Deploying six-dimensional movable antenna (6DMA) systems in Internet-of-Vehicles (IoV) scenarios can greatly enhance spectral efficiency. However, the high mobility of vehicles causes rapid spatio-temporal channel variations, posing a significant challenge to real-time 6DMA optimization. In this work, we pioneer the application of 6DMA in IoV and propose a low-complexity, instantaneous channel state information (CSI)-free dynamic configuration method. By integrating vehicle motion prediction with offline directional response priors, the proposed approach optimizes antenna positions and orientations at each reconfiguration epoch to maximize the average sum rate over a future time window. Simulation results in a typical urban intersection scenario demonstrate that the proposed 6DMA scheme significantly outperforms conventional fixed antenna arrays and simplified 6DMA baseline schemes in terms of total sum rate.

A version of the accepted manuscript is available in arXiv at arXiv:2603.04898v1 [cs.LG] (https://arxiv.org/abs/2603.04898). Comments: This paper has been accepted by infocom. The source code has been released at: https://github.com/qiongwu86/U-Parking . Submission history: From: Qiong Wu: [v1] Thu, 5 Mar 2026 07:38:51 UTC (499 KB).

MOTIVATION: Single-cell RNA sequencing (scRNA-Seq) technology enables detailed exploration of gene expression at the individual cell level, crucial for annotating cell types and understanding cellular diversity. Traditional methods for cell type annotation often rely on marker genes and manual labeling, posing challenges due to low data quality and incomplete reference datasets. RESULTS: We developed CeLLTra, a novel contrastive learning framework that leverages a Transformer-based model integrating biological pathway information to group genes into super tokens, effectively capturing comprehensive gene expression from scRNA-Seq data. By combining this pathway-informed Transformer with a pretrained domain-specific language model, CeLLTra accurately aligns cell-type annotations with gene expression profiles. Evaluations on a large-scale human scRNA-Seq dataset showed that CeLLTra significantly outperformed state-of-the-art methods in supervised and zero-shot cell-type prediction. Additionally, CeLLTra generalized well to external datasets, improving clustering performance and enabling better characterization of cancerous cell states in tumor-infiltrating myeloid cells from non-small cell lung cancer patients. AVAILABILITY AND IMPLEMENTATION: CeLLTra is freely available on GitHub (https://github.com/WJZheng-group/CeLLTra) and Zenodo (https://doi.org/10.5281/zenodo.17666735). The datasets underlying this article are the following: GSE201333 and GSE127465. All these datasets are publicly available and can be freely accessed on the Gene Expression Omnibus repository.

Reconfigurable Intelligent Surface (RIS)-based direct modulation communication systems have garnered significant attention due to their low cost, low power consumption, and baseband-less characteristics. However, these systems face challenges such as the random time-varying coding state of the RIS and the difficulty in implementing beamforming in direct modulation. In this paper, we propose a simple and effective joint space-time coding approach for RIS that enables simultaneous realization of both direct modulation communication and beamforming. By modeling the transmitted signals of the RIS using space-time coding, we show that the time coding determines the direct modulation functionality, while the space coding governs the beamforming. Consequently, we introduce a joint time-space coding technique by performing exclusive-or (XOR) operations on the time and space coding sequences, enabling both functionalities to be achieved concurrently. Numerical simulations demonstrate the effectiveness of the proposed method. Furthermore, we design and fabricate a transmissive 1-bit phase reconfigurable RIS operating in the 3.4-3.79 GHz frequency band for the implementation of a direct modulation communication system. Experimental results reveal that the bit error rate (BER) is significantly reduced when joint space-time coding is used, compared to using time coding alone. Additionally, the root-mean-square error vector magnitude (rmsEVM) of the constellation diagram is reduced by 55%. This technique is promising for applications in the Internet of Things (IoT), contributing to the development of intelligent networks for electronic devices.

This paper proposes a novel IRS-aided framework for secure sensing, which aims to minimize the worst-case Cram´er-Rao Bound (WC-CRB) within an entire surveillance area by optimizing the IRS reflecting beamforming, enabling reliable and secure localization of arbitrary and unknown targets. Specifically, we first establish a general IRS-aided localization coverage model and derive the closed-form expression for the CRB of an arbitrary point, which reveals the relationship between the localization error bound and the Fisher information of the angle of arrival (AOA), angle of departure (AOD) and delay. To solve this challenging min-max optimization problem, we design efficient algorithms for different area types. For sector area, we first represent the Fisher information as trigonometric polynomials, then construct the WC-CRB coverage constraint as a non-negativity problem of these polynomials, and finally approximate it as an efficiently solvable semidefinite program (SDP). For the more challenging case of arbitrarily shaped area, we propose a two-tiered solution comprising a low-complexity heuristic algorithm based on geometric approximation and a high-performance detailed design that accurately solves the problem by decomposing the irregular boundary into multiple continuous segments. Numerical simulations validate the superiority of the proposed framework, demonstrating that our designs significantly outperform various benchmark schemes in terms of robustness and performance uniformity. The results show that the framework not only effectively reduces the WC-CRB but also achieves a highly uniform performance coverage across the entire area, providing a reliable and efficient solution for practical localization security applications.

Simultaneous wireless information and power transfer (SWIPT) has been envisioned as a promising technology to support ubiquitous connectivity and reliable sustainability in Internet-of-Things (IoT) networks, which, however, generally suffers from severe attenuation caused by long distance propagation, leading to inefficient wireless power transfer (WPT) for energy harvesting receivers (EHRs). This paper proposes to introduce emerging intelligent reflecting surface (IRS) and movable antenna (MA) technologies into SWIPT systems aiming at enhancing information transmission for information decoding receivers (IDRs) and improving receive power of EHRs. We consider to maximize the weighted sum-rate of IDRs via jointly optimizing the active and passive beamforming at the base station (BS) and IRS, respectively, together with the positions of MAs, while guaranteeing the individual requirement of each EHR. To tackle this challenging task due to the non-convexity of associated optimization, we develop an efficient algorithm combining weighted minimal mean square error (WMMSE), block coordinate descent (BCD), majorization-minimization (MM), and penalty duality decomposition (PDD) frameworks. Besides, we present a feasibility characterization method to examine the achievability of EHRs’ requirements. Simulation results demonstrate the significant benefits of our proposed solutions. Particularly, the optimized IRS configuration may exhibit higher performance gain than MA counterpart under our considered scenario.

This paper proposes a novel dual-intelligent reflecting surface (IRS) aided interference-limited simultaneous wireless information and power transfer (SWIPT) system with independent power splitting (PS), where each receiving antenna applies different PS factors to offer an advantageous trade-off between the useful information and harvested energy.We separately establish the near- and hybrid-field channel models for IRS-reflected links to evaluate the performance gain more precisely and practically. Specifically, we formulate an optimization problem of maximizing the harvested power by jointly optimizing dual-IRS phase shifts, independent PS ratio, and receive beamforming vector in both near- and hybrid-field cases. In the near-field case, the alternating optimization algorithm is proposed to solve the non-convex problem by applying the Lagrange duality method and the difference-of-convex (DC) programming. In the hybrid-field case, we first present an interesting result that the AP-IRS-user channel gains are invariant to the phase shifts of dual-IRS, which allows the optimization problem to be transformed into a convex one. Then, we derive the asymptotic performance of the combined channel gains in closed-form and analyze the characteristics of the dual-IRS. Numerical results validate our analysis and indicate the performance gains of the proposed scheme that dual-IRS-aided SWIPT with independent PS over other benchmark schemes.

In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-driven cooperative integrated sensing, computing, and communication (ISCC) network to meet the requirement for a diverse network with low energy consumption. The cooperative base stations (BSs) are equipped with TRIS transceivers to accomplish sensing data acquisition, communication offloading, and computation in a time slot. In order to obtain higher cooperation gain, we utilize a signal-level spatial registration algorithm, which is realized by adjusting the beamwidth. Meanwhile, for more efficient offloading of the computational task, multistream communication is considered, and rank-N constraints are introduced, which are handled using an iterative rank minimization (IRM) scheme. We construct an optimization problem with the objective function of minimizing the total energy consumption of the network to jointly optimize the beamforming matrix, time slot allocation, sensing data allocation and sensing beam scheduling variables. Due to the coupling of the variables, the proposed problem is a non-convex optimization problem, which we decouple and solve using a block coordinate descent (BCD) scheme. Finally, numerical simulation results confirm the superiority of the proposed scheme in improving the overall network performance and reducing the total energy consumption of the network.

In intelligent reflecting surface (IRS)-assisted multiple-input multiple-output (MIMO) systems, a strong line-of-sight (LoS) link is required to compensate for the severe cascaded path loss. However, such a link renders the effective channel highly rank-deficient and fundamentally limits spatial multiplexing. To overcome this limitation, this paper leverages the large aperture of sparse arrays to harness near-field spherical wavefronts, and establishes a deterministic deployment criterion that strategically positions the IRS in the near-field of a base station (BS). This placement exploits the spherical wavefronts of the BS–IRS link to engineer decorrelated channels, thereby fundamentally overcoming the rank-deficiency issue in far-field cascaded channels. Based on a physical channel model for the sparse BS array and the IRS, we characterize the rank properties and inter-user correlation of the cascaded BS–IRS–user channel. We further derive a closed-form favorable propagation metric that reveals how the sparse array geometry and the IRS position can be tuned to reduce inter-user channel correlation. The resulting geometry-driven deployment rule provides a simple guideline for creating a favorable propagation environment with enhanced effective degrees of freedom. The favorable channel statistics induced by our deployment criterion enable a low-complexity maximum-ratio transmission (MRT) precoding scheme. This serves as the foundation for an efficient algorithm that jointly optimizes the IRS phase shifts and power allocation based solely on long-term statistical channel state information (CSI). Simulation results validate the effectiveness of our deployment criterion and demonstrate that our optimization framework achieves significant performance gains over benchmark schemes.

This paper presents a mobile computing-based framework for distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenarios under intelligent transportation systems (ITS). A centralized trajectory planning problem is first formulated to optimize merging efficiency and safety. To eliminate reliance on a central controller, a distributed solution is developed using ADMM algorithm based on V2X communication, enabling CAVs to collaboratively compute trajectories in parallel by leveraging their onboard computing power. Building on this, a multi-vehicle model predictive control (MPC) problem is proposed to enhance system stability under strict constraints. To solve it efficiently, a Distributed Cooperative Iterative MPC (DCIMPC) method is introduced, which decomposes and reformulates the problem for real-time distributed execution across CAVs. Together, these methods form a mobile edge computing-driven control framework. Simulations and experiments demonstrate significant improvements in computational efficiency and system performance, highlighting the potential of mobile computing in cooperative CAV control.

This paper investigates a multi-Unmanned Aerial Vehicle (UAV) joint base station-assisted Internet of Vehicles (IoV) task offloading system in dense urban environments. To minimize system delay and energy consumption under strict coupling constraints, the complex non-convex optimization problem is decoupled into a hierarchical execution framework. First, a sequential distributed optimization algorithm based on Second-Order Cone Programming (SOCP) is proposed to optimize the 3D flight trajectory of each UAV, ensuring adaptive network coverage. Second, a novel hybrid resource scheduling paradigm synergizing Deep Reinforcement Learning (DRL) and Large Language Models (LLMs) is developed. Within this framework, the DRL agent dictates the initial resource allocation, while the LLM acts as a semantic macro-scheduler to rectify long-tail allocation imbalances for failed and surplus tasks. Crucially, a reward decoupling mechanism is introduced to isolate DRL training from external LLM interventions, thereby ensuring policy convergence. Finally, the task offloading ratios are precisely determined via Linear Programming (LP) within an alternating optimization loop. Simulation results demonstrate that the proposed method significantly outperforms traditional multi-agent reinforcement learning baselines in terms of task success rate and system efficiency.

In this paper, we address the problem of fair access and Age of Information (AoI) optimization in 5G New Radio (NR) Vehicle to Everything (V2X) Mode 2. Specifically, vehicles need to exchange information with the road side unit (RSU). However, due to the varying vehicle speeds leading to different communication durations, the amount of data exchanged between different vehicles and the RSU may vary. This may poses significant safety risks in high-speed environments. To address this, we define a fairness index through tuning the selection window of different vehicles and consider the image semantic communication system to reduce latency. However, adjusting the selection window may affect the communication time, thereby impacting the AoI. Moreover, considering the re-evaluation mechanism in 5G NR, which helps reduce resource collisions, it may lead to an increase in AoI. We analyze the AoI using Stochastic Hybrid System (SHS) and construct a multi-objective optimization problem to achieve fair access and AoI optimization. Sequential Convex Approximation (SCA) is employed to transform the non-convex problem into a convex one, and solve it using convex optimization. We also provide a large language model (LLM) based algorithm. The scheme's effectiveness is validated through numerical simulations.

This paper investigates detection orientated integrated sensing and communication (ISAC) system aided by hybrid reconfigurable intelligent surface (RIS). Target detection is conducted through communication signal echoes under the practical condition of unknown attenuation coefficient and sensing noise covariance, which makes our study more challenging than existing pertinent works. Firstly, we develop a closedform based generalized likelihood ratio test (GLRT) detector, which first effectively extrapolates unknown parameters through maximum likelihood estimation and then conducts hypothesis testing. Besides, we derive the asymptotic detection probability of the proposed GLRT detector in an analytic form, which is highly accurate for moderate sample size. Based on the above analysis, we propose robust beamforming design to maximize the worst-case detection probability while ensuring ergodic communication rate in awareness of channel state information (CSI) uncertainties. We provide a semidefinite programming (SDP) formulation to solve the robust beamforming problem. Additionally, by converting the variational and ergodic forms in robust formulation into explicit approximations, we further develop an efficient second order cone programming (SOCP) based solution, which is highly reliable when the CSI uncertainty becomes low. Numerical results validate the efficacy of the proposed GLRT detector, the correctness of the detection performance analysis, and the benefit of robust beamforming against CSI uncertainty.

This paper investigates a multi-intelligent reflecting surface (IRS) aided integrated sensing and communication (ISAC) system, where multiple IRSs are strategically deployed not only to assist the communication from a multi-antenna base station (BS) to a multi-antenna communication user (CU), but also enable the sensing service for a point target in the non-line-of-sight (NLoS) region of the BS. First, we propose a hybrid multi-IRS architecture, which consists of several passive IRSs and one semi-passive IRS equipped with both active sensors and reflecting elements. To be specific, the active sensors are exploited to receive the echo signals for estimating the target’s angle information, and the multiple reflecting paths provided by multi-IRS are employed to improve the degree of freedoms (DoFs) of communication. Under the given budget on the number of total IRSs elements, we theoretically show that increasing the number of deployed IRSs is beneficial for improving DoFs of spatial multiplexing for communication while increasing the Crámer-Rao bound (CRB) of target estimation, which unveils a fundamental tradeoff between the sensing and communication performance. To characterize the rate-CRB tradeoff, we study a rate maximization problem, by optimizing the BS transmit covariance matrix, IRSs phase-shifts, and the number of deployed IRSs, subject to a maximum CRB constraint. Analytical results reveal that the communication-oriented design becomes optimal when the total number of IRSs elements exceeds a certain threshold, wherein the relationships of the rate and CRB with the number of IRS elements/sensors, transmit power, and the number of deployed IRSs are theoretically derived and demystified. Simulation results validate our theoretical findings and also demonstrate the superiority of our proposed designs over the benchmark schemes.

This paper investigates a movable antenna (MA)-enabled wireless-powered communication network (WPCN), where multiple wireless devices (WDs) first harvest energy from the downlink signal broadcast by a hybrid access point (HAP) and then transmit information in the uplink using non-orthogonal multiple access. Unlike conventional WPCNs with fixed-position antennas (FPAs), this MA-enabled WPCN allows the MAs at the HAP and the WDs to adjust their positions twice: once before downlink wireless power transfer and once before uplink wireless information transmission. Our goal is to maximize the system sum throughput by jointly optimizing the MA positions, the time allocation, and the uplink power allocation. Considering the characteristics of antenna movement, we explore both continuous and discrete positioning designs, which, after formulation, are found to be non-convex optimization problems. Before tackling these problems, we rigorously prove that using identical MA positions for both downlink and uplink is the optimal strategy in both scenarios, thereby greatly simplifying the problems and enabling easier practical implementation of the system. We then propose alternating optimization-based algorithms to obtain suboptimal solutions for the resulting simplified problems. Simulation results show that: 1) the proposed continuous MA scheme can enhance the sum throughput by up to 395.71% compared to the benchmark with FPAs, even when additional compensation transmission time is provided to the latter; 2) a step size of one-quarter wavelength for the MA motion driver is generally sufficient for the proposed discrete MA scheme to achieve over 80% of the sum throughput performance of the continuous MA scheme; 3) when each moving region is large enough to include multiple optimal positions for the continuous MA scheme, the discrete MA scheme can achieve comparable sum throughput without requiring an excessively small step size.

This paper investigates an intelligent reflecting surface (IRS)-aided movable antenna (MA) system, where multiple IRSs cooperate with a multi-MA base station to extend wireless coverage to multiple target areas. The objective is to maximize the worst-case signal-to-noise ratio (SNR) across all locations within these areas through joint optimization of MA positions, IRS phase shifts, and transmit beamforming. To achieve this while balancing the performance-cost trade-off, we propose three coverage-enhancement schemes: thearea-adaptive MA-IRSscheme, where both the MA positions and IRS phase shifts are adaptively adjusted for each target area; thearea-adaptive MA-staIRSscheme, where only the MA positions are adjusted, while the IRS phase shifts remain unchanged after initial configuration (withstaIRSdenoting static IRSs); and theshared MA-staIRSscheme, where a common MA placement and static IRS configuration are applied across all areas. These schemes lead to challenging non-convex optimization problems with implicit objective functions, which are difficult to solve optimally. To address these problems, we propose a general algorithmic framework that can be applied to solve each problem efficiently albeit suboptimally. Simulation results demonstrate that: 1) the proposed MA-based schemes consistently outperform their fixed-position antenna (FPA)-based counterparts under both area-adaptive and static IRS configurations, with the area-adaptive MA-IRS scheme achieving the highest worst-case SNR; 2) as transmit antennas are typically far fewer than IRS elements, the area-adaptive MA-staIRS scheme may underperform the baseline FPA scheme with area-adaptive IRSs in terms of the worst-case SNR, but a modest increase in antenna number can reverse this trend; 3) under a fixed total cost, the optimal MA-to-IRS-element ratio for the worst-case SNR maximization is empirically found to be proportional to the reciprocal of their unit cost ratio.

Integrated sensing and communication (ISAC) is a key technology for future 6G networks. Most existing studies focus on monostatic and/or bistatic setups with limited coverage and capabilities. Networked ISAC systems with distributed base stations (BSs) can overcome these limitations. Moreover, movable antenna (MA) architectures offer improved ISAC performance over fixed-position antennas (FPAs) by enabling adaptable antenna movement. In this paper, we utilize the MA to promote communication capability with guaranteed sensing performance via jointly designing beamforming, power allocation, receiving filters and position configuration of transmit/receive MA towards maximizing the sum rate for both downlink (DL) and uplink (UL) users. The optimization problem is highly difficult due to the unique channel model derived from the position coefficient of the MA. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) method, we develop an efficient solution that optimizes all variables via convex optimization techniques. Extensive simulation results verify the effectiveness of our proposed algorithms and demonstrate the substantial performance promotion by deploying the MA framework in the networked ISAC system.

The Six-Dimensional Movable Antenna (6DMA) system has emerged as a promising technology to enhance wireless capacity by fully exploiting spatial degrees of freedom. However, applying 6DMA to high-mobility Internet of Vehicles (IoV) scenarios faces significant challenges, primarily due to the difficulty of acquiring instantaneous Channel State Information (CSI) and the risk of service interruptions caused by mechanical reconfiguration delays. To address these issues, this paper proposes a low-complexity, CSI-free single-step reconfiguration framework. First, we design a deterministic discrete position generation scheme based on a latitude-longitude grid with inherent topological structures. Leveraging graph theory, we explicitly model and theoretically derive the lower bounds of movement and time costs for antenna reconfiguration. Subsequently, utilizing the directional sparsity of 6DMA channels, we develop an adaptive optimization strategy that fuses offline environmental priors with online historical feedback. Furthermore, a periodic reconfiguration mechanism based on predicted cumulative vehicle distributions is introduced. By strictly restricting antenna adjustments to the first-order spatial neighborhood, the proposed single-step method effectively eliminates service interruptions. Simulation results demonstrate that the proposed scheme significantly outperforms traditional fixed and global-search-based benchmarks in terms of uplink sum rate, while incurring negligible mechanical overhead and latency, thereby validating its feasibility and robustness in highly dynamic vehicular networks.

Severe signal blockage and fast-varying channels in vehicular environments pose critical challenges to reliable semantic communication. To address these, this paper proposes a novel Row-Movable Active Reconfigurable Intelligent Surface (RM-A-RIS) assisted vehicular semantic communication system. This architecture uniquely combines active signal amplification with element mobility to compensate for multiplicative fading and reconstruct channel geometry, thereby enhancing spatial diversity. We formulate a joint optimization problem to maximize Semantic Spectral Efficiency (SSE) by coordinating RIS element positions, active reflection coefficients, and semantic symbol length. An efficient Alternating Optimization (AO) algorithm is developed to tackle the coupled non-convexity. Simulation results demonstrate that the proposed scheme substantially outperforms existing benchmarks, achieving up to 132.9%, 9.2%, and 35.2% improvements in Sum-Semantic Spectral Efficiency (Sum-SSE) compared to the passive RIS, fixed-position active RIS, and QPSO baselines, respectively.

Sensing-assisted predictive beamforming shows significant promise for enhancing various future unmanned aerial vehicle (UAV) applications in integrated sensing and communication (ISAC) systems. However, the impact of such beamforming technique on the communication reliability was largely unexplored and challenging to characterize. To fill this research gap and tackle this issue, this paper proposes a cellular-connected UAV tracking scheme leveraging extended Kalman filtering (EKF), where the predicted UAV trajectory, sensing duration ratio, and target constant received signal-to-noise ratio (SNR) are jointly optimized to maximize the outage capacity at each time slot. To address the implicit nature of the objective function, analytical outage probability (OP) approximations are proposed based on second-order Taylor expansions, providing an efficient and full characterization of outage capacity. Subsequently, an efficient algorithm is proposed based on a combination of bisection search and successive convex approximation (SCA) to address the non-convex optimization problem with guaranteed convergence. To further reduce computational complexity, a second efficient algorithm is developed based on alternating optimization (AO). Simulation results validate the accuracy of the derived OP approximations, the effectiveness of the proposed algorithms, and the significant outage capacity enhancement over various benchmarks. Furthermore, we show that the optimized predicted UAV trajectory tends to be parallel to the base station’s uniform linear array antennas with a nonzero minimum distance, indicating a trade-off between decreasing path loss and enjoying wide beam coverage for outage capacity maximization.

In this paper, we propose a full-duplex integrated sensing and communication (ISAC) system enabled by a movable antenna (MA). By leveraging the characteristic of MA that can increase the spatial diversity gain, the performance of the system can be enhanced. We formulate a problem of minimizing the total transmit power consumption via jointly optimizing the discrete position of MA elements, beamforming vectors, sensing signal covariance matrix and user transmit power. Given the significant coupling of optimization variables, the formulated problem presents a non-convex optimization challenge that poses difficulties for direct resolution. To address this challenging issue, the discrete binary particle swarm optimization (BPSO) algorithm framework is employed to solve the formulated problem. Specifically, the discrete positions of MA elements are first obtained by iteratively solving the fitness function. The difference-of-convex (DC) programming and successive convex approximation (SCA) are used to handle non-convex and rank-1 terms in the fitness function. Once the BPSO iteration is complete, the discrete positions of MA elements can be determined, and we can obtain the solutions for beamforming vectors, sensing signal covariance matrix and user transmit power. Numerical results demonstrate the superiority of the proposed system in reducing the total transmit power consumption compared with fixed antenna arrays.

In our original paper, we omitted a key step involving the transformation of variableR̃ISACk,j[n]. In this work, we recognize that our initial conclusion, stating that "Hk,jis a negative definite matrix in the feasible region" requires additional clarification and adjustments. To ensure the correctness of the work, we provide the necessary modifications and detailed discussions in this revised version.

Conventional antenna arrays rely primarily on digital beamforming for spatial control. While adding more elements can narrow beamwidth and suppress interference, such scaling incurs prohibitive hardware and power costs. Rotatable antennas (RAs), which allow mechanical or electronic adjustment of element orientations, introduce a new degree of freedom to exploit spatial flexibility without enlarging the array. By dynamically optimizing orientations, RAs can substantially improve desired link alignment and interference suppression. This paper investigates RA-enabled multiple-input single-output (MISO) interference channels under co-channel spectrum sharing and formulates a weighted sum-rate maximization problem that jointly optimizes transmit beamforming and antenna orientations. To tackle this nonconvex problem, we develop an alternating optimization (AO) framework that integrates weighted minimum mean-square error (WMMSE)-based beamforming with Frank-Wolfe-based orientation updates. To reduce complexity, we further study orientation optimization under maximum-ratio transmission (MRT) and zero-forcing (ZF) beamforming schemes. For finite-resolution actuators, we construct spherical Fibonacci codebooks and design a cross-entropy method (CEM)-based algorithm for discrete orientation selection. Simulations show that integrating RAs with conventional beamforming markedly increases weighted sum-rate, with gains rising with element directivity. Under discrete orientation control, the proposed CEM algorithm consistently outperforms the nearest-projection baseline.

In this paper, we model the minimum achievable throughput within a transmission block of restricted duration and aim to maximize it in movable antenna (MA)-enabled multiuser downlink communications. Particularly, we account for the antenna movement delay caused by mechanical movement, which has not been fully considered in previous studies, and reveal the trade-off between the delay and signal-to-interference-plus-noise ratio at users. To this end, we first consider a single-user setup to analyze the necessity of antenna movement. By quantizing the virtual angles of arrival, we derive the requisite region size for antenna moving, design the initial MA position, and elucidate the relationship between quantization resolution and moving region size. Furthermore, an efficient algorithm is developed to optimize MA position via successive convex approximation, which is subsequently extended to the general multiuser setup. Numerical results demonstrate that the proposed algorithms outperform fixed-position antenna schemes and existing ones without consideration of movement delay. Additionally, our algorithms exhibit excellent adaptability and stability across various transmission block durations and moving region sizes, and are robust to different antenna moving speeds. This allows the hardware cost of MA-aided systems to be reduced by employing low rotational speed motors.

Terahertz (THz) simultaneous wireless information and power transfer (SWIPT) is a promising technology for enabling ultra-high-rate and low-latency communications in massive battery-free Internet of Things (IoT) deployments for 6G networks. However, conventional THz systems rely on narrow directional beams that necessitate precise alignment, typically achieved through high-overhead beam scanning procedures, which fundamentally at odds with the energy constraints of battery-free IoT devices. In this paper, we propose a novel self-alignment architecture for THz SWIPT leveraging a reconfigurable intelligent surface (RIS) to eliminate complex beam scanning. By integrating phase conjugate circuits at both the base station and user equipment, the RIS facilitates a resonance-based bidirectional retroreflection mechanism, enabling the system to autonomously converge to an aligned state without manual intervention. We develop an analytical channel transfer model and a power cycle model to characterize the resonance-assisted beam alignment process and power transfer efficiency. Simulation results demonstrate that the RIS-enabled system achieves effective spatial power concentration with significant sidelobe suppression, leading to a communication capacity of 127.84 Gbit/s and a received power of 13.62 mW over a 2.2-meter link.

This paper proposes a novel multiple intelligent reflecting surfaces (IRSs) collaborative hybrid localization system, which involves deploying multiple IRSs near the target area and achieving target localization through joint time delay and angle estimation. Specifically, echo signals from all reflective elements are received by each sensor and jointly processed to estimate the time delay and angle parameters. Based on the above model, we derive the Fisher Information Matrix (FIM) for cascaded delay, Angle of Arrival (AOA), and Angle of Departure (AOD) estimation in semi passive passive models, along with the corresponding Cramer Rao Bound (CRB). To achieve precise estimation close to the CRB, we design efficient algorithms for angle and location estimation. For angle estimation, reflective signals are categorized into three cases based on their rank, with different signal preprocessing. By constructing an atomic norm set and minimizing the atomic norm, the joint angle estimation problem is transformed into a convex optimization problem, and low-complexity estimation of multiple AOA and AOD pairs is achieved using the Alternating Direction Method of Multipliers (ADMM). For location estimation, we propose a three-stage localization algorithm that combines weighted least squares, total least squares, and quadratic correction to handle errors in the coefficient matrix and observation vector, thus improving accuracy. Numerical simulations validate the superiority of the proposed system, demonstrating that the system's collaboration, hybrid localization, and distributed deployment provide substantial benefits, as well as the accuracy of the proposed estimation algorithms, particularly in low signal to noise ratio (SNR) condition.

This paper considers an active reconfigurable intelligent surface (RIS) aided device-based uplink integrated sensing and communication (ISAC) system. In this context, base station (BS) receives pilot and communication signals transmitted concurrently from mobile users to provision sensing and communication services. For the considered setup, we investigate beamforming design by jointly optimizing RIS configuration, mobile users’ transmit power and linear combiner at the BS to minimize Cramér-Rao bound (CRB) of angle-of-arrival (AoA) estimation for the sensing users while ensuring spectral efficiency of communication users. The considered device-based sensing paradigm raises unique challenge since communication signals contribute to noise covariance in AoA measurements, which leads to a highly complicated CRB expression. To resolve this challenge, we transfer the problem into a quartic form, equivalently represent covariance matrix inverse into an equation condition, decouple the intractable covariance equality constraint by introducing splitting variables followed by penalty dual-decomposition (PDD) methodology, which develops an iterative process updating all variable blocks alternatively. Extensive numerical results verify the effectiveness of our proposed algorithm and demonstrate the significant advantage of device-based sensing scheme over the device-free counterpart when the sensing targets can get connected in the ISAC network.

Reconfigurable intelligent surfaces (RISs) have been recognized as a revolutionary technology for future wireless networks. However, RIS-assisted communications have to continuously tune phase-shifts relying on accurate channel state information (CSI) that is generally difficult to obtain due to the large number of RIS channels. The joint design of CSI acquisition and subsection RIS phase-shifts remains a significant challenge in dynamic environments. In this paper, we propose a diffusion-enhanced decision Transformer (DEDT) framework consisting of a diffusion model (DM) designed for efficient CSI acquisition and a decision Transformer (DT) utilized for phase-shift optimizations. Specifically, we first propose a novel DM mechanism, i.e., conditional imputation based on denoising diffusion probabilistic model, for rapidly acquiring real-time full CSI by exploiting the spatial correlations inherent in wireless channels. Then, we optimize beamforming schemes based on the DT architecture, which pre-trains on historical environments to establish a robust policy model. Next, we incorporate a fine-tuning mechanism to ensure rapid beamforming adaptation to new environments, eliminating the retraining process that is imperative in conventional reinforcement learning (RL) methods. Simulation results demonstrate that DEDT can enhance efficiency and adaptability of RIS-aided communications with fluctuating channel conditions compared to state-of-the-art RL methods.

In this paper, we propose a Satellite-Terrestrial Integrated Network (STIN) assisted vehicular multi-tier distributed computing (VMDC) system leveraging hybrid terahertz (THz) and radio frequency (RF) communication technologies. Task offloading for satellite edge computing is enabled by THz communication using the orthogonal frequency division multiple access (OFDMA) technique. For terrestrial edge computing, we employ non-orthogonal multiple access (NOMA) and vehicle clustering to realize task offloading. We formulate a non-convex optimization problem aimed at maximizing computation efficiency by jointly optimizing bandwidth allocation, task allocation, subchannel-vehicle matching and power allocation. To address this non-convex optimization problem, we decompose the original problem into four sub-problems and solve them using an alternating iterative optimization approach. For the subproblem of task allocation, we solve it by linear programming. To solve the subproblem of sub-channel allocation, we exploit many-to-one matching theory to obtain the result. The subproblem of bandwidth allocation of OFDMA and the subproblem of power allocation of NOMA are solved by quadratic transformation method. Finally, the simulation results show that our proposed scheme significantly enhances the computation efficiency of the STIN-based VMDC system compared with the benchmark schemes.

Reconfigurable intelligent surfaces (RISs) enhance wireless systems by reshaping propagation environments. However, dynamic metasurfaces (MSs) with numerous phase-shift elements may incur undesired hardware costs and control overhead. In contrast, static MSs (SMSs), configured with static phase shifts that are pre-designed for specific communication demands, offer a cost-effective alternative by eliminating electronic element-wise tuning. Nevertheless, SMSs typically support only a single beam pattern, limiting flexibility in dynamic and multi-user scenarios. In this paper, we propose a novel Movable Intelligent Surface (MIS) technology that enables dynamic beamforming while maintaining static phase shifts. Specifically, we design a MIS architecture comprising two closely stacked transmissive MSs: a larger fixed-position MS 1 and a smaller movable MS 2. By differentially shifting MS 2’s position relative to MS 1, the MIS synthesizes distinct desired beam patterns, overcoming the SMSs’ single-pattern limitation. Then, we model the interaction between MS 2 and MS 1 using binary selection matrices and padding vectors, which allow us to formulate a new optimization problem that jointly designs the MIS phase shifts and selects shifting positions for worst-case signal-to-noise ratio (SNR) maximization. This position selection, equal to beam pattern scheduling, offers a new degree of freedom for RIS-aided systems. To solve the intractable problem, we develop an efficient algorithm that handles unit-modulus and binary constraints and employs manifold optimization methods. Finally, extensive validation results are provided, including both experimental and numerical analysis. We first implement a MIS prototype and perform proof-of-concept experiments, demonstrating the MIS’s ability to synthesize desired beam patterns that achieve beam steering. Numerical results further validate our theoretical modeling and the proposed algorithm. Encouragingly, by introducing a movable MS 2 with a few elements, MIS effectively offers beamforming flexibility for significantly improved performance compared to SMSs. We also draw insights into the optimal MIS configuration and element allocation strategy.

In this paper, we propose a Satellite-Terrestrial Integrated Network (STIN) assisted vehicular task offloading leveraging hybrid terahertz (THz) and radio frequency (RF) communication technologies. Task offloading for satellite edge computing is enabled by THz communication using the orthogonal frequency division multiple access (OFDMA) technique. For terrestrial edge computing, we employ non-orthogonal multiple access (NOMA) and vehicle clustering to realize task offloading. We formulate a non-convex optimization problem aimed at maximizing computation efficiency by jointly optimizing bandwidth allocation, task allocation and power allocation. To address this non-convex optimization problem, we decompose the original problem into three sub-problems and solve them using an alternating iterative optimization approach. For the subproblem of task allocation, we solve it by linear programming. The subproblem of bandwidth allocation of OFDMA and the sub-problem of power allocation of NOMA are solved by quadratic transformation method. Finally, the simulation results show that our proposed scheme significantly enhances the computation efficiency of the STIN-based vehicular task offloading compared with the benchmark schemes.

In this paper, we study employing movable components on both base station (BS) and intelligent reflecting surface (IRS) in a wideband terahertz (THz) multiple-input-single-output (MISO) system, where the BS is equipped with a movable antenna (MA) array and the IRS consists of movable subarrays. To alleviate double beam squint effect caused by the coupling of beam squint at the BS and IRS, we propose to maximize the minimal received power across a wide THz spectrum by delicately configuring the positions of MAs and IRS subarrays, which is highly challenging. By adopting majorization-minimization (MM) methodology, we develop an algorithm to tackle the aforementioned optimization. Numerical results demonstrate the effectiveness of our proposed algorithm and the benefit of utilizing movable components on the BS and IRS to mitigate double beam squint effect in wideband THz communications.

Spatial scattering modulation (SSM), an emerging millimeter-wave multiple-input multiple-output (MIMO) modu-lation technique, exploits spatial beam resources to enhance the modulation degree of freedom. However, to address the problem that the detection performance of existing scalar-based maximum likelihood (SML) detector is not optimal and the complexity is too high, this paper develops vector-based maximum likelihood (VML) and low-complexity (LC) detectors for the structural char-acteristics of SSM system receivers, respectively. The complexity of the proposed VML algorithm is slightly higher than that of the traditional SML detection algorithm, while the complexity of the proposed LC detection algorithm is reduced by 50% compared with the traditional SML detection algorithm. Numerical results show that at average bit error probability (ABEP) = 10–5, the signal-to-noise ratio (SNR) required for the VML-based ABEP values is 5.5 dB less than that obtained from SML detection. Moreover, the proposed LC algorithm detection performance also saves SNR of 1 dB over SML detector.

Programmable metasurfaces promise a great potential to construct low-cost phased array systems due to their capability of elaborate control over electromagnetic waves for Internet of Things (IoT) applications. However, they are usually in either reflective or transmissive scheme, and possess a relatively high profile as a result of the external feed source. Besides, the phase resolution is insufficient comparing with conventional phased array antennas. In this article, we propose a low-profile wideband radiation-type programmable metasurface and further present the high-precision beam modulating for IoT applications. It could avoid the external space-feed source required by its traditional counterpart, thus achieving a significant reduction of profile height through integration of a high-efficiency microwave excitation network and metasurface radiators. Furthermore, high-precision amplitude-phase weight algorithm was then synchronously carried out for low side-lobe beam scanning through time-coding sequences modulation strategy. Experimental results illustrate that our programmable metasurface could obtain a peak gain of 20.6 dBi at 5.5 GHz with reduced sidelobe level of lower than -23 dB.

In the Internet of Vehicles (IoV), federated learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data. Traditional supervised learning requires image data with labels, but data labeling involves significant manual effort. Federated self-supervised learning (FSSL) utilizes self-supervised learning (SSL) for local training in FL, eliminating the need for labels while protecting privacy. Compared to other SSL methods, Momentum Contrast (MoCo) reduces the demand for computing resources and storage space by creating a dictionary. However, using MoCo in FSSL requires uploading the local dictionary from vehicles to base station (BS), which poses a risk of privacy leakage. Simplified contrast (SimCo) addresses the privacy leakage issue in MoCo-based FSSL by using dual temperature instead of a dictionary to control sample distribution. Additionally, considering the negative impact of motion blur on model aggregation, and based on SimCo, we propose a motion blur-resistant FSSL method, referred to as BFSSL. Furthermore, we address energy consumption and delay in the BFSSL process by proposing a deep reinforcement learning (DRL)-based resource allocation scheme, called DRL-BFSSL. In this scheme, BS allocates the central processing unit (CPU) frequency and transmission power of vehicles to minimize energy consumption and latency, while aggregating received models based on the motion blur level. Simulation results validate the effectiveness of our proposed aggregation and resource allocation methods.

In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, cellular vehicle-to-everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput. Resource allocation within C-V2X is crucial for ensuring the transmission of safety information and meeting the stringent requirements for ultralow latency and high reliability in vehicle-to-vehicle (V2V) communication. This article proposes a method that integrates graph neural networks (GNNs) with deep reinforcement learning (DRL) to address this challenge. By constructing a dynamic graph with communication links as nodes and employing the graph sample and aggregation (GraphSAGE) model to adapt to changes in graph structure, the model aims to ensure a high success rate for V2V communication while minimizing interference on vehicle-to-infrastructure (V2I) links, thereby ensuring the successful transmission of V2V link information and maintaining high transmission rates for V2I links. The proposed method retains the global feature learning capabilities of GNN and supports distributed network deployment, allowing vehicles to extract low-dimensional features that include structural information from the graph network based on local observations and to make independent resource allocation decisions. Simulation results indicate that the introduction of GNN, with a modest increase in computational load, effectively enhances the decision-making quality of agents, demonstrating superiority to other methods. This study not only provides a theoretically efficient resource allocation strategy for V2V and V2I communications but also paves a new technical path for resource management in practical IoV environments.

In this article, a novel multistream downlink communication system based on the transmissive reconfigurable intelligent surface (RIS) transceiver is proposed. Specifically, a transmissive RIS transceiver architecture is first elaborated, where the downlink communication mechanism and the difference from the conventional multiantenna transceivers are introduced, respectively. More importantly, the generation of RIS element control signals based on time-modulated array (TMA) is illustrated in detail, which can jointly take into account multistream modulation signals and beamforming design. Correspondingly, the harmonic signal extraction scheme at the user is also given. Then, since the design of beamforming has an impact on the system performance, we propose a beamforming optimization algorithm based on matrix lifting, successive convex approximation (SCA) and difference-convex (DC) programming under the constraints of user signal-to-interference-plus-noise ratio (SINR) and available useful power of RIS elements. Furthermore, we analyze the bit error rate (BER) performance of the proposed architecture from two perspectives of transmit multiplexing and transmit diversity. Finally, the convergence behavior of the beamforming algorithm, the impact of different system parameter configurations on system performance and the BER performance of different schemes under the system are verified by numerical simulations.

Vehicular edge computing (VEC) is an emerging technology with significant potential in the field of Internet of Vehicles (IoV), enabling vehicles to perform intensive computational tasks locally or offload them to nearby edge devices. However, the quality of communication links may be severely deteriorated due to obstacles such as buildings, impeding the offloading process. To address this challenge, we introduce the use of reconfigurable intelligent surface (RIS), which provide alternative communication pathways to assist vehicle communication. By dynamically adjusting the phase-shift of the RIS, the performance of VEC systems can be substantially improved. In this work, we consider an RIS-assisted VEC system, and design an optimal scheme for local execution power, offloading power, and RIS phase-shift, where random task arrivals and channel variations are taken into account. To address the scheme, we propose an innovative deep reinforcement learning (DRL) framework that combines the deep deterministic policy gradient (DDPG) algorithm for optimizing RIS phase-shift coefficients and the multiagent DDPG (MADDPG) algorithm for optimizing the power allocation of vehicle user (VU). Simulation results show that our proposed scheme outperforms the traditional centralized DDPG, twin delayed DDPG (TD3), and some typical stochastic schemes.

In the digital twin mobile edge network, the maintenance of the vehicle twin model and vehicular task processing in the server require the support of computing resources. In addition, they are performed simultaneously. Therefore, how to allocate resources for twin maintenance and task processing under limited server resources is crucial. However, current research tends to ignore the aspect of resource competition for twin maintenance. In this study, we analyze the delays of these two affected by resource allocation under a generic digital twin mobile edge network (DTMEN) to construct the optimization problem. For this problem, we transformed the problem using a Markov decision process. Meanwhile, we propose a multi-agent reinforcement learning (MADRL) based twin maintenance and task processing resource collaborative scheduling (TMTPRCS) algorithm to solve the problem. Experiments show that our proposed approach is effective in terms of resource allocation compared to other alternative algorithms.

In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transmitter-enabled spatial modulation (SM) multiple-input multiple-output (MIMO) system. In the transmission stage, a column-control activation strategy is implemented for the TRIS panel, where the specific column elements are activated per time slot. Concurrently, the receiver employs the maximum likelihood detection technique. Based on this, for the transmit signals, we derive the closed-form expressions for the upper bounds of the average bit error probability (ABEP) of the proposed scheme from different perspectives, employing both vector-based and element-based approaches. Furthermore, we provide the asymptotic closed-form expressions for the ABEP of the TRIS-SM scheme, as well as the diversity gain. To improve the performance of the proposed TRIS-SM system, we optimize ABEP with a fixed data rate. Additionally, we provide lower bounds to simplify the computational complexity of improved TRIS-SM scheme. The Monte Carlo simulation method is used to validate the theoretical derivations exhaustively. The results demonstrate that the proposed TRIS-SM scheme can achieve better ABEP performance compared to the conventional SM scheme. Furthermore, the improved TRIS-SM scheme outperforms the TRIS-SM scheme in terms of reliability.

To meet the growing demand for data traffic, spectrum-rich optical wireless communication (OWC) has emerged as a key technological driver for the development of 6G. The resonant beam communication (RBC) system, which employs spatially separated laser cavities as the transmitter and receiver, is a high-speed OWC technology capable of self-alignment without tracking. However, its transmission through the air is susceptible to losses caused by obstructions. In this paper, we propose an intelligent reflecting surface (IRS) assisted RBC system with the optical frequency doubling method, where the resonant beam in frequency-fundamental and frequency-doubled is transmitted through both direct line-of-sight (LoS) and IRS-assisted channels to maintain steady-state oscillation and enable communication without echo-interference, respectively. Then, we establish the channel model based on Fresnel diffraction theory under the near-field optical propagation to analyze the transmission loss and frequency-doubled power analytically. Furthermore, communication power can be maximized in real-time by dynamically controlling the beam-splitting ratio between the two channels according to the varying loss levels encountered over air. Numerical results validate that the IRS-assisted channel can compensate for the losses in the obstructed LoS channel and misaligned receivers, ensuring that communication performance reaches an optimal value with dynamic ratio adjustments.

This paper presents a novel multi-stream downlink communication system that utilizes a transmissive reconfigurable intelligent surface (RIS) transceiver. Specifically, we elaborate the downlink communication scheme using time-modulated array (TMA) technology, which enables high order modulation and multi-stream beamforming. Then, an optimization problem is formulated to maximize the minimum signal-to-interference-plus-noise ratio (SINR) with user fairness, which takes into account the constraint of the maximum available power for each transmissive element. Due to the non-convex nature of the formulated problem, finding optimal solution is challenging. To mitigate the complexity, we propose a linear-complexity beamforming algorithm based on consensus alternating direction method of multipliers (ADMM). Specifically, by introducing a set of auxiliary variables, the problem can be decomposed into multiple sub-problems that are amenable to parallel computation, where the each sub-problem can yield closed-form expressions, bringing a significant reduction in the computational complexity. The overall problem achieves convergence by iteratively addressing these sub-problems in an alternating manner. Finally, the convergence of the proposed algorithm and the impact of various parameter configurations on the system performance are validated through numerical simulations.

In this paper, we investigate a secure communication architecture based on unmanned aerial vehicle (UAV), which enhances the security performance of the communication system through UAV trajectory optimization. We formulate a control problem of minimizing the UAV flight path and power consumption while maximizing secure communication rate over infinite horizon by jointly optimizing UAV trajectory, transmit beamforming vector, and artificial noise (AN) vector. Given the non-uniqueness of optimization objective and significant coupling of the optimization variables, the problem is a non-convex optimization problem which is difficult to solve directly. To address this complex issue, an alternating-iteration technique is employed to decouple the optimization variables. Specifically, the problem is divided into three subproblems, i.e., UAV trajectory, transmit beamforming vector, and AN vector, which are solved alternately. Additionally, considering the susceptibility of UAV trajectory to disturbances, the model predictive control (MPC) approach is applied to obtain UAV trajectory and enhance the system robustness. Numerical results demonstrate the superiority of the proposed optimization algorithm in maintaining accurate UAV trajectory and high secure communication rate compared with other benchmark schemes.

In this paper, we propose a transmissive reconfigurable intelligent surface (TRIS)-empowered distributed cooperative integrated sensing and communication (ISAC) network, which enhances the coverage and wireless environment understanding through the joint design of cooperative users (CUEs) and destination users (DUEs). Rate-splitting multiple access (RSMA) is implemented at the base station (BS), where the common stream is decoded and recoded by the CUEs and forwarded to the DUEs, while the private stream meets the CUEs’ own communication requirements. We construct an optimization problem with the objective of maximizing the minimum Radar mutual information (RMI), and jointly optimize the BS beamforming matrix, the CUE beamforming matrixs, common stream rate, and user scheduling vectors. To address the challenges of the nonconvex optimization problem, the consensus alternating direction multiplier framework (ADMM) is utilized to decouple the variables, and the subproblems are solved independently through iterative optimization until overall convergence is achieved. Numerical results validate the superiority of the proposed scheme in terms of improving communication sum-rate and RMI, and greatly reduce the algorithm complexity.

In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-enhanced robust and secure integrated sensing and communication (ISAC) network. A time-division sensing communication mechanism is designed for the scenario, which enables communication and sensing to share wireless resources. To address the interference management problem and hinder eavesdropping, we implement rate-splitting multiple access (RSMA), where the common stream is designed as a useful signal and an artificial noise (AN), while taking into account the imperfect channel state information and modeling the channel for the illegal users in a fine-grained manner as well as giving an upper bound on the error. We introduce the secrecy outage probability and construct an optimization problem with secrecy sum-rate as the objective functions to optimize the common stream beamforming matrix, the private stream beamforming matrix and the timeslot duration variable. Due to the coupling of the optimization variables and the infinity of the error set, the proposed problem is a nonconvex optimization problem that cannot be solved directly. In order to address the above challenges, the block coordinate descent (BCD)-based second-order cone programming (SOCP) algorithm is used to decouple the optimization variables and solving the problem. Specifically, the problem is decoupled into two subproblems concerning the common stream beamforming matrix, the private stream beamforming matrix, and the timeslot duration variable, which are solved by alternating optimization until convergence is reached. To solve the problem, S-procedure, Bernstein’s inequality and successive convex approximation (SCA) are employed to deal with the objective function and non-convex constraints. Numerical simulation results verify the superiority of the proposed scheme in improving the secrecy energy efficiency (SEE) and the Cramér-Rao boundary (CRB).

Different types of intelligent reflecting surfaces (IRS) are exploited for assisting wireless communications. The joint use of passive IRS (PIRS) and active IRS (AIRS) emerges as a promising solution owing to their complementary advantages. They can be integrated into a single hybrid active-passive IRS (HIRS) or deployed in a distributed manner, which poses challenges in determining the IRS element allocation and placement for rate maximization. In this paper, we investigate the capacity of an IRS-aided wireless communication system with both active and passive elements. Specifically, we consider three deployment schemes: 1) base station (BS)$\rightarrow $HIRS$\rightarrow $user (BHU); 2) BS$\rightarrow $AIRS$\rightarrow $PIRS$\rightarrow $user (BAPU); 3) BS$\rightarrow $PIRS$\rightarrow $AIRS$\rightarrow $user (BPAU). Under the line-of-sight channel model, we formulate a rate maximization problem via a joint optimization of the IRS element allocation and placement. We first derive the optimized number of active and passive elements for BHU, BAPU, and BPAU schemes, respectively. Then, low-complexity HIRS/AIRS placement strategies are provided. To obtain more insights, we characterize the system capacity scaling orders for the three schemes with respect to the large total number of IRS elements, amplification power budget, and BS transmit power. Finally, simulation results are presented to validate our theoretical findings and show the performance difference among the BHU, BAPU, and BPAU schemes with the proposed joint design under various system setups.

In this paper, we propose an efficient joint precoding design method to maximize the weighted sum-rate in wideband intelligent reflecting surface (IRS)-assisted cell-free networks by jointly optimizing the active beamforming of base stations and the passive beamforming of IRS. Due to employing wideband transmissions, the frequency selectivity of IRSs has to been taken into account, whose response usually follows a Lorentzian-like profile. To address the high-dimensional non-convex optimization problem, we employ a fractional programming approach to decouple the non-convex problem into subproblems for alternating optimization between active and passive beamforming. The active beamforming subproblem is addressed using the consensus alternating direction method of multipliers (CADMM) algorithm, while the passive beamforming subproblem is tackled using the accelerated projection gradient (APG) method and Flecher-Reeves conjugate gradient method (FRCG). Simulation results demonstrate that our proposed approach achieves significant improvements in weighted sum-rate under various performance metrics compared to primal-dual subgradient (PDS) with ideal reflection matrix. This study provides valuable insights for computational complexity reduction and network capacity enhancement.

Integrated sensing and communication (ISAC) systems may face a heavy computation burden since the sensory data needs to be further processed. This paper studies a novel system that integrates sensing, communication, and computation, aiming to provide services for different objectives efficiently. This system consists of a multi-antenna multi-functional base station (BS), an edge server, a target, and multiple single-antenna communication users. The BS needs to allocate the available resources to efficiently provide sensing, communication, and computation services. Due to the heavy service burden and limited power budget, the BS can partially offload the tasks to the nearby edge server instead of computing them locally. We consider the estimation of the target response matrix, a general problem in radar sensing, and utilize Cramér-Rao bound (CRB) as the corresponding performance metric. To tackle the non-convex optimization problem, we propose both semidefinite relaxation (SDR)-based alternating optimization and SDR-based successive convex approximation (SCA) algorithms to minimize the CRB of radar sensing while meeting the requirement of communication users and the need for task computing. Furthermore, we demonstrate that the optimal rank-one solutions of both the alternating and SCA algorithms can be directly obtained via the solver or further constructed even when dealing with multiple functionalities. Simulation results show that the proposed algorithms can provide higher target estimation performance than state-of-the-art benchmarks while satisfying the communication and computation constraints.

Movable antennas (MAs), which can be swiftly repositioned within a defined region, offer a promising solution to the limitations of fixed-position antennas (FPAs) in adapting to spatial variations in wireless channels, thereby improving channel conditions and communication between transceivers. However, frequent MA position adjustments based on instantaneous channel state information (CSI) incur high operational complexity, making real-time CSI acquisition impractical, especially in fast-fading channels. To address these challenges, we propose a two-timescale transmission framework for MA-enabled multiuser multiple-input-multiple-output (MU-MIMO) systems. In the large timescale, statistical CSI is exploited to optimize MA positions for long-term ergodic performance, whereas, in the small timescale, beamforming vectors are designed using instantaneous CSI to handle short-term channel fluctuations. Within this new framework, we analyze the ergodic sum rate and develop efficient MA position optimization algorithms for both maximum-ratio-transmission (MRT) and zero-forcing (ZF) beamforming schemes. These algorithms employ alternating optimization (AO), successive convex approximation (SCA), and majorization-minimization (MM) techniques, iteratively optimizing antenna positions and refining surrogate functions that approximate the ergodic sum rate. Numerical results show significant ergodic sum rate gains with the proposed two-timescale MA design over conventional FPA systems, particularly under moderate to strong line-of-sight (LoS) conditions. Notably, MA with ZF beamforming consistently outperforms MA with MRT, highlighting the synergy between beamforming and MAs for superior interference management in environments with moderate Rician factors and high user density, while MA with MRT can offer a simplified alternative to complex beamforming designs in strong LoS conditions.

This paper proposes a millimeter-wave (mmWave) multiple-input multiple-output (MIMO) transmission scheme termed spatial scattering shift keying (SSSK), which exploits spatial scattering modulation (SSM) to encode information through the indices of channel scatterers rather than conventional symbol constellations. The proposed SSSK achieves superior reliability compared to amplitude-phase modulation (APM) schemes, while simultaneously reducing hardware complexity. Specifically, the scatterer-index-based signaling mechanism mitigates the detection complexity inherent in APM systems by avoiding explicit symbol-level demodulation. In addition, we illustrate the advantages of SSSK by investigating the interaction between SSSK and fading channels. We derive closed-form expressions for the average bit error probability (ABEP) tight upper bound of the proposed scheme using two different approaches based on the greedy detection algorithm. To gain more insights, we further derive the asymptotic ABEP expression and diversity gain. To characterize the performance, we rigorously derive tight upper bounds on the ABEP using two complementary approaches: union bound and pairwise error probability analysis under a greedy detection framework. Furthermore, asymptotic ABEP expressions are established to reveal the achievable diversity gain. Moreover, we design maximum likelihood (ML) detectors with serial and parallel architectures and corresponding ABEP upper bounds. Simulations validate the analytical derivations and demonstrate SSSK outperforms APM in ABEP at high signal-to-noise ratios. The proposed greedy detector reduces computational complexity compared to the serial ML detector while maintaining comparable ABEP performance.

Decentralized federated learning (DFL) has the potential to address the issue of the aggregator’s single-point failure. However, in the absence of centralized coordination, DFL systems are vulnerable to malicious behaviors from clients. In this article, we propose a blockchain-based DFL framework to regulate the behaviors of enterprise clients in the context of cross-enterprise modeling. To be specific, we first design a novel mechanism for model plagiarism detection, wherein pseudonoise sequences are incorporated into local models, enabling to identify enterprises’ plagiarism behaviors. Then, we propose a model aggregation algorithm to improve the learning performance of the global model. Furthermore, we develop a plagiarism-aware proof-of-work consensus mechanism by adaptively adjusting enterprises’ mining difficulty based on their plagiarism records, which can efficiently demotivate them from engaging in plagiarism. The experimental results based on industrial datasets, including CWRU, PU, Milan, PV, NEU-CLS, and X-SDD, demonstrate that the proposed framework can achieve approximately 4%, 7%, and 12% of the learning accuracy improvement in the scenarios of 20%, 40%, and 60% plagiarism rates, respectively, compared to the conventional DFL system.

In ultra-dense autonomous aerial vehicle (AAV) networks, it is challenging to coordinate the resource allocation and interference management among large-scale AAVs, for providing flexible and efficient service coverage to the ground users (GUs). In this paper, we propose a learning-based resource allocation scheme in an ultra-dense AAV communication network, where the GUs’ service demands are time-varying with unknown distributions. We formulate the non-cooperative game among multiple co-channel AAVs as a stochastic game, where each AAV jointly optimizes its trajectory, user association, and downlink power control to maximize the expectation of its locally cumulative energy efficiency under the interference and energy constraints. To cope with the scalability issue in a large-scale network, we further formulate the problem as a mean-field game (MFG), which simplifies the interactions among the AAVs into a two-player game between a representative AAV and a mean-field. We prove the existence and uniqueness of the equilibrium for the MFG, and propose a model-free mean-field reinforcement learning algorithm named maximum entropy mean-field deep Q network (ME-MFDQN) to solve the mean-field equilibrium in both fully and partially observable scenarios. The simulation results reveal that the proposed algorithm improves the energy efficiency compared with the benchmark algorithms. Moreover, the performance can be further enhanced if the GUs’ service demands exhibit higher temporal correlation or if the AAVs have wider observation capabilities over their nearby GUs.

This paper studies a multiple intelligent reflecting surfaces (IRSs) collaborative localization system where multiple semi-passive IRSs are deployed in the network to locate one or more targets based on time-of-arrival. It is assumed that each semi-passive IRS is equipped with reflective elements and sensors, which are used to establish the line-of-sight links from the base station (BS) to multiple targets and process echo signals, respectively. Based on the above model, we derive the Fisher information matrix of the echo signal with respect to the time delay. By employing the chain rule and exploiting the geometric relationship between time delay and position, the Cramér-Rao bound (CRB) for estimating the target’s Cartesian coordinate position is derived. Then, we propose a two-stage algorithmic framework to minimize CRB in single- and multi-target localization systems by joint optimizing active beamforming at BS, passive beamforming at multiple IRSs and IRS selection. For the single-target case, we derive the optimal closed-form solution for multiple IRSs coefficients design and propose a low-complexity algorithm based on alternating direction method of multipliers to obtain the optimal solution for active beaming design. For the multi-target case, alternating optimization is used to transform the original problem into two subproblems where semi-definite relaxation and successive convex approximation are applied to tackle the quadraticity and indefiniteness in the CRB expression, respectively. Finally, numerical simulation results validate the effectiveness of the proposed algorithm for multiple IRSs collaborative localization system compared to other benchmark schemes as well as the significant performance gains.

Spatial scattering modulation (SSM), an emerging millimeter-wave (mmWave) multiple-input multiple-output (MI-MO) modulation technique, exploits spatial beam resources to enhance the modulation degree of freedom. However, to address the problem that the detection performance of existing scalarbased maximum likelihood (SML) detector is not optimal and the complexity is too high, this paper develops vector-based maximum likelihood (VML) and low-complexity (LC) detectors for the structural characteristics of SSM system receivers, respectively. Then, we give corresponding analysis for the complexity of each of the three detectors. Based on the SML, VML, and LC detectors, we derive the union upper bound of average bit error probability (ABEP) for the SSM scheme, respectively. Monte Carlo simulations validate the correctness of the analytical derivation and show that when ABEP = 10–5, the signal-to-noise ratio (SNR) required for the VML-based ABEP values is 5.5 dB less than that obtained from SML detection. Moreover, compared with SML, the detection complexity of the proposed LC algorithm is reduced by about 50% and the transmit SNR also saves 1 dB SNR. Furthermore, when the number of scatterers is higher, the ABEP performance advantage of the SSM system is more fully unlocked.

Free space optical (FSO) transmission has emerged as a key candidate technology for 6G to expand new spectrum and improve network capacity due to its advantages of large bandwidth, low-electromagnetic interference, and high-energy efficiency. Resonant beam operating in the infrared band utilizes spatially separated laser cavities to enable safe and mobile high-power energy and high-rate information transmission but is limited by Line-of-Sight (LoS) channel. In this article, we propose a reconfigurable intelligent surface (RIS) assisted resonant beam simultaneous wireless information and power transfer (SWIPT) system and establish an optical field propagation model to analyze the channel state information (CSI), in which LoS obstruction can be detected sensitively and non line-of-sight (NLoS) transmission can be realized by changing the phased of resonant beam in RIS. Numerical results demonstrate that, apart from the transmission distance, the NLoS performance depends on both the horizontal and vertical positions of RIS. The maximum NLoS energy efficiency can achieve 55% within a transfer distance of 10 m, a translation distance of ±4 mm, and rotation angle of ±50°.

In this article, we propose a novel uplink communication system enabled by a transmissive reconfigurable intelligent surface (RIS) transceiver, where orthogonal frequency division multiple access (OFDMA) is applied to multiple users. Specifically, we explore a novel receiver architecture that includes a transmissive RIS and a single horn antenna for reception. Additionally, a channel model based on both planar and spherical waves is developed, accounting for far-field and near-field effects. To achieve the maximum system sum-rate of uplink communications while adhering to Quality-of-Service (QoS) constraints, we propose a joint optimization problem that optimizes power allocation, subcarrier allocation, and transmissive RIS coefficient. However, this problem is nonconvex in view of the strong interdependence among the optimization variables, posing significant challenges for direct solution. Thus, the alternating optimization (AO) algorithm architecture is employed, which decouples optimization variables and divide the problem into two subproblems. The first subproblem focuses on jointly optimizing power allocation and subcarrier allocation, and it is addressed by utilizing the Lagrangian dual decomposition method. Meanwhile, concerning the design of the transmissive RIS coefficient, the second subproblem is tackled by means of the successive convex approximation (SCA) approach. Subsequently, these two subproblems are solved in an alternating manner until the convergence criterion is met. Finally, the numerical results indicate that the proposed algorithm exhibits excellent convergence performance and effectively enhances the system sum-rate compared to other benchmark algorithms.

Federated learning (FL) based on the centralized design faces both challenges regarding the trust issue and a single point of failure. To alleviate these issues, blockchain-aided decentralized FL (BDFL) introduces the decentralized network architecture into the FL training process, which can effectively overcome the defects of centralized architecture. However, deploying BDFL in wireless networks usually encounters challenges, such as limited bandwidth, computing power, and energy consumption. Driven by these considerations, a dynamic stochastic optimization problem is formulated to minimize the average training delay by jointly optimizing the resource allocation and client selection under the constraints of limited energy budget and client participation. We solve the long-term mixed integer nonlinear programming problem by employing the tool of Lyapunov optimization and thereby propose the dynamic resource allocation and client scheduling BDFL (DRC-BDFL) algorithm. Furthermore, we analyse the learning performance of DRC-BDFL and derive an upper bound for convergence regarding the global loss function. Extensive experiments conducted on the SVHN and CIFAR-10 data sets demonstrate that the DRC-BDFL achieves comparable accuracy to the baseline algorithms while significantly reducing the training delay by 9.24% and 12.47%, respectively.

In this paper, a novel transmissive reconfigurable intelligent surface (TRIS) transceiver empowered integrated sensing and communications (ISAC) system is proposed for future multi-demand terminals. To address interference management, we implement rate-splitting multiple access (RSMA), where the common stream is independently designed for the sensing service. We introduce the sensing quality of service (QoS) criteria based on this structure and construct an optimization problem with the sensing QoS criteria as the objective function to optimize the sensing stream precoding matrix and the communication stream precoding matrix. Due to the coupling of optimization variables, the formulated problem is a non-convex optimization problem that cannot be solved directly. To tackle the above-mentioned challenging problem, alternating optimization (AO) is utilized to decouple the optimization variables. Specifically, the problem is decoupled into three subproblems about the sensing stream precoding matrix, the communication stream precoding matrix, and the auxiliary variables, which is solved alternatively through AO until the convergence is reached. For solving the problem, successive convex approximation (SCA) is applied to deal with the sum-rate threshold constraints on communications, and difference-of-convex (DC) programming is utilized to solve rank-one non-convex constraints. Numerical simulation results verify the superiority of the proposed scheme in terms of improving the communication and sensing QoS.

This article investigates semantic communication in high-speed mobile Internet of Vehicles (IoV), focusing on spectrum sharing between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. We propose a semantic-aware spectrum-sharing (SSS) algorithm using deep reinforcement learning (DRL) with a soft actor-critic (SAC) approach. We start with semantic information extraction, redefining metrics for V2V and V2I spectrum sharing in IoV environments, introducing high-speed semantic spectrum efficiency (HSSE) and semantic transmission rate (HSR). We then apply the SAC algorithm to optimize decisions V2V and V2I spectrum-sharing decisions on semantic information. This optimization aims to maximize HSSE and enhance the success rate of effective semantic information transmission (SRS), including determining the optimal V2V and V2I sharing strategies, transmission power, and the length of transmitted semantic symbols. Experimental results show that the SSS algorithm outperforms other baseline algorithms, including other traditional-communication-based spectrum-sharing algorithms and spectrum-sharing algorithm using other reinforcement learning approaches. The SSS algorithm exhibits a 15% increase in HSSE and approximately a 7% increase in SRS.

In this article, we investigate the performance of reconfigurable intelligent surface (RIS)-aided spatial shift keying (SSK) wireless communication systems with imperfect channel state information (CSI). Specifically, we study the average bit error probability (ABEP) of two RIS-SSK systems based on intelligent reflection and blind reflection modes. For the intelligent RIS-SSK scheme, we first derive the conditional pairwise error probability of the composite channel through maximum-likelihood (ML) detection. Subsequently, we derive the probability density function of the combined channel. Due to the intricacies of the composite channel formulation, an exact closed-form ABEP expression is unattainable through direct derivation. To this end, we resort to employing the Gaussian–Chebyshev quadrature method to estimate the results. Additionally, we employ$Q$-function approximation to derive the nonexact closed-form expression in the presence of channel estimation errors. For the blind RIS-SSK scheme, we derive both closed-form ABEP expression and asymptotic ABEP expression with imperfect CSI by adopting the ML detector. To offer deeper insights, we explore the impact of discrete reflection phase shifts on the performance of the RIS-SSK system. Finally, we extensively validate all the analytical derivations via Monte Carlo simulations.

Autonomous driving and intelligent transportation applications have dramatically increased the demand for high-accuracy and low-latency localization services. While cellular networks are potentially capable of target detection and localization, achieving accurate and reliable positioning faces critical challenges. Particularly, the relatively small radar cross sections (RCS) of moving targets and the high complexity for measurement association give rise to weak echo signals and discrepancies in the measurements. To tackle this issue, we propose a novel approach for multi-target localization by leveraging the controllable signal reflection capabilities of intelligent reflecting surfaces (IRSs). Specifically, IRSs are strategically mounted on the targets (e.g., vehicles and robots), enabling effective association of multiple measurements and facilitating the localization process. We aim to minimize the maximum Cramér-Rao lower bound (CRLB) of targets by jointly optimizing the target association, the IRS phase shifts, and the dwell time. However, solving this CRLB optimization problem is non-trivial due to the non-convex objective function and closely coupled variables. For single-target localization, a simplified closed-form expression is presented for the case where base stations (BSs) can be deployed flexibly, and the optimal BS location is derived to provide a lower performance bound of the original problem. Then, we prove that the transformed problem is a monotonic optimization, which can be optimally solved by the Polyblock-based algorithm. Moreover, based on derived insights for the single-target case, we propose a heuristic algorithm to optimize the target association and time allocation for the multi-target case. Furthermore, we provide useful guidance for the practical implementation of the proposed localization scheme by theoretically analyzing the relationship between time slots, BSs, and targets. Simulation results verify that deploying IRS on vehicles and effective phase shift design can effectively improve the resolution ability of multi-vehicle positioning and reduce the requirements of the number of BSs.

Intelligent reflecting surface (IRS) has garnered growing interest and attention due to its potential for facilitating and supporting wireless communications and sensing. This paper studies a semi-passive IRS-enabled sensing system, where an IRS consists of both passive reflecting elements and active sensors. Our goal is to minimize the Cramér-Rao bound (CRB) for parameter estimation under both point and extended target cases. Towards this goal, we begin by deriving the CRB for the direction-of-arrival (DoA) estimation in closed-form and then theoretically analyze the IRS reflecting elements and sensors allocation design based on the CRB under the point target case with a single-antenna base station (BS). To efficiently solve the corresponding optimization problem for the case with a multi-antenna BS, we propose an efficient algorithm by jointly optimizing the IRS phase shifts and the BS beamformers. Under the extended target case, the CRB for the target response matrix (TRM) estimation is minimized via the optimization of the BS transmit beamformers. Moreover, we explore the influence of various system parameters on the CRB and compare these effects to those observed under the point target case. Simulation results show the effectiveness of the semi-passive IRS and our proposed beamforming design for improving the performance of the sensing system.

Compared with traditional half-duplex wireless systems, the application of emerging full-duplex (FD) technology can potentially double the system capacity theoretically. However, conventional techniques for suppressing self-interference (SI) adopted in FD systems require exceedingly high power consumption and expensive hardware. In this paper, we consider employing an intelligent reflecting surface (IRS) in the proximity of an FD base station (BS) to mitigate SI for simultaneously receiving data from uplink users and transmitting information to downlink users. The objective considered is to maximize the system weighted sum-rate by jointly optimizing the IRS phase shifts, the BS transmit beamformers, and the transmit power of the uplink users. To visualize the role of the IRS in SI cancellation, we first study a simple scenario with one downlink user and one uplink user. To address the formulated non-convex problem, a low-complexity algorithm based on successive convex approximation is proposed. For the more general case considering multiple downlink and uplink users, an efficient alternating optimization algorithm based on element-wise optimization is proposed. Numerical results demonstrate that the FD system with the proposed schemes can achieve a larger gain over the half-duplex system, and the IRS is able to achieve a balance between suppressing SI and providing beamforming gain.

This paper studies the fair transmission design for an intelligent reflecting surface (IRS) aided rate-splitting multiple access (RSMA). IRS is used to establish a good signal propagation environment and enhance the RSMA transmission performance. The fair rate adaption problem is constructed as a max-min optimization problem. To solve the optimization problem, we adopt an alternative optimization (AO) algorithm to optimize the power allocation, beamforming, and decoding order, respectively. A generalized power iteration (GPI) method is proposed to optimize the receive beamforming, which can improve the minimum rate of devices and reduce the optimization complexity. At the base station (BS), a successive group decoding (SGD) algorithm is proposed to tackle the uplink signal estimation, which trades off the fairness and complexity of decoding. At the same time, we also consider robust communication with imperfect channel state information at the transmitter (CSIT), which studies robust optimization by using lower bound expressions on the expected data rates. Extensive numerical results show that the proposed optimization algorithm can significantly improve the performance of fairness. It also provides reliable results for uplink communication with imperfect CSIT.

Drawing inspiration from the advantages of intelligent reflecting surfaces (IRS) in wireless networks, this paper presents a novel design for intelligent omni surface (IOS) enabled integrated sensing and communications (ISAC). By harnessing the power of multi-antennas and a multitude of elements, the dual-function base station (BS) and IOS collaborate to realize joint active and passive beamforming, enabling seamless 360-degree ISAC coverage. The objective is to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of multi-target sensing while ensuring the multi-user multi-stream communications. To achieve this, a comprehensive optimization approach is employed, encompassing the design of radar receive vector, transmit beamforming matrix, and IOS transmissive and reflective coefficients. Due to the non-convex nature of the formulated problem, an auxiliary variable is introduced to transform it into a more tractable form. Consequently, the problem is decomposed into three sub-problems based on the block coordinate descent algorithm. Semidefinite relaxation and successive convex approximation methods are leveraged to convert the sub-problem into a convex problem, while the iterative rank minimization algorithm and penalty function method ensure the equivalence. Furthermore, the scenario is extended to mode switching and time switching protocols. Simulation results validate the convergence and superior performance of the proposed algorithm compared to other benchmark algorithms.

Reconfigurable intelligent surface (RIS)-assisted index modulation system schemes are considered to be a promising technology for sixth-generation (6G) wireless communication systems, which can enhance various system capabilities such as coverage and reliability. However, obtaining perfect channel state information (CSI) is challenging due to the lack of a radio frequency chain in RIS. In this paper, we investigate the RIS-assisted full-duplex (FD) two-way space shift keying (SSK) system under imperfect CSI, where the signal emissions are augmented by deploying RISs in the vicinity of two FD users. The maximum likelihood detector is utilized to recover the transmit antenna index. With this in mind, we derive closed-form average bit error probability (ABEP) expression based on the Gaussian-Chebyshev quadrature (GCQ) method, and provide the upper bound and asymptotic ABEP expressions in the presence of channel estimation errors. To gain more insights, we also derive the outage probability and provide the throughput of the proposed scheme with imperfect CSI. The correctness of the analytical derivation results is confirmed via Monte Carlo simulations. It is demonstrated that increasing the number of elements of RIS can significantly improve the ABEP performance of the FD system over the half-duplex (HD) system. Furthermore, in the high SNR region, the ABEP performance of the FD system is better than that of the HD system.

In decentralized federated learning (DFL), clients exchange their models with each other for global aggregation. Due to a lack of centralized supervision, a client may easily duplicate shared models to save its computing resources. Generally, this plagiarism behavior is hard to detect, while it is harmful to model training performance. To address this issue, we propose an anti-plagiarism DFL framework to efficiently detect plagiarism misconduct. Specifically, we first design a method for detecting plagiarism by adding a time-shift pseudo-noise (PN) sequence to each client's local model before broadcasting. Second, we develop an upper bound of the loss function of DFL with the proposed PN sequence detection method, which is proved to be the convex function of both the amplitude of PN sequences ($\alpha$) and the detection threshold ($\lambda$). Next, we propose an adaptive plagiarism detection (APD) algorithm by jointly optimizing$\alpha$and$\lambda$to enhance the learning performance. Finally, we conduct extensive experiments on MNIST, Adult, Cifar-10, and SVHN datasets to demonstrate that our analytical bounds are consistent with the experimental results. Remarkably, the proposed framework can recover up to a 10% classification accuracy loss in the presence of 40% plagiaristic clients.

Intelligent reflecting surfaces (IRSs) have recently attained growing popularity in wireless networks owning to their capability to customize the wireless channel via smartly configured passive reflections. In addition to optimizing IRS reflection patterns, the flexible deployment of IRSs offers another design degree of freedom (DoF) to reconfigure the wireless propagation environment in favour of signal transmission. To unveil the impact of IRS deployment on the system capacity, we investigate the capacity of a broadcast channel with a multi-antenna base station (BS) sending independent messages to multiple users, aided by IRSs with N elements. In particular, both the distributed and centralized IRS deployment architectures are considered. Regarding the distributed IRS, the N IRS elements form multiple IRSs and each of them is installed near a user cluster; while for the centralized IRS, all IRS elements are located in the vicinity of the BS. To draw essential insights, we first derive the maximum capacity achieved by the distributed IRS and centralized IRS, respectively, under the assumption of line-of-sight (LoS) propagation and homogeneous channel setups. By carefully capturing the fundamental tradeoff between the spatial multiplexing gain and passive beamforming gain, we rigourously prove that the capacity of the distributed IRS is higher than that of the centralized IRS provided that the total number of IRS elements is above a threshold. Motivated by the superiority of the distributed IRS, we then focus on the transmission and element allocation design under the distributed IRS. By exploiting the user channel correlation of intra-clusters and inter-clusters, an efficient hybrid multiple access scheme relying on both spatial and time domains is proposed to fully exploit both the passive beamforming gain and spatial DoF. Moreover, the IRS element allocation problem is investigated for the objectives of the sum-rate maximization and the minimum user rate maximization, respectively. Finally, extensive numerical results are provided to validate our theoretical finding and also to unveil the effectiveness of the distributed IRS for improving the system capacity under various system setups.

Movable antenna (MA) is an emerging technology that utilizes localized antenna movement to achieve better channel conditions for enhancing communication performance. In this paper, we study the MA-enhanced multicast transmission from a base station equipped with multiple MAs to multiple groups of single-MA users. Our goal is to maximize the minimum weighted signal-to-interference-plus-noise ratio (SINR) among all the users by jointly optimizing the position of each transmit/receive MA and the transmit beamforming. To tackle this challenging problem, we first consider the single-group scenario and propose an efficient algorithm based on the techniques of alternating optimization and successive convex approximation. Particularly, when optimizing transmit or receive MA positions, we construct a concave lower bound for the signal-to-noise ratio (SNR) of each user using only the second-order Taylor expansion, which simplifies the problem-solving process compared to the existing two-step approximation method. The proposed design is then extended to the general multi-group scenario. Simulation results show that the proposed algorithm converges faster than the existing two-step approximation method, achieving a 3.4% enhancement in max-min SNR. Moreover, it can improve the max-min SNR/SINR by up to 22.5%, 181.7%, and 343.9% compared to benchmarks employing only receive MAs, only transmit MAs, and both transmit and receive FPAs, respectively.

This paper studies an intelligent reflecting surface (IRS)-aided multi-antenna simultaneous wireless information and power transfer (SWIPT) system where anM-antenna access point (AP) servesKsingle-antenna information users (IUs) andJsingle-antenna energy users (EUs) with the aid of an IRS with phase errors. We explicitly concentrate on overloaded scenarios whereK+J>MandK≥M. Our goal is to maximize the minimum throughput among all the IUs by optimizing the allocation of resources (including time, transmit beamforming at the AP, and reflect beamforming at the IRS), while guaranteeing the minimum amount of harvested energy at each EU. Towards this goal, we propose two user grouping (UG) schemes, namely, the non-overlapping UG scheme and the overlapping UG scheme, where the difference lies in whether identical IUs can exist in multiple groups. Different IU groups are served in orthogonal time dimensions, while the IUs in the same group are served simultaneously with all the EUs via spatial multiplexing. The two problems corresponding to the two UG schemes are mixed-integer non-convex optimization problems and difficult to solve optimally. We first provide a method to check the feasibility of these two problems, and then propose efficient algorithms for them based on the big-M formulation, the penalty method, the block coordinate descent, and the successive convex approximation. Simulation results show that: 1) the non-robust counterparts of the proposed robust designs are unsuitable for practical IRS-aided SWIPT systems with phase errors since the energy harvesting constraints cannot be satisfied; 2) the proposed UG strategies can significantly improve the max-min throughput over the benchmark schemes without UG or adopting random UG; 3) the overlapping UG scheme performs much better than its non-overlapping counterpart when the absolute difference betweenKandMis small and the EH constraints are not stringent.

This paper considers intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) in a multi-user multiple-input single-output (MISO) interference channel (IFC), where multiple transmitters (Txs) serve their corresponding receivers (Rxs) in a shared spectrum with the aid of IRSs. Our goal is to maximize the sum rate of the Rxs by jointly optimizing the transmit covariance matrices at the Txs, the phase shifts at the IRSs, and the resource allocation subject to the individual energy harvesting (EH) constraints at the Rxs. Towards this goal and based on the well-known power splitting (PS) and time switching (TS) receiver structures, we consider three practical transmission schemes, namely the IRS-aided hybrid TS-PS scheme, the IRS-aided time-division multiple access (TDMA) scheme, and the IRS-aided TDMA-D scheme. The latter two schemes differ in whether the Txs employ deterministic energy signals known to all the Rxs. Despite the non-convexity of the three optimization problems corresponding to the three transmission schemes, we develop computationally efficient algorithms to address them suboptimally, respectively, by capitalizing on the techniques of alternating optimization (AO) and successive convex approximation (SCA). Moreover, we conceive feasibility checking methods for these problems, based on which the initial points for the proposed algorithms are constructed. Simulation results demonstrate that our proposed IRS-aided schemes significantly outperform their counterparts without IRSs in terms of sum rate and maximum EH requirements that can be satisfied under various setups. In addition, the IRS-aided hybrid TS-PS scheme generally achieves the best sum rate performance among the three proposed IRS-aided schemes, and if not, increasing the number of IRS elements can always accomplish it.

With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely considered for application in wireless networks for distributed model training. However, data heterogeneity, e.g., non-independently identically distributions and different sizes of training datasets among clients, poses major challenges to wireless FL. Limited communication resources complicate the implementation of fair scheduling which is required for training on heterogeneous data, and further deteriorate the overall performance. To address this issue, this paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. Specifically, we first develop a closed-form expression for an upper bound on the FL loss function, with a particular emphasis on data heterogeneity described by a dataset size vector and a data divergence vector. Then we formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, uplink transmission power, channel allocation and the number of local epochs. Next, via the Lyapunov drift technique, we transform the optimization problem into a series of tractable problems. Extensive experiments on real-world datasets demonstrate that our method outperforms other benchmarks in terms of the learning accuracy and energy consumption.

In this paper, an intelligent reflecting surface (IRS) is leveraged to enhance the physical layer security of an integrated sensing and communication (ISAC) system in which the IRS is deployed to not only assist the downlink communication for multiple users, but also create a virtual line-of-sight (LoS) link for target sensing. In particular, we consider a challenging scenario where the target may be a suspicious eavesdropper that potentially intercepts the communication-user information transmitted by the base station (BS). To ensure the sensing quality while preventing the eavesdropping, dedicated sensing signals are transmitted by the BS. We investigate the joint design of the phase shifts at the IRS and the communication as well as radar beamformers at the BS to maximize the sensing beampattern gain towards the target, subject to the maximum information leakage to the eavesdropping target and the minimum signal-to-interference-plus-noise ratio (SINR) required by users. Based on the availability of perfect channel state information (CSI) of all involved user links and the potential target location of interest at the BS, two scenarios are considered and two different optimization algorithms are proposed. For the ideal scenario where the CSI of the user links and the potential target location are perfectly known at the BS, a penalty-based algorithm is proposed to obtain a high-quality solution. In particular, the beamformers are obtained with a semi-closed-form solution using Lagrange duality and the IRS phase shifts are solved for in closed form by applying the majorization-minimization (MM) method. On the other hand, for the more practical scenario where the CSI is imperfect and the potential target location is uncertain in a region of interest, a robust algorithm based on the$\cal S$-procedure and sign-definiteness approaches is proposed. Simulation results demonstrate the effectiveness of the proposed scheme in achieving a trade-off between the communication quality and the sensing quality, and also show the tremendous potential of IRS for use in sensing and improving the security of ISAC systems.

This paper studies a communication-centric integrated sensing and communication (ISAC) system, where a multi-antenna base station (BS) simultaneously performs downlink communication and target detection. A novel target detection and information transmission protocol is proposed, where the BS executes the channel estimation and beamforming successively and meanwhile jointly exploits the pilot sequences in the channel estimation stage and user information in the transmission stage to assist target detection. We investigate the joint design of the pilot matrix, training duration, and transmit beamforming to maximize the probability of target detection, subject to the minimum achievable rate required by the user. However, designing the optimal pilot matrix is rather challenging since there is no closed-form expression of the detection probability with respect to the pilot matrix. To tackle this difficulty, we resort to designing the pilot matrix based on the information-theoretic criterion to maximize the mutual information (MI) between the received observations and BS-target channel coefficients for target detection. We first derive the optimal pilot matrix for both channel estimation and target detection, and then propose a unified pilot matrix structure to balance minimizing the channel estimation error (MSE) and maximizing MI. Based on the proposed structure, a low-complexity successive refinement algorithm is proposed. In addition, we rigorously analyze the impact of pilot length and pilot matrix on two fundamental tradeoffs, namely MSE-MI and Rate-MI. Simulation results demonstrate that the proposed pilot matrix structure can well balance the MSE-MI and the Rate-MI tradeoffs, and show the significant region improvement of our proposed design as compared to other benchmark schemes. Furthermore, it is unveiled that as the communication channel is more spatially correlated, the Rate-MI region can be further enlarged.

Multiple input multiple output (MIMO) beamforming has been recognized as a key element to support challenging requirements in the vehicular communication environment. In order to provide accurate beam alignment and tracking results in the high mobility scenario, the mobility induced channel prediction mechanism has been proposed in the line-of-sight (LOS) fading environment, while the application to the practical mixed LOS and non-LOS fading environment is still open. In this paper, we propose a novel mobility and channel prediction combined beamforming (MCPCB) scheme to deal with this issue. Specifically, we rely on per-cluster angular based information and non-linear tracking scheme for angular-delay profile to obtain reliable single input multiple output (SIMO) channel prediction. By linking the estimated mobility parameters and the per-cluster beam directions, our proposed MCPCB scheme is able to provide a higher receiving energy with reduced prediction errors, and achieve more robust prediction performance when the cluster level channel blocking happens. Through analytical and numerical results, we show that the proposed MCPCB scheme can achieve about −50.6 dB and −87.7 dB of average received power gain in the LOS and NLOS scenarios, respectively, and outperform many conventional channel prediction methods.

Federated learning (FL) is a promising distributed learning framework where distributed clients collaboratively train a machine learning model coordinated by a server. To tackle the stragglers issue in asynchronous FL, we consider that each client keeps local updates and probabilistically transmits the local model to the server at arbitrary times. We first derive the (approximate) expression for the convergence rate based on the probabilistic client selection. Then, an optimization problem is formulated to trade off the convergence rate of asynchronous FL and mobile energy consumption by joint probabilistic client selection and bandwidth allocation. We develop an iterative algorithm to solve the non-convex problem globally optimally. Experiments demonstrate the superiority of the proposed approach compared with the traditional schemes.

In an intelligent reflecting surface (IRS)-assisted wireless communication system, obtaining the real-time channel state information (CSI) through channel estimation (CE) is crucial for achieving the IRS’s passive beamforming gain, which however shortens the effective data transmission time due to the CSI feedback overhead. It is of utmost importance to decide how often to estimate the channels in an IRS-assisted system. In this paper, we propose an integrated CE and beamforming scheme to jointly optimize the adaptive CE interval and passive beamforming strategy, based on the past observation sequences composed of imperfect CSI and data rate feedback. We formulate the two-stage optimization problem as a bilevel partially observable Markov decision process (POMDP), aiming to maximize the expectation of cumulative throughput of the system. We propose two bilevel deep reinforcement learning (DRL) algorithms, namely recurrent neural network (RNN) based proximal policy optimization (PPO) algorithm and Belief-based PPO algorithm, to solve this problem. In these two algorithms, the CSI features from the past observation sequences are implicitly extracted by the RNN network or explicitly inferred by the belief network, which then serve as the inputs for the two-stage policy networks to determine the necessity of CE and the IRS beamforming vector based on the PPO algorithm. Simulation results demonstrate the superiority of the proposed adaptive CE scheme over the periodic counterpart in terms of throughput. Moreover, the results show that it is profitable to estimate the channels less frequently if the channels exhibit a higher correlation across time.

In this paper, we investigate a practical structure of reconfigurable intelligent surface (RIS)-based double spatial scattering modulation (DSSM) for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. A suboptimal detector is proposed, in which the beam direction is first demodulated according to the received beam strength, and then the remaining information is demodulated by adopting the maximum likelihood algorithm. Based on the proposed suboptimal detector, we derive the conditional pairwise error probability expression. Further, the exact numerical integral and closed-form expressions of unconditional pairwise error probability (UPEP) are derived via two different approaches. To provide more insights, we derive the upper bound and asymptotic expressions of UPEP. In addition, the diversity gain of the RIS-DSSM scheme was also given. Furthermore, the union upper bound of average bit error probability (ABEP) is obtained by combining the UPEP and the number of error bits. Simulation results are provided to validate the derived upper bound and asymptotic expressions of ABEP. We found an interesting phenomenon that the ABEP performance of the proposed system-based phase shift keying is better than that of the quadrature amplitude modulation. Additionally, the performance advantage of ABEP is more significant with the increase in the number of RIS elements.

In this study, we explore the performance of a reconfigurable reflecting surface (RIS)-assisted transmit spatial modulation (SM) system for downlink transmission, wherein the deployment of RIS serves the purpose of blind area coverage within the channel. At the receiving end, we present three detectors, i.e., maximum likelihood (ML) detector, two-stage ML detection, and greedy detector to recover the transmitted signal. By utilizing the ML detector, we initially derive the conditional pair error probability expression for the proposed scheme. Subsequently, we leverage the central limit theorem (CLT) to obtain the probability density function of the combined channel. Following this, the Gaussian-Chebyshev quadrature method is applied to derive a closed-form expression for the unconditional pair error probability and establish the union tight upper bound for the average bit error probability (ABEP). Furthermore, we derive a closed-form expression for the ergodic capacity of the proposed RIS-SM scheme. Monte Carlo simulations are conducted not only to assess the complexity and reliability of the three detection algorithms but also to validate the results obtained through theoretical derivation results.

This paper investigates an intelligent reflecting surface (IRS) to enhance the physical layer security of an integrated sensing and communication (ISAC) system in which the IRS is deployed to not only assist the downlink communication for multiple users, but also create a virtual line-of-sight (LoS) link for target sensing. In particular, we consider a challenging scenario where the target may be a suspicious eavesdropper that potentially intercepts the communication-user information transmitted by the base station (BS). To ensure the sensing quality while preventing the eavesdropping, dedicated sensing signals are transmitted by the BS. We investigate the joint design of the phase shifts at the IRS and the communication as well as radar beamformers at the BS to maximize the sensing beampattern gain towards the target, subject to the maximum information leakage to the eavesdropping target and the minimum signal-to-interference-plus-noise ratio (SINR) required by users. To solve this non-convex optimization problem, a penalty-based algorithm is proposed to obtain a high-quality solution. In particular, the beamformers are obtained with a semi-closed-form solution using Lagrange duality and the IRS phase shifts are solved for in closed form by applying the majorization-minimization (MM) method. Simulation results show that dedicated sensing signals are required to further improve the system performance, and also validate the tremendous potential of IRS to achieve significant beampattern gains and guarantee ISAC system security.

In this paper, we propose a rate-splitting multiple access (RSMA) scheme for uplink wireless communication systems with intelligent reflecting surface (IRS) aided. In the considered model, IRS is adopted to overcome power attenuation caused by path loss. We construct a max-min fairness optimization problem to obtain the resource allocation, including the receive beam-forming at the base station (BS) and phase-shift beamforming at IRS. We also introduce a successive group decoding (SGD) algorithm at the receiver, which trades off the fairness and complexity of decoding. In the simulation, the results show that the proposed scheme has superiority in improving the fairness of uplink communication.

In this paper, we investigate a state-of-the-art reconfigurable intelligent surface (RIS)-assisted spatial scattering modulation (SSM) scheme for millimeter-wave (mmWave) systems, where a more practical scenario that the RIS is near the transmitter while the receiver is far from RIS is considered. To this end, the line-of-sight (LoS) and non-LoS links are utilized in the transmitter-RIS and RIS-receiver channels, respectively. By employing the maximum likelihood detector at the receiver, the conditional pairwise error probability (CPEP) expression for the RIS-SSM scheme is derived under the two scenarios that the received beam demodulation is correct or not. Furthermore, the union upper bound of average bit error probability (ABEP) is obtained based on the CPEP expression. Finally, the derivation results are exhaustively validated by the Monte Carlo simulations.

Vehicle-to-everything (V2X) communication is expected to support many promising applications in next-generation wireless networks. The recent development of integrated sensing and communications (ISAC) technology offers new opportunities to meet the stringent sensing and communication (S&C) requirements in V2X networks. However, considering the relatively small radar cross section (RCS) of the vehicles and the limited transmit power of the road site units (RSUs), the power of echoes may be too weak to achieve effective target detection and tracking. To handle this issue, we propose a novel sensing-assisted communication scheme by employing an intelligent omni-surface (IOS) on the surface of the vehicle. First, a two-phase ISAC protocol, including the S&C phase and the communication-only phase, was presented to maximize the throughput by jointly optimizing the IOS phase shifts and the sensing duration. Then, we derive a closed-form expression of the achievable rate which achieves a good approximation. Furthermore, a sufficient and necessary condition for the existence of the S&C phase is derived to provide useful insights for practical system design. Simulation results demonstrate the effectiveness of the proposed sensing-assisted communication scheme in achieving a high throughput with low transmit power requirements.

With the rapid development of the Internet of Things (IoT), there are a dramatically increasing number of devices, leading to the fact that only using terrestrial infrastructure can hardly provide high-quality services to all devices. Due to their flexibility, maneuverability, and economy, unmanned aerial vehicles (UAVs) are widely used to improve the performance of IoT networks. UAVs can not only provide wireless access to IoT devices in the absence of a terrestrial network but can also perform rich IoT services and applications such as video surveillance, cargo transportation, pesticide spraying, and so forth. However, due to the high complexity, dynamics, and heterogeneity of the UAV-assisted IoT networks, growing attention has focused on using artificial intelligence (AI)-based methods to optimize, schedule, and orchestrate UAV-assisted IoT networks. In this article, we comprehensively analyze the impact of applying advanced AI architectures, models, and methods to different aspects of UAV-assisted IoT networks, including key IoT technologies, tasks, and applications. In addition, this article also explores challenges and discusses potential research directions of AI-enabled UAV-assisted IoT networks.

In this article, we propose a state-of-the-art downlink communication transceiver design for transmissive reconfigurable metasurface (RMS)-enabled simultaneous wireless information and power transfer (SWIPT) networks. Specifically, a feed antenna is deployed in the transmissive RMS-based transceiver, which can be used to implement beamforming. According to the relationship between wavelength and propagation distance, the spatial propagation models of plane and spherical waves are built. Then, in the case of imperfect channel state information (CSI), we formulate a robust system sum-rate maximization problem that jointly optimizes RMS transmissive coefficient, transmit power allocation, and power splitting ratio design while taking account of the nonlinear energy harvesting model and outage probability criterion. Since the coupling of optimization variables, the whole optimization problem is nonconvex and cannot be solved directly. Therefore, the alternating optimization (AO) framework is implemented to decompose the nonconvex original problem. In detail, the whole problem is divided into three subproblems to solve. For the nonconvexity of the objective function, successive convex approximation (SCA) is used to transform it, and the penalty function method and difference-of-convex (DC) programming are applied to deal with the nonconvex constraints. Finally, we alternately solve the three subproblems until the entire optimization problem converges. Numerical results show that our proposed algorithm has convergence and better performance than other benchmark algorithms.

Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed intelligence of industrial automation thanks to its capability of distributed machine learning without any raw data exchange. However, it is rather challenging for lightweight IIoT devices to perform computation-intensive local model training over large-scale deep neural networks (DNNs). Driven by this issue, we develop a communication-computation efficient FL framework for resource-limited IIoT networks that integrates DNN partition technique into the standard FL mechanism, wherein IIoT devices perform local model training over the bottom layers of the objective DNN, and offload the top layers to the edge gateway side. Considering imbalanced data distribution, we derive the device-specific participation rate to involve the devices with better data distribution in more communication rounds. Upon deriving the device-specific participation rate, we propose to minimize the training delay under the constraints of device-specific participation rate, energy consumption and memory usage. To this end, we formulate a joint optimization problem of device scheduling and resource allocation (i.e. DNN partition point, channel assignment, transmit power, and computation frequency), and solve the long-term min-max mixed integer non-linear programming based on the Lyapunov technique. In particular, the proposed dynamic device scheduling and resource allocation (DDSRA) algorithm can achieve a trade-off to balance the training delay minimization and FL performance. We also provide the FL convergence bound for the DDSRA algorithm with both convex and non-convex settings. Experimental results demonstrate the derived device-specific participation rate in terms of feasibility, and show that the DDSRA algorithm outperforms baselines in terms of test accuracy and convergence time.

In this paper, a novel transmissive reconfigurable meta-surface (RMS) transceiver enabled multi-tier computing network architecture is proposed for improving computing capability, decreasing computing delay and reducing base station (BS) deployment cost, in which transmissive RMS equipped with a feed antenna can be regarded as a new type of multi-antenna system. We formulate a total energy consumption minimization problem by a joint optimization of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient while taking into account the constraints of communication resources and computing resources. This formulated problem is a non-convex optimization problem due to the high coupling of optimization variables, which is NP-hard to obtain its optimal solution. To address the above challenging problems, block coordinate descent (BCD) technique is employed to decouple the optimization variables to solve the problem. Specifically, the joint optimization problem of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient is divided into three subproblems to solve by applying BCD. Then, the decoupled three subproblems are optimized alternately by using successive convex approximation (SCA) and difference-convex (DC) programming until the convergence is achieved. Numerical results verify that our proposed algorithm is superior in reducing total energy consumption compared to other benchmarks.

Multi-tier computing can enhance the task computation by multi-tier computing nodes. In this paper, we propose a cell-free massive multiple-input multiple-output (MIMO) aided computing system by deploying multi-tier computing nodes to improve the computation performance. At first, we investigate the computational latency and the total energy consumption for task computation, regarded as total cost. Then, we formulate a total cost minimization problem to design the bandwidth allocation and task allocation, while considering realistic heterogenous delay requirements of the computational tasks. Due to the binary task allocation variable, the formulated optimization problem is non-convex. Therefore, we solve the bandwidth allocation and task allocation problem by decoupling the original optimization problem into bandwidth allocation and task allocation subproblems. As the bandwidth allocation problem is a convex optimization problem, we first determine the bandwidth allocation for given task allocation strategy, followed by conceiving the traditional convex optimization strategy to obtain the bandwidth allocation solution. Based on the asymptotic property of received signal-to-interference-plus-noise ratio (SINR) under the cell-free massive MIMO setting and bandwidth allocation solution, we formulate a dual problem to solve the task allocation subproblem by relaxing the binary constraint with Lagrange partial relaxation for heterogenous task delay requirements. At last, simulation results are provided to demonstrate that our proposed task offloading scheme performs better than the benchmark schemes, where the minimum-cost optimal offloading strategy for heterogeneous delay requirements of the computational tasks may be controlled by the asymptotic property of the received SINR in our proposed cell-free massive MIMO-aided multi-tier computing systems.

-1To efficiently exploit the massive amounts of raw data that are increasingly being generated in mobile edge networks, federated learning (FL) has emerged as a promising distributed learning technique by collaboratively training a shared learning model on edge devices. The number of resource blocks when using traditional orthogonal transmission strategies for FL linearly scales with the number of participating devices, which conflicts with the scarcity of communication resources. To tackle this issue, over-the-air computation (AirComp) has emerged recently which leverages the inherent superposition property of wireless channels to performone-shotmodel aggregation. However, the aggregation accuracy in AirComp suffers from the unfavorable wireless propagation environment. In this paper, we consider the use of intelligent reflecting surfaces (IRSs) to mitigate this problem and improve FL performance with AirComp. Specifically, a novel performance-oriented long-term design scheme that integrated design multiple communication rounds to minimize the optimality gap of the loss function is proposed. We first analyze the convergence behavior of the FL procedure with the absence of channel fading and noise. Based on the obtained optimality gap which characterizes the impact of channel fading and noise in different communication rounds on the ultimate performance of FL, we propose both online and offline schemes to tackle the resulting design problem. Simulation results demonstrate that such a long-term design strategy can achieve higher test accuracy than the conventional isolated design approach in FL. Both the theoretical analysis and numerical results exhibit a “later-is-better” principle, which demonstrates the later rounds in the FL procedure are more sensitive to aggregation error, and hence more resources are required over time.

This paper investigates the reconfigurable intelligent surface (RIS) assisted spatial scattering modulation (SSM) scheme for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems, in which line-of-sight (LoS) and non-line-of-sight (NLoS) paths are respectively considered in the transmitter-RIS and RIS-receiver channels. Based on the maximum likelihood detector, the expression for the conditional pairwise error probability (CPEP) of the RIS-SSM scheme is derived for both cases of correct demodulation of the received beam or not. Furthermore, we derive the closed-form expressions of the unconditional pairwise error probability (UPEP) by employing two different methods: the probability density function and the moment-generating function expressions with a descending order of scatterer gains. To provide more useful insights, we derive the asymptotic UPEP and the diversity gain of the RIS-SSM scheme in the high SNR region. Depending on UPEP and the corresponding Euclidean distance, we further give the union upper bound of the average bit error probability (ABEP). To acquire the effective capacity of the proposed system, a new framework for ergodic capacity analysis is also provided. Finally, all derivation results are validated via extensive Monte Carlo simulations and reveal that the proposed RIS-SSM scheme outperforms the benchmarks in terms of reliability.

Personalized federated learning (PFL), as a novel federated learning (FL) paradigm, is capable of generating personalized models for heterogenous clients. Combined with with a meta-learning mechanism, PFL can further improve the convergence performance with few-shot training. However, meta-learning based PFL has two stages of gradient descent in each local training round, therefore posing a more serious challenge in information leakage. In this paper, we propose a differential privacy (DP) based PFL (DP-PFL) framework and analyze its convergence performance. Specifically, we first design a privacy budget allocation scheme for inner and outer update stages based on the Rényi DP composition theory. Then, we develop two convergence bounds for the proposed DP-PFL framework under convex and non-convex loss function assumptions, respectively. Our developed convergence bounds reveal that 1) there is an optimal size of the DP-PFL model that can achieve the best convergence performance for a given privacy level, and 2) there is an optimal tradeoff among the number of communication rounds, convergence performance and privacy budget. Evaluations on various real-life datasets demonstrate that our theoretical results are consistent with experimental results. The derived theoretical results can guide the design of various DP-PFL algorithms with configurable tradeoff requirements on the convergence performance and privacy levels.

This work presents non-orthogonal multiple access (NOMA) enabled energy-efficient alternating optimization framework for backscatter aided wireless powered uplink sensors communications for beyond 5G intelligent transportation system (ITS). Specifically, the transmit power of carrier emitter (CE) and reflection coefficients of backscatter aided roadside sensors are optimized with channel uncertainties for the maximization of the energy efficiency (EE) of the network. The formulated problem is tackled by the proposed two-stage alternating optimization algorithm named AOBWS (alternating optimization for backscatter aided wireless powered sensors). In the first stage, AOBWS employs an iterative algorithm to obtain optimal CE transmit power through simplified closed-form computed through Cardano’s formulae. In the second stage, AOBWS uses a non-iterative algorithm that provides a closed-form expression for the computation of optimal reflection coefficient for roadside sensors under their quality of service (QoS) and a circuit power constraint. The global optimal exhaustive search (ES) algorithm is used as a benchmark. Simulation results demonstrate that the AOBWS algorithm can achieve near-optimal performance with very low complexity, which makes it suitable for practical implementations.

Building upon the prevailing concept of edge computing (EC), a distributed EC market requires decentralized and verified transaction management to trade computing resources. Towards this goal, we study a blockchain-aided EC market wherein each data service operator (DSO) rents a group of edge computing nodes (ECNs) and leases the ECNs to the user terminals (UTs) to provide computation offloading services. A trustworthiness model is introduced to evaluate the quality of each network entity throughout the transactions. We develop a two-level trading mechanism over smart contract to enable the automatic and efficient transactions among the network entities and provide high quality services. First, we propose a smart contract based matching mechanism to establish the renting association between the DSOs and ECNs with the aim of maximizing the social welfare. Second, we propose a social welfare improved double auction (SWIDA) mechanism to build up the leasing association between the DSOs and UTs, and determine the pricing of the winners. We show that the proposed double auction mechanism can achieve individual rationality, balanced budget, truthfulness in expectation, and an improved social welfare than the benchmark mechanisms. Moreover, we put forth a trustworthiness driven Proof-of-Stake (PoS) consensus mechanism to enable verified transaction and fair allocation of block generation reward. Following the principle of PoS, we formulate the block generation as a coalitional game, wherein each stakeholder votes according to its trustworthiness and coinage, and shares the reward among the coalition according to the Shapley values. The simulation results show that the proposed PoS consensus mechanism can reduce the wealth inequality among the network entities compared with the conventional consensus mechanisms.

Anintelligent reflecting surface (IRS)-aided wireless-powered mobile edge computing (WP-MEC) system is conceived, where each device’s computational task can be divided into two parts for local computing and offloading to mobile edge computing (MEC) servers, respectively. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) schemes are considered for uplink (UL) offloading. To fully unleash the potential benefits of the IRS, employing multiple IRS beamforming (BF) patterns/vectors in the considered operating frame to create time-selectivity channels, i.e., dynamic IRS BF (DIBF), is in principle possible at the cost of additional signaling overhead. To strike a balance between the system performance and associated signalling overhead, we propose three cases of DIBF configurations based on the maximum number of IRS reconfiguration times. The degree-of-freedom provided by the IRS may introduce different impacts on the TDMA and NOMA-based UL offloading schemes. Thus, it is still fundamentally unknown which multiple access scheme is superior for MEC UL offloading by considering the impact of the IRS. To answer this question, we provide a comprehensively theoretical performance comparison for the TDMA and NOMA-based offloading schemes under the three cases of DIBF configurations by characterizing their achievable computation rate. Analytical results demonstrate that offloading adopting TDMA can achieve the same computation rate as that of NOMA, when all the devices share the same IRS BF vector during the UL offloading. By contrast, computation offloading exploiting TDMA outperforms NOMA, when the IRS BF vector can be flexibly adapted for UL offloading. Then, we propose computationally efficient algorithms by invoking alternating optimization for solving their associated computation rate maximization problems. Our numerical results demonstrate the significant performance gains achieved by the proposed designs over various benchmark schemes and also unveil that the optimal time allocated to downlink wireless power transfer can be effectively reduced with the aid of IRSs, which is beneficial for both the system’s spectral efficiency and its energy efficiency.

In this paper, we investigate the fundamental multiple access (MA) scheme in an active intelligent reflecting surface (IRS) aided energy-constrained Internet-of-Things (IoT) system, where an active IRS is deployed to assist the uplink transmission from multiple IoT devices to an access point (AP). Our goal is to maximize the sum throughput by optimizing the IRS beamforming vectors across time and resource allocation. To this end, we first study two typical active IRS aided MA schemes, namely time division multiple access (TDMA) and non-orthogonal multiple access (NOMA), by analytically comparing their achievable sum throughput and proposing corresponding algorithms. Interestingly, we prove that given only one available IRS beamforming vector, the NOMA-based scheme generally achieves a larger throughput than the TDMA-based scheme, whereas the latter can potentially outperform the former if multiple IRS beamforming vectors are available to harness the favorable time selectivity of the IRS. To strike a flexible balance between the system performance and the associated signaling overhead incurred by more IRS beamforming vectors, we then propose a general hybrid TDMA-NOMA scheme with device grouping, where the devices in the same group transmit simultaneously via NOMA while devices in different groups occupy orthogonal time slots. By controlling the number of groups, the hybrid TDMA-NOMA scheme is applicable for any given number of IRS beamforming vectors available. Despite of the non-convexity of the considered optimization problem, we propose an efficient algorithm based on alternating optimization, where each subproblem is solved optimally. Simulation results illustrate the practical superiorities of the active IRS over the passive IRS in terms of the coverage extension and supporting multiple energy-limited devices, and demonstrate the effectiveness of our proposed hybrid MA scheme for flexibly balancing the performance-cost tradeoff.

Blockchain technology has been extensively studied to enable distributed and tamper-proof data processing in federated learning (FL). Most existing blockchain assisted FL (BFL) frameworks have employed a third-party blockchain network to decentralize the model aggregation process. However, decentralized model aggregation is vulnerable to pooling and collusion attacks from the third-party blockchain network. Driven by this issue, we propose a novel BFL framework that features the integration of training and mining at the client side. To optimize the learning performance of FL, we propose to maximize the long-term time average (LTA) training data size under a constraint of LTA energy consumption. To this end, we formulate a joint optimization problem of training client selection and resource allocation (i.e., the transmit power and computation frequency at the client side), and solve the long-term mixed integer non-linear program based on a Lyapunov technique. In particular, the proposed dynamic resource allocation and client scheduling (DRACS) algorithm can achieve a trade-off of [$\mathcal {O}(1/V)$,$\mathcal {O}(\sqrt {V})$] to balance the maximization of the LTA training data size and the minimization of the LTA energy consumption with a control parameter$V$. Our experimental results show that the proposed DRACS algorithm achieves better learning accuracy than benchmark client scheduling strategies with limited time or energy consumption.

Active intelligent reflecting surface (IRS) has been recently proposed to alleviate the product path loss attenuation inherent in the IRS-aided cascaded channel. In this paper, we study an active IRS-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, an active IRS is deployed to assist a multi-antenna access point (AP) to convey information and energy simultaneously to multiple single-antenna information users (IUs) and energy users (EUs). Two joint transmit and reflect beamforming optimization problems are investigated with different practical objectives. The first problem maximizes the weighted sum-power harvested by the EUs subject to individual signal-to-interference-plus-noise ratio (SINR) constraints at the IUs, while the second problem maximizes the weighted sum-rate of the IUs subject to individual energy harvesting (EH) constraints at the EUs. The optimization problems are non-convex and difficult to solve optimally. To tackle these two problems, we first rigorously prove that dedicated energy beams are not required for their corresponding semidefinite relaxation (SDR) reformulations and the SDR is tight for the first problem, thus greatly simplifying the AP precoding design. Then, by capitalizing on the techniques of alternating optimization (AO), SDR, and successive convex approximation (SCA), computationally efficient algorithms are developed to obtain suboptimal solutions of the resulting optimization problems. Simulation results demonstrate that, given the same total system power budget, significant performance gains in terms of operating range of wireless power transfer (WPT), total harvested energy, as well as achievable rate can be obtained by our proposed designs over benchmark schemes (especially the one adopting a passive IRS). Moreover, it is advisable to deploy an active IRS in the proximity of the users for the effective operation of WPT/SWIPT.

In this paper, we study an intelligent reflecting surface (IRS)-aided radar-communication (Radcom) system, where the IRS is leveraged to help Radcom base station (BS) transmit the joint of communication signals and radar signals for serving communication users and tracking targets simultaneously. The objective of this paper is to minimize the total transmit power at the Radcom BS by jointly optimizing the active beamformers, including communication beamformers and radar beamformers, at the Radcom BS and the phase shifts at the IRS, subject to the minimum signal-to-interference-plus-noise ratio (SINR) required by communication users, the minimum SINR required by the radar, and the cross-correlation pattern design. In particular, we consider two cases, namely, case I and case II, based on the presence or absence of the radar cross-correlation design and the interference introduced by the IRS on the Radcom BS. For case I where the cross-correlation design and the interference are not considered, we prove that the dedicated radar signals are not needed, which significantly reduces implementation complexity and simplifies algorithm design. Then, a penalty-based algorithm is proposed to solve the resulting non-convex optimization problem. Whereas for case II considering the cross-correlation design and the interference, we unveil that the dedicated radar signals are needed in general to enhance the system performance. Since the resulting optimization problem is more challenging to solve as compared with the case I, the semidefinite relaxation (SDR) based alternating optimization (AO) algorithm is proposed. Particularly, instead of relying on the Gaussian randomization technique to obtain an approximate solution by reconstructing rank-one solution, the tightness is achieved by our proposed reconstruction strategy. Simulation results demonstrate the effectiveness of proposed algorithms and also show the superiority of the proposed scheme over various benchmark schemes.

Driven by unmanned aerial vehicle (UAV)’s advantages of flexible observation and enhanced communication capability, it is expected to revolutionize the existing integrated sensing and communication (ISAC) system and promise a more flexible joint design. Nevertheless, the existing works on ISAC mainly focus on exploring the performance of both functionalities simultaneously during the entire considered period, which may ignore the practical asymmetric sensing and communication requirements. In particular, always forcing sensing along with communication may make it is harder to balance between these two functionalities due to shared spectrum resources and limited transmit power. To address this issue, we propose a new integrated periodic sensing and communication (IPSAC) mechanism for the UAV-enabled ISAC system to provide a more flexible trade-off between two integrated functionalities. Specifically, the system achievable rate is maximized via jointly optimizing UAV trajectory, user association, target sensing selection, and transmit beamforming, while meeting the sensing frequency and beam pattern gain requirement for the given targets. Despite that this problem is highly non-convex and involves closely coupled integer variables, we derive the closed-form optimal beamforming vector to dramatically reduce the complexity of beamforming design, and present a tight lower bound of the achievable rate to facilitate UAV trajectory design. Based on the above results, we propose a two-layer penalty-based algorithm to efficiently solve the considered problem. To draw more important insights, the optimal achievable rate and the optimal UAV location are analyzed under a special case of infinity number of antennas. Furthermore, we prove the structural symmetry between the optimal solutions in different ISAC frames without location constraints in our considered UAV-enabled ISAC system. Based on this, we propose an efficient algorithm for solving the problem with location constraints. Numerical results validate the effectiveness of our proposed designs and also unveil a more flexible trade-off in ISAC systems over benchmark schemes.

The acquisition of channel state information (CSI) is a vital and challenging task in wireless communications, especially for high mobility scenarios with rapidly varying channels. Channel prediction, as an effective approach to acquire CSI, is considered a promising approach to improve the communication performance. However, most of the current prediction methods with the assumption of slowly varying channel components cannot cope with rapid variation. In this paper, we propose a novel channel prediction scheme to incorporate the mobility of both transceivers and scatterers. Based on the estimated mobility parameters, the component-wise channel extraction results are derived and then the channel frequency response is predicted. In addition, the prediction performance and the computational complexity of the proposed scheme are analyzed and evaluated, and the relationship between the mobility parameter estimation error and the channel component prediction accuracy is revealed as well. Through predicted results based on the simulated and measured data, the performance of the proposed scheme surpasses the conventional schemes with genie-aided information.

In this paper, a novel intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) architecture is proposed for power-constrained Internet-of-Things (IoT) smart devices, where IRS is exploited to improve the performance of WPCN under imperfect channel state information (CSI). We formulate a hybrid access point (HAP) transmit energy minimization problem by jointly optimizing time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient under the imperfect CSI and non-linear energy harvesting model. On account of the high coupling of optimization variables, the formulated problem is a non-convex optimization problem that is difficult to solve directly. To address the above-mentioned challenging problem, alternating optimization (AO) technique is applied to decouple the optimization variables to solve the problem. Specifically, through AO, time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient are divided into three sub-problems to be solved alternately. The difference-of-convex (DC) programming is used to solve the non-convex rank-one constraint in solving IRS energy reflection coefficient and information reflection coefficient. Numerical simulations verify the superiority of the proposed optimization algorithm in decreasing HAP transmit energy compared with other benchmark schemes.

Besides improving communication performance, intelligent reflecting surfaces (IRSs) are also promising enablers for achieving larger sensing coverage and enhanced sensing quality. Nevertheless, in the absence of a direct path between the base station (BS) and the targets, multi-target sensing is generally very difficult, since IRSs are incapable of proactively transmitting sensing beams or analyzing target information. Moreover, the echoes of different targets reflected via the IRS-assisted virtual links arrive at the BS from the same direction. In this paper, we study a wireless system comprising a multi-antenna BS and an IRS for multi-target sensing, where the beamforming vector and the IRS phase shifts are jointly optimized to improve the sensing performance. To meet the different sensing requirements, such as a minimum received power and a minimum sensing frequency, we propose three novel IRS-assisted sensing schemes: Time division (TD) sensing, signature sequence (SS) sensing, and hybrid TD-SS sensing. For TD sensing, the sensing tasks are performed in sequence over time. In contrast, the novel SS sensing scheme senses all targets simultaneously and establishes a relationship between the target directions and SSs. To strike a flexible balance between the beam pattern gain and sensing efficiency, we also propose a general hybrid TD-SS sensing scheme with target grouping, where targets belonging to the same group are sensed simultaneously via SS sensing, while the targets in different groups are assigned to orthogonal time slots. By controlling the number of groups, hybrid TD-SS sensing can provide a more flexible balance between beam pattern gain and sensing frequency. Moreover, we propose a two-layer penalty-based algorithm to solve the challenging non-convex optimization problem for the joint design of the BS beamformers, IRS phase shifts, and target grouping. Simulation results demonstrate the effectiveness of the proposed hybrid scheme in achieving a flexible trade-off between beam pattern gain and sensing frequency. Our results also reveal that the power leakage in unintended directions is larger for tighter interference constraints.

In this paper, a massive multiple-input multiple-output (MIMO) relay assisted multi-tier computing (MC) system is employed to enhance the task computation. We investigate the joint design of the task scheduling, service caching and power allocation to minimize the total task scheduling delay. To this end, we formulate a robust non-convex optimization problem taking into account the impact of imperfect channel state information (CSI). In particular, multiple task nodes (TNs) offload their computational tasks either to computing and caching nodes (CCN) constituted by nearby massive MIMO-aided relay nodes (MRN) or alternatively to the cloud constituted by nearby fog access nodes (FAN). To address the non-convexity of the optimization problem, an efficient alternating optimization algorithm is developed. First, we solve the non-convex power allocation optimization problem by transforming it into a linear optimization problem for a given task offloading and service caching result. Then, we use the classic Lagrange partial relaxation for relaxing the binary task offloading as well as caching constraints and formulate the dual problem to obtain the task allocation and software caching results. Given both the power allocation, as well as the task offloading and caching result, we propose an iterative optimization algorithm for finding the jointly optimized results. The simulation results demonstrate that the proposed scheme outperforms the benchmark schemes, where the power allocation may be controlled by the asymptotic form of the effective signal-to-interference-plus-noise ratio (SINR).

The integration of non-orthogonal multiple access (NOMA) in vehicle-to-everything (V2X) communications has recently shown great potential to improve traffic efficiency, control, and reliability of beyond 5G transportation systems. In V2X communications, it is vital to inspect imperfect channel state information (CSI) because the high mobility of vehicles leads to more channel estimation uncertainties. This paper proposes an energy-efficient power allocation scheme for the road-side unit (RSU) assisted NOMA multicasting in beyond 5G cellular V2X networks. In particular, the energy efficiency maximization problem is investigated under the outage probability of vehicles under imperfect CSI, quality of services (QoS), and power limit constraints. Since the problem is non-convex and difficult to solve directly, we first convert outage probability constraint to non-probabilistic constraint through approximation and adopt a low complexity gradient assisted binary search (GABS) method to obtain the efficient power allocation at RSUs. Then, a successive convex approximation (SCA) technique is exploited to transform the power allocation problem of vehicles associated with each RSU into a tractable concave-convex fractional programming (CCFP) problem. The optimal solution to the CCFP problem is achieved through Dinkelbach and the dual decomposition method. The global optimal power allocation through the GABS-Exhaustive scheme act as a benchmark, which has considerable computational complexity. Simulation results unveil that the proposed suboptimal scheme (GABS-Dinkelbach) can achieve near-optimal performance with very low complexity.

This paper proposes a novel network framework of intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) non-orthogonal multiple access (NOMA) networks, where IRS is used to enhance the NOMA performance and the wireless power transfer (WPT) efficiency of SWIPT. We formulate a problem of minimizing base station (BS) transmit power by jointly optimizing successive interference cancellation (SIC) decoding order, BS transmit beamforming vector, power splitting (PS) ratio and IRS phase shift while taking into account the quality-of-service (QoS) requirement and energy harvested threshold of each user. The formulated problem is non-convex optimization problem, which is difficult to solve it directly. Hence, a two-stage algorithm is proposed to solve the above-mentioned problem by applying semidefinite relaxation (SDR), Gaussian randomization and successive convex approximation (SCA). Specifically, after determining SIC decoding order by designing IRS phase shift in the first stage, we alternately optimize BS transmit beamforming vector, PS ratio, and IRS phase shift to minimize the BS transmit power. Numerical results validate the effectiveness of our proposed optimization algorithm in reducing BS transmit power compared to other baseline algorithms. Meanwhile, compared with non-IRS-assisted network, the IRS-assisted SWIPT NOMA network can decrease BS transmit power by 51.13%.

This paper investigates the task offloading problem in a hybrid intelligent reflecting surface (IRS) and massive multiple-input multiple-output (MIMO) relay assisted fog computing system, where multiple task nodes (TNs) offload their computational tasks to computing nodes (CNs) nearby massive MIMO relay node (MRN) and fog access node (FAN) via the IRS for execution. By considering the practical imperfect channel state information (CSI) model, we formulate a joint task offloading, IRS phase shift optimization, and power allocation problem to minimize the total energy consumption. We solve the resultant non-convex optimization problem in three steps. First, we solve the IRS phase shift optimization problem with the sequential rank-one constraint relaxation (SROCR) algorithm and semidefinite relaxation (SDR) algorithm for a given power- and computational resource allocation. Then, we exploit a differential convex (DC) optimization framework to determine the power allocation decision that minimizes the total energy consumption. Given the IRS phase shifts, the computational resources, and the power allocation, we propose an alternating optimization algorithm for finding the jointly optimized results. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes, and the energy efficient offloading strategy for the proposed fog computing system can be chosen according to the asymptotic form of the effective signal-to-interference-plus-noise ratio (SINR).

In this paper, we propose anew dynamic IRS beamformingframework to boost the sum throughput of an intelligent reflecting surface (IRS) aided wireless powered communication network (WPCN). Specifically, the IRS phase-shift vectors across time and resource allocation are jointly optimized to enhance the efficiencies of both downlink wireless power transfer (DL WPT) and uplink wireless information transmission (UL WIT) between a hybrid access point (HAP) and multiple wirelessly powered devices. To this end, we first study three special cases of the dynamic IRS beamforming, namelyuser-adaptiveIRS beamforming,UL-adaptiveIRS beamforming, andstatic IRS beamforming, by characterizing their optimal performance relationships and proposing corresponding algorithms. Interestingly, it is rigorously proved that the latter two cases achieve the same throughput, thus helping halve the number of IRS phase shifts to be optimized and signalling overhead practically required for UL-adaptive IRS beamforming. Then, we propose a general optimization framework for dynamic IRS beamforming, which is applicable for any given number of IRS phase-shift vectors available. Despite of the non-convexity of the general problem with highly coupled optimization variables, we propose two algorithms to solve it and particularly, the low-complexity algorithm exploits the intrinsic structure of the optimal solution as well as the solutions to the cases with user-adaptive and static IRS beamforming. Simulation results validate our theoretical findings, illustrate the practical significance of IRS with dynamic beamforming for spectral and energy efficient WPCNs, and demonstrate the effectiveness of our proposed designs over various benchmark schemes.

This paper investigates the task offloading problem in a hybrid intelligent reflecting surface (IRS) and massive multiple-input multiple-output (MIMO) relay assisted fog computing system, where multiple task nodes (TNs) offload their computational tasks to computing nodes (CNs) nearby massive MIMO relay node (MRN) and fog access node (FAN) via the IRS for execution. By considering the practical imperfect channel state information (CSI) model, we formulate a joint task offloading, IRS phase shift optimization, and power allocation problem to minimize the total energy consumption. We solve the resultant non-convex optimization problem in three steps. First, we solve the IRS phase shift optimization problem with the semidefinite relaxation (SDR) algorithm. Then, we exploit a differential convex (DC) optimization framework to determine the power allocation decision. Given the IRS phase shifts, the computational resources, and the power allocation, we propose an alternating optimization algorithm for finding the jointly optimized results. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes.

Owing to the low communication costs and privacy-promoting capabilities, federated learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, with the distributed architecture, low-quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. In this article, we model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk. Specifically, we first investigate the impact on the models caused by unreliable clients by deriving a convergence upper bound on the loss function based on the gradient descent updates. Our bounds reveal that with a fixed amount of total computational resources, there exists an optimal number of local training iterations in terms of convergence performance. We further design a novel defensive mechanism, named deep neural network-based secure aggregation (DeepSA). Our experimental results validate our theoretical analysis. In addition, the effectiveness of DeepSA is verified by comparing with other state-of-the-art defensive mechanisms.

Developing resource allocation algorithms for the uplink sparse code multiple access (SCMA) scheme to satisfy multiple objectives is challenging, especially where users are randomly distributed. In this paper, we aim to address this challenge by developing a joint resource allocation method as a multi-objective optimization (MO) problem to maximize the average sum rate and fairness among users as key and sub-key objectives, respectively. For this purpose, the exact analytical expressions for the average sum rate and users' individual rate are extracted based on an adaptive weighted graph matrix (AWGM). An AWGM matrix beneficially replaces the factor graph and the power allocation matrices to simplify the MO problem based on the asymmetric modified bipartite matching (AMBM) algorithm. The power allocation strategy is utilized during the optimal resource assignment process using the AMBM algorithm. After the AMBM process, we propose a low-complexity four-step algorithm to obtain the AWGM. The simulation results show that our proposed method can compromise and improve the multiple objectives' performance and guarantees a stable range of network performance at different times.

The energy-efficient task offloading problem of a massive multiple-input multiple-output (MIMO)-aided fog computing system is solved, where multiple task nodes offload their computational tasks to be solved via a massive MIMO-aided fog access node to multiple processing nodes in the fog for execution. By considering realistic imperfect channel state information (CSI), we formulate a joint task offloading and power allocation problem for minimizing the total energy consumption, including both computation and communication power consumptions. We solve the resultant non-convex optimization problem in two steps. First, we solve the computational task allocation and computational resource allocation for a given power allocation. Then, we conceive a sequential optimization framework for determining the specific power allocation decision that minimizes the total energy consumption of the fog access node. Given the computational tasks, the computational resources, and the power allocation, we propose an iterative algorithm for the system optimization. The simulation results show that the proposed scheme significantly reduces the total energy consumption compared to the benchmark schemes.

Sparse code multiple access (SCMA) is one of the promising candidates for new radio access interface. The new generation communication system is expected to support massive user access with high capacity. However, there are numerous problems and barriers to achieve optimal performance, e.g., the multiuser interference and high power consumption. In this paper, we present optimization methods to enhance the spectral and energy efficiency for SCMA with individual rate requirements. The proposed method has shown a better network mapping matrix based on power allocation and codebook assignment. Moreover, the proposed method is compared with orthogonal frequency division multiple access (OFDMA) and code division multiple access (CDMA) in terms of spectral efficiency (SE) and energy efficiency (EE) respectively. Simulation results show that SCMA performs better than OFDMA and CDMA both in SE and EE.

Caching popular contents at heterogeneous devices, e.g., fog nodes (FNs) or fog access points (FAPs), constitutes a promising technique of reducing both the traffic and the energy consumption of the backhaul links. In this paper, we propose an energy-efficient caching and node association algorithm for cache-aided fog networks. First, we solve the problem of energy-efficient content caching and delivery in the FNs/FAPs. In both caching scenarios, we investigate the relationship between the caching probability of the file and the energy-efficient content delivery by formulating the associated energy efficiency (EE) optimization problem. Then, we derive a joint modulation mode allocation strategy and caching policy for each content caching node and conceive a joint node association and caching algorithm. Finally, we quantify both the overall EE and throughput for demonstrating that the proposed caching and transmission strategy achieves significant performance improvements.

In this paper, we study multiple-antenna wireless communication networks, where a large number of devices simultaneously communicate with an access point. The capacity region of multiple-input multiple-output massive multiple access channels (MIMO mMAC) is investigated. While joint typicality decoding is utilized to establish the achievability of capacity region for conventional MAC with fixed number of the users, the technique is not directly applicable for the MIMO mMAC. Instead, an information-theoretic approach based on Gallager's error exponent analysis is exploited to characterize the finite dimension region of the MIMO mMAC. Theoretical results reveal that the region is dominated by the sum rate constraint only, and the individual user rates are dominated by specific factors that correspond to the allocation of the sum rate. The rate in conventional MAC is not achievable when the number of users is comparable with codelength, which is due to the fact that successive interference cancellation cannot guarantee an arbitrary small error decoding probability for MIMO mMAC. The results further imply that, asymptotically, the individual user rate is independent of the number of transmit antennas, and channel hardening makes the individual user rate close to that when only statistic knowledge of channel is available at transmitter. The finite dimension region of MIMO mMAC is a generalization of the symmetric rate in Chen et al. (2017).

Secure wireless information and power transfer based on directional modulation is conceived for amplify-and-forward relaying networks. Explicitly, we first formulate a secrecy rate maximization (SRM) problem, which can be decomposed into a twin-level optimization problem and solved by a one-dimensional (1D) search and semidefinite relaxation (SDR) technique. Subsequently, in order to reduce the search complexity, we formulate an optimization problem based on maximizing the signal-to-leakage-AN-noise-ratio (Max-SLANR) criterion, and transform it into a SDR problem. In addition, the relaxation is proved to be tight according to the classic Karush-Kuhn-Tucker (KKT) conditions. Finally, to reduce the computational complexity, a successive convex approximation (SCA) scheme is proposed to find a near-optimal solution. The complexity of the SCA scheme is much lower than that of the SRM and the Max-SLANR schemes. Simulation results demonstrate that the performance of the SCA scheme is very close to that of the SRM scheme in terms of its secrecy rate and bit error rate, but much better than that of the zero forcing scheme.

This paper investigates the hybrid precoding design for millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems with finite-alphabet inputs. The mmWave MIMO system employs partially-connected hybrid precoding architecture with dynamic subarrays, where each radio frequency (RF) chain is connected to a dynamic subset of antennas. We consider the design of analog and digital precoders utilizing statistical and/or mixed channel state information (CSI), which involve solving an extremely difficult problem in theory: First, designing the optimal partition of antennas over RF chains is a combinatorial optimization problem, whose optimal solution requires an exhaustive search over all antenna partitioning solutions; Second, the average mutual information under mmWave MIMO channels lacks closed-form expression and involves prohibitive computational burden; and Third, the hybrid precoding problem with given partition of antennas is nonconvex with respect to the analog and digital precoders. To address these issues, this paper first presents a simple criterion and the corresponding low complexity algorithm to design the optimal partition of antennas using statistical CSI. Then, it derives the lower bound and its approximation for the average mutual information, in which the computational complexity is greatly reduced compared to calculating the average mutual information directly. In addition, it also shows that the lower bound with a constant shift offers a very accurate approximation to the average mutual information. This paper further proposes utilizing the lower bound approximation as a low-complexity and accurate alternative for developing a manifold-based gradient ascent algorithm to find near-optimal analog and digital precoders. Several numerical results are provided to show that our proposed algorithm outperforms the existing hybrid precoding algorithms.

In this paper, we study the design of secure communication for time-division duplex multi-cell multi-user massive multiple-input-multiple-output (MIMO) systems with active eavesdropping. We assume that the eavesdropper actively attacks the uplink pilot transmission and the uplink data transmission before eavesdropping the downlink data transmission of the users. We exploit both the received pilot's and the received data signals for uplink channel estimation. We show analytically that when both the number of transmit antennas and the length of the data vector tend to infinity, the signals of the desired user and the eavesdropper lie in different eigenspaces of the received signal matrix at the base station, provided their signal powers are different. This finding reveals that decreasing (instead of increasing) the desired user's signal power might be an effective approach to combat a strong active attack from an eavesdropper. Inspired by this observation, we propose a data-aided secure downlink transmission scheme and derive an asymptotic achievable secrecy sum-rate expression for the proposed design. For the special case of a single-cell single-user system with independent and identically distributed fading, the obtained expression reveals that the secrecy rate scales logarithmically with the number of transmit antennas. This is the same scaling law as for the achievable rate of a single-user massive MIMO system in the absence of eavesdroppers. The numerical results indicate that the proposed scheme achieves significant secrecy rate gains compared with alternative approaches based on matched filter precoding with artificial noise generation and null space transmission.

Existing polar coding schemes for the two-user interference channel follow the original idea of Han and Kobayashi, in which component messages are encoded independently and then mapped by some deterministic functions (i.e., homogeneous superposition coding). In this paper, we propose a new polar coding scheme for the interference channel based on the heterogeneous superposition coding approach of Chong, Motani, and Garg. We prove that fully joint decoding (the receivers simultaneously decode both senders' common messages and the intended sender's private message) in the Han-Kobayashi strategy can be simplified to two types of partial-joint decoding, which are friendly to polar coding with practical decoding algorithms. The proposed coding scheme requires less auxiliary random variables and no deterministic functions and can be efficiently constructed. Furthermore, we extend this result to interference networks and show that partial-joint decoding is a general method for designing heterogeneous superposition polar coding schemes in interference networks.

The conventional grant-based network relies on the handshaking between the base station and active devices to achieve dynamic multi-user scheduling, which may result in large signaling overheads as well as system latency. To address those problems, a grant-free receiver design is considered in this paper based on sparse code multiple access (SCMA), one of the promising air interface technologies for 5G wireless networks. With the presence of unknown multipath fading, the proposed receiver performs joint channel estimation and data decoding without knowing the user activity in the network. Formulating a factor graph representation for the problem, we devise a message-passing receiver for the uplink SCMA that performs joint estimation iteratively. Motivated by the idea of approximate inference, we use expectation propagation to project the intractable distributions into Gaussian families such that a linear complexity decoder is obtained. The simulation results show that the proposed receiver can detect active devices in the network with a high accuracy and can achieve an improved bit-error-rate performance compared with existing methods.

Hybrid precoding provides a tradeoff between spectral efficiency and power consumption in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. In this paper, we investigate the partially-connected hybrid precoding design for mmWave MIMO broadcast channels with finite alphabet inputs. To enhance the spectral efficiency, a new algorithm is proposed to dynamically optimize the mapping strategy from radio frequency (RF) chains to transmit antennas such that the weighted sum of channel gains is maximized. Then we adopt the inexact alternating minimization method to design hybrid precoding matrices with given optimal mapping strategy and finite-alphabet inputs. Simulation results demonstrate the good performance of our proposed algorithm.

In this paper, we study the design of secure communication for time division duplexing multi-cell multi-user massive multiple-input multiple-output (MIMO) systems with active eavesdropping. We assume that the eavesdropper actively attacks the uplink pilot transmission and the uplink data transmission before eavesdropping the downlink data transmission phase of the desired users. We exploit both the received pilots and data signals for uplink channel estimation. We show analytically that when the number of transmit antennas and the length of the data vector both tend to infinity, the signals of the desired user and the eavesdropper lie in different eigenspaces of the received signal matrix at the base station if their signal powers are different. This finding reveals that decreasing (instead of increasing) the desire user's signal power might be an effective approach to combat a strong active attack from an eavesdropper. Inspired by this result, we propose a data-aided secure downlink transmission scheme and derive an asymptotic achievable secrecy sum-rate expression for the proposed design. Numerical results indicate that under strong active attacks, the proposed design achieves significant secrecy rate gains compared to the conventional design employing matched filter precoding and artificial noise generation.

In this paper, we propose a low-complexity, secrecy capacity achieving polar coding scheme for the cognitive interference channel with confidential messages (CICC) under the strong secrecy criterion. Existing polar coding schemes for interference channels rely on the use of polar codes for the multiple access channel, the code construction problem of which can be complicated. We show that the whole secrecy capacity region of the CICC can be achieved by simple point-to-point polar codes due to the cognitivity, and our proposed scheme requires the minimum rate of randomness at the encoder.

This paper investigates the hybrid precoding design for millimeter wave multiple-input multiple-output systems with finite-alphabet inputs. The precoding problem is a joint optimization of analog and digital precoders, and we treat it as a matrix factorization problem with power and constant modulus constraints. This paper presents three main contributions. First, we present a sufficient condition and a necessary condition for hybrid precoding schemes to realize unconstrained optimal precoders exactly when the number of data streams Nssatisfies Ns= min{rank(H), Nrf}, where H represents the channel matrix and Nrfis the number of radio frequency chains. Second, we show that the coupled power constraint in our matrix factorization problem can be removed without loss of optimality. Third, we propose a Broyden-Fletcher-Goldfarb-Shanno-based algorithm to solve our matrix factorization problem using gradient and Hessian information. Several numerical results are provided to show that our proposed algorithm outperforms existing hybrid precoding algorithms.

This paper investigates the hybrid precoding design for millimeter wave (mmWave) multiple-input multiple- output(MIMO) systems with finite alphabet inputs. The precoding problem is a joint optimization of analog and digital precoders,and it imposes nonconvex constant modulus constraints on the analog precoder. We treat this problem as a matrix factorization problem with constant modulus constraints. The main contributions of our work are listed as follows: First, we propose sufficient and necessary conditions for hybrid precoding schemes to realize any unconstrained optimal precoders exactly when the number of data streams is equal to the number of radio frequency chains. Second, we show that the power constraint in the hybrid precoding problem can be removed without loss of optimality. Third, we present a trust region Newton method to solve our problem using gradient and Hessian information, and the proposed algorithm converges to a stationary point satisfying the first and second order necessary optimality conditions. Several numerical examples are provided to show that the proposed algorithm outperforms existing hybrid precoding algorithms.

In this paper, we propose device-to-device (D2D) overlaying communications with spectrum-power trading where D2D users (DUs) consume transmit power to relay the data of cell-edge cellular users (CUs) for uplink transmission in exchange for bandwidth from CUs for D2D communications. The proposed spectrum-power trading aims at exploiting individual disparities from both the spectrum and the power perspectives. Our goal is to maximize the weighted sum EE (WSEE) of DUs via a joint D2D relay selection, bandwidth allocation, and power allocation while guaranteeing the quality of service of each CU. We show that for a given D2D relay selection, the objective function of the WSEE maximization problem in a fractional form can be transformed into a subtractive-form that is more tractable based on the fractional programming theory. To perform D2D relay selection, we first reveal an important property, which connects the WSEE with both the system-centric EE and the fairness- centric EE. Based on this insight, the D2D relay selection problem is cast into a minimum weighted bipartite matching problem that can be solved efficiently with optimality. Simulation results demonstrate the effectiveness of the proposed scheme and algorithm.

Heterogeneous network is a novel network architecture proposed in long-term-evolution, which highly increases the capacity and coverage compared with the conventional networks. However, in order to provide the best services, appropriate resource management must be applied. In this paper, we consider the joint optimization problem of user association, subchannel allocation, and power allocation for downlink transmission in multi-cell multi-association orthogonal frequency division multiple access heterogeneous networks. To solve the optimization problem, we first divide it into two subproblems: 1) user association and subchannel allocation for fixed power allocation and 2) power allocation for fixed user association and subchannel allocation. Subsequently, we obtain a locally optimal solution for the joint optimization problem by solving these two subproblems alternately. For the first subproblem, we derive the globally optimal solution based on graph theory. For the second subproblem, we obtain a Karush-Kuhn-Tucker optimal solution by a low complexity algorithm based on the difference of two convex functions approximation method. In addition, the multi-antenna receiver case and the proportional fairness case are also discussed. Simulation results demonstrate that the proposed algorithms can significantly enhance the overall network throughput.

Sparse code multiple access (SCMA) is one of the most promising methods among all the non-orthogonal multiple access techniques in the future 5G communication. Compared with some other non-orthogonal multiple access techniques, such as low density signature, SCMA can achieve better performance due to the shaping gain of the SCMA code words. However, despite the sparsity of the code words, the decoding complexity of the current message passing algorithm utilized by SCMA is still prohibitively high. In this paper, by exploring the lattice structure of SCMA code words, we propose a low-complexity decoding algorithm based on list sphere decoding (LSD). The LSD avoids the exhaustive search for all possible hypotheses and only considers signal within a hypersphere. As LSD can be viewed a depth-first tree search algorithm, we further propose several methods to prune the redundancy-visited nodes in order to reduce the size of the search tree. Simulation results show that the proposed algorithm can reduce the decoding complexity substantially while the performance loss compared with the existing algorithm is negligible.

Recently, content-aware-enabled distributed caching relying on local small-cell base stations (SBSs), namely, smallcell caching, has been intensively studied for reducing transmission latency as well as alleviating the traffic load over backhaul channels. In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several content providers (CPs), and multiple mobile users (MUs). The NSP, as a network facility monopolist in charge of the SBSs, leases its resources to the CPs for gaining profits. At the same time, the CPs are intended to rent the SBSs for providing better downloading services to the MUs. We focus on solving the profit maximization problem for the NSP within the framework of contract theory. To be specific, we first formulate the utility functions of the NSP and the CPs by modeling the MUs and SBSs as two independent Poisson point processes. Then, we develop the optimal contract problem for an information asymmetric scenario, where the NSP only knows the distribution of CPs' popularity among the MUs. Also, we derive the necessary and sufficient conditions of feasible contracts. Lastly, the optimal contract solutions are proposed with different CPs' popularity parameter γ. Numerical results are provided to show the optimal quality and the optimal price designed for each CP. In addition, we find that the proposed contract-based mechanism is superior to the benchmarks from the perspective of maximizing the NSP's profit.

In this paper, we investigate device-to-device (D2D) overlaying communications with spectrum-power trading, where D2D users (DUs) consume transmit power to relay cell-edge cellular users (CUs) for uplink transmission in exchange for bandwidth from CUs for D2D communications. The proposed spectrum-power trading aims at exploiting individual disparities from both the spectrum and the power perspectives. Recently, energy efficiency (EE) defined by the ratio of the date rate to the power consumption has become increasingly important for devices due to their limited capacity batteries. As such, our goal is to maximize the weighted sum EE (WSEE) of DUs via a joint D2D relay selection, bandwidth allocation, and power allocation while guaranteeing the quality of service of each CU. Specifically, we study WSEE maximization problems for two different cases, i.e., public-interest DUs and self-interest DUs, depending on whether the DUs are willing to share their obtained bandwidth with each other or not. For the case of public-interest DUs, we show that for a given D2D relay selection, the objective function of the WSEE maximization problem in a fractional form can be transformed into a subtractive form that is more tractable based on the fractional programming theory. To perform D2D relay selection, we first reveal a fundamental relationship between the WSEE and two other EE metrics, i.e., system-centric EE and fairness-centric EE, which, to the best of our knowledge, has never been found in the existing works. Based on this insight, the D2D relay selection problem can be cast as a minimum weighted bipartite matching problem. For the case of self-interest DUs, we show that the corresponding problem can also be solved with optimality by the algorithm proposed for the previous case. Simulation results demonstrate the effectiveness of the proposed algorithm.

This paper investigates a new class of nonconvex optimization, which provides a unified framework for linear precoder design. The new optimization is called generalized quadratic matrix programming (GQMP). Due to the non-deterministic polynomial time (NP)-hardness of GQMP problems, we provide a polynomial time algorithm that is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) point. In terms of application, we consider the linear precoder design problem for spectrum-sharing secure broadcast channels. We design linear precoders to maximize the average secrecy sum rate with finite-alphabet inputs and statistical channel state information (CSI). The precoder design problem is a GQMP problem and we solve it efficiently by our proposed algorithm. A numerical example is also provided to show the efficacy of our algorithm.

Non-orthogonal multiple access is one of the key techniques developed for the future 5G communication systems among which, the recent proposed sparse code multiple access (SCMA) has attracted a lots of researchers' interests. By exploring the shaping gain of the multi-dimensional complex codewords, SCMA is shown to have a better performance compared with some other non-orthogonal schemes such as low density signature (LDS). However, although the sparsity of the codewords makes the near optimal message passing algorithm (MPA) possible, the decoding complexity is still very high. In this paper, we propose a low complexity decoding algorithm based on list sphere decoding. Complexity analysis and simulation results show that the proposed algorithm can reduce the computational complexity substantially while achieve the near maximum likelihood (ML) performance.

This paper investigates spectrum-power trading between a small cell (SC) and a macro-cell (MC), where the SC consumes power to serve the macro-cell users (MUs) in exchange for some bandwidth from the MC. Our goal is to maximize the system energy efficiency (EE) of the SC while guaranteeing the quality of service (QoS) of each MU as well as small cell users(SUs). Specifically, given the minimum data rate requirement and the bandwidth provided by the MC, the SC jointly optimizes MU selection, bandwidth allocation, and power allocation while guaranteeing its own minimum required system data rate. The problem is challenging due to the binary MU selection variables and the fractional form objective function. We first show that in order to achieve the maximum system EE, the bandwidth of an MU is shared with at most one SU in the SC. Then, for a given MU selection, the optimal bandwidth and power allocations are obtained by exploiting the fractional programming. To perform MU selection, we first introduce the concept of trading EE. Then, we reveal a sufficient and necessary condition for serving an MU without considering the total power constraint and the minimum data rate constraint. Based on this insight, we propose a low computational complexity MU selection algorithm. Simulation results demonstrate the effectiveness of the proposed scheme.

This paper investigates the linear precoder design for cognitive multiple-access wiretap channel (CMAC-WT), where two secondary-user transmitters (STs) communicate with one secondary-user receiver (SR) in the presence of an eavesdropper and subject to interference threshold constraints at primary-user receivers (PRs). It designs linear precoders to maximize the ergodic secrecy sum rate for multiple-input multiple-output (MIMO) CMAC-WT under finite-alphabet inputs and statistical channel state information (CSI). For this non-convex problem, a two-layer algorithm is proposed by embedding the convex-concave procedure into an outer approximation framework. The key idea of this algorithm is to reformulate the approximated ergodic secrecy sum rate as a difference of convex (DC) functions, and then generate a sequence of simpler relaxed sets to approach the non-convex feasible set. In this way, near optimal precoding matrices are obtained by maximizing the approximated ergodic secrecy sum rate over a sequence of relaxed sets. Numerical results show that the proposed precoder design provides a significant performance gain over the Gaussian precoding method in the medium and high SNR regimes.

Heterogeneous network is a novel network architecture proposed in Long-Term-Evolution (LTE) which can highly increase the capacity and coverage compared with the conventional networks. However, in order to provide the best possible services, appropriate resource management must be applied. In this paper, we consider the joint optimization of subcarrier and power allocation for downlink transmission in multi- cell orthogonal frequency division multiple access (OFDMA) heterogeneous networks. As far as we know, this is the first paper which joint optimize subcarrier and power allocation when full frequency reuse is assumed. We maximize the throughput by a mutual iterative algorithm. We show that our algorithm has a good performance and a low complexity.We also prove the convergence of our algorithm.

In this paper, we investigate spectrum-power trading between a small cell (SC) and a macro cell (MC), where the SC consumes power to serve the MC users (MUs) in exchange for some bandwidth from the MC. Our goal is to maximize the system energy efficiency (EE) of the SC while guaranteeing the quality of service of each MU as well as SC users (SUs). Specifically, given the minimum data rate requirement and the bandwidth provided by the MC, the SC jointly optimizes MU selection, bandwidth allocation, and power allocation while guaranteeing its own minimum required system data rate. The problem is challenging due to the binary MU selection variables and the fractional-form objective function. We first show that the bandwidth of an MU is shared with at most one SU in the SC. Then, for a given MU selection, the optimal bandwidth and power allocation are obtained by exploiting the fractional programming. To perform MU selection, we first introduce the concept of the trading EE to characterize the data rate obtained as well as the power consumed for serving an MU. We then reveal a sufficient and necessary condition for serving an MU without considering the total power constraint and the minimum data rate constraint: the trading EE of the MU should be higher than the system EE of the SC. Based on this insight, we propose a low complexity MU selection method and also investigate the optimality condition. Simulation results verify our theoretical findings and demonstrate that the proposed resource allocation achieves near-optimal performance.

In this paper, we propose adaptive techniques for multiuser multiple-input and multiple-output (MU-MIMO) cellular communication systems, to solve the problem of energy efficient communications with heterogeneous delay-aware traffic. In order to minimize the total transmission power of the MU-MIMO, we investigate the relationship between the transmission power and the M-ary quadrature amplitude modulation (MQAM) constellation size and get the energy efficient modulation for each transmission stream based on the minimum mean square error (MMSE) receiver. Since the total power consumption is different for MU-MIMO and multiuser single input and multiple output (MU-SIMO), by exploiting the intrinsic relationship among the total power consumption model, and heterogeneous delay-aware services, we propose an adaptive transmission strategy, which is a switching between MU-MIMO and MU-SIMO. Simulations show that in order to maximize the energy efficiency and consider different Quality of Service (QoS) of delay for the users simultaneously, the users should adaptively choose the constellation size for each stream as well as the transmission mode.

In this paper, the problem of joint transmit power and bandwidth allocation over multiple channels is investigated for a secondary user in underlay mode, when partial information of interference channel is known. The target is to maximize the capacity of a secondary user under a probabilistic constraint of the interference to the primary user. A robust optimization problem is formulated, which is nondeterministic and cannot be solved directly. We then transform the original optimization problem into an equivalent convex optimization problem. For general case, an optimal solving algorithm, which is a combination of analytical and bisection-search methods, is given. For some special cases, simple and optimal solving algorithms are also devised. Numerical results are presented to show that our proposed optimal algorithm has low computation complexity when the number of channels is not large and can achieve global optimal utility in general case and our proposed simple algorithms have much lower computation complexity and can achieve global optimal utility in special cases.

In this paper, we consider wireless powered communication networks (WPCNs) where multiple users harvest energy from a dedicated power station and then communicate with an information receiving station in a time-division manner. Thereby, our goal is to maximize the weighted sum of the user energy efficiencies (WSUEEs). In contrast to the existing system-centric approaches, the choice of the weights provides flexibility for balancing the individual user EEs via joint time allocation and power control. We first investigate the WSUEE maximization problem without the quality of service constraints. Closed-form expressions for the WSUEE as well as the optimal time allocation and power control are derived. Based on this result, we characterize the EE tradeoff between the users in the WPCN. Subsequently, we study the WSUEE maximization problem in a generalized WPCN where each user is equipped with an initial amount of energy and also has a minimum throughput requirement. By exploiting the sum-of-ratios structure of the objective function, we transform the resulting non-convex optimization problem into a two-layer subtractive-form optimization problem, which leads to an efficient approach for obtaining the optimal solution. The simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed approach.

Most of the existing works on energy-efficient wireless communications only consider the transmitter (Tx) or the receiver (Rx) side power consumption, but not both. Moreover, the circuit power consumption is often assumed to be constant regardless of the transmission rate or the bandwidth. In this paper, we investigate the system-level energy-efficient transmission in multi-radio access networks by considering joint Tx and Rx power consumption and adopting link-dependent dynamic circuit power model. A combinatorial-type optimization problem for user scheduling, radio-link activation, and power control is formulated with the objective of maximizing joint Tx and Rx energy efficiency (EE). We tackle this problem using a divide-and-conquer approach. Specifically, the concepts of link EE and user EE are first introduced, which have structures similar to the system EE. Then, we explore their hierarchical relationships and propose an optimal algorithm whose complexity is linear in the product of the total number of users and radio links. Furthermore, we investigate the EE maximization problem with minimum user data rate constraints. The divide-and-conquer approach is also applied to find a sub-optimal but efficient solution. Finally, comprehensive numerical results are provided to validate the theoretical findings and demonstrate the effectiveness of the proposed algorithms.

This paper considers a wireless powered communication network (WPCN), where multiple users harvest energy from a dedicated power station and then communicate with an information receiving station. Our goal is to investigate the maximum achievable energy efficiency (EE) of the network via joint time allocation and power control while taking into account the initial battery energy of each user. We first study the EE maximization problem in the WPCN without any system throughput requirement. We show that the EE maximization problem for the WPCN can be cast into EE maximization problems for two simplified networks via exploiting its special structure. For each problem, we derive the optimal solution and provide the corresponding physical interpretation, despite the nonconvexity of the problems. Subsequently, we study the EE maximization problem under a minimum system throughput constraint. Exploiting fractional programming theory, we transform the resulting nonconvex problem into a standard convex optimization problem. This allows us to characterize the optimal solution structure of joint time allocation and power control and to derive an efficient iterative algorithm for obtaining the optimal solution. Simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed joint time and power optimization.

Orthogonal frequency-division multiplexing (OFDM) has been widely adopted for broadband wireless communications due to its high spectral efficiency. However, it is sensitive to the time selectivity caused by the high-mobility, which largely degrades the accurate of estimating the channel state information (CSI). Therefore, the channel estimation in high-mobility OFDM systems has been a long-standing challenge. Recently, numerous experimental studies have shown that high-mobility broadband wireless channels tend to have some inherent sparsity. In this paper, we introduce the compressed sensing (CS) to utilize the inherent channel sparsity and estimate the high-mobility channel. Based on the CS minimization criterion, we propose two off-line pilot design algorithms to improve the estimation performance. One is to design the pilot symbol only and the other is to jointly design the pilot symbol and the pilot pattern. Simulation results show that the proposed methods achieve better estimation performances than conventional linear methods in high-mobility environments.

Most of the existing works on energy-efficient wireless communication systems only consider the transmitter (Tx) or the receiver (Rx) side power consumption but not both. Moreover, they often assume the static circuit power consumption. To be more practical, this paper considers the joint Tx and Rx power consumption in multiple-access radio networks, where the power model takes both the transmission power and the dynamic circuit power into account. We formulate the joint Tx and Rx energy efficiency (EE) maximization problem which is a combinatorial-type one due to the indicator function for scheduling users and activating radio links. The link EE and the user EE are then introduced which have the similar structure as the system EE. Their hierarchical relationships are exploited to tackle the problem using a divide-and-conquer approach, which is only of linear complexity. We further reveal that the static receiving power plays a critical role in the user scheduling. Finally, comprehensive numerical results are provided to validate our theoretical findings and demonstrate the effectiveness of the proposed algorithm for improving the system EE.

This paper considers wireless powered communication networks (WPCN). Our goal is to investigate the maximum network energy efficiency (EE) by joint time allocation and power control while taking account the initial battery energy level of each user. It is shown that the EE maximization problem for the WPCN can be cast into the EE maximization problems for two independent networks, i.e., purely wireless powered communication networks (PWPCN) or initial energy limited communication networks (IELCN). For the PWPCN, we find that: 1) in the wireless energy transfer (WET) stage, the power station always transmits with its maximum power; 2) it is not necessary for all users to transmit signals in the wireless information transmission (WIT) stage, but all scheduled users will deplete all of their energy; 3) the maximum system EE can always be achieved by exhausting all the available time. Based on these observations, we derive a closed-form expression for the system EE based on the user EE, which transforms the original problem into a user scheduling problem that can be solved efficiently. While for the IELCN, we reveal that the most energy-efficient transmission strategy is to only schedule the user who has the highest user EE. Simulation results validate our theoretical findings and demonstrate the effectiveness of the proposed scheme.

In this paper, we address the robust source and relay matrices design for the multiple-input multiple-output (MIMO) relaying broadcast channels (BC) with imperfect channel state information at the transmitter (CSIT). Our objective is to maximize the minimum achievable rate among all users, which dominates the quality of service (QoS) performance of the system. In the proposed scheme, we first set up an equivalent problem, and then relax the constraints of the new problem to decouple it into three tractable subproblems. Finally, an iterative algorithm is proposed to jointly optimize the source and relay matrices. The advantage of the proposed scheme is demonstrated by numerical experiments.

Heterogeneous cellular networks (HCNs) with embedded small cells are considered, where multiple mobile users wish to download network content of different popularity. By caching data into the small-cell base stations, we will design distributed caching optimization algorithms via belief propagation (BP) for minimizing the downloading latency. First, we derive the delay-minimization objective function and formulate an optimization problem. Then, we develop a framework for modeling the underlying HCN topology with the aid of a factor graph. Furthermore, a distributed BP algorithm is proposed based on the network's factor graph. Next, we prove that a fixed point of convergence exists for our distributed BP algorithm. In order to reduce the complexity of the BP, we propose a heuristic BP algorithm. Furthermore, we evaluate the average downloading performance of our HCN for different numbers and locations of the base stations and mobile users, with the aid of stochastic geometry theory. By modeling the nodes distributions using a Poisson point process, we develop the expressions of the average factor graph degree distribution, as well as an upper bound of the outage probability for random caching schemes. We also improve the performance of random caching. Our simulations show that 1) the proposed distributed BP algorithm has a near-optimal delay performance, approaching that of the high-complexity exhaustive search method; 2) the modified BP offers a good delay performance at low communication complexity; 3) both the average degree distribution and the outage upper bound analysis relying on stochastic geometry match well with our Monte-Carlo simulations; and 4) the optimization based on the upper bound provides both a better outage and a better delay performance than the benchmarks.

In this paper, we propose a cross-layer design framework for transmitting Markov modulated Poisson process (MMPP) traffic over Nakagami-m fading channel with delay demands. The adaptive modulation and coding (AMC) technique is used at the physical layer. The energy efficiency is described as the average throughput over the average transmission power, where both of throughput and transmit power have full consideration of the queuing system. We first derive the closed-form expressions of the delay and the energy efficiency with the stationary distribution of the system. We then derive the energy efficient thresholds to choose the AMC transmission modes. At last, we derive the transmission policy to maximize the energy efficiency with delay constraints. Numerical results are provided to support the theoretical development.

In this paper, we propose a generalized analog network coding (GANC) scheme for a non-orthogonal multiple-access relay channel (MARC), where two sources transmit their information simultaneously to the destination with the help of a relay. In the GANC scheme, the relay receives interfered signals from the two sources and generates signals to be transmitted with a relay function. We focus on the design of the optimal relay function to achieve the minimum pair-wise error probability (PEP) of the system. Specifically, we first covert the relay function optimization problem to a transformation matrix (TM) design problem by presenting the received complex signals as signal matrices composed of real and imaginary parts. Then, we propose an optimization criteria, i.e.,maximizing the minimal squared Euclidean distance(MMSED), to improve the PEP performance, since the PEP is determined by the Euclidean distance of the received constellation at destination. Next, we prove that the MMSED can be equivalently converted to a convex problem by introducing an intermediate matrix. We solve this convex problem by using the Lagrangian method and obtain the closed-form expression of the optimal TM. We further improve the PEP performance by optimizing transmission power of the two sources. Simulation results show that the proposed GANC scheme has a better PEP performance compared to other alternative schemes.

This paper investigates the joint transmitter and receiver optimization for the energy efficiency (EE) in orthogonal frequency-division multiple-access (OFDMA) systems. We first establish a holistic power dissipation model for OFDMA systems, including the transmission power, signal processing power, and circuit power from both the transmitter and receiver sides, while existing works only consider the one side power consumption and also fail to capture the impact of subcarriers and users on the system EE. The EE maximization problem is formulated as a combinatorial fractional problem that is NP-hard. To make it tractable, we transform the problem of fractional form into a subtractive-form one by using the Dinkelbach transformation and then propose a joint optimization method, which leads to the asymptotically optimal solution. To reduce the computational complexity, we decompose the joint optimization into two consecutive steps, where the key idea lies in exploring the inherent fractional structure of the introduced individual EE and the system EE. In addition, we provide a sufficient condition under which our proposed two-step method is optimal. Numerical results demonstrate the effectiveness of proposed methods, and the effect of imperfect channel state information is also characterized.

This paper studies the beamforming design problem of a multiuser downlink network, assuming imperfect channel state information known to the base station. In this scenario, the base station is equipped with multiple antennas, and each user is wiretapped by a specific eavesdropper where each user or eavesdropper is equipped with one antenna. It is supposed that the base station employs transmit beamforming with a given requirement on sum transmitting power. The objective is to maximize the sum secrecy rate of the network. Due to the uncertainty of the channel, it is difficult to calculate the exact sum secrecy rate of the system. Thus, the maximum of lower bound of sum secrecy rate is considered. The optimization of the lower bound of sum secrecy rate still makes the considered beamforming design problem difficult to handle. To solve this problem, a beamforming design scheme is proposed to transform the original problem into a convex approximation problem, by employing semidefinite relaxation and first-order approximation technique based on Taylor expansion. In addition, with the advantage of low complexity, a zero-forcing-based beamforming method is presented in the case that base station is able to nullify the eavesdroppers' rate. When the base station does not have the ability, user selection algorithm would be in use. Numerical results show that the former strategy achieves better performance than the latter one, which is mainly due to the ability of optimizing beamforming direction, and both outperform the signal-to-leakage-and-noise ratio-based algorithm.

In this paper, we propose adaptive techniques for multiple input and multiple output (MIMO) systems, to solve the problem of energy efficient communications with delay constraint, where the energy efficiency is defined as the number of bits per second correctly received per power consumed. We first investigate the optimal multiple quadrature amplitude modulation (MQAM) constellation size for each transmission stream and the optimal packet size. By exploiting the intrinsic relationship among the constellation size, the packet size, the symbol error rate (SER) and delay, we propose an adaptive transmission mode for different delay demands. For the case of user's buffer overflow, we use the congestion control algorithm to schedule the average queue length, and maintain the optimal delay performance for energy efficiency. Simulations show that to maximize the energy efficiency and offer different Quality of Service (QoS) of delay simultaneously, the transmitter should adaptively choose the constellation size and the packet size as well as the transmission mode. In this framework, the tradeoff between energy efficiency and delay demand are well demonstrated.

Small cells are envisioned to embrace dynamic time division duplexing (TDD) in order to tailor downlink (DL)/uplink (UL) subframe resources to the quick variations and burstiness of their DL/UL traffic. In this paper, and for the first time, we perform a theoretical analysis on the signal-to-interference-plus-noise ratio performance of dynamic TDD transmissions in homogeneous small cell networks. The significant accuracy of the proposed theoretical analysis is corroborated by simulations. Based on our work, a partial interference cancellation scheme is proposed to reduce the outage probabilities, and it is shown that cancelling one interfering small cell on average is good enough when the traffic load is low to medium to avoid radio link failures.

In this paper, we study a more practical cognitive radio (CR) scenario where the primary user (PU) operates under more than one transmit power levels. Different from the existing research where PU is assumed to have only one constant transmit power, the new consideration well matches the existing and ongoing standards, i.e., IEEE 802.11 Series, GSM, LTE, LTE-A, etc. The primary target in this new consideration is still to detect the presence of PU, while a secondary target to identify the PU's transmit power level could also be achieved. By doing this, the secondary user (SU) could realize more "cognition" compared to the conventional strategy where only the `on-off" status of PU is detected. To make the study complete, we derive closedform results for multiple thresholds as well as the performance analysis. Numerical examples are provided to corroborate the proposed studies.

In this paper, we investigate the resource allocation for joint transmitter and receiver energy efficiency maximization in orthogonal frequency division multiple access (OFDMA) systems. An elaborate power dissipation model is proposed for OFDMA systems considering the transmission power from the base station side, the signal processing power and radio frequency (RF) circuit power from both sides. Then we formulate the energy efficiency maximization problem and propose a two-step method based on the relationship analysis of the single subcarrier-user (SU) pair energy efficiency and system energy efficiency. Specifically, we first pair each subcarrier with the user resulting in highest SU pair energy efficiency, which is motivated by a special case study. Then, we propose a linear complexity scheme by exploring the inherent fractional structure of the system energy efficiency, which is proved to be optimal for the power allocation with given SU pairing in the first step. Finally, we provide a sufficient condition under which our proposed two-step method is globally optimal. Numerical results demonstrate the effectiveness of the proposed method and we also find that exploiting more user diversity is not always beneficial from the perspective of energy efficiency.

This paper investigates the optimal energy-efficient transmission policy of multi-channels in energy harvesting systems. We configure the transmitter with the active mode in which the energy cost includes the basic operation cost and transmission cost and signal processing cost, while with the sleep mode only counting the basic operation cost. Then the energy efficiency maximization problem of joint transmission time and power allocation is formulated and studied in the offline manner. Based on the fractional optimization theory, we transform the original fractional optimization problem into a series of subtractive-form optimization problems which are then further transformed into convex optimization problems. Then characteristics of the optimal solution are described based on the analysis of transmission time and power allocation. Finally, a special case without considering the basic operation cost as previous works assumed is studied. We find that the optimal policy results a best subchannel scheduling which can be viewed as the peaky transmission. Through this, the energy cost of the sleep mode in previous case can be interpreted as the switching operation cost.

Future wireless communication systems feature heterogeneous networks (HetNets), with small cells underlying existing macrocells. In order to maximize the off-loading benefits of small cells and mitigate the interference from the macrocell tier to the small cell tier, cell range expansion (CRE) and almost blank subframes (ABSs) have been designed for small cells and macrocells, respectively. Besides, enhanced 4th generation (4G) networks are also envisaged to adopt dynamic time division duplexing (TDD) transmissions for small cells to adapt their communication service to the fast variation of downlink (DL) and uplink (UL) traffic demands. However, up to now, it is still unclear whether it is technically feasible to introduce dynamic TDD into HetNets. In this paper, we investigate this fundamental problem and propose a feasible scheme to enable small cell dynamic TDD transmissions in HetNets. Simulation results show that compared with the static TDD scheme with CRE and ABS operations, the proposed scheme can achieve superior performance gains in terms of DL and UL packet throughputs when the traffic load is low to medium, at the expense of introducing the DL-to-UL interference cancellation (IC) functionality in macrocell base stations (BSs) and/or small cell BSs.

In this paper, we propose a threshold-based one-bit soft forwarding (TOB-SF) protocol for a multi-source relaying uplink system with network coding. In the TOB-SF protocol, the relay calculates the log-likelihood ratio (LLR) value of each network coded symbol, compares this LLR value with a pre-optimized threshold, and determines whether to transmit or keep silent. We first derive the bit error rate (BER) expression at the destination, based on which, we optimize the threshold to minimize the BER. Then we theoretically prove that the system can achieve the full diversity gain by using this threshold. Further, we optimize the power allocation at the relay to achieve a higher coding gain. Simulation results show that the proposed TOB-SF protocol outperforms other conventional relaying protocols in terms of error performance.

In this paper, we propose a novel distributed optimization method for dynamic user association in a downlink Heterogeneous cellular network (Hetnet). We aim at maximizing the utilization of the base stations (BSs) in such a network by jointly considering the effect of the channel gains and load balancing of different BSs. Specifically, we first formulate the user association as a combinatorial optimization problem, whose global optimal solution is difficult to obtain. This is due to the high complexity caused by the large network scale and prohibitive signaling overhead. To address this issue, we then consider the optimization problem under the stochastic decision framework, and propose a distributed heuristic algorithm to independently and dynamically associate each user with the best BS. By posing a price factor to the BS evaluation update, the convergence of the heuristics is guaranteed. Numerical results indicate that the proposed heuristics can perform better than the conventional best-SNR (signal-to-noise ratio) method in the presence of large number of users, and achieves the nearly optimal solution.

In this paper we propose a new power adaptive network coding (PANC) strategy for a non-orthogonal multiple-access relay channel (MARC), where two sources transmit their information simultaneously to the destination with the help of a relay. In contrast to the conventional XOR-based network coding (CXNC), the relay in PANC generates network coded symbols by considering the coefficients of the source-to-relay channels, and forwards each symbol with a pre-optimized power level. Next, we obtain the optimal power level by decomposing it as a multiplication of a power scaling factor and a power adaptation factor. We prove that with the power scaling factor at the relay, our PANC scheme can achieve a full diversity gain, i.e., an order of two diversity gain, while the CXNC can achieve only an order of one diversity gain. In addition, we optimize the power adaptation factor at the relay to minimize the SPER at the destination by considering of the relationship between SPER and minimum Euclidean distance of the received constellation, resulting in an improved coding gain. Simulation results show that the PANC scheme with power adaptation optimizations and power scaling factor design can achieve a full diversity, and obtain a much higher coding gain than other network coding schemes.

In this study, the authors analyse the throughput of random beamforming (RBF) for a multi‐antenna cognitive radio (CR) broadcast network. Using extreme value theory, the asymptotic average throughput of a single‐beam RBF and a multiple‐beam RBF are derived from the limiting distribution of the sample maximum of received signal‐to‐noise ratio (signal‐to‐interference‐plus‐noise ratio) with maximal ratio combiner at the secondary user. It is shown that the throughput of the single‐beam RBF scales as logarithm of the number of users, which stands in contrast to the double logarithm of the number of users in non‐CR networks. They also found that the single‐beam RBF is always much preferable to the multi‐beam RBF on the throughput in the CR networks, which is opposite to the previous results in the non‐CR networks. Simulation results show that the author's approximated expressions match with the simulation results very well.

In this paper, we propose a threshold-based one-bit soft forwarding (TOB-SF) protocol for a multi-source relaying system with network coding, where two sources communicate with the destination with the help of a relay. Specifically in the TOB-SF protocol, the relay calculates the log-likelihood ratio (LLR) value of each network coded symbol, compares this LLR value with a pre-optimized threshold, and determines whether to transmit or keep silent. We are interested in optimizing the TOB-SF protocol in fading channels, and consider both the uncoded and low-density parity check coded systems. In the uncoded system, we first derive the bit error rate (BER) expressions at the destination, based on which, we derive the optimal threshold. Then we theoretically prove that the system can achieve the full diversity gain by using this threshold. Further, we optimize the power allocation at the relay to achieve a higher coding gain. In the coded system, we first optimize the LLR threshold. Then we develop a methodology to track the BER evolution at the destination by using Gaussian approximations. Based on the BER evolution, we further optimize the power allocation at the relay which minimizes the system BER. Simulation results show that the proposed TOB-SF protocol outperforms other conventional relaying protocols in terms of error performance.

In this paper we propose a novel power adaptive network coding (PANC) for a non-orthogonal multiple-access relay channel (MARC), where two sources transmit their information simultaneously to the destination with the help of a relay. In contrast to the conventional XOR-based network coding (CXNC), the relay in PANC generates network coded symbols by considering the coefficients of the source-to-relay channels, and forwards each symbol with a pre-optimized power level. Specifically, by defining a symbol pair as two symbols from the two sources, we first derive the expression of symbol pair error rate (SPER) for the system. Noting that deriving the exact SPER are complex due to the irregularity of the decision regions caused by random channel coefficients, we propose a coordinate transform (CT) method on the received constellation to simplify the derivations of the SPER. Next, we obtain the optimal power level by decomposing it as a multiplication of a power scaling factor and a power adaptation factor. We prove that with the power scaling factor at the relay, our PANC scheme can achieve a full diversity gain, i.e., an order of two diversity gain, while the CXNC can achieve only an order of one diversity gain. In addition, we optimize the power adaptation factor at the relay to minimize the SPER at the destination by considering of the relationship between SPER and minimum Euclidean distance of the received constellation, resulting in an improved coding gain. Simulation results show that (1) the SPER derived based on our CT method can well approximate the exact SPER with a much lower complexity; (2) the PANC scheme with power adaptation optimizations and power scaling factor design can achieve a full diversity, and obtain a much higher coding gain than other network coding schemes.

In this paper, we consider the performance-optimized nonbinary low-density parity check code over general linear group, i.e.,$\bar{\cal C}$. A new methodology for constructing the binary representation [generalized binary representation (GBR)] of$\bar{\cal C}$is proposed, which can be optimized with regard to both degree distributions and girth. As to the decoding of the GBR, we develop a low-complexity hybrid parallel decoding process. It is shown that the decoding performance of the GBR under the proposed binary decoding process could closely approach the decoding performance of its mother code$\bar{\cal C}$under nonbinary belief propagation decoding. A simple code optimization algorithm for the GBR is also provided. Simulations show the comparative results and justify the advantages of the proposed constructions.

In this paper, we investigate the robust beamforming for multi-user multiple-input single-output systems under quantized channel direction information (CDI) with per-antenna power constraints. The robustness of the considered beamforming design is achieved in the sense that the stochastic interference leakage is below a certain level by a given probability. Our design objective is to maximize the expectation of the weighted sum-rate performance. From the discussion of the non-robust optimal beamforming based on the polyblock algorithm, we propose a robust beamforming scheme for the quantized CDI case with per-antenna power constraints. In the proposed beamforming scheme, we use Jensen's inequality to generate a tractable feasibility problem for the polyblock algorithm and apply the semi-definite programming relaxation, as well as the randomization technique to find its approximate rank-one matrix solution and user equipments' beamforming vectors. Simulation results show that substantial gains can be achieved by the proposed scheme compared with the existing schemes in terms of the average weighted sum-rate performance. Although very high complexity is required for the implementation of the proposed scheme, it stands as a good benchmark for robust beamforming designs.

Recent studies show that dynamic scheduling using the latest available information is superior to the flooding scheduling. While the existing works on informed dynamic scheduling focus on belief propagation (BP) based algorithm for binary LDPC codes, in this paper we devise a novel method which is appropriate for majority-logic decoding of non-binary LDPC codes. We firstly clarify that previous methods are not suitable for majority-logic decoding of non-binary LDPC codes. Then we give a practical scheduling strategy which utilizes the stability of check nodes to select the messages for propagation. Furthermore, we discuss the computational complexity of this proposed scheme in detail. Simulation results verify that our approach can achieve better performance compared with layered and flooding schemes.

In this paper, a low coherence compressed channel estimation method is proposed for high mobility MIMO OFDM systems. High mobility always causes large Doppler frequency spread which costs large spectrum and time resources to obtain the accurate channel state information (CSI). As numerous recent experimental studies have shown that high mobility broadband wireless channels tend to have some inherent sparsity, compressed sensing (CS) has been introduced to utilize the inherent sparsity and reduce the CSI estimation complexity. In this paper, the coherence of CS is studied and we prove that lower coherence leads to better CS performance. An iterative algorithm is proposed to reduce the coherence by designing pilots with the known channel model before transmitted. Numerical results confirm that the proposed method has satisfied channel estimation performance in high mobility environments.

In this paper, we propose a novel linear beamforming scheme for multiuser two-way MIMO relay channels to maximize the system sum rate. To achieve a low complexity, our scheme is designed by using the non-iterative method based on generalized channel inversion (GCI). Both perfect and imperfect channel estimations are considered in the design. Simulation results shown that the proposed scheme outperforms the existing scheme in the scenarios with or without channel estimation error

In this paper, we consider a dual-hop Multiple Input Multiple Output (MIMO) wireless multi-relay network, in which a source-destination pair both equipped with multiple antennas communicates through multiple half-duplex amplify-and-forward (AF) relay terminals which are also with multiple antennas. Imperfect channel estimations for all nodes are considered. We propose a novel robust linear beamforming at the relays, based on the QR decomposition filter at the destination node which performs successive interference cancellation (SIC). Using Law of Large Number, we obtain the asymptotic rate, upon which the proposed relay beamforming is optimized. Simulation results show that the asymptotic rate matches with the ergodic rate well. Analysis and simulation results demonstrate that the proposed beamforming outperforms the conventional beamforming schemes.

In this paper we design novel decoders for non-binary low density parity check (LDPC) codes. For a non-binary LDPC code C over the field Fqof size q for some q > 0, we propose two novel cooperative decoders, each composed of a binary component decoder and a q-ary component decoder in a concatenated manner, to obtain excellent decoding performance. Specifically to reduce the complexity of the cooperative decoders, we design a hybrid q-ary component decoder. Then, we propose an algorithm to construct the parity check matrix to eliminate the bit-level cycles. Simulations show that the decoding performance of the proposed algorithm approaches the capacity limit within 0.2dB at BER= 10−4.

This paper considers the wireless multicast cell, in which two source nodes with multiple antennas communicate with two destinations over a common medium simultaneously assisted by a multi-antenna relay node. Assume that the sources and relay have individual power constraints. The purpose of this work is to develop an optimal algorithm to compute the source beamforming vectors and the relay beamforming matrix to maximize the system sum-rate, subject to the power constraints in the sources and relay. The problem is nonconvex and apparently has no simple solution. In this paper, We first develop the beamforming vectors at the sources, and then explore the linear beamforming matrix for the nonregenerative relay, Finally, we present a coordinated optimal algorithm to jointly optimize the vectors and matrix, which performance is compared with the conventional methods. Simulations demonstrate that our algorithm outperforms the conventional methods.

This paper studies the precoder design of a multi-relay multi-user bidirectional networks in which each relay is equipped with multiple antennas. We study the relay precoder design in the case of imperfect channel state information (CSI) and a norm-bounded error model is adopted. Then, we solve the power minimization problem under this scenario. Numerical results demonstrate that the proposed robust precoding algorithm outperforms the non-robust solution.

In this paper, we will consider a coordinated multipoint (CoMP) transmission system which accords with the current Long Term Evolution-Advanced (LTE-A) system and the 5th generation wireless system (5G). This paper will consider two different circumstances, one concerning the condition that transmitters knows exactly which user will be wiretapped; the other concerning the condition that transmitters have no knowledge about the wiretapped user's index. Accordingly, we provide the corresponding precoding strategies and simulation results to verify the effectiveness of our proposed algorithms.

Based on the recently proposed efficient inverse Cholesky factorization, we propose three efficient implementations of Orthogonal Matching Pursuit (OMP), and compare them with the existing implementations of OMP by theoretical and empirical analysis. The proposed implementation 1 theoretically needs the least computational complexity, and is the fastest in the simulations for almost all problem sizes. Among the implementations that store the Gram matrix of the dictionary, the proposed implementation 1 needs the least memories in the k-th iteration (k > 1). As the memory-saving variants of the proposed implementation 1, the proposed implementations 2 and 3 save the memories for the Gram matrix at the expense of higher computational complexity, and in the simulations they are faster than the existing implementations for most problem sizes. With respect to the existing efficient implementation that does not store the Gram matrix, the proposed implementation 2 needs less computational complexity and a little more memories, while the proposed implementation 3 needs the same complexity and less memories.

Network coding is a paradigm for modern communication networks by allowing intermediate nodes to mix messages received from multiple sources. Recently, space-time analog network coding (STANC) has been proposed for non-regenerative multi-way relaying, where multiple nodes are exchanging information through the multi-antenna relay without direct links. In this paper, we investigate STANC in multiple-access relay channels, where multiple nodes communicate to a common destination through a multi-antenna relay with direct links. We discuss several different possible schemes including STANC with Alamouti scheme under three time slots constraints and compare the sum rate and error rate performance.

Recently, a network equivalence theory has been proposed by Koetter et al. to model the behavior of wireless and noisy wireline network components in terms of noiseless wireline models in combinatorial essence. Meanwhile the truncated deterministic model is proposed by Avestimehr et al. to approximate Gaussian relay network. In this paper, we utilize the deterministic approximation to model Multiple Access Relay Channel (MARC), gives the admissible rate regions for deterministic MARC (D-MARC) and designs simplified transmission schemes for MARC. Moreover, by allowing source cooperation, we give an algebraic formulation with non-fixed adjacency matrix to guarantee an proper code construction for DMARC and interpret the set of demands by an efficient deterministic transmission. Results in this paper can be extended to more complicated equivalent networks by seeing MARC as a component network.

This paper presents a low-complexity network error correction (NEC) code for wireless relay networks (WRN), where the non-binary low density parity check (LDPC) code is jointly designed with a random network code. We give different transmission schemes under which the decoding complexity of the network code can be much reduced by dividing the transfer matrix into smaller decodable sub-matrices. We also propose a complexity optimization algorithm to the non-binary LDPC code based on an upper bound of message error probability. Simulations show that the complexity optimized codes can outperform the threshold optimized codes in higher SNR regime.

Random beamforming (RBF) has received much attention recently in downlink beamforming because of its simple structure, low‐feedback load and same throughput scaling as that obtained using dirty paper coding at the transmitter. In this study, the authors analyse the performance of RBF for cognitive downlink multi‐antenna system in terms of the throughput of the secondary network. The authors consider a secondary broadcast station with multiple antennas utilising the licensed spectrum of each primary receiver to broadcast information to multiple secondary users (SUs) simultaneously, as long as the interference power inflicted at each primary receiver is less than a predefined threshold. Firstly, they derived the secondary network closed‐form throughput approximation of a single‐beam RBF by exploiting extreme value order statistics. Then, closed‐form approximation for secondary network throughput on multiple‐beam RBF is presented. Simulation results verify the validity of the authors approximation results analysis even with fewer SUs.

This paper investigates the throughput-reliability tradeoff (TRT) for dual-hop amplify-and-forward relay systems with one source, one destination, and multiple relays, and its relationship with the diversity-multiplexing tradeoff (DMT). The TRT was proposed in the context of MIMO block fading channels to reveal the interplay between the signal-to-noise ratio (SNR), rate R, and outage probability that are not accessible through the DMT. The contributions of this paper include the calculation of the TRT expressions for two classes of amplify-and-forward protocols: the slotted amplify-and-forward and the non-orthogonal amplify-and-forward. Based on the derived expressions, relationships between the SNR, rate and outage probability are explored. The relationship between the TRT and the DMT is investigated. One of the goals of the TRT is to predict the slope and offset of the outage vs. SNR set of curves parameterized by different rates. We verify the accuracy of the TRT predictions in the context of amplify-and-forward relays.

Secure communication in the multiuser and multieavesdropper (MUME) scenario is considered in this paper. It has be shown that secrecy can be improved when the transmitter simultaneously transmits an information-bearing signal to the intended receivers and artificial noise to confuse the eavesdroppers. Several processing schemes have been proposed to limit the cochannel interference (CCI). In this paper, we propose the increasing security degree of freedom (ISDF) method, which takes an idea from dirty-paper coding (DPC) and ZF beam-forming. By means of known interference precancellation at the transmitter, we design each precoder according to the previously designed precoding matrices, rather than other users' channels, which in return provides extra freedom for the design of precoders. Simulations demonstrate that the proposed method achieves the better performance and relatively low complexity.

We consider signature waveform design for synchronous code division multiplexing in the presence of interference and wireless multipath fading channels. The adaptive real/complex signature that maximizes the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum-SINR filter is the minimum-eigenvalue eigenvector of the disturbance autocovariance matrix. In digital communication systems, the signature alphabet is finite and digital signature optimization is NP-hard. In this paper, first we convert the maximum-SINR objective of adaptive binary signature design into an equivalent minimization problem. Then we present an adaptive binary signature design algorithm based on modified Fincke-Pohst (FP) method that achieves the optimal exhaustive search performance with low complexity. In addition, with the derivation of quaternary-binary equivalence, we extend and propose the optimal adaptive signature design algorithm for quaternary alphabet. Numerical results demonstrate the optimality and complexity reduction of our proposed algorithms.

Compute-and-forward (CPF) strategy is one category of network coding in which a relay will compute and forward a linear combination of source messages according to the observed channel coefficients, based on the algebraic structure of lattice codes. Recently, based on the idea of CPF, integer forcing (IF) linear receiver architecture for MIMO system has been proposed to recover different integer combinations of lattice codewords for further original message detection. In this paper, we consider the problem of IF linear receiver design with respect to the channel conditions. Instead of exhaustive search, we present practical and efficient suboptimal algorithms to design the IF coefficient matrix with full rank such that the total achievable rate is maximized, based on the slowest descent method. Numerical results demonstrate the effectiveness of our proposed algorithms.

In this paper, we consider a dual-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay system, where there is one source, one destination and K relay nodes. We propose a minimum mean squared error (MMSE)-based eigenmode selection algorithm to choose several relay nodes to minimize MSE. We use eigenmode-based definitions for the equivalent backward and forward channels, and extend the present MMSE-based selection scheme to multiple power amplifier configuration. According to the closed form expression of MSE, we iteratively select eigenmode pairs to minimize the MSE. Simulation results show that our scheme outperforms the existing ones.

Motivated by recently presented integer-forcing linear receiver architecture, we propose algorithms to design optimal integer-forcing coefficient matrix such that the total achievable rate is maximized.

When data symbols modulate a signature waveform to move across a channel in the presence of disturbance, the adaptive real/complex signature that maximizes the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum-SINR filter is the minimum-eigenvalue eigenvector of the disturbance autocovariance matrix. In digital communication systems the signature alphabet is finite and digital signature optimization is NP-hard. In this paper, we propose a new adaptive binary signature assignment for CDMA systems based on improved Fincke-Pohst (FP) algorithm that achieve the optimal exhaustive search performance with low complexity. Then, we extend and propose the optimal adaptive signature assignment algorithm with quaternary signature sets. Simulation studies included herein offer performance comparisons with known adaptive signature designs and the theoretical upper bound of the complex/real eigenvector maximizer.

Secure communication in the Multi-user and Multi-eavesdropper (MUME) scenario is considered in this paper. It has be shown that secrecy can be improved when the transmitter simultaneously transmits information signal to the legitimate receivers and artificial noise to confuse the eavesdroppers. Several processing schemes have been proposed to limit the co-channel interference (CCI). The conventional method and the ZF beamforming method are simple but of little ideal performance. While the block diagonalization (BD) method is of ideal performance but too complex. In this paper, we propose a new alternative approach based on maximizing the signal-to-leakage ratio (SLR). Simulations demonstrates that the proposed SLR method can achieve compromise between the secrecy performance and complexity.

In this paper, we analyze the asymptotic capacity in a spectrum sharing system where a secondary access point (SAP) restrictively utilizes the licensed spectrum of an active primary user (PU) to broadcast information to multiple secondary users (SUs) simultaneously, as long as the interference power inflicted on the PU is less than a predefined threshold. At the SAP, interference channel state information (CSI) between the SAP and the PU is used to calculate the maximum allowable SAP transmit power to limit the interference. We first derive the average capacity when perfect CSIs are known at the SAP based on extreme value theory. When the CSIs are imperfect, specifically, the interference CSI is imperfect, or the CSI of the SU transmission channel is imperfect, which is an important scenario for the secondary system. Then we characterize the capacity loss where perfect CSIs are not always available at the SAP.

This paper considers a multiple-access relay channel (MARC) with two sources, one relay and one destination, where the relay decides what it transmits to the destination according to the outage condition of source-relay links. The outage probability and the approximate bit error rate (BER) are derived, which are shown to be in tight match with Monte-Carlo simulation results. Simulation results reveal that adaptive DF strategy yields better performance than the traditional fixed DF strategies.

We apply the efficient inverse Cholesky factorization to Alamouti matrices in Groupwise Space-time Block Code (G-STBC) and Alamouti-like matrices in Orthogonal Matching Pursuit (OMP) for the sub-Nyquist sampling system. By utilizing some good properties of Alamouti or Alamouti-like matrices, we save about half the complexity. Then we propose the whole square-root algorithms for G-STBC and OMP, respectively. The proposed square-root G-STBC algorithm has an average speedup of 2.96~3.6 with respect to the sub-optimal G-STBC algorithm. On the other hand, when comparing the complexities of all the steps except the projection step, the complexity for the proposed square-root OMP algorithm is about 30% of that for the fast OMP algorithm by matrix inverse update.

This paper investigates the resource allocation problem in a relay-aided cognitive radio (CR) system under the orthogonal frequency division multiplexing (OFDM) transmission. Different from the conventional CR resource allocation problem, the relay node here is capable of performing subcarrier permutation over two hops such that the signal received over a particular subcarrier is forwarded on a different subcarrier. The objective is to maximize the throughput of the CR network subject to a limited power budget at the secondary source and relay node, as well as the interference constraints at the primary receiver. The optimization is performed under a unified framework where the power allocation at the source node, power allocation at the relay node, and subcarrier pairing at the two hops are optimized jointly. Finally, numerical examples are provided to corroborate the proposed studies.

In this study, the authors consider the asymptotic capacity of wireless multicast and unicast transmission schemes in a spectrum-sharing system. In these schemes, a secondary access point (SAP) utilises the licensed spectrum of an active primary user (PU) to send a common information to multiple secondary users simultaneously, as long as the interference power inflicted on the PU is less than a predefined threshold. At the SAP, interference channel-state information between the SAP and the PU is used to calculate the maximum allowable SAP transmit power to limit the interference. The authors derive the average capacity of these schemes based on extreme value theory. From the derived asymptotic capacities, the insights to the capacity behaviour can be drawn.

Network coding is a new and promising paradigm for modern communication networks by allowing intermediate nodes to mix messages received from multiple sources. Compute-and-forward strategy is one category of network coding in which a relay will decode and forward a linear combination of source messages according to the observed channel coefficients, based on the algebraic structure of lattice codes. The destination will recover all transmitted messages if enough linear equations are received. In this work, we design in a system level, the compute-and-forward network coding coefficients by Fincke-Pohst based candidate set searching algorithm and network coding system matrix constructing algorithm, such that by those proposed algorithms, the transmission rate of the multi-source multi-relay system is maximized. Numerical results demonstrate the effectiveness of our proposed algorithms.

The conventional rateless coded cooperation (RCC) operates in full-duplex and non-orthogonal multiples access channel (MAC) aiming to enlarge outage capacity with multiplexed Raptor code of high complexity. In this paper, we develop a new RCC scheme using rateless code, which operates in the practical half-duplex or orthogonal channel aiming to achieves coding gain in high SNR region with only one raptor code. By deriving the outage probability, we found that our scheme outperforms the conventional RCC scheme. Simulation results also validate the theoretical prediction.

This paper addresses bounds on the transmission capacities of two coexisting wireless networks (a primary and a secondary network), where each with multiple antennas shares the same spectrum and operates in the same geographic region. In the two coexisting network, the secondary (SR) network limits its interference to the primary (PR) network by carefully controlling its active transmitter density. Each transmitter uses a subset of its antennas to transmit multiple data streams, while each receiver uses partial zero forcing (PZF) to cancel interference using some of its spatial receive degrees of freedom (SRDOF). Considering general power-law wireless channels with path-loss exponent α >; 2 and small-scale Rayleigh fading, based on stochastic geometry tools and Markov's inequality, we first derive bound on the transmission capacity for the PR networks. Then the transmission capacity of the SR networks is derived when the transmission density of PR network maintains unchanged. Using the obtained bounds, we derive their optimal number of data streams to transmit and the optimal SRDOF to use for interference cancelation.

In this paper, we propose a dynamic power allocation scheme for a single-relay network based on orthogonal frequency division multiplexing (OFDM) modulation with limited feedback.We consider an Amplify-and-Forward (AF) cooperative diversity model, where one source node communicates with one destination node assisted by one half duplex relay. The power allocation scheme employs a codebook of quantized power allocation vectors designed offline equipped on the source, relay, and destination. The destination, who has full knowledge of channel state information (CSI), chooses the best power allocation vector and sends back its index to the source and relay nodes, which results in a dramatic reduced overhead over conventional resource allocation. First we achieve the optimal power allocation solution as the function of channel realizations with maximum capacity. Then we present an adaptation of the Lloyd algorithm to construct a codebook to quantize the optimal power allocation vectors subject to the amount of feedback. Simulations show that a negligible performance loss could be achieved with just a few feedback bits at different levels of SNRs.

In this paper, we consider the problem of channel estimation for two-way relay networks (TWRN) under time-selective environment. We first parameterize the time-varying channels by the basis expansion model (BEM) and then propose a novel pilot symbol aided modulation (PSAM) for TWRN. A linear approach to estimate the cascaded channels is designed and the optimal training sequences are derived based on minimizing the mean-square error (MSE) criterion. Moreover, we develop an algorithm to recover the individual channel knowledge with which both the channel estimation accuracy and the system performance can be improved. Various simulations are provided to corroborate the proposed studies.

The super codes of the first order Reed-Muller code are widely used in the practical wireless communication systems, such as WCDMA and LTE. However, the super codes are usually obtained by exhaustive computer search, which involves huge computational cost. Therefore a systematic algorithm to construct the super codes beyond exhaustive computer search has been expected for long time. In this paper, we propose a systematic algorithm for the super codes construction base on the m-sequence pairs, which only involves some item permutation within the m-sequence. In addition, the constructed super codes outperform the conventional codes in terms of decoding error rate. Meanwhile, the super codes share the same characteristics as the conventional ones, such as being of arbitrary length and rate, and being efficiently decodable with Fast Hadamard Transform (FHT).

In this letter, we study the problem of resource allocation in amplify-and-forward (AF) based multiuser two-way relay network that is operated under orthogonal frequency division multiple access (OFDMA) modulation. We formulate an end-to-end throughput maximization problem subject to limited power constraint at individual user and relay. The optimization targets to find the best sub-carrier allocation to each user, sub-carrier pairing at the relay, as well as the power allocation at all nodes, which turns out to be a mixed integer programming problem. We then derive an asymptotically optimal solution through Lagrange dual decomposition approach and further design a suboptimal algorithm to trade the performance for computational complexity. Finally, simulation results are provided to demonstrate the performance gain of the proposed algorithms.

This paper proposes a fast recursive algorithm for Group-wise Space-Time Block Code (G-STBC), which takes full advantage of the Alamouti structure in the equivalent channel matrix to reduce the computational complexity. With respect to the existing efficient algorithms for G-STBC, the proposed algorithm achieves better performance and usually requires less computational complexity.

In this paper, channel estimation and training sequence design are considered for amplify-and-forward (AF)-based two-way relay networks (TWRNs) in a time-selective fading environment. A new complex-exponential basis expansion model (CE-BEM) is proposed to represent the mobile-to-mobile time-varying channels. To estimate such channels, a novel pilot symbol-aided transmission scheme is developed such that a low complex linear approach can estimate the BEM coefficients of the convoluted channels. More essentially, two algorithms are designed to extract the BEM coefficients of the individual channels. The optimal training parameters, including the number of the pilot symbols, the placement of the pilot symbols, and the power allocation to the pilot symbols, are derived by minimizing the channel mean-square error (MSE). The selections of the system parameters are thoroughly discussed in order to guide practical system design. Finally, extensive numerical results are provided to corroborate the proposed studies.

A fast algorithm for inverse Cholesky factorization is proposed, to compute a triangular square-root of the estimation error covariance matrix for Vertical Bell Laboratories Layered Space-Time architecture (V-BLAST). It is then applied to propose an improved square-root algorithm for V-BLAST, which speedups several steps in the previous one, and can offer further computational savings in MIMO Orthogonal Frequency Division Multiplexing (OFDM) systems. Compared to the conventional inverse Cholesky factorization, the proposed one avoids the back substitution (of the Cholesky factor), and then requires only half divisions. The proposed V-BLAST algorithm is faster than the existing efficient V-BLAST algorithms. The expected speedups of the proposed square-root V-BLAST algorithm over the previous one and the fastest known recursive V-BLAST algorithm are 3.9 ~ 5.2 and 1.05 ~ 1.4, respectively.

In this paper, we consider the capacity of a multicast network, where a single-antenna secondary base station (SBS) utilizes the spectrum licensed to primary users (PUs) to broadcast the same information to multiple secondary users (SUs) with a single-antenna simultaneously, when the interference received by PUs is less than a predefined threshold. Based on extreme value theory, we first derive the average capacity of the multicast/unicast schemes in the case that perfect channel station information from the SBS to the PUs (interference CSI) is known at the SBS. Due to limited cooperation between the SBS and the PUs, perfect interference CSI is not always available at the SBS. Thus, we also analyze the average capacity of the above-mentiontd schemes in the case of imperfect interference CSI at the SBS. The analytical and simulated results reveal that when interference CSI is perfectly available at the SBS, and the SBS transmit power P is sufficiently larger than the interference temperature threshold Q , the average capacity of the multicast scheme scales as Θ(Q) similar to a previously known scaling law in the non-spectrum sharing multicast systems, while the average capacity of the unicast scheme scales as Θ(log (1 + P (log(NQ)-log log NQ))) in the case of Q ≪ P ≪ NQ or Θ(log (1 + (N-1)Q)) in the case of P ≫ NQ, where N denotes the number of SU. If perfect interference CSI is not available at the SBS, the average capacity of the multicast and unicast schemes will be respectively scaled by the factors λ/(λ+1) and |(λ-1)/λ|, compared to that of perfect interference CSI available at the SBS, where 1/λ denotes the mean of interference CSI estimation error.

Network coding is considered as a promising technique to improve diversity gain and network throughput in multi-source relay systems. This paper presents an opportunistic network coded cooperation scheme in wireless multi-unicast system with non-ideal source-relay channels. In the conventional network-coding-based cooperation schemes, the relay merges the messages received from multiple sources and always forwards them to the destinations without checking their reception status. The destinations recover the sources' messages either from the direct transmissions or from the relay forwarding. Such mode is easy to implement, but may lead to error propagation in the event of decoding failures at relay. In contrast to these works, the proposed scheme is opportunistic, where the relay forwarding is determined by the quality of $S\rightarrow R$ channels, that is, the relay does not assist transmission unless it has correctly decoded the received sources' messages. Systematic performance analysis in the form of outage probability and spectral efficiency is performed in this paper. Comparisons with the conventional network coded multi-unicast and Incremental Relaying protocol are made under a fixed system energy constraint. The outage results show that the proposed scheme performs better than the conventional network-coded schemes when source-relay links has poor quality. Comparing with Incremental Relaying protocol, the opportunistic scheme achieves a reduced system outage probability as well as a higher spectral efficiency. This scenario can be extended to general multi-user environment without much cost.

It has been shown that channel state information (CSI) at transmitter can significantly increase the performance of a relay system. However, most of the existing designs assume perfect CSI at the transmitters. Since most practical systems can only obtain partial CSI at the transmitters, it is necessary to analyze the relay channels with limited CSI feedback. Our objective in this paper is to find the optimal power allocation strategy for relay channel under different levels of transmitter CSI, with the system outage probability constraint. We consider a Decode-and-Forward (DF) cooperative diversity model where one source node communicates with one destination node assisted by one half duplex relay. The Lloyd Algorithm is employed to quantize the CSI at receiver and construct the codebook, whose copies are also equipped on the source and the relay nodes. Each code in the codebook is a power allocation vector. Simulation results show that a few feedback bits can significantly improve the system performance.

In this paper, we propose an opportunistic relay with network coding for multicast cell. Specifically, we propose three strategies for the opportunistic relay. By analyzing the stability regions for the three proposed strategies, we find that the two strategies with network coding outperform that without network coding in terms of stability region. In addition, the strategy with opportunistic network coding outperforms that with relatively static network coding. Finally, simulation results validate our theoretical predictions.

A novel algorithm, named Improved Protograph-based Extrinsic Information Transfer (IP-EXIT), is proposed in this paper. With the proposed algorithm, quite accurate performance evaluations of Quasi-Cyclic LDPC (QC-LDPC) codes with arbitrary puncturing can be provided. LDPC codes combined with IR-HARQ technique have been a hot topic at present and puncturing is definitely one of the most efficient ways to achieve LDPC combined with HARQ. Unlike Turbo codes, different puncturing patterns for the same LDPC code may result in huge performance gap, so researchers are trying to design the puncturing pattern with better performance for LDPC codes. Inevitably, a large number of puncturing pattern with different performance should be compared in order to choose the best one from them, and usually this work is done by simulations with computer. In this paper, a novel algorithm is proposed to replace the work of simulation. It has been proved that the proposed algorithm can accurately evaluate the performance of arbitrary puncturing pattern, with very low complexity and very little time.

We propose efficient algorithms for the extended Vertical Bell Labs Layered Space-Time architecture (V-BLAST) with selective per-antenna rate control (S-PARC). Instead of computing SNIRs from nulling vectors, we compute SNIRs from diagonal entries in the estimation error covariance matrix P, which are obtained from entries in F, i.e. the triangular squareroot of P. Then the square-root V-BLAST algorithm is applied, to compute F for an antenna subset from F for another subset. Moreover, when computing F, we can reuse intermediate results to further reduce the computational complexity dramatically. Assume M transmit/receive antennas. The proposed algorithm for the S-PARC scheme minimizing total transmit power has the speedup of 0.14M + 1.82, with respect to the corresponding SPARC algorithm computing SNIRs from nulling vectors, while the proposed algorithm for the S-PARC scheme maximizing total information rate has the speedup of (M + 58)/24.

We propose efficient square-root and division free algorithms for inverse LDLTfactorization and the wide-sense Givens rotation, mainly for conventional multiply-add digital computers. The algorithms are then employed to propose a fast algorithm for vertical Bell Laboratories Layered Space-Time architecture (V-BLAST). The proposed inverse LDLTfactorization avoids the conventional back substitution of the Cholesky factor, and then has the speedups of 1.19 over the square root and division free alternative Cholesky factorization plus the back substitution. The proposed wide-sense Givens rotation requires the same computational complexity as the efficient Givens rotation. The speedups of the proposed V-BLAST algorithm over the recursive V-BLAST algorithm are 1.05~1.4. The difference between the complexity of the proposed V-BLAST algorithm and that of the square-root V-BLAST algorithm is trivial and even negligible. However, the square-root V-BLAST algorithm requires many square-root and division operations, while the proposed V-BLAST algorithm requires no square-root operations, and requires only one division for each transmit signal, which can be postponed until the signal estimation is required.

We propose a greedy minimum mean squared error (MMSE)-based antenna selection algorithm for amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay systems. Assuming equal-power allocation across the multi-stream data, we derive a closed form expression for the mean squared error (MSE) resulted from adding each additional antenna pair. Based on this result, we iteratively select the antenna-pairs at the relay nodes to minimize the MSE. Simulation results show that our algorithm greatly outperforms the existing schemes.

In this comment, we justify that the computational complexity proposed in the paper "A New ML Based Interference Cancellation Technique for Layered Space-Time Codes" (IEEE Trans. on Communications, vol. 57, no. 4, pp. 930-936, 2009) is O(N3) rather than the claimed O(N2), where N is the number of receive antennas.

One of the major drawbacks of orthogonal frequency division multiplexing (OFDM) signals is the high peak to average power ratio (PAPR) of the transmitted signal. Many PAPR reduction techniques have been proposed in the literature, among which, partial transmit sequence (PTS) technique has been taken considerable investigation. However, PTS technique requires an exhaustive search over all combinations of allowed phase factors, whose complexity increases exponentially with the number of sub-blocks. In this paper, a newly suboptimal method based on modified artificial bee colony (ABC-PTS) algorithm is proposed to search the better combination of phase factors. The ABC-PTS algorithm can significantly reduce the computational complexity for larger PTS subblocks and offers lower PAPR at the same time. Simulation results show that the ABC-PTS algorithm is an efficient method to achieve significant PAPR reduction.

In this paper, we address the incentive-based relay-selection problem over multi-source and multi-relay wireless networks. A two-side market game approach is employed to jointly consider the benefits of all sources and relays. The equilibrium concept in such games is called core. The outcomes in the core of the game cannot be improved upon by any subset of players. These outcomes correspond exactly to the price-lists that competitively balance the benefits of all sources and relays. When the price assumes only discrete values, the core of the game is defined as discrete core. The Distributed Source-Relay Assignment (DSRA) algorithm is proposed for competitive price adjustment and converges to the discrete core of the game. With small enough measurement of price, the algorithm can achieve the optimal performance compared with centralized one in terms of total profit of the system.

A construction scheme of M-QAM (quaternary amplitude modulation) signals using quaternary phase-shift keying (QPSK) constellations for the orthogonal frequency division multiplexing (OFDM) has been proposed, when M= 2kand k is an even number. In this paper, we extend Rudin-Shapiro polynomials (RSP) to the generalized Rudin-Shapiro polynomials (GRSP). Based on the generalized Rudin-Shapiro polynomials (GRSP), upper bound of peak-to-mean envelope power ratio (PMEPR), code rate, the minimum Hamming distance for the M-QAM sequences are derived.

Spectrum under-utilization is one of the bottlenecks of the development of wireless communication, and dynamic spectrum access (DSA) is envisioned as a novel mechanism to solve the problem of spectrum scarcity. Spectrum auction has been recognized as an effective way to achieve DSA, wherein the primary spectrum owner (PO) acts as an auctioneer who has free channels and is willing to sell them for additional revenue, and the secondary user (SU) acts as a bidder who is willing to buy a channel from POs for its service. In this paper, we adopt a progressive spectrum auction named MAP, which has been proved optimal and incentive compatible in DSA networks with distributed POs and SUs. However, in MAP, the profit of POs is not maximized under the equilibrium point due to the scarcity of SUs' private information known by POs. We propose an information sharing mechanism, in which the POs exchange their local information with each other. We show analytically that, allowing information sharing, each PO is able to learn the private information of SUs and increase its profit accordingly. Long term profit acts as the incentive for information sharing that all the POs automatically reveal the true information when they are aware of this. It is notable that information sharing doesn't affect social optimality. Simulation shows the increase of POs' profits in the sense of long term interests.

This paper considers the general multiuser downlink scheduling problem and power minimization with multiuser rate constraints. We present joint user selection algorithms for DPC, ZF-DPC, ZFBF and TDMA for multi-antenna OFDM system in broadcast channels, and we also present a practical waterfilling solution in this paper. By the selected users with the consideration of fairness, we derive the power optimization algorithm with multiuser rate constraints. Simulation results show that the presented user scheduling algorithms and power minimization algorithms can achieve good power performance. Meanwhile, simulation results also show that the scheduling algorithm can guarantee fairness.

In this paper, we propose a novel matrix inversion algorithm, which speeds up the corresponding step in the existing fast recursive algorithm for vertical Bell Laboratories layered space-time architecture (V-BLAST) by 1.67. Totally our improved recursive algorithm for V-BLAST speeds up the existing recursive algorithm for V-BLAST by 1.3. Furthermore, we develop an efficient recursive algorithm to implement the minimum mean- square error (MMSE) successive interference cancellation (SIC) detector with optimal ordering for Groupwise Space-Time Block Coded system (G-STBC), by exploiting the properties of the Alamouti structure in the equivalent channel matrix for G- STBC. Our recursive algorithm for G-STBC speeds up the low- complexity MMSE SIC algorithm with sub-optimal ordering for G-STBC by 2.57 approximately.

Extensive studies on supporting voice traffic over wireless 802.11 networks have been carried out in the literature. Most of them were focused only on one hop infrastructure mode. This paper addresses the issue of voice over multihop wireless networks. Through simulation considering a simple topology, we identify the voice traffic bottleneck in 802.11 MAC and thus propose a burst queue (BQ) scheme to cooperate with 802.11b and 802.11e MAC protocols. Extensive simulation results under variety of circumstances show that BQ scheme can support 50%~100% more voice calls and obtain 20%~50% lower delay at the cost of a little higher loss ratio which is tolerable to voice flows. Even for the grid and random topology, our BQ scheme can provide smaller packet loss ratio, and shorter end-to-end delay.

Network coding is a promising technology designed to reach the min-cut max-flow capacity in wired network. While in wireless cooperative environments, it has been proved that network coding can also increase the system throughput by taking the advantage of the broadcast nature of electromagnetic waves. In this paper, we establish a 2-source and 2-destination cooperative systems with arbitrary number of relays (2-N-2 system), and then the designed signal-superposition-and-forward based complex field network coding protocol (SiSF-CFNC) is applied to the model. We define the system frame error probability (SFEP) to measure the performance of cooperative multicast systems with the proposed protocol. Power allocation schemes as well as precoder design are concentratively studied to improve the system performance without cutting down the system throughput.

VoIP over wireless local area network (WLAN) is an important application of WLAN and gaining more and more attention. In this paper, we analyze the maximum number of VoIP calls in WLAN and propose a new call admission control strategy, namely, adaptive transmitting interval call admission control (ATICAC) to enhance VoIP calls in 802.11 WLANs. In ATICAC strategy, base station (BS) adaptively changes the transmitting interval of the active stations to prevent the network from saturation by controlling the average collision probability pc of the network. The proposed model is in good agreement with our simulation results. ATICAC can not only ensure the QoS of VoIP calls in 802.11 WLANS in the network, but also increase the number of VoIP calls in the network.

The performance of TCP-Vegas is not satisfactory in multihop ad hoc networks over IEEE 802.11 MAC protocol. We analyze the problem with a unified network model and simulation results. We observe that the aggregate throughput of all traffics decreases as the load of the network increases. The main reasons lie in Vegas's large minimum congestion window, large reset slow start threshold and aggressive window increase policy. To fix these problems, we propose a modified TCP protocol based on TCP-Vegas for multihop ad hoc networks, called Vegas- W. We extend the congestion window to fraction; change the probing mechanisms of legacy TCP-Vegas in both slow start and congestion avoidance and update slow start threshold tracking the stable window. We evaluate the performance of Vegas-W through ns-2. Extensive simulation results under a variety of scenarios show that Vegas-W can improve the throughput up to 87% over legacy TCP-Vegas and up to 27% over FeW, which is another improved algorithm based on TCP-Newreno scenarios.

Channel allocation was extensively researched in the framework of cellular networks, but it was rarely studied in the ad-hoc wireless networks, especially in the multi-hop ad-hoc networks. In this paper, we study the problem of competitive multi-radio multi-channel allocation in multi-hop wireless networks in detail. We model the channel allocation problem as a static cooperative game, and then derive a min-max coalition-proof Nash equilibrium (MMCPNE) in this game. We study the existence of MMCPNE in the static game and prove the necessary and sufficient conditions for MMCPNE. Finally, we propose a two-step distributed algorithm that enable the selfish players to converge to MMCPNE.

Cooperative transmission protocols are always designed to reach the largest diversity gain and the largest network capacity simultaneously. The concept of diversity-multiplexing tradeoff (DMT) in MIMO systems put forward by Zheng and Tse has been extended to this field. In fact, many works that follow from this famous rule have been done to achieve more perfect tradeoff curves. However, the concept of multiplexing gain in DMT constrains a better understanding of the asymptotic interplay between transmission rate, frame error probability (FEP) and signal-to-noise ratio (SNR), and also fails to predict FEP curves accurately. Another formulation called the throughput- reliability tradeoff (TRT) was then proposed to avoid such limitation. Under this new rule, Azarian and Gamal well elucidated the asymptotic trends exhibited by the FEP curves in block-fading MIMO channels. Meanwhile they doubted whether the new rule can be used in more general channels and protocols. In this paper, we will prove that it does hold true in amplify-and-forward (AF) cooperative protocols. We propose a symbol based slotted amplify-and-forward (SSAF) protocol as the infrastructure to deduce the relationship between DMT and TRT. Furthermore, we derive the theoretical FEP curves predicted by TRT. We show that the FEP curves by simulation will asymptotically overlap with the theoretical curves predicted by TRT under some circumstance.

This paper studies the problem of queue control and user scheduling in multi-antenna broadcast (downlink) systems under zero forcing beamforming (ZFBF) transmit strategy. In the system, we assume that the data packet arrives randomly to the buffered transmitter. By taking the broadcast channel as a controlled queueing system, we deduce the property of queue control function which maximizes the weighted system throughput while guarantees the delay fairness among users. We also present a low complexity user selection algorithm with the consideration of queue state and channel state together. Simulation results show that the joint queue control and user selection policy can achieve considerable fairness and stability among users.

We propose a fast algorithm to compute an initial triangular square-root of the estimation error covariance matrix for BLAST, which are then applied to develop a square-root algorithm for BLAST. The speedups of our square-root BLAST algorithm over the previous square-root BLAST algorithm in the number of multiplications and additions are 3.78-5.8 and 3.95-5 respectively, and the ratios between the computational complexity of our BLAST algorithm and that of the linear MMSE detection algorithm in the number of multiplications and additions are 1.10-0.71 and 0.90-0.71 respectively, which means that for the first time, the nonlinear MMSE BLAST detector with successive interference cancellation can have even lower complexity than the linear MMSE detector. Moreover, our BLAST algorithm is also numerically stable and hardware friendly, since it uses unitary transformations to avoid the matrix inversions, and gets the initial square-root which is equivalent to a Cholesky factor of the estimation error covariance matrix.

In this letter, we address the problem of fulfilling quality-of-service (QoS) requirements in a multiple-input multiple-output (MIMO) relay system, where a set of target signal-to-noise ratios (SNRs) should be attained on different substreams. By solving a two-step optimization problem, we obtain a power-efficient relaying scheme that can make the SNR requirements asymptotically fulfilled. A simple relay selection method is also proposed such that the relay-power consumption can be largely reduced when there is a large number of relays.

In this letter, we propose two power allocation schemes based on the statistical channel state information (CSI) and instantaneous srarrr CSI at transmitters, respectively, for a 2-N-2 cooperative multicast system with nonregenerative network coding. Then the isolated precoder and the distributed precoder are, respectively, applied to the schemes to further improve the system performance by achieving the full diversity gain. Finally, we demonstrate that joint instantaneous srarrr CSI-based power allocation and distributed precoder design achieve the best performance.

This paper deals with design and performance analysis of transmit precoder optimization for MIMO-OFDM systems with limited feedback of channel state information (CSI). We assume that the receiver has perfect channel knowledge while the transmitter has only partial channel knowledge from limited feedback. We propose MSE-based optimal codebook design algorithm for MIMO-OFDM precoded spatial multiplexing systems under a specific average power constraint. The optimal precoder has the structure of a precoding and power allocation for each mode obtained by water-filling process. We derived the closed form solution for power allocation in the MSE- sense. Simulation results show that the MSE-based codebook construction algorithm with hybrid design of power allocation and precoding can achieve better performance than that of equal power allocattion based codebook in the previous works.

This paper deals with design and performance analysis of transmit precoder optimization for MIMO-OFDM systems with limited feedback of channel state information (CSI). We assume that the receiver has perfect channel knowledge while the transmitter has only partial channel knowledge from limited feedback. We present a BER-based optimal codebook construction algorithm for MIMO-OFDM systems under average power constraint using the Lloyd algorithm. The proposed optimal precoder has the structure of joint precoding and power allocation. Closed form solution has been derived for power allocation in the sense of minimum BER for the MIMO-OFDM systems. Simulation results show that the BER-based codebook construction algorithm with hybrid design of power allocation and multi-mode beamforming can achieve better performance than those of equal power allocation based codebooks in literature.

Golay complementary sequences have been introduced to encode the orthogonal frequency division multiplexing (OFDM) signals. In this paper, we extend Golay complementary sequences to sub-root sequences. Based on sub-root sequences, we propose a new scheme to construct good codes for OFDM with low PMEPR, which has the potential to discover Golay-Davis-Jedwab (GDJ), non-GDJ complementary codes and non-complementary codes. We firstly build the main construction theorem. Then we build short length root codes represented by Boolean functions ℤ2m→ ℤM, which are not the form of GDJ complementary sequences, but with low PMEPR. By our theorem, we then extend them to long length root codes with the same PMEPR. In this way, we offer an efficient method to identify a large number of codes for OFDM with low PMEPR.

In this paper, we propose a new multi-sender(t,n) identity based signcryption scheme with public verifiability using pairings , and give a security proof about the original scheme in the random oracle model.

This paper proposes a new simple efficient electronic auction scheme based on quadratic residue. Our scheme satisfies the basic security requirements for a sealed-bid auction system.

Based on experiments and numerical simulation, it has been widely believed that the time average mean square error in the first order sigma-delta modulator with input of bandlimited signals decays like O(/spl lambda//sup -3/) as the sampling ratio /spl lambda/ goes to infinity. This conjecture remains as an open problem for many years. Combining tools from number theory, harmonic analysis, real analysis and complex analysis, this paper shows that the conjecture holds in some reasonable sense.

Multiple-shift complementary sequences (MCS), a generalized form of Golay complementary sequences, have recently been introduced to encode OFDM signals, allowing a better trade-off between the code rate and peak-to-mean envelope power ratio (PMEPR). However, a table of such sequences needs to be constructed by exhaustive search, a practically impossible task for a moderately large number of sub-carriers. As has been done for Golay complementary sequences and generalized Golay complementary sequences, this paper successfully identifies a class of MCS as the second order cosets of the first order Reed-Muller codes. We also present a new proof for the PMEPR of MCS.

Generalized Golay complementary sequences and multiple-shift complementary sequences have recently been introduced to encode orthogonal frequency division multiplexing (OFDM) signals, reducing the peak-to-mean envelope power ratio (PMEPR). Certain classes of these complementary sequences have been identified as a subset of second order cosets of the first order Reed-Muller codes. Since the code rates of these encoding schemes are prohibitively low for a large number of sub-carriers, it is necessary to find an efficient algebraic way to produce sufficient number of codewords such that the code rate of the encoding scheme is high enough. In this paper, we introduce generalized Rudin-Shapiro polynomials, a subset generalized Golay complementary sequences, to encode OFDM signals. In our encoding scheme, a matrix equation recursively produces a sufficient number of Rudin-Shapiro polynomials such that the code rate increases linearly with respect to the PMEPR. Therefore, it offers an excellent trade-off performance between the code rate and the PMEPR.

This paper extends previous work on modeling stochastic decision discrete-event systems (DDES) through a generalized semi-Markov decision process (GSMDP), which discards any restrictive unrealistic assumptions and can be applied to complex cases including non-Markovian environment. Moreover, as a typical example, we develop a GSMDP model for the optimal call admission control (CAC) problem in an integrated voice/data wireless network supporting multiple traffic types with different resource requirements. In contrast to existing methods, this approach can better model the real world of the next generation wireless network behaviors. Besides, through a form of reinforcement learning algorithm known as Q-learning, we can solve the Bellman optimality equation with requiring neither explicit state transition probabilities nor any assumptions behind the network model.

It has been found that the quantization error e for a conventional oversampled analog-to-digital (A/D) conversion behaves like /spl par/e/spl par//sup 2/=O(/spl tau//sup 2/) with respect to the sampling rate /spl tau/. Recently, conventional A/D conversion has been extended to A/D conversion based on shift-invariant spaces. As consequences of such extension, it offers rich choices to build a nonideal A/D conversion system of high accuracy and low computational complexity, as well as reduces the noise sensitivity and computational complexity in digital-to-analog (D/A) conversion. Therefore, it is necessary to establish the estimate of quantization error for the extended A/D conversion based on shift-invariant spaces. In this paper, we introduce a constructive method to establish an estimate of the quantization error as |e|/sup 2/=O(/spl tau//sup 2/) for oversampled A/D conversion in shift-invariant spaces. Meanwhile, we demonstrate that the bit rate required to encode the converted digital signal in such A/D conversion scheme only increases as the logarithm of the sampling ratio. Therefore, the quantization error is an exponentially decaying function of the bit rate. In order to establish such an estimate, we need the nonuniform sampling theorem for shift-invariant spaces, which, as the necessary preparation, is studied prior to introducing the constructive method.

A conventional A/D converter prefilters a signal by an ideal lowpass filter and performs sampling for bandlimited signals by the Nyquist sampling rate. Recent research reveals that A/D conversion in a shift invariant space provides more flexible choices for designing a practical A/D conversion system of high accuracy. This paper focuses on the maximal gap of a sampling set for the iterative algorithm in shift invariant spaces, which provides an explicit formula to calculate the maximal gap of a sampling set in terms of a generator of the undertaken shift invariant spaces.

A necessary and sufficient condition for sampling in the general framework of shift invariant spaces is derived. Then this result is applied, respectively, to the regular sampling and the perturbation of regular sampling in shift invariant spaces. A simple necessary and sufficient condition for regular sampling in shift invariant spaces is attained. Furthermore, an improved estimate for the perturbation is derived for the perturbation of regular sampling in shift invariant spaces. The derived estimate is easy to calculate, and shown to be optimal in some shift invariant spaces. The algorithm to calculate the reconstruction frame is also presented.

From the Paley-Wiener 1/4-theorem, the finite energy signal f(t) can be reconstructed from its irregularly sampled values f(k+/spl delta//sub /spl kappa//) if f(t) is band-limited and sup/sub /spl kappa//|/spl delta//sub /spl kappa//|<1/4. We consider the signals in wavelet subspaces and wish to recover the signals from its irregular samples by using scaling functions. Then the method of estimating the upper bound of sup/sub /spl kappa//|/spl delta//sub /spl kappa//| such that the irregularly sampled signals can be recovered is very important. Following the work done by Liu and Walter (see J. Fourier Anal. Appl., vol.2, no.2, p.181-9, 1995), we present an algorithm which can estimate a proper upper bound of sup/sub /spl kappa//|/spl delta//sub /spl kappa//|. Compared to Paley-Wiener 1/4-theorem, this theorem can relax the upper bound for sampling in some wavelet subspaces.