Li Huang, Zhongxin Liu, Yifan Wu, Tao Yin, Dong li, Jichao Bi, Nankun Mu, Hongyu Zhang, Meng Yan. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Backscatter communication (BackCom) has gained increasing attention due to its low power consumption and low cost. However, its performance is severely degraded by the near-far effect due to the limited backscattering power at the tag side. In this paper, we consider two effective methods to solve the above problem by using the reconfigurable intelligent surface (RIS) and the relay. The objective is to maximize the received signal-to-noise ratio (SNR) subject to the transmit and circuit power control. To gain insight into both schemes, we derive a closed-form expression for the coverage radius of RIS-aided BackCom. Furthermore, considering the relay as a competing technology to the RIS, we compare their performance in terms of transmission performance, required transmit power, and quantify the number of reflecting elements needed for the RIS to outperform the relay. Simulation results demonstrate the RIS-aided BackCom with a sufficiently large number of elements achieves a superior performance, offers greater deployment flexibility, and is suitable for high self-interference levels.

The growing demands for high-precision sensing and ultra-reliable low-latency communications in Internet of Vehicles (IoV) networks, coupled with increasingly congested spectrum resources, have driven integrated sensing and communication (ISAC) technologies toward millimeter-wave (mmWave) frequency bands. However, apart from the inherent openness of wireless channels, this transition exposes critical security gaps that the conventional physical layer security methods featured by communication interference struggle to mitigate: the cross-domain coupling interference between communication and sensing subsystems among vehicles, forming an emergent threat landscape in ISAC-IoV networks. Based on the fact that conventional forward-facing vehicular mmWave radars are typically equipped with horizontally oriented narrow beam, and have limitations in vertical resolution due to the utilization of 1D horizontally arranged antenna arrays, in this paper, we propose a 3D communication-2D sensing (Com3DSen2D) enabled spatial separation and interference decoupling framework. Furthermore, under the proposed Com3DSen2Dframework, to make a balance between sensing accuracy, communication reliability and security, we formulate a non-convex optimization problem of secrecy rate maximization through jointly optimizing 3D-BF design of communication subsystems and radar sensing power allocation of sensing subsystems, subject to the expected levels of sensing accuracy and communication reliability constraints. Experimental evaluations demonstrate that our joint optimization approach achieves significant performance gains over individual optimization benchmarks, yielding 25.3% and 57.8% improvements in secrecy rate compared to isolated 3D-BF optimization and radar sensing power allocation, respectively. Moreover, experimental results obtained from the hardware platform further validate the effectiveness of our proposed framework. This work establishes a comprehensive solution for coupling interference management and security enhancement in next-generation ISAC-IoV networks.

In strong line-of-sight (LoS) scenarios, the lack of scattering paths leads to significant rank deficiency in the channel, thereby limiting the spatial multiplexing capabilities of multiple-input multiple-output (MIMO) systems. To address this inherent deficiency, reconfigurable intelligent surfaces (RISs) have been proposed as a promising solution for enhancing the channel scattering environment in the context of sixth-generation (6G) networks. However, traditional passive RISs often require a large number of elements to counteract multiplicative fading. In contrast, active RISs (ARISs) can greatly reduce the reflecting elements requirement and effectively overcome the multiplicative fading. This paper investigates using multiple ARISs to reshape the wireless channel and formulates the effective rank (ER) maximization problem under two ARIS phase-shift models. We propose a multi-phase particle swarm optimization (MPSO) scheme to optimize the phase shifts of the ARISs under a continuous phase-shift model. However, the continuous phase-shift model entails substantial hardware implementation costs. To solve this, we introduce the multi-phase maximum cross-swapping algorithm (MMCA) under a discrete phase-shift model. Furthermore, we explore the impact of ARIS deployment positions on the channel ER and employ the sparrow search algorithm (SSA) to adjust the ARIS positions. The SSA-MPSO scheme is then proposed, which alternately optimizes the deployment positions and phase shifts of multiple ARISs, achieving an ER close to the theoretical upper bound. Simulation results demonstrate that the proposed scheme significantly outperforms baseline schemes, effectively enhancing the spectral efficiency.

Movable antennas have recently emerged as a promising paradigm to overcome the inherent inflexibility of conventional fixed antenna arrays. By enabling the physical movement of antenna elements, movable antennas introduce additional spatial degrees of freedom to wireless systems. Although the importance of the movement delay has been recognized, a critical yet unexplored problem is that the movement schemes used to transition from the initial to the target positions are overlooked. This paper presents a systematic investigation of two fundamental movement schemes: concurrent movement and cumulative movement, and addresses a key design question: Should we prioritize minimizing the total configuration time or maximizing the communication performance under a limited movement budget? Specifically, two different optimization problems are formulated to maximize the sum rate under different movement constraints, thereby introducing tighter coupling between antenna positions and beamforming design, increasing computational complexity in joint optimization, and necessitating efficient allocation of delay budgets across multiple antennas. To this end, we develop an alternating-optimization-based algorithm to obtain the corresponding suboptimal solutions. A theoretical degeneration analysis is further conducted to provide fundamental insights. The optimal strategy for a single antenna can surprisingly be to not move. While in multi-antenna systems, the performance gap scales with antenna displacement, movement budgets, and transmit power. Simulation results show that movable antennas substantially improve achievable rates over fixed antennas, with concurrent movement benefiting low-latency scenarios, while cumulative movement favoring high-rate or delay-tolerant scenarios.

Low Earth Orbit (LEO) satellites are playing an important role in earth observation, providing valuable images for training machine learning (ML) models used in tasks such as environmental monitoring and pattern recognition. However, due to unstable communication links and limited downlink bandwidth, it is economically impractical to transmit all raw images to ground stations (GSs) for model training. Federated learning (FL), a privacy-preserving distributed machine learning method, can reduce the communication overhead by exchanging model parameters. Generally, FL needs a fixed central server to aggregate the global model, which is challenging in LEO satellite networks, given the dynamic nature of satellite orbits. To overcome this, we propose a decentralized federated learning (DFL) framework that enables efficient model aggregation through satellite collaboration. Specifically, the proposed framework, named FedHRA, is based on model-agnostic meta-learning (MAML), which jointly optimizes hyperparameters and resource allocation to mitigate straggler effect and address statistical heterogeneity. Extensive numerical results on MNIST and CIFAR-10 datasets demonstrate that FedHRA achieves shorter learning time and higher model accuracy compared to the benchmark frameworks.

The task offloading technology plays a vital role in the Internet of Vehicles (IoV) by satisfying diversified vehicular demands, such as energy consumption and processing delay of computing tasks. Unlike the current related works, which not only ignored wireless interference when making information exchange, but also overlooked the available resources of parked and moving vehicles, this paper proposes a comprehensive solution. First, we model vehicle speed using a truncated Gaussian distri bution, replacing simplistic average speed models in prior studies. Wireless interference in V2V/V2I communications significantly impacts communication quality and reliability, leading to packet loss, increased latency, and reduced throughput. For instance, in high-density traffic scenarios, interference can disrupt com munication links, hindering effective task offloading. Next, by incorporating wireless interference and effective communication duration in V2V and RSUs, we propose an analytical framework for task offloading that jointly optimizes energy consumption and processing delay, leveraging resources from parked/moving vehicles and RSUs. Furthermore, inspired by the Multi-Agent Deep Deterministic Policy Gradient (MADDPG), we design an Interference-Aware Multi-Agent Deep Deterministic Policy Gradient (IA-MADDPG) algorithm. The algorithm ensures resource load balancing while reducing energy consumption and latency, and improves the task offloading completion rate. Simulations validate the effectiveness of IA-MADDPG, demonstrating supe rior convergence speed, energy efficiency, and latency reduction compared to existing methods.

Privacy disclosure from model parameters and malicious attacks are critical issues in federated learning (FL). Existing research has yet to effectively address the simultaneous need for efficient communication design, privacy protection, and attack detection, which impedes the widespread adoption of FL in mobile edge networks over 6G wireless communication. In this paper, we propose a trustworthy FL framework that can ensure privacy, robustness, accountability, fairness, and explainability in mobile edge networks. Specifically, we integrate authenticated zero-knowledge proofs (ZKPs) and Pedersen commitments into the FL process. Despite the lack of direct access between servers and mobile devices, the servers can still identify trustworthy clients for specific tasks. Clients can verify the authenticity of the received global model based on the provided proofs and commitments. Furthermore, we leverage Ethereum to act as the verifier and authenticator of models. This verification and authentication process enables the servers to detect abnormal local models and perform trust-based aggregations. Numerical results demonstrate that the proposed trustworthy FL framework significantly improves the global model's in terms of accuracy, convergence rate, and security.

In Federated Learning (FL), with parameter aggregated by a central node, the communication overhead is a substantial concern. To circumvent this limitation and alleviate the single point of failure within the FL framework, recent studies have introduced Decentralized Federated Learning (DFL) as a viable alternative. Considering the device heterogeneity, and energy cost associated with parameter aggregation, in this paper, the problem on how to efficiently leverage the limited resources available to enhance the model performance is investigated. Specifically, we formulate a problem that minimizes the loss function of DFL while considering energy and latency constraints. The proposed solution involves optimizing the number of local training rounds across diverse devices with varying resource budgets. To make this problem tractable, we first analyze the convergence of DFL with edge devices with different rounds of local training. The derived convergence bound reveals the impact of the rounds of local training on the model performance. Then, based on the derived bound, the closed-form solutions of rounds of local training in different devices are obtained. Meanwhile, since the solutions require the energy cost of aggregation as low as possible, we modify different graph-based aggregation schemes to solve this energy consumption minimization problem, which can be applied to different communication scenarios. Finally, a DFL framework which jointly considers the optimized rounds of local training and the energy-saving aggregation scheme is proposed. Simulation results show that, the proposed algorithm achieves a better performance than the conventional schemes with fixed rounds of local training, and consumes less energy than other traditional aggregation schemes.

Cell-free massive multiple-input multiple-output (CF massive MIMO) is regarded as a promising technology for next-generation wireless communication systems. However, conventional CF massive MIMO systems typically employ fixed-position antennas (FPAs) at access points (APs), which limits the exploitation of spatial degrees of freedom (DoFs) for antenna position optimization. To address this issue, we propose the use of fluid antennas (FAs) in place of FPAs, enabling more DoFs at APs and leading to a novel FA-aided CF massive MIMO (FA-CF) architecture. In this paper, we investigate the uplink spectral efficiency (SE) of FA-CF systems with imperfect channel state information (CSI). We design a minimum mean-square error (MMSE)-based channel estimation scheme to estimate the aggregated channels between APs and user equipments (UEs). We further derive achievable SE expressions for both centralized and distributed processing schemes, including fully centralized processing (FCP), large-scale fading decoding (LSFD), and equal-gain decoding processing (EGDP). Moreover, we formulate a mean-square error (MSE) minimization problem based on the signal transmission model. To solve this problem, we develop an efficient algorithm that combines orthogonal matching pursuit (OMP) with binary search to jointly optimize FA positions and the combining matrix. In addition, we propose a protective weak-ordering (PWO) strategy to enhance the SE of the FCP scheme. Numerical results demonstrate that FA-CF significantly outperforms conventional CF systems in terms of SE, even with a limited number of antennas, and maintains strong robustness under imperfect CSI or heavy UE loads by adaptively adjusting antenna positions. These results highlight FA-CF as a promising architecture offering enhanced SE and robustness for future wireless systems, particularly in scenarios where large-scale AP deployment is infeasible or cost-constrained.

In strong line-of-sight (LoS) communication systems, the lack of scattering paths leads to significant rank deficiency in the channel matrix, thereby limiting the spatial multiplexing gain of multiple-input multiple-output (MIMO) systems. To address this inherent deficiency, this paper utilizes multiple active reconfigurable intelligent surfaces (ARISs) to reshape the wireless channel and solve the effective rank (ER) maximization problem. We propose a multi-phase particle swarm optimization (MPSO) scheme to optimize the phase shifts of the ARISs. Furthermore, we explore the impact of ARIS deployment positions on the channel ER and employ the sparrow search algorithm (SSA) to adjust the ARIS positions. Then we propose the SSA-MPSO scheme, which alternately optimizes the deployment positions and phase shifts of multiple ARISs, achieving an ER close to the theoretical upper bound. Simulation results demonstrate that the proposed scheme significantly outperforms baseline schemes, effectively enhancing the spatial multiplexing gain under imperfect channel state information (CSI).

This paper investigates the covert communication (CC) performance of a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) and rate splitting (RS) systems operating over Rician fading channels. Alice applies RS and NOMA to the downlink transmission of two legitimate users aided by the STAR-RIS in the presence of two non-colluding illegal users. Specifically, closed-form expressions for detection error probability, optimal detection threshold, minimum detection error probability (MDEP) of the warden, and the covert rate of the NOMA user pair are derived. The accuracy of the derived results is verified through Monte Carlo simulations. The results demonstrate that the MDEP depends only on the power allocation factor of the covert users and is independent of the transmit power or STAR-RIS deployment distance. Furthermore, the RS-NOMA system exhibits superior CC performance compared to the conventional NOMA system.

The phase shift information (PSI) overhead poses a critical challenge to enabling real-time intelligent reflecting surface (IRS)-assisted wireless systems, particularly under dynamic and resource-constrained conditions. In this paper, we propose a lightweight PSI compression framework, termed meta-learning-driven compression and reconstruction network (MCRNet). By leveraging a few-shot adaptation strategy via model-agnostic meta-learning (MAML), MCRNet enables rapid generalization across diverse IRS configurations with minimal retraining overhead. Furthermore, a novel depthwise convolutional gating (DWCG) module is incorporated into the decoder to achieve adaptive local feature modulation with low computational cost, significantly improving decoding efficiency. Extensive simulations demonstrate that MCRNet achieves competitive normalized mean square error performance compared to state-of-the-art baselines across various compression ratios, while substantially reducing model size and inference latency. These results validate the effectiveness of the proposed asymmetric architecture and highlight the practical scalability and real-time applicability of MCRNet for dynamic IRS-assisted wireless deployments.

Virtual Programmable Logic Controllers (vPLCs), as a newborn technology, are becoming increasingly important in modern industrial automation due to their flexibility and scalability. There is lack of researches on data synchronization and redundancy mechanisms for vPLCs, limiting applications of vPLCs in critical industrial scenarios. This paper designs and implements a data synchronization and redundancy mechanism between vPLCs based on heartbeat detection to enhance the reliability of vPLC systems. The mechanism continuously monitors for failures and synchronizes data between vPLCs to ensure seamless control task takeover in the event of a failure. Experimental results demonstrate the mechanism’s high effectiveness in fault detection and recovery, achieving a redundancy switchover time that meets industrial application requirements.

Existing reconfigurable intelligent surface (RIS)-enabled symbiotic systems generally rely on a dynamic adjustment of the amplitude of the RIS’s reflection coefficient to continuously change from 0 to 1 to support the underlying symbiotic trans-missions, which is however infeasible for practical RIS hardware. To address this, we propose a novel on-off digitalized symbiotic backscatter non-orthogonal multiple access (NOMA) system that employs an on-off mechanism of the RIS’s reflecting elements. In particular, the adjustment of the on-off state of reflecting elements in batches is adaptively invoked to control the power gain of the backscatter channel, thereby changing the amplitude of backscatter signals to establish symbiotic transmissions. In order to evaluate the practicability of the proposed on-off symbiotic mechanism, an analytical expression of coexistence outage probability at high SNR has been derived. Moreover, the adjustment of RIS’s elements on-off state is discussed and extended to the scenarios of one-shot and one-by-one activation modes. Finally, representative numerical results show that when a sufficient number of reflecting elements is deployed on the RIS, our proposed on-off mechanism can fully support the symbiotic transmissions of the underlying systems.

Abstract Traffic prediction serves as a critical foundation for traffic balancing and resource management in Low Earth Orbit (LEO) satellite networks, ultimately enhancing the efficiency of data transmission. The self‐similarity of traffic sequences stands as a key indicator for accurate traffic prediction. In this article, the self‐similarity of satellite traffic data was first analyzed, followed by the construction of a satellite traffic prediction model based on an improved Long Short‐Term Memory (LSTM). An early stopping mechanism was incorporated to prevent overfitting during the model training process. Subsequently, the Diebold‐Mariano (DM) test method was applied to assess the significance of the prediction effect between the proposed model and the comparison model. The experimental results demonstrated that the improved LSTM satellite traffic prediction model achieved the best prediction performance, with Root Mean Squared Error values of 18.351 and 8.828 on the two traffic datasets, respectively. Furthermore, a significant difference was observed in the DM test compared to the other models, providing a solid basis for subsequent satellite traffic planning.

Semantic communication (SemCom) aims to convey the intended meaning of messages rather than merely transmitting bits, thereby offering greater efficiency and robustness, particularly in resource-constrained or noisy environments. In this paper, we propose a novel framework which is referred to as joint source-channel noise adding with adaptive denoising (JSCNA-AD) for SemCom based on a diffusion model (DM). Unlike conventional encoder-decoder designs, our approach intentionally incorporates the channel noise during transmission, effectively transforming the harmful channel noise into a constructive component of the diffusion-based semantic reconstruction process. Besides, we introduce an attention-based adaptive denoising mechanism, in which transmitted images are divided into multiple regions, and the number of denoising steps is dynamically allocated based on the semantic importance of each region. This design effectively balances the reception quality and the inference latency by prioritizing the critical semantic information. Extensive experiments demonstrate that our method significantly outperforms existing SemCom schemes under various noise conditions, underscoring the potential of diffusion-based models in next-generation communication systems.

Cell-free massive multiple-input multiple-output symbiotic radio (CF-SR) has recently been introduced as a promising solution for the Internet of Things (IoT), offering cost advantages and more uniform coverage performance for user devices. However, most previous studies assume perfect hardware, which is impractical in IoT systems. In this article, we investigate the uplink performance of CF-SR systems in the presence of hardware impairments (HWIs). We adopt two novel schemes for hybrid combining, namely hybrid maximum ratio (HMR) and hybrid local minimum mean square error (HL-MMSE), both of which effectively enhance the spectral efficiency (SE) of the backscattering link. We derive closed-form expressions for the achievable SE of both conventional MR and HMR combining schemes, taking into account the imperfect channel state information (CSI) and HWIs. The simulation results that both the direct and backscattering links are primarily limited by HWIs including multiplicative and additive distortions from the device side. We apply the differential evolution (DE) algorithm for power control to maximize the minimum SE, and the results show that the DE algorithm improves the minimum SE by approximately 52%, avoiding further degradation of the SE of the weakest device due to the impact of HWIs.

Wireless temperature measurement, compared with traditional temperature measurement methods, can not only offer lower costs and higher convenience but also measure the temperature of the target object in real time and continuously. However, current mainstream-based wireless temperature measurement schemes still face issues such as low accuracy, high power consumption and limited data transmission distance. To this end, this paper proposes a novel ultra-high frequency (UHF) battery-free wireless temperature sensing system. The system utilizes the radio frequency (RF) electromagnetic wave signals in the surrounding environment for energy supply, employs a self-compensating temperature sensor to accurately measure the temperature, and achieves data communication by modulating the electromagnetic wave signals through backscattering. This system is characterized by its low power consumption, long working distance, broad temperature measurement scope and high accuracy. The chip is fabricated in Huahong 0.18 μm 1P6M BCD technology, with a tag chip area of 0.097 mm2. Operating at the frequency of 915 MHz, the RF energy harvesting sensitivity is 17 dBm, and the effective distance of RF energy harvesting can be up to 10 m at the equivalent isotropically radiated power (EIRP) of 36 dBm. With a two-point calibration, the measurement covers a range from 0 to 100 with a mean error of +0.47/0.79 . The average power consumption of the chip is as low as 0.97 μW.

Reconfigurable intelligent surface (RIS), a recently emerging technology in wireless communications, has been gradually attracting widespread attention. Generally, a high array gain can be achieved by increasing the number of reflecting elements. However, the number of elements is limited by cost, overhead, deployment environments, power consumption, etc. In particular, the cost and power consumption may be high when a large number of elements are deployed, which results in a low energy efficiency even if there is an increase in the spectral efficiency. On the other hand, an increase in the number of elements does not necessarily increase the performance since it significantly increases the delivery overhead and may compress the data transmission time. Thus, number configuration for the RIS-aided systems appears to be particularly important. To demonstrate the advantages of the number configuration for the RIS-aided systems, in this article, we first introduce the overview of the RIS that is profitable for number configuration. Then, several design frameworks of number configurations are proposed and discussed, such as hybrid RIS, element on-off control, hybrid phase shift, phase delivery, etc., followed by numerical results to demonstrate the achieved benefits of dynamic number configuration. Furthermore, future directions for number configuration applications are presented, including functionality extensions and framework extensions. Finally, the open issues of realizing number configuration for the RIS-aided systems are outlined and elaborated upon.

Semantic communication has emerged as a promising solution to meet the growing demand for efficient data transmission in the information age. Unlike traditional communication methods that focus on transmitting raw data, semantic communication prioritizes preserving the meaning of transmitted information, which significantly reduces the data volume. However, implementing semantic communication systems in resource-constrained environments, such as Internet of Things (IoT) devices, remains challenging due to limited computational resources. In this letter, we propose a novel lightweight deep learning (DL) model, termed the lightweight image compression and reconstruction network (LICRnet). LICRnet leverages depthwise separable convolution (DSC) and a local and nonlocal mixture (LNLM) block to significantly reduce computational costs. Additionally, the LNLM incorporates a variable window size-based multiscale attention mechanism (VW-MSA), enabling it to effectively learn from both local detailed features and global high-level meaningful features. Extensive simulations demonstrate that LICRnet significantly reduces computational complexity while maintaining satisfactory image compression and reconstruction performance, making it highly suitable for deployment in resource-constrained environments.

Ambient backscatter communication (AmBC) is a newly cutting-edge technology for the Internet of Things, which utilizes the ambient radio frequency signal as the carrier to transmit information. Existing works focus on the simple on-off keying modulation which has low channel utilization. However, it is not desirable to develop the high-order modulation in the power domain due to the weak strength of the backscattered signal. In this paper, we extend the high-order modulation in the time domain instead, i.e., high-order time shift keying (TSK). Since the channel coherent time is unknown at the receiver and the tag, the detection methods with training symbols will have a huge performance degradation if the channels are changed and training symbols become outdated. To overcome this challenge, we further propose the transition-aided TSK (TA-TSK) modulation and the frequency-shifting TSK (FS-TSK) modulation, which do not need to send training symbols at the tag. These two methods can work well even if the channel coherent time is as short as one time slot. Meanwhile, the detection methods for TSK are developed and the corresponding closed-form bit-error-rate (BER) expressions are obtained. Simulation results show that a high modulation order is more suitable for the M-ary phase shift keying source than the complex Gaussian source. The high order TSK can provide at least$2~dB$signal-to-noise ratio (SNR) gain at the same BER compared to the on-off keying.

Backscatter communication (BackCom) technology that takes advantage of the radio frequency signals to facilitate the communications of passive devices has attracted much attention in recent years. To enhance its communication performance, the multiple-input and multiple-output (MIMO) technology has been introduced to BackCom. Channel estimation is crucial for the MIMO BackCom system. However, the optimization for the pilot training sequences has not been studied. In this paper, we propose a pilot sequence design algorithm for MIMO BackCom systems with spatially correlated antennas, which can estimate both the direct link and the backscatter link channel information. We derive the optimal structure of the source and the tag pilot sequences which achieves the minimum mean-squared error (MSE) of channel estimation. Then, we optimize the power allocation between the source pilot sequences. Simulation results show that our proposed algorithm can estimate channel efficiently and achieve better sum MSE performance than the benchmark without power allocation.

Physical layer security (PLS) technology based on the fixed-position antenna (FPA) has attracted widespread attention. Due to the fixed feature of the antennas, current FPA-based PLS schemes cannot fully utilize the spatial degree of freedom, and thus a weaken secure gain in the desired/undesired direction may exist. Different from the concept of FPA, movable antenna (MA) is a novel technology that reconfigures the wireless channels and enhances the corresponding capacity through the flexible movement of antennas on a minor scale. MA-empowered PLS enjoys huge potential and deserves further investigation. In this paper, for the first time, we investigate the secrecy performance of MA-enabled PLS system where a MA-based base station (BS) transmits the confidential information to multiple single-antenna Bobs, in the presence of the single-antenna eavesdropper (Eve) with no location information, by jointly optimizing the beamforming and antenna positions at the BS. Furthermore, the non-convex optimization problem can be solved via the methods of projected gradient ascent and alternating optimization. Also, and simulated annealing is adopted to find a high-quality feasible solution. Simulation results demonstrate the effectiveness and correctness of the proposed method. In particular, MA-enabled PLS scheme can significantly enhance the secrecy rate compared to the conventional FPA-based ones for different settings of key system parameters.

Integrated sensing and communication (ISAC) plays a crucial role in the Internet of Vehicles (IoV), serving as a key factor in enhancing driving safety and traffic efficiency. To address the security challenges of the confidential information transmission caused by the inherent openness nature of wireless medium, different from current physical layer security methods, which depends on the additional communication interference costing extra power resources, in this paper, we investigate a novel physical layer security solution, under which the inherent radar sensing interference of the vehicles is utilized to secure wireless communications. To measure the performance of physical layer security methods in ISAC-based IoV systems, we first define an improved security performance metric called by transmission reliability and sensing accuracy based secrecy rate (TRSA_SR), and derive closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), success ranging probability (SRP) for evaluating transmission reliability, security and sensing accuracy, respectively. Furthermore, we formulate an optimization problem to maximize the TRSA_SR by utilizing radar sensing interference and joint design of the communication duration, transmission power and straight trajectory of the legitimate transmitter. Finally, the non-convex feature of formulated problem is solved through the problem decomposition and alternating optimization. Simulations indicate that the sensing interference utilization, combined with joint design of transmission power and straight trajectory of the transmitter, achieves a secrecy rate of 3.92bps/Hz for different noise powers for the case of perfect channel state information (CSI). The proposed method maintains robustness, achieving a 60.17% improvement of TRSA_SR under unavailable CSI and location information of the Eve.

Semantic communication (SemCom) has emerged as a promising technique for the next-generation communication systems, in which the generation at the receiver side is allowed with semantic features’ recovery. However, the majority of existing research predominantly utilizes a singular type of semantic information, such as text, images, or speech, to supervise and choose the generated source signals, which may not sufficiently encapsulate the comprehensive and accurate semantic information, and thus creating a performance bottleneck. In order to bridge this gap, in this paper, we propose and investigate a SemCom framework using multimodal information to supervise the generated image. To be specific, in this framework, we first extract semantic features at both the image and text levels utilizing the Convolutional Neural Network (CNN) architecture and the Contrastive Language-Image Pre-Training (CLIP) model before transmission. Then, we employ a generative diffusion model at the receiver to generate multiple images. In order to ensure the accurate extraction and facilitate high-fidelity image reconstruction, we select the ”best” image with the minimum reconstruction errors by taking both the aided image and text semantic features into account. We further extend multimodal semantic communication (MMSemCom) system to the multiuser scenario for orthogonal transmission. Experimental results demonstrate that the proposed framework can not only achieve the enhanced fidelity and robustness in image transmission compared with existing communication systems but also sustain a high performance in the low signal-to-noise ratio (SNR) conditions.

Battery free Internet of Things (BF IoT), which is realized by harvesting ambient energy to power the IoT devices, has many advantages such as low power, low cost, free-maintenance, easy deployment, and environmental protection. As the two key enabling technologies of BF IoT, the integration of energy harvesting, management and backscatter communication (BackCom) is expected to meet practical applications. However, several fundamental issues need to be addressed to fully develop the potential of such integration. In this paper, we propose an ultra-low power energy harvesting and management scheme that effectively reduces the startup and quiescent power consumption. In the case of extremely limited energy harvested, the Long Range (LoRa) BackCom digital waveform generation algorithm with ultra-low power consumption and low complexity is studied, which can support low-power and low-cost micro-controllers (MCUs). Then, a radio frequency (RF) front-end scheme that is compatible with RF energy harvesting (RFEH), BackCom, and On-Off-Keying (OOK) receiving is proposed. On this basis, we design and implement a fully functional BF LoRa Tag, which integrates tag antennas, LoRa BackCom, low-power OOK receiving, multi-parameter real-time sensing, RFEH and management, at a cost of only 6. Our test results show that the BF LoRa Tag can be self-powered by harvesting the RF energy as low as –19 dBm, with the cold-start power as low as 280 nW, the quiescent power as low as 49 nW, the OOK receiving power consumption as low as$1.74~\mu $W, the LoRa BackCom power consumption of only$251~\mu $W, and the communication distance up to 445 meters. Compared with the prototypes in existing literature, the BF LoRa Tag not only has lower power consumption, wider coverage, and the lowest cost, but also has complete system functions. Finally, we discuss the reciprocity between stations and receivers, as well as the BF LoRa cellular communication network, which provides useful references for the practical application and large-scale deployment of BF IoT in the future.

Task offloading technology plays a crucial role in the Internet of Vehicles (IoV) by minimizing processing delays through the joint optimization of heterogeneous computing resources supported by vehicles, roadside units (RSUs), and macro base stations (MBSs). Previous works have often ignored the wireless interference during the exchange and sharing of task data. Additionally, the potential for vehicles with similar driving behaviors to form vehicle platooning (VEH-PLA) and effectively integrate individual vehicle resources has not been adequately addressed. Furthermore, as a novel resource management paradigm, VEH-PLA should consider task categorization since vehicles within a VEH-PLA may have identical task offloading requestsan aspect that has also received insufficient attention. In this paper, considering wireless interference, vehicle mobility, VEH-PLA, and task categorization, we propose four task offloading models aimed at minimizing processing delays. By utilizing centralized training and decentralized execution (CTDE) based on multi-agent deep reinforcement learning (MADRL), we present a task offloading decision-making method to find the global optimal offloading decision. This results in significant enhancements in resource load balancing and reductions in processing delays. Finally, simulations validate that the proposed method significantly outperforms traditional task offloading approaches in terms of minimizing processing delays while maintaining balanced resource utilization.

With the rapid advancement of data acquisition technologies, multiview data have been widely applied in fields such as social networks, computer vision, and natural language processing. Multiview data typically contain information arising from different views or sensors, offering more perspectives for observation. The multiview nature also brings challenges, such as high dimensionality, noise, heterogeneity, and redundancy. Particularly, in scenarios with limited labeled data, traditional single-view learning methods often struggle to handle these complex issues. To address this, this article proposes an unsupervised multiview learning framework that integrates Takagi–Sugeno–Kang fuzzy systems and graph-based adaptive regularization (MvTSK-GAR) to handle the heterogeneity and redundancy in multiview data effectively. Specifically, this article first captures the uncertainty in multiview data through fuzzy rules and models the structural relationships between the data using graph-based adaptive regularization. The framework does not rely on a large amount of labeled data. Instead, it integrates complementary information coming from different views to automatically mine latent patterns, thus generating more accurate and stable data representations. Experimental results demonstrate that the proposed framework performs well in various real-world applications, particularly excelling in high-dimensional data processing and noise reduction. Good performance in multiple publicly available datasets validates the effectiveness of our approach.

Large Language Models (LLMs) have been widely adopted by developers in software development. However, the massive pretraining code data is not rigorously filtered, allowing LLMs to learn unsafe coding patterns. Several prior studies have demonstrated that code LLMs tend to generate code with potential vulnerabilities. The widespread adoption of intelligent programming assistants poses a significant threat to the software development process. Existing approaches to mitigating this risk primarily involve constructing secure data that are free of vulnerabilities and then retraining or fine-tuning the models. However, such an effort is resource intensive and requires significant manual supervision. When the model parameters are too large (e.g., more than 1 billion) or multiple models with the same parameter scale have the same optimization needs (e.g., to avoid outputting vulnerable code), the above work will become unaffordable. To address this challenge, in previous work, we proposed CoSec, an approach to improve the security of code LLMs with different parameters by utilizing an independent and very small parametric security model as a decoding navigator.Despite CoSec’s excellent performance, we found that there is still room for improving: 1) its ability to maintain the functional correctness of hardened targets, and 2) the security of the generated code. To address the above issues, we propose CoSec+, a hardening framework consisting of three phases: 1) Functional Correctness Alignment, which improves the functional correctness of the security base with knowledge disstillation; 2) Security Training, which yields an independent, but much smaller security model; and 3) Co-decoding, where the security model iteratively reasons about the next token along with the target model. Due to the higher confidence that a well-trained security model places in secure and correct tokens, it guides the target base model to generate more secure code, even as it improves the functional correctness of the target base model. We have conducted extensive experiments in several code LLMs (i.e., CodeGen, StarCoderBase, DeepSeekCoder and Qwen2.5-Coder), and the results show that our approach is effective in improving the functional correctness and security of the models. The evaluation results show that CoSec+ can deliver a 0.8% to 37.7% improvement in security across models of various parameter sizes and families; moreover, it preserves the functional correctness of the target base models—achieving functional-correctness gains of 0.7% to 51.1% for most of those models.

This paper proposes a fast-decoding scheme based on the successive interference cancellation (SIC) framework for symbiotic backscatter multi-user non-orthogonal multiple access (NOMA) systems, which aims to decode the desired primary NOMA signal and the backscatter signal for the end-users in an efficient manner. Under the proposed decoding framework, we derive a closed-form expression of the coexistence outage probability (COP) with perfect SIC for the end-users over Nakagami-m fading channel. More importantly, the diversity order is determined by the bottleneck fading parameter of the two-hop backscatter channels in most general cases, but is dominated by the bottleneck fading parameter of the primary and backscatter channels in a rare special case. Due to the influence of the residual interference, the COP with imperfect SIC would converge to an error floor. Moreover, we formulate the symbiotic constraints to guarantee the minimum decoding times and a shorter decoding time than the conventional solutions, and further derive the corresponding successful fast-decoding probability at high transmit signal-to-noise ratio (SNR). The results show that by keeping a weak primary channel statistics or lowering the threshold to decode the backscatter signal, the proposed fast-decoding scheme outperforms conventional schemes in terms of decoding times.

Massive multiple input multiple output (mMIMO) is a promising technology for the next-generation wireless communication systems, yet accurate channel modeling is still a challenging problem. Recent field experiments revealed that, the channel is non-stationary in space, time and frequency (STF) domains. Meanwhile, it is also disclosed that, the channel changes in a smooth way. Namely, the channel should be consistent in space and time domains. This paper proposes a three-dimensional (3D) space-time consistency modeling method for the STF non-stationary mMIMO environments. Innovatively, a growth-curve based power attenuation factor (PAF) is introduced to evolve the birth and death (BD) process of the scattering clusters, so that the space-time consistency can be guaranteed. In validation experiments, the modeled channel characteristics are in good agreements with the measured ones, indicating that the proposed method is suitable for mMIMO channel modelling.

Two-way backscatter is essential to general-purpose backscatter communication as it provides rich interaction to support diverse applications on commercial devices. However, existing Bluetooth backscatter systems suffer from unstable uplinks due to poor carrier-identification capability and inefficient downlinks caused by packet-length modulation. This paper proposes EffBlue, an efficient two-way backscatter design for commercial Bluetooth devices. EffBlue employs a simple edge backscatter server that alleviates the computational burden on the tag and helps build efficient uplinks and downlinks. Specifically, efficient uplinks are designed by introducing an accurate synchronization scheme, which can effectively eliminate the use of non-compliant packets as carriers. To break the limitation of packet-level modulation, we design a new symbollevel WiFi-ASK downlink where the edge sends ASK-like WiFi signals and the tag can decode such signals using a simple envelope detector. We prototype the edge server using commodity WiFi and Bluetooth chips and build two-way backscatter tags with FPGAs. Experimental results show that EffBlue can identify the target excitations with more than 99% precision. Meanwhile, its WiFi-ASK downlink can achieve up to 124 kbps, which is 25x better than FreeRider.

Large Language Models (LLMs) specialized in code have shown exceptional proficiency across various programming-related tasks, particularly code generation. Nonetheless, due to its nature of pretraining on massive uncritically filtered data, prior studies have shown that code LLMs are prone to generate code with potential vulnerabilities. Existing approaches to mitigate this risk involve crafting data without vulnerability and subsequently retraining or fine-tuning the model. As the number of parameters exceeds a billion, the computation and data demands of the above approaches will be enormous. Moreover, an increasing number of code LLMs tend to be distributed as services, where the internal representation is not accessible, and the API is the only way to reach the LLM, making the prior mitigation strategies non-applicable. To cope with this, we propose CoSec, an on-the-fly Security hardening method of code LLMs based on security model-guided Co-decoding, to reduce the likelihood of code LLMs to generate code containing vulnerabilities. Our key idea is to train a separate but much smaller security model to co-decode with a target code LLM. Since the trained secure model has higher confidence for secure tokens, it guides the generation of the target base model towards more secure code generation. By adjusting the probability distributions of tokens during each step of the decoding process, our approach effectively influences the tendencies of generation without accessing the internal parameters of the target code LLM. We have conducted extensive experiments across various parameters in multiple code LLMs (i.e., CodeGen, StarCoder, and DeepSeek-Coder), and the results show that our approach is effective in security hardening. Specifically, our approach improves the average security ratio of six base models by 5.02%-37.14%, while maintaining the functional correctness of the target model.

Excessive non-actionable warnings generated by static program analysis tools can hinder developers from utilizing these tools effectively. Leveraging learning-based approaches for actionable warning identification has demonstrated promise in boosting developer productivity, minimizing the risk of bugs, and reducing code smells. However, the small sizes of existing datasets have limited the model choices for machine learning researchers, and the lack of aligned fix commits limits the scope of the dataset for research. In this paper, we present AW4C, an actionable warning C dataset that contains 38,134 actionable warnings mined from more than 500 repositories on GitHub. These warnings are generated via Cppcheck, and most importantly, each warning is precisely mapped to the commit where the corrective action occurred. To the best of our knowledge, this is the largest publicly available actionable warning dataset for C programming language to date. The dataset is suited for use in machine/deep learning models and can support a wide range of tasks, such as actionable warning identification and vulnerability detection. Furthermore, we have released our dataset1 and a general framework for collecting actionable warnings on GitHub2 to facilitate other researchers to replicate our work and validate their innovative ideas.

In next-generation wireless networks, the integration of localization and communications techniques are regarded as a paradigmatic shift for enhancing spectrum and hardware utilizations. The channel sensing, encompassing localization and channel estimation, plays a pivotal role in various aspects such as beamforming, precoding and high-quality data transmission. In this paper, we present a method for optimizing the frame structure in the localization and communication integration system, to reveal the intricate relationship among channel estimation, user's localization and communication throughput in terms of the spectral efficiency (SE). Specifically, we initially derive the error bounds for channel estimation and location prediction in dynamic point-to-point communication scenario. Leveraging these bounds, we optimize the sensing and communication duration together with beamwidth design, to maximize the SE while ensuring communication requirements. An efficient iterative algorithm is employed to tackle this non-convex problem. Numerical results demonstrate that our proposed method can achieve a nearoptimal SE performance with significantly lower complexity compared to exhaustive search method. Furthermore, our results underscore the critical role of localization in optimizing sensing and communication durations for SE, particularly in high dynamic scenarios.

The reconfigurable intelligent surface (RIS) has attracted significant research interests recently due to its abilities of dynamic channel reconstruction, flexible deployment and reduced power consumption. However, a malicious RIS can introduce serious signal degradation and even interception risk. This article investigates destructive beamforming design from the perspective of a malicious RIS, where the RIS is active and able to amplify the reflected signals from the base station (BS) to an Internet of Things Device (IoTD). We consider two scenarios where the BS is known and unknown to the identity of malicious RIS, and the objective is to minimize the received signal-to-noise ratio (SNR) at the IoTD with the constraints of total power budget and RIS signal amplification. To solve the above nonconvex optimization problem, we first propose a low-complexity scheme by integrating several classical beamforming methods with the Taylor expansion approach to solve the original problem for the case of known malicious RIS at BS. While for the unknown malicious RIS case, we propose an alternating optimization scheme by using the successive convex approximation method to obtain the beamforming vector and reflection coefficient matrix iteratively. Finally, numerical results verify that, through the proposed destructive beamforming design, the RIS only brings pain without gain for the signal reception.

Doppler mitigation method aided by reconfigurable intelligent surface (RIS) is essential for the high-speed rail (HSR) communications system, which, however, receives little attention. In this letter, a new anti-Doppler spread strategy in double RISs-aided HSR communication is proposed. The phase shift is designed according to the rail information (e.g., timetable, speed, and rail track) by optimizing a multiobjective problem with signal-to-noise ratio (SNR) maximization and Doppler and delay spread minimization. Numerical results verify the effectiveness of the proposed phase shifts design for double RISs, as compared to the various benchmark schemes, and proposed phase shifts design can eliminate the Doppler spread entirely.

Decentralized Federated Learning (DFL), a Federated Edge Learning (FEEL) framework without a server, can avoid the huge communication overhead of the server and the single point of failure within FEEL. Since there is no server, the convergence of DFL depends not only on the communication conditions and number of edge nodes, but also on the graph topology of the network over which edge nodes communicate. Moreover, in practical scenarios, considering the limited communication resources, such as the latency and energy costs, the convergence of DFL also suffers from quantization errors. In this paper, we consider the decentralized gradient descent (DGD), which is widely applied in DFL, and examine the influence of graph topology and quantization on the convergence of DGD in different wireless networks with cost constraints. According to the derived convergence bound, the maximum quantization error acceptable for the DFL convergence is obtained. Furthermore, motivated by the limited iterations caused by the impact of constrained communication costs on each edge node, we compare the convergence bounds between higher and lower connectivity topologies, which face different energy consumption in one round of communication. Based on this comparison, the impact of graph topologies with energy constraints on the convergence can be observed. Numerical simulations confirm the validity of our analyses, supporting the correctness of our theoretical findings.

Mobile edge computing-assisted integrated aerial-ground network (MEC-IAGN) emerges as a promising key component of the sixth-generation (6G) wireless networks due to its potential capabilities in providing ubiquitous connectivity for global coverage and computing services. However, the inevitable existences of computation-intensive tasks, uncontrollable propagation environment, and malicious jamming attacks pose three significant bottlenecks for enabling efficient MEC-IAGN. With these focuses, we propose a novel framework of multi-functional reconfigurable intelligent surface (MF-RIS) aided semantic anti-jamming communication and computing in MEC-IAGN. Under this framework, a semantic transceiver exhibits inherent robustness and data compression capability, and MF-RIS can customize the full-space wireless environment by leveraging its signal reflection, refraction, amplification, and energy harvesting functions, thereby achieving substantial global coverage, reliable connectivity, and high-rate computing. Based on our proposed framework, we formulate a semantic computation rate maximization problem considering the impacts of jammer’s channel state information (CSI) imperfection, while maintaining the energy partition constraint for computation offloading decision, semantic similarity requirement, semantic computation rate target, and MF-RIS’s self-sustainability. Then, by transforming the imperfect CSI into a worst-case one by exploiting a discretization method, we propose a fast-converging monotonic optimization algorithm that is combined with decoupling second-order cone programming to obtain a globally optimal solution with fewer feasibility evaluations. Furthermore, to strike a satisfactory tradeoff between performance and computational complexity, we develop a suboptimal generalized power iteration algorithm. Numerical simulations demonstrate the superiority of our proposed framework and algorithms compared to various benchmarks.

Federated learning (FL) can generate huge communication overhead for the central server, which may cause operational challenges. Furthermore, the central server’s failure or compromise may result in a breakdown of the entire system. To mitigate this issue, decentralized federated learning (DFL) has been proposed as a more resilient framework that does not rely on a central server, as demonstrated in previous works. DFL involves the exchange of parameters between each device through a wireless network. To optimize the communication efficiency of the DFL system, various transmission schemes have been proposed and investigated. However, the limited communication resources present a significant challenge for these schemes. Therefore, to explore the impact of constrained resources, such as computation and communication costs on the DFL, this study analyzes the model performance of resource-constrained DFL using different communication schemes (digital and analog) over wireless networks. Specifically, we provide convergence bounds for both digital and analog transmission approaches, enabling analysis of the model performance trained on DFL. Furthermore, for digital transmission, we investigate and analyze resource allocation between computation and communication and convergence rates, obtaining its communication complexity and the minimum probability of correction communication required for convergence guarantee. For analog transmission, we discuss the impact of channel fading and noise on the model performance and the maximum errors accumulation with convergence guarantee over fading channels. Finally, we conduct numerical simulations to evaluate the performance and convergence rate of convolutional neural networks (CNNs) and Vision Transformer (ViT) trained in the DFL framework on fashion-MNIST and CIFAR-10 datasets. Our simulation results validate our analysis and discussion, revealing how to improve performance by optimizing system parameters under different communication conditions.

The metaplectic operator has shown to be a valid technique for generalizing the notion of cross Wigner distribution to achieve time-frequency superresolution. Inspired by the latest work (Cordero and Rodino, 2022), we revisit the notion of cross Wigner distribution in metaplectic transform domains by introducing two$2N\times 2N$symplectic matrices rather than integrating them into one$4N\times 4N$symplectic matrix. We style the derived general formulation as the cross metaplectic Wigner distribution and obtain its basic properties including time translation property, frequency modulation property, time translation and frequency modulation property, Moyal formula, complex conjugate symmetry, time reversal symmetry, and scaling property. We use it to define the so-called short-time metaplectic transform and clarify the equivalence between them. We establish the standard Heisenberg’s uncertainty principles for the cross metaplectic Wigner distribution, i.e., an attainable lower bound for two real-valued functions in cross metaplectic Wigner distribution domains and a sequence of attainable (unattainable) lower bounds for two complex-valued (one real-valued and the other complex-valued) functions in orthogonal, orthonormal, the minimum eigenvalue commutative and the maximum eigenvalue commutative cross metaplectic Wigner distribution domains. We further demonstrate the time-frequency superresolution superiority of the derived results over the conventional one through theoretical analyses and numerical experiments.

In this paper, we propose a novel UAV aided ground nodes (GNs) localization and communication integrated framework, where the age of information (AoI) is introduced to evaluate the system timeliness. We aim to jointly optimize the UAV trajectory, localization accuracy, bandwidth and beamwidth, to guarantee the information freshness. Specifically, we give a two-stage method, where a low complexity initial UAV trajectory searching algorithm is firstly proposed, based on theroughposition information of GNs. Afterwards, we formulate a joint UAV location and resource optimization problem. This essential mixed integer problem can be solved by efficient successive convex approximation based iterative algorithm. Simulations show that the localization and communication integrated framework can obtain about 50% performance gain compared with the UAV communication aid only solution, and over 37% performance gain via proper resource allocation. Moreover, the analysis reveals that our proposed scheme strikes a balance among the time durations of localization, data transmission and UAV movement. Additionally, we conduct practical experiments to draw valuable insights into the system design and implementations (An experimental can be found on the supplementary materials, or online at https://youtu.be/OX6Bgz6naUA).

Direction of arrival (DOA) estimation is widely used in many applications. Traditional DOA estimation generally adopts the second-order statistics and the uniform linear array (ULA) structure. However, the second-order statistics and the uniform array structure restrict the number of DOAs that can be accurately estimated. In addition, the performance of the second-order statistics-based methods is sensitive to noise. As the wireless environment is becoming increasingly complex with the advent of the 5G era, the propagation channel can be composed of numerous paths. When the number of paths exceeds the size of the antenna array, DOA estimation becomes an under-determined problem, which the second-order statistics-based traditional methods fail to deal with. In order to address the under-determined DOA estimation problem, this paper proposes a method based on higher-order statistics and non-uniform array structure. Higher-order statistics-based methods can not only expand the array aperture, but also suppress the additive Gaussian noise. However, if combined with a uniform array structure, the degrees of freedom of the expanded array is limited. Therefore, we further adopt a non-uniform array structure and optimize its structure. Consequently, the proposed method is capable of achieving$M^{2}$-level DOA estimation with an M-element array, while simultaneously providing good robustness to noise. For instance, using the proposed method, the DOAs of 20 incoming wave directions can be accurately estimated with a 6-antenna non-uniform array when the signal-to-noise ratio is as low as 0 dB.

Mobile edge computing (MEC) has been regarded as a promising technique to support latency-sensitivity and computation-intensive serves. However, the low offloading rate caused by the random channel fading characteristic becomes a major bottleneck in restricting the performance of the MEC. Fortunately, reconfigurable intelligent surface (RIS) can alleviate this problem since it can boost both the spectrum- and energy-efficiency. Different from the existing works adopting either fully active or fully passive RIS, we propose a novel hybrid RIS in which reflecting units can flexibly switch between active and passive modes. To achieve a tradeoff between the latency and energy consumption, an optimization problem is formulated by minimizing the total cost via jointly optimizing the transmission time, the transmit power, the receive beamforming vector, the phase-shift matrix, the mode-switching factor, the amplification factor, the offloading ratio factor, and the computation ability of the user, where the constraints of the maximum energy of users, the maximum power of the RIS, the minimum computation tasks, the transmission time, the offloading ratio factor, the mode-switching factor, the unit moduli of passive units, and the computation ability are taken into account. Considering the complexity of the aforementioned problem, we develop an alternating optimization-based iterative algorithm by combining the successive convex approximation method, the variable substitution, and the singular value decomposition to obtain sub-optimal solutions. Furthermore, in order to gain more insight into the problem, we consider two special cases involving a latency minimization problem and an energy consumption minimization problem, and respectively analyze the tradeoff between the number of active and passive units. Simulation results verify that the proposed algorithm can achieve flexible mode switching and significantly outperforms existing algorithms.

Cognitive radio has been suggested as a solution to address the shortage of accessible spectrum caused by the significant demand for wideband services and the fragmentation of spectrum resources. Nevertheless, the sensing performance is rather inadequate owing to the diminished sensing signal-to-noise ratio, especially in complex environments with severe channel fading. Fortunately, applying reconfigurable intelligent surfaces (RIS) for spectrum sensing can efficiently address the aforementioned problems. However, the passive RIS may experience the “double fading” effect, seriously limiting the effectiveness of passive RIS-aided spectrum sensing. Thus, a crucial challenge is how to fully exploit the potential advantages of the RIS and further improve the sensing performance. In this paper, we utilize the passive and active RIS to further enhance detection probability and subsequently develop two different problems for both the passive and active RIS to achieve the detection probability maximization. Considering the complexity of the above problems, we design a one-stage optimization algorithm featuring inner approximation and a two-stage optimization algorithm that employs the bisection method to derive corresponding solutions, and further establish the upper bound and lower bound of the detection probability by employing the Rayleigh quotient. Moreover, we separately explore how many reflecting elements are needed for passive RIS and active RIS and investigate the detection performance comparison of the two types (passive and active) of RIS. Simulation results show that the proposed algorithms outperform existing algorithms under the same parameter configuration, and achieve a detection probability close to 1 with even fewer reflecting elements or antennas than existing schemes.

A potential 6G technology known as intelligent reflecting surface (IRS) has recently gained much attention from academia and industry. However, acquiring the optimized quantized phase shift (QPS) presents challenges for the IRS due to the phenomenon of signaling storms. In this paper, we attempt to solve the above problem by proposing two deep learning models, the global attention phase shift compression network (GAPSCN) and the simplified GAPSCN (S-GAPSCN). In GAPSCN, we propose a novel attention mechanism that emphasizes a greater number of meaningful features than traditional attention mechanisms. Additionally, S-GAPSCN is built with an asymmetric architecture to meet the practical constraints on the computation resources of the IRS controller. Moreover, in S-GAPSCN, to compensate for the performance degradation caused by simplifying the model, we design a low-computation complexity joint attention-assisted multi-scale network (JAAMSN) module in the decoder of S-GAPSCN. Simulation results demonstrate that the proposed global attention mechanism achieves prominent performance compared to the existing attention mechanisms and the proposed GAPSCN can achieve reliable reconstruction performance compared to existing state-of-the-art models. Furthermore, the proposed S-GAPSCN can approach the performance of the GAPSCN at a much lower computational cost.

Airborne tactical networks (ATN) play a pivotal role in enabling information sharing between manned and unmanned military aircrafts. The design of effective ATNs faces two significant challenges: the network ossification problem and the complexity associated with managing heterogeneous resources. Wireless network virtualization provides a practical solution for the first challenge by abstracting, isolating, and sharing wireless resources among different entities. Flexible and scalable virtual request embedding (VRE) algorithms have the potential ability to address the other challenge. However, existing VRE algorithms are not suitable for the virtualization of an ATN because they do not adequately consider key factors such as global interference, reliability and delay-sensitive information sharing in the air-battlefield context. In this paper, we propose an analytical framework of joint wireless network virtualization and resource allocation in the context of an ATN. This framework ensures coordination between physical node and link resources for the VRE. Based on the proposed framework, we design a centralized embedding mechanism that maps available physical resources to served users by constructing a directed resource topology and designing wireless link scheduling algorithms. Furthermore, we design two VRE algorithms that account for two types of delay sensitivity: transmission time and waiting time, depending on whether virtual requests are split or not. Through simulations, we validate the effectiveness of our algorithms and analyze the impact of key system parameters on the delay performance.

Noise pollution is a pressing concern in urban areas, exacerbated by the rapid pace of urbanization, industrialization, and high population density. It poses significant risks to human health and disrupts ecosystems. While noise pollution monitoring and E-health technologies have individually made substantial contributions to their respective fields, their integration has been largely overlooked in existing research. This oversight has resulted in missed opportunities for valuable insights and fragmented data analysis. To bridge this gap, we present the development of a collaborative sensing network that simultaneously monitors noise pollution and manages E-health. Our proposed solution, AntiNoise, employs a novel architecture that leverages smart devices and data mules to record noise levels and health statuses, transmitting this information to a cloud platform. To optimize the performance of the system, we design an integrated deep reinforcement learning framework for data mule trajectory planning and employ a deep Q-network algorithm for trajectory control. Through extensive evaluation, we demonstrate the efficiency of our approach, surpassing conventional schemes, particularly in scenarios with strict transmission power budgets.

Ultra-reliable and low-latency communication (uRLLC) plays an important role in the development of 5G-advanced and 6G wireless networks. Combining unmanned aerial vehicles (UAVs) with non-orthogonal multiple access (NOMA) offers a promising solution to achieve improved reliability and lower latency. This is made possible by enabling line-of-sight (LoS) links and concurrent transmissions through the use of UAVs and NOMA, respectively. However, because of the inherent openness of wireless channel, uRLLC faces the security challenges against being eavesdropped. Physical Layer Security (PLS) has been proposed as an efficient method to secure uRLLC, since it uses only the properties of wireless channels (such as fading, interference, and noise). Although the potential benefits of NOMA-UAV provide a better coverage for ground users, it remains a significant challenge since it may provide a LoS link to eavesdroppers. Therefore, in this paper, we investigate the security and reliability performance of UAV and NOMA based uRLLC scenario, under which UAV serves two different types of users with different needs, i.e., secret users and public users. By using stochastic geometry tools, we derive the closed-form expression of the secrecy rate, an important metric in the study of PLS. Additionally, the secure performance is enhanced by maximizing the secrecy rate through optimizing the hovering height and power assignment of UAV. It should be noted that the hovering position is optimized via power allocation when there is only one secret user. Evaluations demonstrate the effectiveness and correctness of our theoretical analysis.

With the rapidly increasing data traffic in vehicle-to-infrastructure (V2I) networks, the demand for communication and computing rises proportionally. As such, joint communication, computing and sensing becomes crucial for addressing the demands in V2I networks. This paper proposes an energy-efficient joint communication, sensing and mobile edge computing (MEC) system for V2I networks. In the proposed system, the roadside unit (RSU) is equipped with the functions of communication, sensing, and cache-aided computing. By combining beam prediction and tracking, the optimization problem of system energy consumption is formulated. The problem is divided into three parts, which are solved using the joint communication, sensing and MEC online (JCSMO) strategy. Simulation results show that the proposed JCSMO strategy outperforms the baseline schemes in system energy consumption.

In this article, we investigate and analyze energy recycling for a reconfigurable intelligent surface (RIS)-aided wireless-powered communication network. As opposed to the existing works where the energy harvested by Internet of Things (IoT) devices only comes from the power station, IoT devices are also allowed to recycle energy from other IoT devices. In particular, we propose group switching- and user switching-based protocols with time-division multiple access to evaluate the impact of energy recycling on the system performance. Two different optimization problems are, respectively, formulated for maximizing the sum throughput by jointly optimizing the energy beamforming vectors, the transmit power, the transmission time, the receive beamforming vectors, the grouping factors, and the phase-shift matrices, where the constraints of the minimum throughput, the harvested energy, the maximum transmit power, the phase shift, the grouping, and the time allocation are taken into account. In light of the intractability of the above problems, we, respectively, develop two alternating optimization-based iterative algorithms by combining the successive convex approximation method and the penalty-based method to obtain corresponding suboptimal solutions. Simulation results verify that the energy recycling-based mechanism can assist in enhancing the performance of IoT devices in terms of energy harvesting and information transmission. Besides, we also verify that the group switching-based algorithm can obtain more sum throughput of IoT devices, and the user switching-based algorithm can harvest more energy.

In federated learning (FL), a number of devices train their local models and upload the corresponding parameters or gradients to the base station (BS) for global model updates. However, the eavesdropper can recover data from parameters or gradients, resulting in data leakage. To defend against eavesdropping attacks, in this article, we propose an algorithm that divides the transmit power proportionally between the transmitted signal and artificial noise (AN) to counteract the eavesdropper for wireless FL. In this algorithm, due to the limited communication resources, the ratio of signal power to total power and the aggregation frequency need to be carefully chosen, to guarantee the model accuracy and security at the same time. In order to achieve this goal, we maximize the secrecy rate with the system/user power and model performance constraints. To make this problem tractable, we derive two bounds of the secrecy rate and loss function, which allows us to obtain closed-form expressions for the power of AN and the aggregation frequency. Furthermore, in order to make our analysis more realistic, we consider the FL model with channel fading and additive white Gaussian noise (AWGN) over uplink and downlink, respectively. Specifically, we discuss the convergence of FL over noisy multiple access channels (MACs). Simulation results confirm the convergence and the effectiveness of the proposed algorithm.

Backscatter communication (BackCom), one of the core technologies to realize zero-power communication, is expected to be a pivotal paradigm for the next generation of the Internet of Things (IoT). However, the “strong” direct link (DL) interference (DLI) is traditionally assumed to be harmful, and generally drowns out the “weak” backscattered signals accordingly, thus deteriorating the performance of BackCom. In contrast to the previous efforts to eliminate the DLI, in this paper, we exploit the constructive interference (CI), in which the DLI contributes to the backscattered signal. To be specific, our objective is to maximize the received signal-to-noise ratio (SNR) by jointly optimizing the receive beamforming vectors and tag selection factors under different detection error probability (DEP) requirements, which leads to two different optimization problems. However, the resulting problems are non-convex and unanalyzable due to constraints on the DEP. To solve these problems, the Kullback-Leibler divergence is first applied to transform the DEP into a tractable form. Then, inspired by the alternating optimization, we respectively propose two successive convex approximation (SCA)-based algorithms to solve the corresponding sub-problems with beamforming design, and a greedy algorithm to solve the sub-problem with tag selection. In order to gain insight into the CI, we consider a special case with the single-antenna reader to reveal the channel angle between the backscattering link (BL) and the DL, in which the DLI will become constructive. Simulation results show that significant performance gain can always be achieved with the proposed algorithms compared to the traditional algorithms without the CI in terms of the received SNR. The derived constructive channel angle for the BackCom system with a single-antenna reader is also confirmed by simulation results.

Unmanned aerial vehicles (UAVs) are attractive in some Internet of Things (IoT) applications, due to their flexible deployment and extended coverage. In this paper, we consider an UAV-assisted network where the UAV flies between the resource-limited sensor nodes (SNs) and collects their status updates. The UAV trajectory and SN scheduling are jointly optimized to minimize the Age of Synchronization (AoS). In contrast to the conventional Age of Information (AoI), AoS takes into account both the freshness and the content of the information, which makes AoS a more suitable design criterion for information collection in an energy-constrained wireless network. Since the formulated problem is challenging to solve due to its non convexity, we reformulate the problem as a Markov decision process (MDP) and propose a deep reinforcement learning (DRL) algorithm to obtain the optimal solution with various action and state spaces. Our simulation results show the fast convergence rate of the proposed DRL algorithm and demonstrate that our proposed scheme can improve the performance of the UAV-assisted network compared to AoI-based schemes.

Reconfigurable intelligent surface (RIS) has been regarded as a promising technique due to its high array gain, low cost, and low power. However, the traditional passive RIS suffers from the “double fading” effect, which has become a major bottleneck in restricting the performance of passive RIS-aided communications. Fortunately, active RIS can alleviate this problem by adjusting the phase shift and amplifying the received signal simultaneously. Nevertheless, a high beamforming gain often requires a large number of reflecting elements, which leads to non-negligible power consumption, especially for the active RIS. Thus, one challenge is how to improve the scalability of the RIS and the energy efficiency. Different from the existing works where all reflecting elements are activated, we propose a novel element on-off mechanism where reflecting elements can be flexibly activated and deactivated. To achieve a tradeoff between the transmission rate and energy consumption, two different optimization problems for passive RIS and active RIS are formulated by maximizing the total energy efficiency, where the constraints of the maximum power of users and the RIS, the minimum transmission rate, the element on-off factor, and the unit moduli of passive elements are taken into account. In light of the intractability of the formulated problems, we develop two different alternating optimization-based iterative algorithms by combining quadratic transform, variable substitution, and the successive convex approximation method to obtain sub-optimal solutions. Furthermore, in order to gain more insight into problems, we consider special cases involving transmission rate maximization problems for given the same total power budget, and respectively analyze the number configuration for passive RIS and active RIS. Simulation results verify that the proposed algorithms outperform existing algorithms, and reflecting elements under the proposed algorithms can be flexibly activated and deactivated.

To tackle a challenge in high-dimensional complex features information processing, this study extends the permanent scale Wigner distribution and the single scale$k$-Wigner distribution (kWD, formerly known as$\tau $-Wigner distribution) to a novel multiscale parameterized Wigner distribution. That is the so-called$\mathbf {K}$-Wigner distribution (KWD) which is able to use different scales to extract different types of features at different dimensions. Heisenberg-type uncertainty inequalities of the KWD are established, giving rise to the tightest universal attainable lower bound for all functions on the uncertainty product in time-KWD and Fouier transform-KWD domains, and two versions of attainable lower bounds for complex-valued functions. The obtained results solve an important concern regarding the limit of the KWD’s time-frequency resolution influenced by the parameter matrix. As an application, the derived uncertainty inequalities are applied to estimate the bandwidth in KWD domains. The time-frequency resolution performance of the multiscale KWD, as compared with that of the single scale kWD, is investigated in details. The optimal parameter matrix of the KWD achieving the best performance is then generated, which solves an important concern regarding the KWD’s parameter matrix selection. Examples are also carried out to demonstrate the usefulness and effectiveness of the proposed technique.

Inspired by a definition of the closed-form instantaneous cross-correlation Wigner distribution (Zhang, 2019), we generalize the notion of Wigner distribution to the so-called free metaplectic Wigner distribution (FMWD) through three free metaplectic transforms, in order to tackle a challenge in high-dimensional complex features information processing. We provide some representative special cases for this general form, including the$N$-dimensional nonseparable affine characteristic Wigner distribution, kernel function Wigner distribution, convolution representation Wigner distribution and instantaneous cross-correlation Wigner distribution. We establish the standard Heisenberg’s uncertainty principles (HUPs) of the real-valued function for the FMWD. We also establish the standard HUPs of the complex-valued function for some specific (i.e., the orthogonal, the orthonormal, the minimum eigenvalue commutative and the maximum eigenvalue commutative) FMWDs. In view of the one-dimensional case of our results, we solve a burning question regarding the limit of time-frequency superresolution triggered by the linear canonical transform free parameters.

Fuzzy C-Means (FCM) is a widely used clustering method. However, FCM and its many accelerated variants have low efficiency in the mid-to-late stage of the clustering process. In this stage, all samples are involved in updating their non-affinity centers, and the membership grades of most samples, whose assignments remain unchanged, are still updated by calculating the sample-center distances. All these factors lead to the algorithms converging slowly. In this paper, a new affinity filtering technique is developed to recognize a complete set of non-affinity centers for each sample with low computations. Then, a new membership scaling technique is suggested to set the membership grades between each sample and its non-affinity centers to 0 and maintain the fuzzy membership grades for others. By integrating these two techniques, FCM based on new affinity filtering and membership scaling (AMFCM) is proposed to accelerate the whole convergence process of FCM. Numerous experimental results performed on synthetic and real-world data sets have shown the feasibility and efficiency of the proposed algorithm. Compared with state-of-the-art algorithms, AMFCM is significantly faster and more effective. For example, AMFCM reduces the number of FCM iterations by 80$\%$on average.

The transmission performance of an reconfigurable intelligent surface (RIS)-aided device-to-device (D2D) communication network is fundamentally limited by the devices' energy. To address this challenge, in this paper, the joint radio resource allocation of D2D users (DUs) with piece-wise linear energy harvesting (EH) models and passive beamforming of the RIS is investigated to maximize the sum rate of DUs for an RIS-aided wireless-powered D2D communication underlaying a cellular network. Specifically, multiple wireless-powered DUs harvest radio-frequency energy from a hybrid access point (HAP) with the help of an RIS during the EH phase and achieve data transmission by using the harvested energy during information transmission phase. The optimization problem is formulated by jointly optimizing the transmit power of DUs, transmission time, the active beamforming vector of the HAP, and the passive beamforming matrix of the RIS. An alternating optimization-based algorithm is designed to solve the non-convex problem by using the variable substitution approach and the Lagrangian dual method. Simulation results have shown that our proposed algorithm provides a significant improvement in data rates over the existing algorithm without the RIS.

This paper investigates and analyzes the number of reflecting elements for guaranteed energy- and spectral-efficient intelligent reflecting surface (IRS)-assisted communication systems. As opposed to previous works where the energy efficiency (EE)/the spectral efficiency (SE) maximization or the EE-SE tradeoff was considered, our goal is to minimize the number of reflecting elements in the IRS-assisted system. Besides, both the EE and the SE constraints are considered in the number minimization problem, which has not been addressed in existing works. However, both the EE and the SE performance do not admit exact closed-form expressions due to the non-convexity incurred by joint beamforming and phase shift design. In order to make the optimization problem tractable, we resort to their performance bounds for problem reformulation. By decomposing the problem into two sub-problems, we are able to derive closed-form expressions for the minimum number of reflecting elements, and both the coherent phase shift (CPS)-oriented solution and the random phase shift (RPS)-oriented solution are proposed for comparison. In order to shed light on the practical design, the relationship between the derived number of reflecting elements is established for both schemes, and the upper bounds on both the EE and the SE thresholds and the placement of the IRS to achieve only one reflecting element are obtained. Simulation results confirm the validity of our analysis on the minimum number of reflecting elements and effectiveness of both schemes.

The industrial Internet of Things (IIoT) is one of the key applications in 5G heterogeneous networks. To support high energy efficiency (EE) and reliability of IIoT equipment, it is important to design an efficient resource allocation algorithm in dynamic and complex environments. However, most of the studies on 5G heterogeneous IIoT networks did not address the transceiver hardware impairment (HWI) issues (e.g., phase noises, amplifier nonlinearities, and quantization errors) and the corresponding algorithms may not be applicable in practice. To this end, in this article, we investigate a realistic beamforming algorithm in a multicell downlink multiple-input single-output heterogeneous IIoT network by incorporating HWIs in our design. In particular, a beamforming design problem is formulated as a nonconvex optimization problem for maximizing the total EE of all equipment subject to the quality of service constraints of the IIoT equipment in both the macrocell and femtocells and the maximum transmit power constraints of base stations. In light of the intractability of the considered problem, we develop an EE-based iterative beamforming algorithm to tackle the formulated problem by employing the semidefinite relaxation method, Dinkelbach’s method, and the successive convex approximation method. Simulation results show that the proposed algorithm can achieve higher EE and bring less interference power to the macrocell IIoT equipment by comparing it with baseline algorithms.

A sharper uncertainty inequality which exhibits a lower bound tighter than that in the classical N-dimensional Heisenberg's uncertainty principle is obtained. The condition that reaches an equality relation of the uncertainty inequality is deduced. Example and simulations are carried out to illustrate that the newly derived uncertainty principle is truly sharper than the classical one. The new proposal's applications in time-frequency analysis, optical signal processing and physical optics propagation are also given.

In this paper, we propose and analyze an opportunistic ambient backscatter (AmBack)-assisted decode-and-forward (DF) (i.e., ODF-AmBack) relaying scheme, where the relay can not only forward the signal from the source as in the traditional DF scheme but also backscatter/transmit the signal of the tag embedded in the relay. Motivated by the fact that the capacity is dominated by the “weak” link in the traditional DF scheme, we are able to split the received power at the relay or the transmit power of the relay to support the AmBack transmission while the DF relaying is not affected, by exploiting the “extra power” due to the power difference between the first-hop link and the second-hop link. For comparison, both the traditional AmBack and the DF schemes are also investigated for performance benchmarks. Since the exact closed-form expressions of the ergodic capacities are difficult to obtain for the proposed ODF-AmBack scheme and the traditional AmBack scheme, their lower bounds are derived to facilitate our analysis. Simulations results show that significant performance gain can be achieved in the proposed ODF-AmBack scheme compared with benchmark schemes, and confirm the tightness of the derived expressions.

In this article, we propose and investigate hybrid active and passive antenna selection for backscatter-assisted multiple input and single output (MISO) systems. Specifically, each transmit antenna is allowed to work on either the active radio (AR) mode or the passive radio (PR) mode, which are respectively powered by the battery as in traditional communication and the external power source as in the backscatter communication (BackCom). The advantage is that the link reliability can be enhanced, and the power consumption can be opportunistically reduced by switching between the AR and the PR modes. Adaptive antenna selection is investigated and analyzed, where the antenna is selected over all combinations of antennas and their working modes. However, the antenna with the best channel quality will always be chosen. In order to circumvent this problem, two alternative schemes, referred to as reactive antenna selection and proactive antenna selection, are considered, where all antennas take turns to transmit/backscatter the signal. Specifically, in the reactive antenna selection scheme, the working mode for each antenna is determined by choosing the maximum value of the transmit power in the AR mode and the backscatter power in the PR mode. The working mode for the proactive antenna selection scheme is, however, chosen beforehand. Since the exact closed-form expression for the average symbol error rate (SER) is not attainable, approximations and their asymptotic performance are derived for proposed schemes. The power consumption is also analyzed. Simulation results demonstrate the correctness and the tightness of our analysis, and the effectiveness of proposed schemes.

In this article, we investigate a communication and computation problem for industrial Internet of Things (IoT) networks, where K relays can help accomplish the computation tasks with the assist of M computational access points. In industrial IoT networks, latency and energy consumption are two important metrics of interest to measure the system performance. To enhance the system performance, a three-hierarchical optimization framework is proposed to reduce the latency and energy consumption, which involves bandwidth allocation, off-loading, and relay selection. Specifically, we first optimize the bandwidth allocation by presenting three schemes for the second-hop wireless relaying. We then optimize the computation off-loading based on the discrete particle swarm optimization algorithm. We further present three relay selection criteria by taking into account the tradeoff between the system performance and implementation complexity. Simulation results are finally demonstrated to show the effectiveness of the proposed three-hierarchical optimization framework.

Signal classification plays an important role in cognitive communication networks to identify and avoid interference. Contrary to traditional cooperative spectrum sensing based on binary hypothesis testing, we study a network of cognitive radios that jointly perform linear cooperation based signal classification via M-ary hypothesis testing. To maximize the probability of successful classification subject to constraints on individual probabilities of misclassification, we divide the problem into M independent binary hypothesis testing subproblems in parallel before selecting the hypothesis that is most likely true. Furthermore, we consider a problem that maximizes the probability of successful classification subject to a constraint on the total probability of misclassification. We reformulate such an optimization problem into two different subproblems, where the optimal solution is obtained by alternating the two optimization sub-problems iteratively. Numerical simulations demonstrate the near-optimality of the proposed methods with low computational complexity for the cooperative signal classification problems.

Ultra dense heterogeneous network (UDHN) has become one of the main frameworks of 5G. Traditional user association methods are difficult to satisfy this new scenario for load balancing. On the other hand, the concept of green communication requires the network to increase energy efficiency. Therefore, it is necessary to study power allocation and user association in UDHN. This paper focuses on load balancing and energy efficiency of UDHN. The joint user association and power allocation is modelled as an appropriate optimization problem. Then we introduce reinforcement learning and propose a multiagent Q-learning based algorithm for solving the optimization problem. According to analysis of simulation result, the convergence of the proposed scheme is verified and the proposed approach is effective on achieving load balancing and enhancing energy efficiency in UDHN.

Wireless body area networks (WBANs) are not only an extension and branch of wireless sensor networks (WSNs) but also a practical application area of Internet of Things (IoT). With the extensive development of IoT technology, WBAN can monitor human physiological parameters in real time. Reliable information transmission is an important factor limiting the development of WBAN owing to special path loss and shadowing effect. Therefore, maximizing throughput is a pivotal part of improving system performance. In this paper, a multipoint WBAN (MP-WBAN) with energy harvesting for normal and abnormal scenarios is studied. We propose two different protocols, including a time switching (TS) strategy and a hybrid TS and power splitting (PS) strategy, respectively. In the abnormal scenarios, the access point (AP) harvests independent command signals from sensor nodes in the uplink (UL) and broadcasts dedicated energy signals to all sensor nodes in the downlink (DL). At the same time, the AP simultaneously broadcasts wireless command and energy signals to all sensor nodes in the normal situation. After all sensors harvest energy from the radio frequency (RF) signals, physiological datas can be transfered to the AP in a specific time sequence. We optimize TS ratios to achieve the abnormal situation sum-throughput maximization by utilizing convex optimization techniques. For sum-throughput maximization in normal situation, a near-optimal solution can be acquired by iteratively updating TS ratios and PS ratios. Numerical simulation results show the system performances of sum-throughput can be significantly improved by the proposed algorithms.

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

Signal classification plays an important role in spectrum sensing for cognitive radios to identify and avoid interference from other wireless devices. In this paper, we study a network of cognitive radios that jointly perform signal classification via cooperation. We propose a simple but effective linear cooperation scheme to fuse pre-processed measurements collected from spatially distributed cognitive radios. Our objective is to maximize the probability of successful classification subject to some constraints on the probabilities of misclassification. By applying a divide-and-conquer strategy and new constraint relaxation methods, we are able to derive the closed-form expressions for the optimal weight coefficient for each contributing cognitive radio. The design of such a cooperative signal classification system is further studied through numerical simulation.

ABSTRACT In this paper, we investigate the fundamental capacity limit of the cognitive broadcast channels with opportunistic scheduling, where cognitive users (CUs) can share the same spectrum with the primary user (PU) as long as the interference introduced to the PU is kept at an acceptable level. In this context, we analyze the capacity gains offered by this opportunistic spectrum sharing in Rayleigh fading environment. Specifically, we analyze the outage and effective capacity of the selected CU, and derive closed‐form expressions for these capacity metrics. We also obtain closed‐form expressions for the asymptotic performance as the bandwidth approaches infinity. Numerical results are provided to corroborate our theoretical analysis and quantify the effects of the system parameters. Copyright © 2011 John Wiley & Sons, Ltd.

There are a number of studies on using directional antennas in wireless networks. Many of them concentrate on analyzing the theoretical capacity improvement by using directional antennas. Other studies focus on designing proper Medium Access Control (MAC) protocols to improve the practical network throughput. There are few works on the security improvement using directional antennas. In this paper, we explore the benefits of directional antennas in security improvements on both single-hop and multi-hop wireless networks. In particular, we found that using directional antennas in wireless networks can significantly reduce the eavesdropping probabilities of both single-hop transmissions as well as multi-hop transmissions and consequently improve the network security.

In this study, the author investigates the outage performance of cognitive relay networks, in which the best relay is selected based on full and partial channel state information, respectively. The author derives exact closed-form expressions for both relay selection schemes, and study their asymptotic performance in terms of the diversity order based on the performance bound analysis. Simulation results are provided to confirm the accuracy of the analytical results, and highlight performance gains provided by the relay selection in cognitive radio networks.

Channel estimation for multiple-input multiple-output/orthogonal frequency-division multiplexing (MIMO/ OFDM) systems in linearly time-varying (LTV) wireless channels using superimposed training (ST) is considered. The LTV channel is modeled by truncated discrete Fourier bases. Based on this model, a two-step approach is adopted to estimate the LTV channel over multiple OFDM symbols. We also present a performance analysis of the channel estimation and derive a closed-form expression for the channel estimation variances. It is shown that the estimation variances, unlike that of the conventional ST-based schemes, approach to a fixed lowerbound as the training length increases, which is directly proportional to information-pilot power ratios. To further enhance the channel estimation performance with a limited pilot power, an interference cancellation procedure is introduced to iteratively mitigate the information sequence interference to channel estimation. Simulation results show that the proposed algorithm outperforms frequency-division multiplexed trainings schemes.