freedom (DoF) of 3-user interference channel is known, the entire DoF region is still unknown in terms of both outer-bound and achievable region. In this paper, we first give the outer-bound of DoF region. Then, we present a linear beamforming scheme based on interference alignment chain whose achievable DoF region is the same as the outer bound, with the consideration of integer DoF only.

We consider an asynchronous cooperative communication system where two distributed transmitters are communicating with one destination. In this paper, a bounded delay-tolerant time interleave reversal Alamouti code (BDT-TIR AC) is proposed. BDT-TIR AC achieves full diversity, given that the path delay difference is within a tolerance limit. Furthermore, we design an efficient maximum-likelihood (ML) decoder. By employing a divide-and-conquer strategy, the original decoding problem is decoupled into several sub-problems in small size. In fact, these sub-problems contain either Alamouti code structure or only four symbols. By parallel decoding on the sub-problems, the proposed ML decoder exhibits low complexity advantage in large scale problems. Simulations of BDT-TIR AC confirm the full diversity gain. The bit error rate performance of BDT-TIR AC in the considered asynchronous scenarios is comparable with that of synchronized Alamouti code, and outperforms the latest delay-tolerant space-time code.

In this paper, we propose a cooperative spectrum sharing scheme between cellular network downlink and mobile ad-hoc network based on the analysis using stochastic geometry theory. The licensed spectrum belongs to the cellular network and the strong interference at cell-edge becomes a bottleneck to guarantee the quality of service requirement. In this case, the secondary ad-hoc users can assist the transmission between the base station and cell-edge mobile users in exchange for spectrum usage. Through maximizing the transmission capacity of secondary system under the constraint of throughput improvement of primary system, an optimal spectrum allocation can be obtained. Numerical and simulation results are provided to validate the analysis and verify the efficiency of the proposed scheme.

In this paper, we study space-time block code (STBC) designs for two-user MIMO X channels to achieve full diversity when the inter-user interference is cancelled by the group zero-forcing receiver. To do so, we first propose a design criterion and then propose a systematic design to satisfy the criterion so that it achieves the full diversity. The code rate approaches one when encoding length gets large. We prove that full diversity can be achieved by the proposed STBC without the channel information at the transmitters. Simulation results are provided to demonstrate the theory.

In this paper, we propose an underlay spectrum sharing scheme where a secondary broadcast system shares the spectrum with a pair of primary users. Both primary and secondary users are discrete rate-power adaptive systems equipped with limited feedback of the channel quality information. By overhearing the primary feedback and receiving the feedback from each secondary receivers, the best secondary receiver is scheduled at the secondary transmitter which chooses an appropriate power-rate pair from the pre-designed quantization codebook. The secondary quantization codebook is designed with the aim of finding the optimal power and rate allocation which maximize the average secondary rate under the average secondary power constraint and the primary rate loss constraint. Numerical results illustrate that, the secondary performance improves with the increase of secondary feedback load. In addition, for a fixed total feedback load, increasing the number of the secondary receivers brings more benefits than increasing the number of feedback bits at each secondary receiver.

Degrees of freedom (DoF) of heterogeneous two-cell interfering uplink network is investigated in this paper. In each cell, there is one base station (BS) equipped with M antennas and arbitrary number of users equipped with different number of antennas (smaller than M). Suppose k and l active users are selected to communicate with the two BSs, respectively, it is shown that the DoF of k + l is achievable. A distributed opportunistic interference alignment scheme is then proposed which can obtain the optimal achievable DoF of the network. Numerical result shows that the scheme outperforms other existing schemes in terms of achievable sum rate.

This paper studies a MIMO Two-Way Relay Channel (TWRC) where two single-antenna nodes communicate with each other through a K-antenna relay. In the uplink phase, the two end nodes send their respective information simultaneously to the relay, and it is usually regarded as a virtual MIMO system. With traditional VBLAST MIMO detection, the maximum achievable diversity is K - 1. By noting that only the network coded packet, rather than the two individual packets, is needed at the relay node in TWRC, we propose a channel quantized physical-layer network coding (CQ-PNC) scheme based on VBLAST to achieve the full diversity K. Specifically, relay first uses QR decomposition to convert the K received signals into two valid signals, the first signal is a weighted summation of the two end nodes' symbols and the second signal is a scaled version of one end node's symbol. The relay first quantizes the first signal with the channel coefficient of one node and estimate the Gaussian integer summation of the two end nodes' information. At the same time, the quantization error is further canceled with the help of the second signal. After that, we adaptively map the Gaussian integer weighted summation to the network coding form. Moreover, we theoretically prove that our CQ-PNC can achieve the maximum diversity K. Finally, the numerical simulation shows that CQ-PNC performs within 2dB gap from the theoretical bound.

In underlay cognitive radio, a secondary user transmits in the transmission band of a primary user without serious degradation in the performance of the primary user. This paper proposes a method of underlay cognitive radio where the secondary pair listens to the primary ARQ feedback to glean information about the primary channel. The secondary transmitter may also probe the channel by transmitting a packet and listening to the primary ARQ, thus getting additional information about the relative strength of the cross channel and primary channel. The method is entitled Spectrum Harvesting with ARQ Retransmission and Probing (SHARP). The probing is done only infrequently to minimize its impact on the primary throughput. Two varieties of spectrum sharing, named conservative and aggressive SHARP, are introduced. Both methods avoid introducing any outage in the primary; their difference is that conservative SHARP leaves the primary operations altogether unaffected, while aggressive SHARP may occasionally force the primary to use two instead of one transmission cycle for a packet, in order to harvest a better throughput for the secondary. The performance of the proposed system is analyzed and it is shown that the secondary throughput can be significantly improved via the proposed approach, possibly with a small loss of the primary throughput during the transmission as well as probing period.

Partial interference cancellation (PIC) group decoding is an attractive decoding alternative for multiple-input multiple-output (MIMO) wireless communications. It can well deal with the tradeoff among rate, diversity and decoding complexity of space-time block codes. In this paper, a design criterion of full-diversity space-frequency codes (SFC) is proposed for MIMO-OFDM systems with the PIC group decoding. Based on the criterion, a systematic design of full-diversity SFC is proposed that can achieve full diversity under the PIC group decoding. Simulation results of the newly proposed codes demonstrate the theory.

In this paper, we study space-time block code (STBC) designs for two-user MIMO X channels to achieve full diversity when the inter-user interference is cancelled by the group zero-forcing receiver. To do so, we first propose a design criterion and then propose a systematic design to satisfy the criterion so that it achieves full diversity. The code rate approaches one when encoding length gets large. We prove that full diversity can be achieved by the proposed STBC without the channel information at the transmitters. Simulation results are provided to demonstrate the theory.

A spectrum sharing scheme is proposed for overlaid wireless networks based on the decode-and-forward two-path successive relaying technique. In this scheme, two secondary transmitters are used to transmit secondary information alternately to their respective receivers while relaying the primary signal at the same time. The transmission of the primary system is continuous, and the secondary system can opportunistically help the primary transmission in exchange for the spectrum sharing. Superposition coding is used at the secondary transmitters, where the primary signal and secondary signal are linearly combined. Successive interference decoding and cancelation is performed by secondary users to extract their desired signals. For the primary system, joint decoding is performed by treating the secondary signal as noise at the receiver side. The optimal power allocation is determined to maximize the success probability of the secondary system without violating the outage performance of the primary system. Numerical and simulation results show that the proposed scheme can conservatively protect the primary performance while realizing the secondary transmission requirement.

In this paper, an underlay spectrum sharing scheme is proposed where both primary and secondary users are discrete power-rate adaptive systems with limited feedback from receivers. By receiving secondary and overhearing primary quantized channel quality information from the secondary receiver and primary receiver, respectively, the secondary transmitter adapts its resource allocation to the current channel quality by selecting a proper power-rate pair from a pre-designed secondary quantization codebook. The secondary quantization codebook is designed to maximize the secondary average rate subject to the primary rate loss constraint and the average secondary transmit power constraint, which is discussed in three cases when different amount of side information of the primary codebook and the cross interference link are available at the secondary receiver side. Differential Evolution algorithm is employed to provide the global optimal solutions to the proposed secondary quantization codebook optimization problems. Numerical results show that, by introducing the secondary feedback, the secondary throughput is greatly improved. Furthermore, more secondary feedback bits or more primary side information result in better secondary performance.

In this paper, an adaptive spectrum-leasing scheme is proposed for multiuser cognitive radio networks. Based on two-path successive relaying and decode-and-forward protocols, each cognitive user intelligently switches the spectrum leasing protocol to cooperate in the primary data transmission. As a reward, the spectrum is then leased to the cognitive user for its own data transmission. For given m cognitive users, the best one that can achieve the largest secondary rate while satisfying primary rate target is scheduled for the spectrum leasing. It is shown that the cooperative diversity gain of m+1 is achieved for the primary system and the selection diversity gain of m is obtained for the secondary system. Moreover, multiuser diversity of secondary system is studied and the throughput scales as log(log m) when m is large. Performance results are provided to verify the efficiency of the proposed adaptive spectrum leasing scheme compared with the decode-and-forward based scheme and two-path successive relaying based scheme.

In this paper, an underlay spectrum sharing scheme is proposed where both primary and secondary users are discrete power-rate adaptive systems with limited feedback from receivers. By receiving secondary and overhearing primary quantized channel quality information from the secondary receiver and primary receiver, respectively, the secondary transmitter adapts its resource allocation to the current channel quality by selecting a proper power-rate pair from the pre-designed secondary quantization codebook. The secondary quantization codebook is designed to maximize the secondary average achievable rate subject to the long term power constraint of the secondary transmitter and the rate loss constraint at the primary receiver. Numerical results show that, by introducing the secondary feedback, the secondary throughput is greatly improved. More secondary feedback bits result in better secondary performance.

Outage probability and transmission capacity are two metrics that are often used together to quantify the achievable capacity of decentralized wireless networks, where the outage probability measures the probability that a direct transmission fails and the transmission capacity measures the maximum spatial density of successful concurrent transmissions, subject to a constraint on the outage probability. In CSMA networks, spatial correlations between concurrent transmitters makes the analysis of the outage probability and the transmission capacity a challenging task. In this paper, we analyze the transmission capacity of CSMA networks subject to a designated outage probability constraint by first deriving an upper bound on the outage probability in CSMA networks subject to Rayleigh fading, which is applicable for any node distribution. On that basis, we provide a sufficient condition on the transmission power required to meet a designated outage probability constraint. Finally, we obtain an upper bound on the transmission capacity in CSMA networks satisfying a pre-determined outage probability constraint.

A spectrum leasing scheme is proposed for overlaid wireless networks based on the spectral efficient relaying. The potential secondary transmitters compete to assist the primary transmission using the one-path alternate or two-path successive relaying in exchange for the opportunity of spectrum access. The cognitive relaying mode is dynamically chosen based on the availability of secondary receiver for the cooperation. The secondary user that can support the primary transmission and has the highest secondary rate is selected as the best to join the spectrum leasing. The rate and outage performance of both systems are studied. Numerical results show that the primary performance is greatly improved compared with direct transmission, and the secondary requirement is also well satisfied.

In this paper a spectrum sharing scheme is proposed to maximize the successful transmission probability of a single-hop cognitive network coexisting with a random primary network. Both cognitive and primary networks exhibit randomness in topologies and endure imperfect wireless channel conditions. For a given primary outage probability bound, the maximum secondary transmit power is determined and then the maximum transmission capacity of the cognitive user is derived. Numerical results show that the proposed spectrum sharing scheme indeed boosts the transmission capacity of the cognitive network dramatically whilst having little performance loss of the primary network.

A spectrum sharing scheme is proposed for overlaid wireless networks based on the two-path successive relaying technique. In this scheme, two secondary transmitters are used to alternately transmit secondary information to their respective receivers whilst relaying the primary signal at the same time. By using superposition coding at the secondary transmitters and successive decoding at the receivers, a diversity gain of 2 is obtained for the primary system without losing the bandwidth efficiency. Outage probabilities of the primary system and secondary system are analyzed. The optimal power allocation factor of the secondary system is determined to minimize the outage probability of the secondary system while maintaining the performance of the primary system. Numerical results show that the proposed scheme can indeed enhance the outage performance of the primary and secondary systems simultaneously.

In this paper, we propose a relay assisted spectrum sharing (RASS) scheme based on the mixed sharing strategy in cognitive radio networks. Mixed sharing is a more general sharing strategy which provides a higher spectrum utilization efficiency than underlay sharing and interweave sharing. Compared to conventional mixed sharing, the proposed approach enhances the throughput of secondary users while not causing harmful interference to the primary receiver. The optimal time allocation which maximizes the achievable capacity of the secondary system is derived. At the same time, the existence of the optimal sensing time is proved and the optimal sharing time allocation between the two-hop relay links is presented. Numerical results show that there exists global optimal time allocation for sensing and sharing in the proposed RASS scheme.

A partial interference cancellation (PIC) group decoding as well as a full diversity design criterion were recently proposed for space-time block code. The PIC group decoding complexity is between the lowest linear decoding complexity and the highest ML decoding complexity, and so is the code rate. Based on the criterion for the PIC group decoding, families of space-time block codes were also proposed recently. However, the interference cancellation operator is hard to determine and with the direct zero-forcing (ZF) PIC group decoding, these codes may not achieve the full diversity. In this paper, we propose a new PIC group decoding method for these existing codes, which has a similar decoding complexity to the ZF PIC group decoding, but achieves full diversity (or nearly).

In this paper, we study the throughput of interference-limited three dimensional (3D) CSMA networks. Specifically, we consider a network with a total of n nodes uniformly i.i.d. in a cube of edge length n1/3. Further, CSMA random access scheme is employed and the SINR model is used to simulate a successful transmission. We first give a sufficient condition on the transmit power required for the CSMA network to be asymptotically almost surely (a.a.s.) connected as n → ∞ under the SINR model. Then, we demonstrate constructively that a throughput of Θ(1/(n log2n)1/3) is obtainable by each node for an arbitrarily chosen destination.

Degree of freedom of MIMO interference channels with constant channel coefficients is studied in this paper. An asymmetric interference alignment and cancelation scheme is proposed for the 3-user MIMO interference channels with constant channel coefficients. With M antennas at each transmitter and N antennas at each receiver, the total degree of freedom (M+2N)/2 can be achieved by the proposed scheme for N <; M ≤ 2N.

In this paper, we propose two uncoordinated cooperative truncated ARQ schemes based on node position or local SNR information. If the original transmission between a source and a destination fails, the source and all the potential relays that have correctly received the data packet will contend for the channel to retransmit the packet without coordination. The competition for the channel access is governed by the retransmission probability which is computed in a distributed fashion using the stochastic geometry theory. Theoretical system success probabilities of both schemes are derived. Compared with the scheme in which only the source retransmits, the uncoordinated schemes are shown to achieve better performance, particularly when the distance between the source and the destination is large.

Connectivity is one of the most fundamental properties of wireless multi-hop networks. In a wireless network with many concurrent transmissions, signals transmitted at the same time may mutually interfere with each other. In this paper we consider the impact of interference on the connectivity of CSMA networks using the SINR model. On the basis of our earlier work in which we give a sufficient condition, i.e. an upper bound, on the critical transmission power required for a CSMA network with a total of n nodes i.i.d. on a √n × √n square following a uniform distribution to be a.a.s. connected as n → ∞ under the SINR model, in this paper we continue to study the necessary condition for the above CSMA network to be a.a.s. connected. A lower bound is obtained on the critical transmission power required for the above CSMA network to be a.a.s. connected under any scheduling scheme satisfying the carrier-sensing constraint. The lower bound differs from the upper bound by a constant factor only. Compared with previous literature assuming a unit disk model, it is shown that the critical transmission power for a CSMA network under the SINR model to be a.a.s. connected is within a constant factor of that required for a network under the unit disk model, which does not consider the impact of interference, to be a.a.s. connected. That is, transmission power only needs to be increased by a constant factor to combat interference and maintain connectivity. This result is also in sharp contrast with previous results considering the connectivity of ALOHA networks under the SINR model.

Group zero-forcing (GZF) detection is an efficient and powerful detection tool that has flexible detection complexity and performance gain between the optimum detection and the ZF detection. In this paper, we give diversity and multiplexing tradeoff (DMT) analysis for a 2-layer D-BLAST transmission under GZF detection over Rayleigh MIMO channels. With M transmit antennas and N receive antennas, the DMT of such a system is shown as d_{out}(r)=(N-1)(M-\frac{r(M+1)}{2})^+ for \frac{2}{M+1}≤ r≤ \frac{2M}{M+1} and d_{out}(r)=(MN-M+1)-\frac{rN(M+1)}{2} for 0≤ r< \frac{2}{M+1}, where d_{out} denotes the diversity gain and r denotes the multiplexing gain.

A partial interference cancellation (PIC) group decoding based space-time block code (STBC) design criterion was recently proposed by Guo and Xia, where the decoding complexity and the code rate traeoff is dealt when the full diversity is achieved. In this paper, two designs of STBC are proposed for any number of transmit antennas that can obtain full diversity when a PIC group decoding (with a particular grouping scheme) is applied at receiver. With the PIC group decoding and an appropriate grouping scheme for the decoding, the proposed STBC are shown to obtain the same diversity gain as the ML decoding, but have a low decoding complexity. The first proposed STBC is designed with multiple diagonal layers and it can obtain the full diversity for two-layer design with the PIC group decoding and the rate is up to 2 symbols per channel use. With PIC-SIC group decoding, the first proposed STBC can obtain full diversity for any number of layers and the rate can be full. The second proposed STBC can obtain full diversity and a rate up to 9/4 with the PIC group decoding. Some code design examples are given and simulation results show that the newly proposed STBC can well address the rate-performance-complexity tradeoff of the MIMO systems.

In this paper, a design criterion for space-time block codes (STBC) is proposed for two-user MIMO interference channels when a group zero-forcing (ZF) algorithm is applied at each receiver to eliminate the inter-user interference. Based on the design criterion, a design of STBC for two-user interference channels is proposed that can achieve full diversity for each user with the group ZF receiver. The code rate approaches one when the time delay in the encoding (or code block size) gets large. Performance results demonstrate that the full diversity can be guaranteed by our proposed STBC with the group ZF receiver.

Wireless multi-hop networks are being increasingly used in military and civilian applications. Connectivity is a prerequisite in wireless multi-hop networks for providing many network functions. In a wireless network with many concurrent transmissions, signals transmitted at the same time will mutually interfere with each other. In this paper we consider the impact of interference on the connectivity of CSMA networks. Specifically, consider a network with n nodes uniformly and i.i.d. on a square [-\frac{\sqrt{n}}{2},\frac{\sqrt{n}}{2}]^{2} where a node can only transmit if the sensed power from any other active transmitter is below a threshold, i.e. subject to the carrier-sensing constraint, and the transmission is successful if and only if the SINR is greater than or equal to a predefined threshold. We provide a sufficient condition and a necessary condition, i.e. an upper bound and a lower bound on the transmission power, required for the above network to be asymptotically almost surely (a.a.s.)connected as n → ∞. The two bounds differ by a constant factor only as n → ∞. It is shown that the transmission power only needs to be increased by a constant factor to combat interference and maintain connectivity compared with that considering a unit disk model (UDM) without interference. This result is also in stark contrast with previous results considering the connectivity of ALOHA networks under the SINR model.

In traditional cooperative communications, coordination is required among relay nodes to help data transmission through distributed signal transmission or coding techniques. For large cooperative networks, the overhead for coordination is huge and the synchronization among relays is very difficult. In this paper, we propose uncoordinated cooperative communication schemes in a large wireless network that do not need the coordination among relays while realizing cooperative diversity for the source-destination link. Without a central controller, all relays that are spatially randomly placed contend for the channel to relay the packet from the source to the destination in a distributed fashion. The competition for the channel access is governed by the retransmission probability that is independently calculated by the relays according to the location or channel quality information. Three schemes of uncoordinated cooperative communications are proposed to determine the retransmission probabilities of the potential relays based on the local distance, direction, and channel quality, referred to as distance based, sectorized, and local SNR based schemes, respectively. Success probabilities for the proposed uncoordinated schemes are analyzed. Numerical and simulation results show that the local SNR based scheme has the best performance and the distance based scheme outperforms the sectorized scheme.

Partial interference cancellation (PIC) group decoding proposed by Guo and Xia is an attractive decoding alternative for multiple-input multiple- output (MIMO) wireless communications. It can well deal with the tradeoff among rate, diversity and decoding complexity of space-time block codes. In this paper, two design criteria of full diversity space-frequency codes (SFC) are proposed for MIMO- OFDM systems with the PIC group decoding. Based on the criteria, a systematic design of full-diversity SFC with the PIC group decoding is proposed that can achieve full diversity and a rate of $2M_t/(M_t+1)$ for $M_t$ transmit antennas. Performance results demonstrate that the full diversity is offered by the newly proposed SFC with the PIC group decoding.

A fast-decodable distributed space-time block code (STBC) is proposed for a two-path successive relay network that can achieve both full diversity and full transmission rate. The proposed STBC employs a precoder to rotate the constellation of transmitted symbols at source. After decode-and-forward by relays, the rotated information symbols are decoded at destination. It is shown that single complex symbol decoding can be obtained at the destination. Simulation results demonstrate that the full diversity is offered by the newly proposed distributed STBC for the two-path successive relay network.

We consider a set of cognitive nodes cooperatively sensing the activities of a primary system. Two cooperative protocols are proposed, where cognitive nodes opportunistically transmit to each others, and finally each node makes a decision based its own observations. In the first protocol (Protocol 1), a node relays the signal received from the primary to the other nodes only if the signal energy is sufficiently large. In the second protocol (Protocol 2), given the knowledge of channel gains to the other nodes, a node transmits only if the channel gains are large. With two cognitive nodes, Protocol 1 improves the average detection probability by up to 50% relative to the protocol studied in. Compared to Protocol 1, while additional channel knowledge is needed, Protocol 2 saves up to 50% transmit power and maintains almost the same detection probability.

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An opportunistic spectrum sharing scheme for a pair of secondary users co-existing with a pair of primary users is proposed in this letter. The primary user is considered to exploit the limited feedback of channel quality information (CQI) to allocate its transmission power and rate. By overhearing the limited feedback of the primary CQI, the secondary user accesses the channel with proper transmission power and rate while causing interference to the primary user. Under the primary rate loss constraint, the optimal transmit power and transmission rate for the secondary is obtained to maximize the secondary user throughput. Numerical results show that, with only 3 or 4 bits of feedback, the effective throughput of the secondary user is comparable to the case when both primary and secondary transmitters have the perfect CQI of the primary link.

In this letter, we propose a systematic design of space-time block codes (STBC) which can achieve high rate and full diversity when the partial interference cancellation (PIC) group decoding is used at receivers. The proposed codes can be applied to any number of transmit antennas and admit a low decoding complexity while achieving full diversity. For M transmit antennas, in each codeword real and imaginary parts of PM complex information symbols are parsed into P diagonal layers and then encoded, respectively. With PIC group decoding, it is shown that the decoding complexity can be reduced to a joint decoding of M/2 real symbols. In particular, for 4 transmit antennas, the code has real symbol pairwise (i.e., single complex symbol) decoding that achieves full diversity and the code rate is 4/3. Simulation results demonstrate that the full diversity is offered by the newly proposed STBC with the PIC group decoding.

Two-path successive relaying has recently been proposed as a mechanism for recovering the multiplexing loss of conventional relay networks; however, the full diversity cannot be guaranteed with this method. In this paper, distributed space-time block coding (STBC) is proposed for a two-path successive decode-and-forward relay network that can achieve both full rate and full diversity when perfect decoding is enabled at the relays. For the case of imperfect decoding, selection relaying is proposed with the distributed STBC to recover the full diversity. For the practical scenario in which the inter-relay channel is subject to fading, the two-path successive relaying transmission may not always be successful; for such cases an adaptive relaying scheme is proposed that can select a proper transmission mode between two-phase relaying transmission and two-path successive relaying transmission based on the channel quality of the relay network. Analytical results show that the proposed distributed STBC can offer both full diversity and a large symbol rate gain.

In this letter, a blind spectrum sensing method is proposed which does not need any information of primary users and the noise power. For a given number of observation samples of primary channels, the spectrum sensing is reformulated into a Student's t-distribution testing problem. The analytical results of the blind spectrum sensing are given. It is shown that over flat fading channels in noise of uncertainty power the blind spectrum sensing greatly outperforms the energy detection at about 4 dB gain.

In this paper, we consider a pair of cognitive radio (CR) users co-existing with a pair of ARQ- based primary users (PU). The secondary user (SU) overhears the ACK/NACK feedback sent from the receiver of the primary system, and then decides to access the spectrum or not. An opportunistic sharing scheme, referred to as Spectrum sHaring with ARQ Retransmission and Probing timeslots (SHARP), is proposed to exploit spectrum opportunities based on the ACK/NACK of PU only. Numerical results show that the analytical outcomes perfectly match those from the Monte Carlo simulation. Moreover, the goodput of SU increases dramatically while the outage probability of the primary remains small.

In this paper, we propose a systematic design of space-time block codes (STBC) which can achieve high rate and full diversity when the partial interference cancellation (PIC) group decoding is used at receivers. The proposed codes can be applied to any number of transmit antennas and admit a low decoding complexity while achieving full diversity. For $M$ transmit antennas, in each codeword real and imaginary parts of $PM$ complex information symbols are parsed into $P$ diagonal layers and then encoded, respectively. With PIC group decoding, it is shown that the decoding complexity can be reduced to a joint decoding of $M/2$ real symbols. In particular, for $4$ transmit antennas, the code has real symbol pairwise (i.e., single complex symbol) decoding that achieves full diversity and the code rate is $4/3$. Simulation results demonstrate that the full diversity is offered by the newly proposed STBC with the PIC group decoding.

A distributed space-time block code (STBC) based on a decode-and-forward (DF) protocol is proposed for two-path successive relaying. By making the two relay nodes transmit and listen in turn, the proposed distributed STBC can achieve not only full rate but also full diversity when the relay nodes can correctly decode the information symbols from the source node and when the inter-relay channels are sufficiently strong. We also analyze the situation when there are decoding errors at the relay nodes. We notice in this case full diversity will not be achieved. We then propose the application of selection relaying to the proposed distributed STBC to ensure that full rate and full diversity can still be obtained with decoding errors at the relay nodes.

In this paper we investigate opportunistic spectrum access in cognitive radio networks when a decode and forward relay is employed. To better exploit the white space in primary networks, we propose two cognitive spectrum access approaches based on white space modeling, referred to as successive sensing based spectrum access and simultaneous sensing based spectrum access. We further study the optimal time allocation for the two-hop relay to minimize the outage probability of the cognitive data transmission. Numerical results show that the optimal time allocation heavily depends on the primary user traffic rate.

Recently, space-time block codes (STBC) with a partial interference cancellation (PIC) group decoding was proposed to deal with the tradeoff among rate, diversity and complexity by Guo and Xia. In this paper, we propose a systematic design of STBC with reduced-complexity PIC group decoding. The proposed STBC is featured as Alamouti-type block code in which each entry of Alamouti code matrix is replaced by a block of multi-layer coded symbols. With the PIC group decoding and a particular grouping scheme, the proposed STBC can achieve full diversity, rate- 2M/M+2 and a low-complexity decoding for M transmit antennas. Simulation results show that the proposed codes can achieve the full diversity with PIC group decoding while requiring half decoding complexity of the existing codes.

Collaborative sensing in cognitive radio networks is able to significantly improve the detection probability for the existence of primary user transmission. However, it is usually assumed that there exists orthogonal channels for the cognitive users to send the detection information to the decision center, which is not realistic. In this paper, we consider a practical scenario in which there is a random access channel shared by different cognitive radio users. We then propose a two-threshold opportunistic feedback scheme to judiciously select some cognitive users who have much confidence in the spectrum sensing decision to share the random access channel. The thresholds are determined to maximize the detection probability while keeping the collision of the random access as minimal. The analytical results of the distributed spectrum sensing with the proposed opportunistic feedback are derived. Numerical results show that the proposed opportunistic feedback scheme in distributive spectrum sensing is superior to the conventional cooperative spectrum sensing where the orthogonal access channels are applied.

We consider an uplink communication in a wireless network where each source-user is assisted by M half-duplex relays. The source users and relays are equipped with single-antenna whereas the destination has N antennas. We show that the degree of freedom (d.o.f.) of this network is limited by the links from the source to the collection of relays and destination. This phenomenon is referred to as a "d.o.f. bottleneck problem" which compromise the cooperative multiplexing gain (CMG) and the achievable rate of the system. To improve the CMG, we propose an auxiliary frequency bands (AFBs)-based approach to tackle the d.o.f. bottleneck problem. Information theoretic analysis and numerical results show that the proposed scheme can achieve a CMG of min(M+1,N) when the number of AFBs is sufficiently large. As compared with conventional cooperative communications, the improved multiplexing gain of the proposed scheme is translated into tremendously increased achievable rate at a relatively high SNR.

Cognitive radio (CR) enables dynamic spectrum access (DSA). In CR networks (CRNs), unlicensed cognitive users (CUs) can share the radio spectrum with the licensed primary users (PUs) if the incurred signal-to-noise-and-interference ratio (SNIR) to the PUs is above a predetermined threshold. In this paper, we investigate the use of relaying to enhance the spectrum utilization of an idealized two-dimensional geometric network in Rayleigh fading channels. To do so, the CUs should be made transparent to the PUs while achieving acceptable reliability themselves. Based on these conditions, we first derive the geometric condition under which CUs could transmit over a single hop with the PUs being active. If the destination is beyond the reach of the source in a single hop, multihop relaying will be used for shorter-distance less-power transmission. Then, we propose and analyze two multihop routing strategies, termed nearest-neighbor routing (NNR) and farthest-neighbor routing (FNR). In multihop relaying, we also study the benefits of concurrency, in which more than one CUs are allowed to transmit at the same time to improve the end-to-end (e2e) channel utilization. Finally, we compare the e2e performance of CRNs with and without relaying.

A partial interference cancellation (PIC) group decoding based space-time block code (STBC) design criterion was recently proposed by Guo and Xia, where the decoding complexity and the code rate trade-off is dealt when the full diversity is achieved. In this paper, we propose a systematic design of STBC that satisfy the criterion, i.e., achieve full diversity with the PIC group decoding for any number of transmit antennas. Moreover, the proposed full-diversity STBC can achieve a rate of 2 symbols per channel use for any number of transmit antennas when the code length is large. Some design examples are given. Simulation results show that the newly proposed STBC can obtain full diversity over flat Rayleigh fading channels and outperform some existing codes given the same bandwidth efficiency.

Orthogonal space-time block codes (OSTBC) are attractive in that they can achieve full diversity and linear complexity of maximum likelihood (ML) decoding. However, the OSTBC have a low symbol rate due to the limitation of the orthogonality of the code structure. Most of the high-rate STBC achieve full diversity based on ML decoding at the receiver that is computationally expensive. In order to achieve full diversity with linear receivers, recently Liu-Zhang-Wong and Shang-Xia introduced new STBC. In this paper, we propose a simple design of STBC which have a high rate and achieve full diversity with linear receivers. The proposed STBC are constructed by embedding Alamouti codes into a Toeplitz matrix. Simulation results show that in comparison with some existing codes for a given codeword length the proposed STBC can give a better bit error rate (BER) performance while having a high rate.

Recently several STBC designs were proposed for MIMO systems with linear receivers. In this paper, we propose a new design of high-rate STBC with linear receivers. Compared to the overlapped Alamouti code (OAC) recently proposed by Shang and Xia, the new STBC has a higher symbol rate without guarantee of achieving full diversity. To achieve the same rate, the new STBC has only a half of the block length (code delay) of the OAC. Simulation results show that in comparison with some existing codes for a given rate the proposed STBC can give a better outage probability performance.

Cognitive radio is an exciting emerging technology that has the potential of dealing with the stringent requirement and scarcity of the radio spectrum. Such revolutionary and transforming technology represents a paradigm shift in the design of wireless systems, as it will allow the agile and efficient utilization of the radio spectrum by offering distributed terminals or radio cells the ability of radio sensing, self-adaptation, and dynamic spectrum sharing. Cooperative communications and networking is another new communication technology paradigm that allows distributed terminals in a wireless network to collaborate through some distributed transmission or signal processing so as to realize a new form of space diversity to combat the detrimental effects of fading channels. In this paper, we consider the application of these technologies to spectrum sensing and spectrum sharing. One of the most important challenges for cognitive radio systems is to identify the presence of primary (licensed) users over a wide range of spectrum at a particular time and specific geographic location. We consider the use of cooperative spectrum sensing in cognitive radio systems to enhance the reliability of detecting primary users. We shall describe spectrum sensing for cognitive radios and propose robust cooperative spectrum sensing techniques for a practical framework employing cognitive radios. We also investigate cooperative communications for spectrum sharing in a cognitive wireless relay network. To exploit the maximum spectrum opportunities, we present a cognitive space-time-frequency coding technique that can opportunistically adjust its coding structure by adapting itself to the dynamic spectrum environment.

Cognitive radio has been recently proposed as a promising technology to improve the spectrum utilization. In this paper, we consider the spectrum sharing between a large number of cognitive radio users and a licensed user in order to enhance the spectrum efficiency. With the deployment of M antennas at the cognitive base station, an opportunistic spectrum sharing approach is proposed to maximize the downlink throughput of the cognitive radio system and limit the interference to the primary user. In the proposed approach, cognitive users whose channels are nearly orthogonal to the primary user channel are pre-selected so as to minimize the interference to the primary user. Then, M best cognitive users, whose channels are mutually near orthogonal to each other, are scheduled from the preselected cognitive users. A lower bound of the proposed cognitive system capacity is derived. It is then shown that opportunistic spectrum sharing approach can be extended to the multipleinput/ multiple-output (MIMO) case, where a receive antenna selection is utilized in order to further reduce the computational and feedback complexity. Simulation results show that our proposed approach is able to achieve a high sum-rate throughput, with affordable complexity, when considering either single or multiple antennas at the cognitive mobile terminals.

One of the most important challenges in cognitive radio is how to measure or sense the existence of a signal transmission in a specific channel, that is, how to conduct spectrum sensing. In this letter, we first formulate spectrum sensing as a goodness of fit testing problem, and then apply the Anderson-Darling test, one of goodness of fit tests, to derive a sensing method called Anderson-Darling sensing. It is shown by both analysis and numerical results that under the same sensing conditions and channel environments, Anderson-Darling sensing has much higher sensitivity to detect an existing signal than energy detector-based sensing, especially in a case where the received signal has a low signal-to-noise ratio (SNR) without prior knowledge of primary user signals.

We consider cooperative spectrum sensing in which multiple cognitive radios collaboratively detect the spectrum holes through energy detection and investigate the optimality of cooperative spectrum sensing with an aim to optimize the detection performance in an efficient and implementable way. We derive the optimal voting rule for any detector applied to cooperative spectrum sensing. We also optimize the detection threshold when energy detection is employed. Finally, we propose a fast spectrum sensing algorithm for a large network which requires fewer than the total number of cognitive radios in cooperative spectrum sensing while satisfying a given error bound.

In wireless relay networks, the use of multiple antennas at both the source and destination can increase the capacity linearly with the number of antennas. In this paper, we investigate amplify-and-forward dual-hop MIMO relay networks without channel state information at the relay nodes, i.e., noncoherent case. In contrast to the conventional approach where the available power is equally distributed over all relay nodes, we propose an opportunistic relaying approach in which only a few very important relays (VIR) are selected to share the power. By doing so, the array gain of MIMO relay networks can be enhanced and thus maximize the ergodic capacity. Simulation results show that the optimal number of selected VIR to maximize the ergodic capacity is identical to the number of antennas employed at the source or destination. Compared to the conventional approach, the proposed opportunistic relaying approach can achieve a higher capacity.

In this paper, we consider the coexistence between a large number of cognitive radio users and a licensed user in order to enhance the spectrum efficiency. Zero-forcing beamforming (ZFB) is utilized as a downlink preceding technique that precancels inter-user interference in our multiuser cognitive multiple-input/multiple-output (MIMO) system. When there is a large number of users, the system performance can be significantly increased by selecting a subset of users and a set of receive antennas for each user. In particular, to reduce the complexity, we propose a receive antenna selection method followed by multiuser selection in order to maximize the downlink throughput as well as mitigate the interference to the primary user. Performance results show that our proposed method is able to achieve significant complexity reduction at the expense of a little sum-rate throughput loss.

In this paper, we propose a joint cooperative multiplexing and user scheduling approach over wireless relay networks. While multiuser diversity gain is achieved in traditional opportunistic scheduling techniques, the proposed method achieves both gains from multiuser diversity and multiplexing simultaneously. Theoretical bounds of the achievable throughput for the proposed method are obtained and verified to approach the simulation results very well. Performance comparison shows that the proposed method significantly outperforms traditional opportunistic techniques in terms of spectral efficiency.

Cognitive radio is being recognized as an intelligent technology due to its ability to rapidly and autonomously adapt operating parameters to changing environments and conditions. In order to reliably and swiftly detect spectrum holes in cognitive radios, spectrum sensing must be used. In this paper, we consider cooperative spectrum sensing in order to optimize the sensing performance. We focus on energy detection for spectrum sensing and find that the optimal fusion rule is the half-voting rule. Next, the optimal detection threshold of energy detection is determined numerically. Finally, we propose a fast spectrum sensing algorithm for a large network which requires fewer than the total number of cognitive radios to perform cooperative spectrum sensing while satisfying a given error bound.

A full-rate distributed space-time (ST) code design is proposed for amplify-and-forward cooperative wireless channels. By employing a signal space diversity technique at the source node and a unique signature vector at each relay node, the proposed distributed ST codes can achieve full cooperative diversity and full rate. The achievable diversity gain is shown to be M + 1, where M is the number of relay nodes. Optimal power allocation is also proposed to maximize the coding gain under a total power constraint.

In the letter the problem of cooperative spectrum sensing is investigated in cognitive radio (CR) networks over Rayleigh fading channels. By taking into account the error effect on the decision reporting, a general performance analysis of cooperative spectrum sensing is given. The analytical detection results show that the performance of cooperative spectrum sensing is limited by the probability of reporting errors. To deal with this limitation, we propose a transmit diversity based cooperative spectrum sensing method. By regarding multiple CRs as a virtual antenna array, space-time coding and spacefrequency coding are applied into CR networks over flat-fading and frequency-selective fading channels, respectively. Moreover, we propose a relay diversity based cooperative spectrum sensing approach to increase the diversity of detection when some CRs are in heavy shadowing. It is then shown that, when combined with algebraic coding, relay diversity can further improve the cooperative spectrum sensing performance.

A distributed space-frequency (SF) coded cooperative technique is proposed for broadband wireless ad-hoc networks, where both the channels from the source node to relay nodes and from the relay nodes to the destination node are characterized by frequency-selective fading. Using an SF coding at the source node and a circular shift at each relay node, we propose a scheme that can achieve both cooperative and multi-path diversity. The pairwise error probability is analyzed and the result demonstrates that a diversity gain of min(ML1, ML2) can be achieved, where M is the number of the relay nodes, and L1and L2are the number of taps of the multipath fading channels from the source node to the relay nodes and from the relay nodes to the destination node, respectively. Furthermore, the proposed distributed SF coding can achieve full-rate transmission for any number of relay nodes. In particular, it does not need IFFT/FFT processing or decoding at each relay node, thus providing a low-complexity design of the relay nodes in broadband wireless ad-hoc networks.

Cognitive radio has been recently proposed as a promising technology to improve the spectrum utilization. In this paper, we consider the coexistence between a large number of cognitive radio users and a licensed user in order to enhance the spectrum efficiency. With the deployment of M antennas at the cognitive base station, an efficient transmit beamforming technique combined with user selection is proposed to maximize the downlink throughput and satisfy the signal-to-interference- and-noise ratio (SINR) constraint as well as limit interference to the primary user. In the proposed user selection algorithm, cognitive users who are nearly orthogonal to the primary user are first pre-selected so as to minimize the mutual interference. Then, M best cognitive users who are nearly mutual orthogonal to each other are scheduled from those pre-selected cognitive users. Simulation results show that our proposed method is able to achieve high sum-rate throughput, with affordable complexity. Moreover, our proposed technique with equal power allocation suffers a negligible performance loss compared to the one with the optimal power allocation.

Cooperative diversity has been recently proposed as a way to form distributed antenna arrays that improve error performance and capacity. In this paper, a bandwidth efficient cooperative diversity scheme is proposed for half-duplex amplify- and-forward wireless relay networks. By employing a signal space diversity technique and allowing the source node to keep on transmitting over the whole duration of the cooperation frame, the proposed cooperative scheme is proved to be able to achieve both full-rate and full-diversity simultaneously. Diversity analysis shows that the achievable diversity gain is M + 1, where M is the number of relay nodes. Moreover, a power allocation scheme is proposed to maximize the coding gain under a total power constraint. Simulation results are shown to demonstrate the full- rate and full-diversity of the proposed scheme.

A distributed space-frequency (SF) coded cooperative technique is proposed for broadband wireless ad-hoc networks. By employing an SF coding at the source node and a circular shift at each relay node, the proposed cooperative diversity technique can exploit the maximum relay diversity and multipath diversity. Pairwise error probability analysis shows that the achievable diversity gain is min (ML1, ML2), where M is the number of the relay nodes, and L1and L2are the number of taps of the multipath fading channels from the source node to the relay nodes and from the relay nodes to the destination node, respectively.

Cognitive radio has been recently proposed as a promising technology to improve the spectrum utilization efficiency by intelligently sensing and accessing some vacant bands of licensed users. In this paper, we consider the coexistence between a cognitive radio and a licensed user in order to enhance the spectrum efficiency. We develop an approach to allow the cognitive radio to operate in the presence of the licensed user. In order to minimize the interference to the licensed user, the transmit power of the cognitive radio is controlled by using the side information of spectrum sensing. Numerical results will show that the quality of service for the licensed user can be guaranteed in the presence of the cognitive radio by the proposed approach.

In cognitive radio systems, secondary users can be coordinated to perform cooperative spectrum sensing so as to detect the primary user more accurately. However, when the sensing observations are forwarded to a common receiver through fading channels, the sensing performance can be severely degraded. In this paper, we propose a cluster-based cooperative spectrum sensing method to improve the sensing performance. By separating all the secondary users into a few clusters and selecting the most favorable user in each cluster to report to the common receiver, the proposed method can exploit the user selection diversity so that the sensing performance can be enhanced. Furthermore, decision fusion and energy fusion are both studied and the analytical performance results are given. Numerical results show that the sensing performance is improved significantly as opposed to conventional spectrum sensing.

While a great number of space-time (ST) code and space-frequency (SF) code designs have been extensively studied for point-to-point single-user links and directly applied to each user for multiuser links, little effort has been dedicated to multiuser ST/SF code design. Recently, this issue was considered by Gartner and Bolcskei and from a perspective of increasing system throughput, they proposed a joint code design for a 2-user scenario. In this paper, by applying a signal space diversity technique and a judicious phase rotation to each of a group of uncoordinated users, we propose a systematic design of SF codes for multiuser MIMO-OFDM systems over frequency-selective fading channels. We prove that multiuser SF codes with maximum-likelihood (ML) detection can achieve the maximum diversity for each user while minimizing the interference introduced from all the other users.

We consider multiuser scheduling with limited feedback of partial channel state information in MIMO broadcast channels. By using spatial multiplexing at the base station (BS) and antenna selection for each user, we propose a multiuser scheduling method that allocates independent information streams from allMtransmit antennas to theMmost favorable users with the highest signal-to-interference-plus-noise ratio (SINR). A close approximation of the achievable sum-rate throughput for the proposed method is obtained and shown to match the simulation results very well. Moreover, a scheduling algorithm with finite rate feedback of SINR is proposed and the achievable throughput is derived. Performance analysis will show that even with only 1-bit quantization of SINR, the proposed scheduling approach can exploit multiuser diversity at an expense of slight decrease of throughput.

Scheduling schemes have been extensively studied in the framework of cellular networks, but the emergence of new system concepts, such as cognitive radio, brings this topic into the focus of research again. In this paper, we investigate multiuser uplink scheduling with quality-of-service (QoS) provisioning for multiple cognitive users working at the same area with a primary user. In this work, the proposed scheduling schemes attempt to provide a satisfactory tradeoff between maximizing the system capacity, achieving fairness among cognitive users, minimizing the interference to the primary user, and satisfying delay constraints to individual cognitive user. Simulation results are presented to evaluate the performance of the proposed scheduling schemes.

In cognitive radio systems, cooperative spectrum sensing is conducted among the cognitive users so as to detect the primary user accurately. However, when the number of cognitive users tends to be very large, the bandwidth for reporting their sensing results to the common receiver will be very huge. In this paper, the authors employ a censoring method with quantization to decrease the average number of sensing bits to the common receiver. By censoring the collected local observations, only the users with enough information will send their local one bit decisions (0 or 1) to the common receiver. The performance of spectrum sensing is investigated for both perfect and imperfect reporting channels. Numerical results show that the average number of sensing bits decreases greatly at the expense of a little sensing performance loss.

We consider multiuser scheduling with limited feedback of partial channel state information in MIMO broadcast channels. By using spatial multiplexing at the base station (BS) and antenna selection for each user, we propose a multiuser scheduling method that allocates independent information streams from allMtransmit antennas to theMmost favorable users with the highest signal-to-interference-plus-noise ratio (SINR). A close approximation of the achievable sum-rate throughput for the proposed method is obtained and shown to match the simulation results very well. Moreover, two reduced feedback scheduling approaches are proposed. In the first approach, which we shall refer to as selected feedback scheduling, the users are selected based on their SINR compared to a predesigned threshold. Only those selected users are allowed to feed back limited information to the BS. The resultant feedback load and achievable throughput are derived. It will then be demonstrated that with a proper choice of the threshold, the feedback load can be greatly reduced with a negligible performance loss. The second reduced feedback scheduling approach employs quantization for each user, in which only few bits of quantized SINR are fed back to the BS. Performance analysis will show that even with only 1-bit quantization, the proposed quantized feedback scheduling approach can exploit the multiuser diversity at the expense of slight decrease of throughput.

A systematic design of high-rate full-diversity space-time-frequency (STF) codes is proposed for multiple-input multiple-output frequency-selective block-fading channels. It is shown that the proposed STF codes can achieve rate Mtand full-diversity MtMrMbL, i.e., the product of the number of transmit antennas Mt, receive antennas Mr, fading blocks Mb, and channel taps L. The proposed STF codes are constructed from a layered algebraic design, where each layer of algebraic coded symbols are parsed into different transmit antennas, orthogonal frequency-division multiplexing tones, and fading blocks without rate loss. Simulation results show that the proposed STF codes achieve higher diversity gain in block-fading channels than some typical space-frequency codes

In this paper, we propose a new pilot tone placement scheme and a novel pilot sequence design to estimate the carrier frequency offset (CFO) in OFDM systems. Unlike the conventional approach in which isolated pilot tones are used, we cluster two pilot tones together as a group and these tone groups are equally spaced along the subcarrier axis. The pilot sequence of each group is carefully designed, in which the pilot symbol on the left side in each cluster is made antipodal to the one on the right. The performance in terms of the signal-to-interference ratio (SIR) can be significantly improved by the proposed scheme. Theoretical analysis and simulation results show that the clustered pilot tones can give a substantially lower variance of CFO estimate than that of the isolated pilot tones. For a given CFO, it demonstrates that the variance of the estimate can be reduced by 3 - 14 dB. The bit error rate (BER) is reduced by about 1 dB both in AWGN and multipath fading channels in the simulated cases

In this letter we apply the general orthogonal space-time block codes (OSTBC) to MIMO-OFDM systems over frequency-selective fading channels and aim to exploit the potential multipath diversity. By replacing the scalar entry of an OSTBC matrix with the vector of repeated symbols, we obtain a new OSTBC which can achieve both spatial diversity and multipath diversity. Moreover, a fast maximum likelihood (ML) decoding is admitted. Simulation results show that the proposed OSTBC, for two transmit antennas, can obtain a higher diversity gain than the Alamouti code at the same ML decoding complexity

Using OFDM and space-time coding to achieve asynchronous cooperative diversity has been proposed by Mei et. al. and Shin et al, where orthogonal space-time block codes, Alamouti code in particular, were used at relay nodes. This proposed space-time coding can not achieve the multipath diversity when the channels from nodes to nodes are frequency selective fading. In this paper, we also consider asynchronous cooperative communications where the channels are assumed to be frequency-selective fading. We design high rate distributed space-frequency codes to achieve both full asynchronous cooperative diversity and multipath diversities.

In this paper, a design of orthogonal space-time block codes (OSTBC) is proposed for MIMO-OFDM systems operating in frequency-selective fading channels. By stacking the repeated symbols as entries of an OSTBC matrix, the proposed OSTBC can achieve full diversity in frequency-selective fading channels. Moreover, the ML decoding can be performed separately on each symbol, i.e., single-symbol decoding. Simulation results show that for two transmit antennas the proposed OSTBC can achieve a higher diversity gain than the Alamouti code yet maintaining a single-symbol decoding simplicity.

In user cooperative communications, relay nodes are usually asynchronous. By realizing that the processing in the frequency domain is insensitive to the errors in the time domain, Mei et. al. [13] recently applied the OFDM technique to achieve the full cooperative diversity for asynchronous cooperative communications, where orthogonal space-time block codes, Alamouti code in particular, were used for relay nodes and the channels from nodes to nodes are assumed flat fading. In this paper, we also consider asynchronous cooperative communications, but the channels from nodes to nodes are assumed to be frequency-selective fading. We use high rate space-frequency codes to encode the correctly detected information symbols across some subcarriers at relay nodes to achieve both full cooperative diversity and full multipath diversity for asynchronous communications, which, we show, holds if the recent high rate space-frequency codes constructed in [15] are used.

It was claimed in the above correspondence that "single-carrier frequency-domain equalization (SC-FDE) is capable of collecting full multipath diversity even without channel coding." However, this is not correct because Proposition 2 and Theorem 1 of the above correspondence, which lead to the claim, are both incorrect. In this correspondence, Proposition 2 and Theorem 1 are disproved. As a consequence, the main claim of the above correspondence is denied. Furthermore, a new theorem is given to show that the multipath diversity cannot be achieved by SC-FDE without channel coding. An example is also presented to disprove the main claim of of the above correspondence meanwhile supporting the new theorem proposed in this correspondence.

In this paper, we propose a systematic design of rate-M/sub t/ full-diversity space-time-frequency (STF) code for MIMO frequency-selective block-fading channels. The full-diversity can be proved to be the product of number of transmit antennas M/sub t/, receiver antennas M/sub r/, fading blocks M/sub b/ and channel taps L. The performance of the proposed STF codes are simulated and compared with some existing SF codes. The full-diversity of STF is further validated from the simulated case.

In this paper, a design of high-rate space-frequency codes (SFC) is proposed for MIMO-OFDM systems. The proposed SFC is achieved by partitioning data symbols in one OFDM block into many blocks and then precoding each block with an unitary matrix. For M/sub t/ transmit antennas, it can achieve a symbol rate M/sub t/ and it is numerically shown that it has full diversity in some interesting cases.

The orthogonal frequency division multiplexing (OFDM) system has been demonstrated to be effective in a frequency selective multipath fading environment. To compensate the deleterious effects of the fading channels, pilot symbol assisted modulation (PSAM) technique has been used to obtain the channel estimate. In order to have an accurate estimate, a high percentage of pilot symbols is usually needed and thus reducing the spectrum efficiency. Therefore, the number of pilot symbols is a tradeoff between channel estimation accuracy and bandwidth efficiency. In this paper, we propose a novel method of deciding the optimal number of pilots in OFDM transmission. The method is based on the derivation and analysis of bit error rate (BER) of a PSAM-OFDM system in multipath fading channel in terms of the pilot spacing for a given block size.