We propose a distributed random space-time coding based on rotated Alamouti code to address the loss of orthogonality in Alamouti codes and the resulting inter-symbol interference (ISI) caused by link asymmetry in distributed cooperation of high-density unmanned ground vehicles (UGVs) barrage relay networks (BRNs) under complex tactical environments. This approach incorporates rotation coding techniques, applying specific angular rotations to the coding matrix to circumvent ISI without increasing relay communication overhead, thereby restoring system diversity gains. Combined with random space-time coding (RSTC), it utilizes a randomly weighted coefficient matrix to enhance spatial diversity capabilities and physical layer security in multipath fading channels. Furthermore, a composite forest channel model incorporating path loss, vegetation attenuation, and shadow fading is constructed, with end-to-end outage probability derived. Simulation results demonstrate that the proposed scheme achieves signal-to-noise ratio (SNR) gain over the conventional phase rotation (PR)-based BRN, while significantly reducing outage probability. This validates its effectiveness and reliability in tactical environments.

This paper investigates intelligent decision-making and resource allocation for self-powered sensors with the age of information (AOI) orientation in high-speed railway (HSR) communication networks. The sensors innovatively access the network via non-orthogonal multiple access (NOMA), thereby alleviating the train’s burden in terms of both energy and communication resources. For the sensors’ energy management and data offloading, we formulate a resource allocation problem aimed at maximizing the data offloading volume within a single base station (BS) while ensuring the average AOI requirement. As a complex cumulative reward problem, directly applying deep reinforcement learning (DRL) for solution is neither elegant nor efficient. Therefore, we propose a convex constrained soft actorcritic (CCSAC) scheme, decomposing the optimization problem into long-term and short-term subproblems. Specifically, at the long-term problem, a DRL-based policy is used to make intelligent decisions on energy variations. In each time slot, the energy variation derived from DRL serves as input to a convex optimization model that determines the optimal resources allocation for that slot. In the simulation section, we compare multiple baseline algorithms and conduct extensive tests under various conditions, including different train speeds, sensor positions, numbers of sensors, and channel estimation error. The simulation results demonstrate the effectiveness and robustness of the algorithm, and indicate the important trade-off between the volume of data transmission and AOI in HSR scenarios.

This paper presents a Dual-Security-Assured Computation Offloading (DSACO) scheme for space-air-ground networks (SAGNs), which exploits the integrated sensing, communication, and computing (ISCC) capability of the low Earth orbit (LEO) satellite to support secure and efficient computation offloading. In the proposed scheme, the air-ground mode serves as the default edge-processing strategy due to its low latency and energy consumption, while the LEO satellite senses the malicious aerial eavesdropper and protects the ground device (GD)-to-UAV links through directional anti-eavesdropping jamming. When such protection becomes insufficient, the system switches to direct GD-to-LEO offloading via dedicated frequency bands. Accordingly, the secure offloading process is formulated as a worst-case average long-term energy minimization problem under sensing uncertainty. To solve the resulting dual-timescale mixed-integer nonlinear problem, we develop a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework for the joint optimization of sensing duration, UAV trajectories, computation offloading, and resource allocation. Simulation results demonstrate that the proposed scheme significantly enhances system security while maintaining offloading efficiency, and that the H-MADRL framework outperforms benchmark methods.

Backscatter communication is emerging as a promising solution for enabling low-power and large-scale IoT applications. However, it faces challenges in terms of widespread deployment, wireless resource management, and quality of service (QoS). In this paper, we first propose a secure transmission architecture to integrate the backscatter network with the existing 5G/IoT infrastructure. Next, we introduce a multi-device scheduling and time-switching strategy aimed at optimizing both capacity and secure throughput. In the time-switching scheme, BDs primarily operate in symbiotic mode without requiring additional spectrum, but can dynamically switch to opportunistic spectrum access mode when necessary, with adaptive time allocation to improve QoS. For multi-device scheduling, BDs are assigned to function as a master transmission node, cooperation node, or spoofing/jamming node, thereby enhancing the system’s resistance to proactive eavesdropping. The optimization problem is formulated to minimize spectrum resource usage while ensuring QoS and following the energy constraint. To solve this, we introduce an enumeration-based interior-point algorithm (EIA) and design a novel progressive greedy algorithm (PGA). The EIA method provides optimal solutions, while the PGA algorithm achieves high-quality suboptimal solutions with lower complexity. Extensive simulation results demonstrate that the proposed strategy stands out in ensuring QoS, enhancing security, and reducing spectrum resource usage.

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

Recent years have witnessed growing interest in leveraging the bidirectional wave control of intelligent omni-surfaces (IOS) for integrated sensing and communication (ISAC) systems. Nevertheless, acquiring precise channel state information (CSI) is particularly challenging due to the inherent interplay between the electromagnetic properties of IOS and the dual functions of ISAC. In this paper, we propose a robust beamforming design for IOS-enabled ISAC systems. We jointly optimize the transmit beamforming, sensing waveform and IOS phase shifts to minimize the Cram´er-Rao bound (CRB) for sensing while ensuring communication reliability under an outage probability constraint. The resulting mixed-integer non-convex problem is tackled via a dual-loop penalty dual decomposition (PDD) algorithm. This framework solves the augmented Lagrangian (AL) subproblem in the inner loop, while the outer loop adjusts dual variables and penalty parameters to enforce constraint satisfaction. Simulation results demonstrate that our design substantially enhances sensing accuracy and communication reliability in scenarios with large CSI errors or fluctuating service requirements. Furthermore, it is shown that an optimal ratio between sensing and passive IOS elements must be maintained to balance energy utilization and spatial sampling capability in ISAC systems.

This paper proposes a vital-sign monitoring system based on near-field WBAN, with integrated sensing, communication, and powering. A two-layer communication medium composed of air and human tissue is established to model both in vitro and in vivo environments. In the in vivo scenario, the propagation, reflection, and scattering of electromagnetic signals are described for vital sign detection. Conversely, in the in vitro setting, a multi-antenna access point (AP) operates in the near-field regime to transmit wireless energy to a wearable vital-sign sensor node, collects vital-sign data to the AP via backscatter communication, and senses the sensor’s position based on the echo signals. We formulate a multi-stage stochastic optimization problem that jointly optimizes the AP’s transmission strategy, time-slot allocation, and beamforming, incorporating the age of information (AoI) to ensure timely data transmission. Since the information-theoretic limit of the monitoring task can be characterized by mutual information (MI), it is adopted as the optimization metric. The objective is to maximize MI for vital-sign monitoring under constraints on communication rate, wireless power transfer, and position sensing accuracy. To solve the resulting joint optimization problem, we use a Lyapunov optimization framework to transform the long-term AoI constraint into a tractable per-slot control form. Building on this formulation, we propose the JO-VSM algorithm, which employs a block coordinate descent (BCD) method to decouple and solve the coupled optimization variables within each slot. Simulation results demonstrate that the proposed JO-VSM algorithm can effectively balance vital-sign monitoring performance, communication rate, and position sensing accuracy, ensuring information freshness and robustness, as indicated by stable AoI convergence over time-slot evolution.

Integrated sensing and communication (ISAC) is pivotal for enabling simultaneous environment perception and data transmission in intelligent transportation systems (ITS). However, mission-critical ITS management applications, such as collision avoidance and autonomous driving, require stable and reliable ISAC services. Unfortunately, dense urban canyons, with their skyscraper-induced occlusions, create persistent coverage blind zones, posing significant challenges to these applications. To address these challenges, this paper explores a novel aerial intelligent reflecting surface (AIRS)-assisted ISAC system, where multiple AIRSs dynamically reconfigure the wireless propagation environment to enhance multi-vehicle sensing and base station (BS)-to-multiuser communication. To maximize the minimum achievable communication rate while ensuring sensing performance, we formulate a joint resource allocation problem considering BS beamforming, AIRS trajectory optimization, AIRS phase shift control, and user association. Given its highly coupled and nonconvex nature, we develop an alternating optimization framework tackling each subproblem sequentially. Specifically, we employ the Lagrangian dual transform and semi-definite relaxation (SDR) for BS beamforming, the successive convex approximation (SCA) method for AIRS trajectory optimization, matrix decomposition and equivalent rank-constrained transformation techniques for AIRS phase shift design, and a penalty dual decomposition (PDD)-based approach for user association. Furthermore, considering uncertainties in the vehicle’s angle of departure (AoD) due to urban mobility, we derive a worst-case sensing performance bound and generalize the proposed algorithm to a more complex scenario. Simulations validate the algorithm’s effectiveness, demonstrating superior communication rates and sensing performance over benchmark schemes, while ensuring robustness against AoD uncertainties.

In this paper, we propose a novel federated learning (FL) scheme, called Device-Cooperative FL (DCFL), to mitigate the negative impact of probabilistic transmission errors on FL performance and improve the convergence rate in Industrial Internet of Things (IIoT) environments with unreliable wireless communication. After local model training, a cooperative local update stage is introduced, utilizing a fully connected device-to-device (D2D) relaying scheme to ensure that model parameters from devices with failed device-to-server (D2S) transmissions are included in the global aggregation, while avoiding the need for real-time relay selection. To address inconsistent participation due to varying communication conditions, we introduce local update weights that reflect each device’s transmission reliability, ensuring balanced contributions to the global model. Simulation results validate that the proposed DCFL scheme can accelerate convergence and improve test accuracy by up to 20.26% compared to baseline schemes under conditions of high transmission error probability.

In this paper, we propose a novel channel access and power control scheme for smart grids in communication networks. The scheme is named digital twin-based memory recall optimization (DMRO), which aims to extract meaningful patterns from noisy network traffic measurements and support more sophisticated decision-making processes for optimizing channel access and power control for smart grids. Specifically, we design a pattern extraction method that minimizes the Frobnius norm between the collected measurements and the expected k-rank approximation of the measurements in order to extract useful information. Then, considering the interference and signal-to-interference-plus-noise ratio (SINR) constraints in the wireless environment, we develop a digital twin-based distributed channel access and power control scheme to improve the latency taming and energy utilization efficiency of the phasor measurements units (PMU). We consider both the real-time traffic prediction and the paired optimization scheme on the digital twin side, and utilize memory recall to enhance local model robustness to optimize from a more diverse set of situations by replaying underrepresented experiences. Simulation results demonstrate that the proposed DMRO scheme can achieve high traffic prediction accuracy and improve the latency taming and energy utilization efficiency even increasing industrial channel interference or the number of PMUs.

In this paper, the method of convex optimization combined with reinforcement learning is used to solve the problem of non-orthogonal multiple access (NOMA) for self-powered sensor in high-speed railway (HSR) scenario. The self-powered sensor on the high-speed train collects ambient energy, and offloads the sensing data by using NOMA in time slots when the train-to-ground (T2G) communication system employs time-division multiple access (TDMA) to serve multiple carriages. In each time slot, when the self-powered sensor is in energy collecting mode, the T2G communication is used for energy collecting, and when the sensor is in data offloading mode, the T2G communication becomes interference noise. Our goal is to maximize the amount of self-powered sensor data offload within the scope of a base station. This is a long-term return problem, which cannot be optimized only slot by slot, but needs to be optimized as a whole, so we use the principle of reinforcement learning for optimization. Experiments show that the proposed algorithm is better than the greedy algorithm and the random algorithm, and has better performance in multiple scenarios.

Physical layer security (PLS) in symbiotic radio (SR) systems is primarily considered for passive eavesdropping scenarios. However, overlooking the impact of proactive eavesdroppers poses significant risks. In this paper, we focus on secure transmission in SR systems under proactive eavesdropping conditions. A PLS strategy is investigated for a full-duplex facultative symbiotic radio (FD-FSR) system. First, we introduce an innovative FSR protocol. It allows backscatter devices (BDs) to dynamically switch between cognitive and symbiotic patterns. Next, we develop a multi-device scheduling method. It adaptively assigns BDs as transmitters, cooperators, or jammers to enhance the secrecy rate. We formulate the pattern switching and BD scheduling as a mixed integer programming problem (MIP). To solve this, we first decompose it into binary decision-making and multi-variable optimization sub-problems. Then, a low-complexity two-stage optimization strategy is employed. Numerical results demonstrate that our proposed strategy significantly outperforms existing schemes.

Mobile robots and autonomous vehicles rely on 3-D point cloud technology for environmental perception, which often employ various visual perception sensors within their Internet of Things (IoT) systems to acquire point cloud information. Nevertheless, the obtained point cloud data is frequently incomplete, making it difficult to effectively perform perception tasks. Multitask point cloud learning networks can not only achieve point cloud reconstruction under high occlusion rates but also assist the system in accomplishing various point cloud tasks. However, existing multitask point cloud learning models based on advanced Transformer frameworks often suffer from quadratic complexity, limiting their performance in massive point cloud data learning. In this article, we propose a novel approach named Hybrid-Fusion Mamba (HFMamba), as a pretraining network model specifically designed for multitask point cloud learning. Compared to Transformer-based networks, the proposed HFMamba possesses linear complexity, which can significantly reduce IoT systems’ computational cost. HFMamba employs a unique first-layer feature fusion design that integrates point cloud features from three different perspectives, enabling it to capture deeper dependencies among the point clouds. Moreover, a hybrid scan strategy is proposed to separately scan the hidden states and residuals, aiming to model the sequence from different directions. Experimental results demonstrate that the proposed HFMamba network model outperforms many state-of-the-art methods without applying any serialization strategies to the original point cloud data. In particular, the proposed HFMambda approach achieves classification accuracies of 93.7% and 93.89% on ModelNet40 and ScanObjectNN, respectively.

Uncrewed aerial vehicle (UAV)-assisted joint communication and localization (JCAL) system have great potential and capacity to make future Internet of Things efficient, safe, smart, reliable, and sustainable. Generally, the traditional UAV path planning methods set the flying duration and hovering duration of UAVs as constants, and ignore the importance of UAV operation time in emergency rescue and other scenarios. In this article, we consider the path planning and time scheduling problem of UAV-assisted JCAL system for minimizing the UAV operation time under the constraints of the localization accuracy, communication message, and energy loss. Specifically, we first formulate path planning and time scheduling problem for UAV-assisted JCAL system and derive Cramér-Rao bound (CRB) as the localization accuracy constraint. The variables in the constraints of localization accuracy, communication overhead, and energy loss are deeply coupled, which leads to nonconvex optimization problems. Next, to solve the high nonconvex problem, we divide the original problem into two subproblems, i.e., time scheduling subproblem and path planning subproblem. We use equivalent convex transformation and successive convex approximation (SCA) to transform the nonconvex constraints into convex forms for solving the subproblems, respectively. Lastly, aiming to the robust problem of target and channel parameters, we convert the robust constraints into convex constraint forms by equivalent proof and S-Procedure. On this basis, we develop a robust algorithm for solving the uncertainty of target and channel parameters. Simulation results verify the feasibility of the proposed methods.

In this paper, we introduce a wireless-powered communication network (WPCN) which is composed of a base station (BS), an access point (AP), and an N-element intelligent reflecting surface (IRS). Specifically, several groups of internet of things (IoT) users will collect energy radiated from BS in the wireless energy transmission (WET) time scheduling, and transfer their collected information for AP in the wireless information transmission (WIT) time scheduling in the direct/reflecting way. Hybrid frequency-time division multiple access (HFTDMA) transmission protocol is adopted, and an optimization problem is formulated to maximize the sum throughput of the system. Since the optimization variables are strongly coupled together, the optimization problem is non-convex. Therefore, we utilize the Lagrangian function and Karush-Kuhn-Tucker (KKT) conditions to derive the optimal bandwidth allocation and the optimal WIT time scheduling. Then, the Lambert W function is applied to obtain the optimal WET time scheduling. Finally, we propose alternating direction method of multipliers based alternating optimization (AO) algorithm to acquire the optimal WET/WIT phase-shift matrices. Numerical results corroborate that the proposed algorithm significantly outperforms the benchmark schemes, and the trade-off between energy harvesting and information transmission plays the pivotal role in the IRS-assisted multigroup WPCNs.

With the increasing complexity of next generation network applications and the coexistence of diverse service requirements, Generative AI (GAI) and Large Models (LMs) based semantic communication are widely regarded as promising solutions to address these challenges. The goal of these systems is not only to reduce system burden by reducing transmission data, but also to adapt to new and complex requirements. In this paper, we propose a semantic communication system designed to meet diverse requirements of speech applications while enabling accurate speech transmission. The semantic encoder comprises an unsupervised model wav2vec 2.0 for learning universal speech representations to enable adaptability across various speech-related tasks. It also includes a prosodic feature encoder from the style embedding module of Global Style Tokens (GST) Tacotron. The semantic decoder integrates a phoneme recognition module and a GST-Tacotron-based text-to-speech (TTS) module to facilitate accurate and expressive reconstruction of the original speech signal, with the incorporation of prosodic features enhancing the naturalness and intelligibility of the synthesized speech. The proposed system has been tested in noisy channels. It demonstrates that the system maintains superior and robust performance even at Bit Error Rate (BER) of 10−1, as reflected by a stable Character Error Rate (CER) approximately 0.0940 and 0.0649 for the base and large versions of wav2vec 2.0 respectively in speech recognition, and consistent cFDSD scores approximately 0.9 in speech quality assessment. This performance surpasses that of the existing semantic communication systems, while also providing reliable support for a wider range of downstream speech applications.

Intelligent omni-surface (IOS) has been identified as an innovative technology to achieve omnidirectional wireless coverage for mobile users. However, due to the passive characteristics of the IOS, accurate channel state information (CSI) is difficult to acquire in IOS-assisted communication systems. In this article, we investigate a novel IOS-assisted multiuser multiple-input-single-output (MISO) downlink communication system. Specifically, the cascaded channel errors on both sides of the IOS are modeled separately to improve the flexibility and stability of the robust beamforming schemes. Considering the diverse practical communication requirements posed by the bounded and statistical CSI error models, we formulated the system sum-rate maximization and transmission power minimization problems for the worst-case and outage-constrained robust beamforming, respectively.$\boldsymbol {S}$-Procedure and Bernstein-type inequality are introduced to approximate the original nonconvex problems. Finally, we decompose the transformed problem into two subproblems and present an alternate optimization (AO) algorithm based on the success convex approximation (SCA) technique and the branch and bound method. Simulation results demonstrate that our robust beamforming schemes can effectively mitigate the system performance degradation caused by CSI error and enhance the downlink transmission robustness of the IOS-assisted communication system.

Natural disasters often destroy critical infrastructure, such as terrestrial communication networks and transportation routes, thereby severely disrupting post-disaster rescue operations. To rapidly re-establish communication links and provide flexible computational support in disaster rescue scenarios, the integration of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) has emerged as a promising solution. Nevertheless, the highly complex and resource-constrained characteristics of disaster environments pose significant challenges for UAV-assisted computation task offloading. In this paper, we investigate the joint task offloading and resource allocation (JTORA) problem to minimize the energy consumption associated with communication and computation during task offloading. Specifically, we develop a twin-delayed deep deterministic policy gradient (TD3)-based JTORA (JTORA-TD3) algorithm, which enables the UAV to optimize decisions of task offloading and resource allocation intelligently. To further enhance the training efficiency of the JTORA-TD3 algorithm in a complex disaster rescue environment, we integrate a large AI model (LAM) into the TD3 framework. Based on the textual interaction, we propose an LAM-enabled TD3-based JTORA (JTORA-LAM4TD3) algorithm. Simulation results demonstrate that the proposed JTORA-LAM4TD3 algorithm significantly outperforms baselines. These findings confirm the effectiveness of integrating LAMs with deep reinforcement learning (DRL) for solving the decision optimization problem.

This paper investigates a symbiotic radio (SR)-assisted mobile edge computing (MEC) network for railway Internet of Things (RIoT) services, where IoT devices parasitize in a train-ground primary network for passively modulating their computation tasks over computation offloading by associating a mobile relay (MR) on the high-speed railway (HSR). The multi-antenna base station (BS) integrated with the MEC server recovers computation task data from MRs and IoT devices through joint decoding. With the objective of maximizing the total computation efficiency (CE) of all MRs while satisfying the computation requirements of IoT devices, we formulate a computation offloading and resource allocation problem that jointly optimizes the association strategy between MRs and IoT devices, the received beamforming of the BS, the transmission power and computation frequency of MRs. However, since the rapid variation of channel conditions in HSRs poses difficulties to centralized optimization methods in terms of both accurate model acquisition and computation overhead, we utilize a model-free deep reinforcement learning (DRL) approach to propose a fingerprint-based distributed dueling double deep Q-network (FD4QN)-based computation offloading and resource allocation scheme to solve the above problem. In particular, this scheme describes the original problem as a partially observable Markov decision process (POMDP), and then incorporates low-dimensional fingerprint markers in each computing agent to stabilize the experience replay mechanism in a multi-agent environment, thereby enhancing training robustness. Moreover, each agent makes a decision for each MR at each time frame by using a dueling double deep Q-network (D3QN) framework based on the local observation state. Simulation results show that the proposed scheme achieves superior performance compared to other benchmark schemes.

In this paper, we investigate a novel symbiotic radio (SR)-aided high-speed railway (HSR) wireless network, in which the Internet of Things (IoT) device, operating as a secondary transmitter, transmits its own information to the mobile relay (MR) on the HSR by backscattering radio frequency (RF) signals from the base station (BS). With the assistance of SR, the designed network facilitates the transmission of locally collected environmental sensing messages from the IoT network to the HSR, simultaneously enhancing the primary communication between the BS and MRs. Aiming to maximize the sum transmission rate of the primary and the IoT network, we focus on a joint power control and device access (JPCDA) problem. Specifically, each IoT device accesses the network through appropriate time slot selection and appropriate power control, thereby achieving satisfactory overall network performance. However, since the fast channel variations arising from the high mobility of HSRs make it impractical to acquire accurate channel state information (CSI), it is challenging to achieve an optimal resource allocation scheme. To address this challenge, we develop a distributed deep reinforcement learning (DRL)-based algorithm that utilizes historical CSI to infer real-time CSI for decision making. In particular, each computing unit of the agent performs action selection for only one IoT device at one time based on the current local observation information. Numerical results illustrate that our proposed algorithm outperforms other baselines, and still works effectively when the environment changes.

Age of information (AoI) serves as a key metric for characterizing information freshness. In this article, we investigate the AoI minimization of a non-orthogonal multiple access (NOMA)-capable wireless-powered Internet of Things (WP-IoT) network, where a base station (BS) consistently sends radio frequency (RF) signals to power IoT sensors (IoT-Ss), and selected IoT-Ss are scheduled to transmit status update packets to the BS in each time slot. We first formulate a scheduling problem for average AoI (AAoI) minimization with NOMA transmission and solve for a near-optimal scheduling policy with a deep Q-network (DQN)-based solution. Next, we propose a novel joint transmission mode selection and scheduling (JTMSS) design to further minimize the AAoI of the network. Specifically, the system adaptively selects one of three transmission modes: NOMA, orthogonal multiple access (OMA), and no transmission and schedules two, one, or none sensors for transmission, respectively. Considering the discrete hierarchical action space of the JTMSS problem, we formulate a parameterized-action Markov Decision Process (PAMDP) and develop a deep transfer learning (DTL)-based solution with a two-tier DQN framework to find a near-optimal JTMSS policy. Besides, we present a partial tuning approach during online operation to alleviate the effects of environmental changes. Simulation results verify that the JTMSS design with DTL achieves a significant performance gain over the scheduling-only policy for NOMA transmission. Moreover, the online tuning with DTL converges quickly during online operation.

The rapid development of High-Speed Railway (HSR) puts higher requirements on comprehensive perception and reliable transmission in tunnel scenarios. To realize efficient and reliable perception information transmission of HSR in the tunnel, we propose a multisensor perception communication system, which consists of an Access Point (AP) deployed on each carriage for perception information transmission and self-powered wireless sensors. The AP remote transmits the perception information through the leaky cable deployed in the tunnel. We construct an optimization problem for minimizing the transmission time of the whole system’s perception information in the multi-network system and the adjacent area of the carriage. A Multi-Agent Cooperation-based Deep Reinforcement Learning (MA-CDRL) algorithm is proposed to get the optimal scheduling strategy for reducing the transmission time. We construct the CDRL neural network for the algorithm to introduce the states of other APs, resulting in the system making more efficient transmission strategies. In the simulations, the proposed algorithm gets a better performance than the comparison algorithms and is verified in various dynamic HSR scenarios, such as different travel speeds and sensor distributions.

Pedestrian trajectory prediction within open social environments is a vital component of mobile robotics and autonomous driving. The inherent uncertainty and multimodality introduce considerable challenges for this task. Currently, an effective approach is to first evaluate the goal distribution of pedestrian trajectories to provide potential destinations for prediction. However, this process neglects the global perspective, weakening the global dependency among trajectory points, which can increase the divergence in the goal distribution and reduce prediction accuracy. To address this, we propose a Markov Random Field (MRF) clique model method for pedestrian trajectory prediction network. In this approach, each future trajectory point is treated as a node variable, forming cliques with historical trajectory points, thereby constructing a global clique model between them. Subsequently, clique models are established in both the goal network and the prediction network. This clique-based approach enhances the ability to capture and represent complex dependency in trajectories. Additionally, to further deepen feature fusion in the prediction network, we design a fusion network based on the Kolmogorov-Arnold Network (KAN). Specifically, we propose MCK-net, which primarily includes a two-tiered clique goal module and a clique prediction module based on CK-LSTM (Clique and KAN LSTM). These designs achieve dual performance improvements in both the goal and prediction networks. Comparative experiments with various previous outstanding algorithms demonstrate that our MCK-net achieves state-of-the-art (SOTA) performance on all the scenes of the ETH/UCY datasets. Code will be available at:https://github.com/SunshineDHW/MCK-net

In this paper, we propose a joint non-line-of-sight (NLoS) predictive beamforming and power allocation (JNPB-PA) scheme to enhance the power efficiency of the road side units in the integrated sensing and communication (ISAC)-assisted vehicular networks. This scheme decouple the spatial and amplitude components in power allocation by exploiting angular domain discretization of a novel modulation technique–spatially-spread orthogonal time frequency space (SS-OTFS). Specifically, we first develop an auxiliary target method to achieve predictive beamforming in NLoS scenarios, which initially determines the power allocation vector’s non-zero positions corresponding to discrete angles of the vehicles. Then, we further refine the power allocation by solving a multi-objective optimization problem (MOP) aimed at minimizing both the age of information (AoI) for communication and the Cramér-Rao bound (CRB) for sensing. A low-complexity algorithm based on the proposed reconstruction-contraction-constraint (RCC) approach is developed to solve the formulated MOP based on its inherent features. Simulation shows that our proposed JNPB-PA scheme can achieve higher power utilization rate, lower AoI, and lower CRB in comparison with benchmark schemes. Besides, RCC solves the formulated MOP more efficiently by avoiding iterative searching of traditional methods.

In this paper, we investigate the sensing and communication (S&C) trade-off in the integrated sensing and communication (ISAC)-assisted vehicular network with spatially spread orthogonal time frequency space (SS-OTFS) modulation technique, where power allocation is the trigger leading to S&C performance shift. We tailor S&C metrics specifically for the vehicular network where information freshness and sensing accuracy are critical due to safety concerns, indicated by age of information (AoI) and Cramér-Rao bound (CRB), respectively. Then we propose an AoI-and-CRB-based power allocation (ACPA) scheme and develop a reconstruction-contraction-constraint (RCC) approach to derive the non-dominated solutions, which delineate the S&C trade-off. Simulation shows that our proposed ACPA scheme can identify the S&C performance frontier of the system, and the RCC approach is more efficient than the traditional non-dominated sorting genetic algorithm II (NSGA-II). In addition, the intrinsic mechanism of how power allocation affects S&C performances in the SS-OTFS-enabled ISAC system is analyzed.

In this paper, we propose a novel intrusion detection scheme within smart grid networks, which is named digital twin-based multi-kernel knowledge replay (DtMKR) scheme. The scheme is designed to improve resilience against the noise and interference, and efficiently address the clustering of diverse and multi-sourced phasor measurement units (PMUs) packets. Specifically, we create channel-vectors to represent the packet features using the power gain and delay spread properties of the channel impulse response derived from the received packets. Then, we investigate the DtMKR scheme to alleviate the impact of noise and interference effects and enhance the precision of the malicious PMU packets detection from benign ones without the need for a comprehensive pre-established database of channel features for all PMUs in the network. In the scheme, the designed multi-kernel enabled Markov decision process (MDP) clustering functions are implemented to map the channel-vectors into a new feature space, thereby the dispersive effects on the channel-vectors are minimized. In addition, we consider both the realtime packet clustering and the paired learning scheme on the digital twin side, and utilize memory recall to mitigate the model overestimation problem and enhance local model robustness to optimize from a more diverse set of situations by replaying underrepresented experiences. Simulation results demonstrate the efficacy of the proposed DtMKR scheme in accurately identifying malicious packets originating from PMUs attackers, distinguishing them from benign traffic, and mitigating the impact of transmission impairments typical in environment.

In this paper, we propose a novel channel access and power control scheme for industrial IoT environments within heterogeneous networks. This scheme, termed Digital Twin-Based Prediction and Optimization (DTPO), extracts significant patterns from noisy network traffic data to enhance decision-making for channel access and power control. Specifically, we develop a pattern extraction method that minimizes the Frobenius norm between the collected traffic measurements and their expected k-rank approximation, thereby isolating useful information. Taking into account the interference and signal-to-interference-plus-noise ratio (SINR) constraints prevalent in wireless settings, we propose a digital twin-enabled distributed channel access and power control scheme to better manage latency and enhance energy efficiency for IoT devices. We consider both the real-time traffic prediction and a coupled optimization process on the digital twin side. Additionally, we employ a memory recall technique to improve the robustness of local models, allowing for optimization across a broader range of scenarios by revisiting underrepresented data. Simulation results demonstrate that the DTPO scheme can achieve high accuracy in traffic prediction and effectively enhances latency management and energy efficiency, even with increased industrial channel interference or a growing number of IoT devices.

This paper focuses on a symbiotic radio (SR)-aided high-speed railway (HSR) wireless network, where the base station (BS) in the primary network serves the mobile relays (MRs) on the HSR via orthogonal frequency division multiple access (OFDMA) and the Internet of Things (IoT) devices deployed around the HSR serve as secondary transmitters for information transmission by selecting appropriate time slots. By using the SR technique, the proposed network not only facilitates the transmission of locally collected environmental messages from the IoT network to MRs, but also enhances the primary communications from the BS to MRs. With the aim of maximizing the sum transmission rate of the primary and the IoT network, we formulate a device access problem under time slot allocation constraints. However, the time-varying channel due to the high mobility of HSR makes it challenging to obtain an optimal policy for the problem. To overcome this challenge, we develop a distributed deep reinforcement learning (DRL) algorithm, which utilizes historical knowledge to infer real-time information to make decisions. Particularly, the proposed algorithm performs action selection for only one IoT device at one time based on the current local observation information. Numerical results demonstrate that the performance of the proposed distributed DRL algorithm closely approximates the optimal strategy that requires perfect instantaneous information.

In this paper, we propose a reliable federated learning (FL) scheme for vehicular communication networks. The scheme is named intelligent vehicle selection and resource optimization (IVSRO), which aims to improve the federate learning reliability by reducing the probability of incorrect packet transmission in mobility scenario, and determining the most suitable vehicle for learning based on the incorrect packet probability. Specifically, we introduce a FL model for the vehicular communication network and analyze the probability of incorrect packet transmission caused by dynamic channel changes under this network. In consideration of FL convergence accuracy, an optimization problem is formulated to minimize the incorrect packet transmission rate, which is achieved through selecting the optimal connected vehicles from the training set, allocating transmission power and wireless spectrum resources to the selected vehicles. By employing convergence analysis and determining the optimal power for each selected vehicle, the proposed optimization problem can be handled using a bipartite matching algorithm. Simulation results show that the identification accuracy of the proposed IVSRO scheme is higher than that of existing baseline schemes. The results of this study demonstrate how the proposed IVSRO scheme improve the reliability of the FL scheme in vehicular communication networks while considering the varying channel conditions and proper vehicle selection, making it valuable for FL implementations in the domains of intelligent transportation and road safety management.

Reasonable robust beamforming design has been identified as a promising approach to enhance the adaptability, transmission efficiency and anti-interference capacity of the communication system. In this paper, we propose a beamforming design aimed at maximizing the sum-rate of communication in an intelligent omni-surface (IOS)-assisted multi-user multiple-input single-output (MISO) downlink communication system. Specifically, the presented scheme resolves the transmission rate optimization problem while considering constraints such as transmission power limitation at the base station (BS) and discrete phase shifts at the IOS. Additionally, we investigate S-Procedure to transform the produced semi-infinite objective function for addressing the non-convex problem due to the infinite inequality constraints. Then, we decompose the transformed problem into two subproblems to tackle the deep coupling between variables. Finally, a novel success convex approximation (SCA) algorithm is presented based on semi-definite programming (SDP) technology, branch and bound method to solve two subproblems iteratively until convergence. Simulation results demonstrate that our robust beamforming scheme can effectively mitigate the communication sum-rate degradation caused by channel state information (CSI) error and enhance the downlink transmission robustness of the IOS-assisted communication system.

In the pursuit of achieving optimal functionality for internet of vehicles (IoV), the integration of multi-access edge computing (MEC) emerges as a solution, offering high bandwidth, low latency, robust security, and reliability services. In this article, we consider a multi-user multi-server vehicular network scenario, where the non-orthogonal multiple access (NOMA) technology in 5G is used to optimize spectrum resource utilization. We firstly formulate the problem using mixed integer non-linear programming (MINLP) and propose a task scheduling scheme based on deep reinforcement learning (DRL) to handle high-dimensional state and action spaces and to approximate the optimal solution. We then proposed solutions to the NOMA clustering and power allocation problems in order to further reducing system latency in the uplink transmission stage. Simulation results underscore the efficacy of our proposed algorithm in systems with unevenly distributed computing resources, showcasing superior performance compared to alternative algorithms.

Energy-efficient transmission is essential to wireless body area networks (WBAN) as most biosensors in WBAN have limited energy supply. In this paper, we study the energy consumption minimization problem for each amplify-and-forward (AF) relay transmission session while satisfying a certain reliability requirement in WBAN. To minimize the energy consumption during successful transmission and wasted energy due to failed transmission attempts, over finite blocklength (FBL) regime, we design an intelligent agent that can determine: i) whether to transmit or not for given current channel state information (CSI) and available resources and ii) the best power levels and blocklength values for the current transmission session if the agent decides to transmit. To perform these two tasks simultaneously, we propose a novel hybrid supervised/reinforcement learning solution. Specifically, we design a classification network following the supervised learning approach to determine whether to transmit or not based on predicted minimal packet error probability. We then develop a deep reinforcement learning (DRL)-based solution that determines the optimal values of the transmission parameters. We also propose a DRL-based online parameter tuning (DRL-OPT) algorithm to minimize the impact of model inaccuracy and/or environment changes. Simulation results reveal that the performance of the proposed hybrid solution is almost identical to that of the exhaustive search. The DRL-OPT algorithm can follow environment variation and maintain a good performance with low computational complexity. Moreover, we numerically analyze the effect of slot duration on energy consumption and develop a guideline for practical WBAN design.

In post-disaster scenarios, it is challenging for affected users to transmit data as quickly as possible before their residual energy (RE) is exhausted. Besides, the problem of limited users’ RE causes severe transmission delay unfairness within the network. In this paper, we propose a novel two-phase scheme, called energy-aware unmanned aerial vehicle (UAV) relay transmission (EURT), to balance transmission delay of users and network fairness. Specifically, in the first phase, we pair users two-by-two based on their RE and minimize the maximum transmission delay among all pairs by jointly optimizing the bandwidth allocation, transmit powers, and UAV altitude. In the second phase, we design a cognitive radio (CR) inspired rate-splitting multiple access (RSMA) scheduling strategy to obtain the optimal power splitting factor for each pair. This strategy considers the user with a lower RE value in each pair as the primary user (PU) and the other one as the secondary user (SU), then minimizes the transmission delay of the SU while ensuring the quality of service (QoS) of the PU. Furthermore, we propose a novel evaluation framework to explore the degree of impact of RE and channel state information (CSI) on network delay fairness. Simulation results demonstrate that: i) the proposed EURT algorithm effectively improves performance metrics of networks in terms of transmission delays, throughput, energy consumption and energy efficiency; ii) The proposed algorithm achieves a trade-off between the minimum delay and the optimal network fairness by adjusting the QoS threshold of the PU.

With the advent of the 5G and 6G eras and the explosive growth of mobile users, machine learning (ML) is increasingly used for extracting important information from a large amount of generated data and making intelligent decisions for complex environments. Especially, distributed ML techniques are getting more attention to enable training ML models in a distributed manner by exploiting distributed computational resources at the network edge. Federated learning (FL) as a classical distributed learning approach can not only protect data privacy but also reduce communication overhead. However, it requires synchrony among users, which is hard to satisfy due to the heterogeneity of the wireless networks. Hence, we first propose a clustering-based semi-asynchronous Online FL with AoU-based local update (CSAOFL-ALU) with importance-based user clustering and AsynFL-ALU-based local update. After that, the BS aggregates the cluster model of each cluster with synchronous FL. We also provide mathematical convergence analysis of the CSAOFL-ALU algorithm. The results show that the global model convergence rate is inversely proportional to the users’ AoU, at the same time, the convergence bound of the global loss function is inversely proportional to the size and the importance of the user dataset. The experiments are conducted on the non-IID MINST dataset. Numerical results demonstrate that the proposed AsynFL-ALU with priority-based user scheduling achieves better learning performance than fully AsynFL, and converges faster than the baseline user scheduling schemes. The CSAOFL-ALU converges faster with less communication time than the baseline algorithms and increases the fairness of user participation.

Due to the constraints on power supply and limited encryption capability, data security based on physical layer security (PLS) techniques in backscatter communications has attracted a lot of attention. In this work, we propose to enhance PLS in a full-duplex symbiotic radio (FDSR) system with a proactive eavesdropper, which may overhear the information and interfere legitimate communications simultaneously by emitting attack signals. To deal with the eavesdroppers, we propose a security strategy based on pseudo-decoding and artificial noise (AN) injection to ensure the performance of legitimate communications through forward noise suppression. A novel AN signal generation scheme is proposed using a pseudo-decoding method, where AN signal is superimposed on data signal to safeguard the legitimate channel. The phase control in the forward noise suppression scheme and the power allocation between AN and data signals are optimized to maximize security throughput. The formulated problem can be solved via problem decomposition and alternate optimization algorithms. Simulation results demonstrate the superiority of the proposed scheme in terms of security throughput and attack mitigation performance.

In this paper, we propose a novel age-of-information (AoI)-aware waveform design scheme for the cooperative joint radar-communications (JRC) system, called AoI-aware Online Prediction (A-OnP) scheme. To be specific, we optimize the power allocation of the orthogonal frequency division multiplexing (OFDM) signal. We aim to maximize the radar mutual information (RMI) with considering the communication data rate (CDR) and AoI performance. Specifically, we design a cognitive operating framework for the JRC system, with a particular emphasis on the closed-loop signal processing for online prediction of the radar target scattering coefficient (TSC). Then, considering the obtained TSC prediction result and corresponding communication performance requirement, we optimize the power allocation of the transmit waveform and the signal-to-interference-plus-noise ratio (SINR) threshold of the communication users. Accordingly, we propose a constraints-splitting coordinate descent (CS-CD) method to solve the formulated non-convex problem by strategically splitting the sum-constraints and assign a quota to each channel, where the allocation criteria is automatically decided during iteration. Simulation results demonstrate that, the cooperative radar-centric communication-constrained (RC-CC) waveform outperforms the separately optimized radar-optimal plus communication-optimal (RO-CO) waveform. Additionally, the A-OnP scheme can increase RMI while meeting the communication CDR and AoI requirements.

Future vehicle localization will be committed to improving the positioning accuracy and energy efficiency of localization systems in the intelligent transportation. Recently, reconfigurable intelligent surface (RIS) as an emerging technology has gained widespread attention and is favorable to enhance the performance of vehicle localization systems because of its capacity of customizing the wireless channel. In this paper, in order to minimize the transmit power, we consider the joint active and passive beamforming problem of RIS-assisted vehicle localization system under the constraints of the localization accuracy and the phase shift parameters of the RIS. Specifically, we establish the model of RIS-assisted vehicle localization system and derive the Cramér-Rao bound (CRB) as the localization performance metric. Next, for the scenario of single vehicle localization, we derive the optimal RISs’ phases, and obtain the optimal solution for joint active and passive beamforming based on semidefinite programming relaxation of the non-convex beamforming problem and the corresponding equivalent analysis. Lastly, aimming to the scenario of multiple vehicles localization, we transform the nonconvex joint active and passive beamforming problem into semidefinite programming (SDP) and geometric programming (GP) form subproblems through alternating optimization. Simulation results verify the feasibility of the proposed methods.

This paper considers a reconfigurable intelligent surface (RIS)-aided multi-user simultaneous localization and communication (SLAC) system with statistical position uncertainty, where an RIS is deployed to simultaneously enhance the quality of service. To this end, we first derive the closed-form Cramér-Rao lower bound concerning position parameters as the localization metric and also provide the achievable rate metric for communication services. Then, the joint robust design of subcarrier groups, beamforming vectors, and the phase-shift matrix of the RIS is formulated as a stochastic bi-objective optimization problem to maximize expected localization and communication metrics. Due to the nonlinearity of the multi-objective function and the coupling between optimizing variables, the resulting problem is highly non-convex. Accordingly, we transform the expected achievable rate into an analytical form and further develop a novel unified successive convex approximation (U-SCA)-based iterative algorithm to obtain a robust resource allocation strategy. In particular, we derive closed-form solutions of beamforming vectors and the phase-shift matrix of RIS to decrease the computational complexity. In addition, we also analyse the convergence of the proposed U-SCA-based algorithm. Simulation results demonstrate the effectiveness of the presented method.

Multi-target tracking (MTT) plays a significant role in intelligent transportation systems, serving as an enabling technology for applications such as self-driving, surveillance, and navigation. To enhance the MTT performance, the unmanned aerial vehicles (UAVs) have emerged as effective assistants to MIMO radar system, due to their advantages of high flexibility, controllable deployment and cost-effectiveness. Towards this end, this work investigates a multi-UAV-enabled MIMO radar system, in which each UAV is equipped with a MIMO radar unit and dispatched to track multiple targets simultaneously. We are interested in the joint trajectory planning and transmit resource optimization (i.e. radar waveform optimization and transmit power allocation) to minimize the system power consumption, subject to constraints related to UAVs motion, system resources, and tracking accuracy. Specifically, the posterior Cramér-Rao Lower Bound (PCRLB) is derived and employed as a guideline for the joint optimization. Given the non-convex and inter-variable coupling nature of the formulated problem, we decompose it into three sub-problems and design an alternating optimization method. Firstly, for the UAVs trajectory planning, we obtain sub-optimal results leveraging the successive convex approximation (SCA)-based algorithm. Next, we present a feasible solution set for radar waveform optimization. For transmit power allocation, we perform a convex transformation and find the numerical solution. In addition, through introducing the Lagrange dual method, we further obtain the optimal analytical solution. Finally, simulation results demonstrate the effectiveness and advantages of the developed strategy.

In this paper, we propose a novel orthogonal frequency division multiplexing (OFDM) waveform power allocation approach in the spectrum-sharing dual-functional radar-communication (DFRC) systems, with a particular emphasis on improving radar recognition performance while considering the specific communication requirements of individual users. Specifically, the radar mutual information (RMI) is maximized in terms of allocating the radar power to the OFDM subcarrier within the limits of the power constraints and meeting the age of information (AoI) expectation requirements. By considering the impact of radar interference, we measure the AoI performance of individual users using the transmission outage probability. Then, an iterative constraints-splitting (ICS) method is developed to find the optimal radar power allocation results from the formulated non-convex problem by transforming it into an equivalent convex problem using an iteratively determined factor. Simulation results demonstrate that the proposed individual user AoI preference-based approach can improve RMI performance while meeting the AoI communication requirements of individual users. Additionally, higher RMI and more stable AoI performance can be achieved while maintaining total communication performance by allocating more sub-carriers to fewer users.

In this paper, a novel reconfigurable intelligent surfaces (RIS)-assisted joint localization and communication (JLAC) scheme is presented to supply both position-sensing and data transmission functions for a multi-user system by a frequency division strategy. In particular, considering the parameter uncertainty, we formulate the robust resource design problem as a statistical mixed-integer form, aiming to maximize localization and communication performance by joint subcarrier group, beamforming, and phase-shift optimization. To tackle the formulated non-convex problem efficiently, we develop an iterative method based on the stochastic successive convex approximation technology to handle the original problem. Simulation studies are presented to demonstrate the effectiveness of the proposed JLAC scheme and method.

To improve the reliability of dynamic multiple wireless body area networks (WBANs) system, it is indispensable to comprehensively consider the interference mitigation and user data differences. In this paper, we study a multi-WBAN system, where sensors receive radio frequency (RF) signals from the access point (AP), then transmit the monitoring sign to the sink node. Considering the dynamic network topology and the individuality of users, we propose a data-driven wasser-stein distributionally robust weight-based joint power allocation (DW-JPA) scheme. In particular, we formulate a sum-weighted transmission rate maximization problem by optimizing dynamic weight and transmit power ratio subject to the data transmission and energy limitation constraints. We divide the problem into dynamic weight subproblem and transmission power control subproblem. We utilize the collected physiological data to predict the optimal actual weight assignment. Then, we quantify the criticality of sensors and build an ambiguity set based on wasserstein distance for probability distributions of the critically. In essence, the optimal weight is obtained by using the distributionally robust optimization (DRO) method. Furthermore, due to the non-convexity of the power control subproblem, we convert the subproblem to a difference of convex (DC) problem and use an iterative algorithm to alternately optimize the power ratio. The results reveal that the proposed scheme achieves a significantly higher weighted transmission rate with physiological data compared with traditional schemes.

Massive multiple-input multiple-output (MIMO) assisted grant-free access is a compelling approach to support uplink ultra-reliable low-latency communications (URLLCs). It is a natural idea to combine massive MIMO and repetition transmission to further improve the reliability. However, excessive repetitions may increase the collision probability in multiuser scenarios and thus limit the reliability improvement. In this paper, we propose a novel semi-probabilistic repetition (SPRe) scheme for the massive multiuser MIMO (MU-MIMO) systems with grant-free access. By introducing a transmission probability for the replicas, the SPRe is more flexible than the popular repetition scheme, i.e.,$K$-repetition, and covers the$K$-repetition as a special case. Considering practical MU-MIMO systems with finite block-length coding, channel estimation errors, pilot collisions and the sporadic arrivals of URLLC traffic, the reliability of the proposed SPRe is thoroughly analyzed and derived in closed form. Through further asymptotic analysis, the optimal transmission probability to maximize the reliability of MU-MIMO is derived for the high-SNR regime and low-load scenarios. Moreover, the diversity gain of the proposed SPRe is also analyzed and it is proved that the optimal SPRe is superior to the$K$-repetition in terms of reliability. The analysis and superiority of the SPRe are finally verified through computer simulations.

This paper investigates a high-speed railway (HSR) network with device-to-device (D2D)-aided underlaying cellular communications, where cellular-based train-to-infrastructure (T2I) and D2D-supported train-to-train (T2T) transmissions co-exist. Considering the diverse quality-of-service (QoS) requirements of different types of links, age of information (AoI) is adopted as a new metric to evaluate the information freshness performance of T2T links. With the objective to maximize the sum data rate of T2I links, we formulate a resource allocation problem under the minimum data rate constraints of T2I links and the maximum average AoI constraint of T2T links. As the problem with a set of binary variables, it is intractable to be solved directly. Thus, we propose an AoI-aware power control and subcarrier assignment (AoI-PCSA) scheme, which decomposes the optimization problem into a power control subproblem and a subcarrier assignment subproblem. More specifically, we derive the optimal analytical solutions of power control for each T2I-T2T subcarrier reusing pair with algebraic methods. Then, we transform the subcarrier assignment subproblem into a weighted bipartite matching problem and obtain the optimal reusing pattern based on the Kuhn-Munkres algorithm. Simulation results demonstrate that our proposed scheme can achieve a sum data rate gain of up to 19.94% on average for T2I links as compared with other benchmark schemes.

To align with the vision of future intelligent high-speed railway (HSR) networks, integrating sensor monitoring and remote communication are challenging for ensuring the lightweight of train equipment, high-quality transmissions, and dynamic interaction between monitoring and communication. In this paper, we propose a self-powered multisensor monitoring and communication integrated system in HSR. A low-power backscatter communication working framework of the self-powered monitoring system is designed in the monitoring network model, and a finite Gaussian mixture model (GMM) clustering method is used to analyze the communication cell coverage area in the communication network model. Aiming to minimize the total task completion time, we formulate a data monitoring and remote communication problem with the energy transfer constraint, data collection constraint, and transmission data rate constraint. As for the non-convex minimum time optimization problem, we develop a novel option-based hierarchical deep reinforcement learning (OHDRL) method to deal with the complex continuous variation characteristics of the monitoring and communication integrated HSR system. The system learns to select options at a high level, and the action is executed according to the policy of the selected option at a low level. This approach enables us to handle stochastic HSR environments, closed-loop policies, and goals in a temporal abstraction way. Numerical results reveal that the proposed algorithm for the integrated monitoring and communication HSR achieves a significantly higher reward and more stable learning performance than other algorithms in the literature.

It is expected that the future intelligent transportation system will be endowed with the sensing ability to cope with the complex road environment. Therefore, the integrated sensing and communications (ISAC) system can complement the development of intelligent transportation. In this work, a novel reconfigurable intelligent surface (RIS)-aided ISAC system is investigated, in which an RIS reflects signals to the vehicle target and user by creating a directional path to enhance sensing and communication performance. We are interested in the joint robust design of transmitted beamformer at the dual-functional radar-communication (DFRC) base station and phase-shift at the RIS to maximize the radar mutual information subject to user achievable rate constraint under imperfect angles knowledge and channel state information (CSI). Specifically, two CSI error models, namely, the bounded and the mixed bounded-moment error models, are considered. Then, a worst-case robust (WCR) beamforming problem, as well as a mixed chance-constrained and worst-case robust (MCWR) beamforming problem, are separately formulated. Furthermore, we develop two efficient methods to convert the formulated semi-infinite constraint problems into feasibility ones, and an alternate optimization framework is proposed to obtain stationary points of the original problems. Simulation results are provided to validate the effectiveness of the proposed transformation methods and solution.

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered sensor network (WPSN), where sensor nodes of multiple types can simultaneously harvest radio-frequency energy from the UAV and then transmit sensing data by using harvested energy. A joint distributed beamforming (DBF) and backscattering scheme is designed, in which the sensor nodes of one type can perform DBF while the sensor nodes of other types perform distributed backscattering (DBS) to improve the received signal strength. A sum-throughput maximization problem is formulated by jointly optimizing DBF phases, DBS phases, and time allocation (TA), subject to the received signal-to-noise ratio constraints. Since the formulated problem is difficult to be solved due to the tightly coupled optimizing variables, the problem is decoupled into a TA subproblem and a phase optimization subproblem, and then a two-step algorithm is proposed to solve them. Firstly, the closed-form solution for the TA subproblem is derived according to Karush-Kuhn-Tucker conditions. Secondly, based on iterative optimization and one-dimensional search methods, a centralized algorithm is proposed to obtain the optimal solution for the phase optimization subproblem. Moreover, a decentralized algorithm that obtains the suboptimal solution is proposed to reduce the computational complexity. Extensive simulation results validate the effectiveness of the proposed scheme on throughput enhancement.

Federated learning (FL) is able to utilize the computing capability and maintain the privacy of the end devices by collecting and aggregating the locally trained learning model parameters while keeping the local personal data. As the most widely-used FL framework,Jederated averaging (FedAvg) suffers an expensive communication cost especially when there are large amounts of devices involving the FL process. Moreover, when considering asynchronous FL, the slowest device becomes the bottleneck for the cask effect and determines the overall latency. In this work, we propose a communication-efficient federated learning framework with partial model aggregation (CE-FedPA) algorithm to utilize compression strategy and weighted device selection, which can significantly reduce the size of uploaded data and decrease the communication time. We perform a series of experiments on the MNIST/CIFAR-10 datasets, in both lID and non-lID data settings. We compare the communication time of different aggregation schemes, in terms of iteration rounds and target accuracy. Simulation results demonstrate that the uploading time of the proposed scheme is up to 4.3 times shorter than other existing ones. Experiments on an end - to-end FL framework also verify the communication efficiency of CE-FedPA in a real-world setting.

In this paper, we study a self-supplied multi-sensor monitoring and communication integrated system in a high-speed railway (HSR), where each access point (AP) simultaneously transmits radio frequency (RF) energy signal to charge sensors, monitors the train working state via backscatter communication technology, and communicates remotely with the base station (BS). Aiming to minimize the total task completion time, we formulate a data monitoring and remote communication integrated optimization problem under energy transfer, data collection and transmission data rate constraints. To reduce the system task's complexity in a high-speed movement scenario, we propose a finite Gaussian mixture model (GMM) clustering method to analyze the communication handover area. For the complex action space in the highly-dynamic HSR communication handover environment, we develop a novel option-based hierarchical deep reinforcement learning (OHDRL) algorithm to deal with the sparse reward and non-stationary problem. An agent learns to select options at higher levels of decomposed subtasks, and the action is executed according to the internal policy of the selected option at a low level. Numerical results reveal that the proposed algorithm achieves a significantly higher reward and more stable learning performance than the traditional Deep Q-Network (DQN) and Double Deep Q-Network (DDQN) algorithms.

Joint localization and communication systems have drawn significant attention due to their high resource utilization. In this paper, we consider a reconfigurable intelligent surface (RIS)-aided simultaneously localization and communication system. We first determine the sum squared position error bound (SPEB) as the localization accuracy metric for the presented localization-communication system. Then, a joint RIS discrete phase shifts design and subcarrier assignment problem is formulated to minimize the SPEB while guaranteeing each user’s achievable data rate requirement. For the presented non-convex mixed-integer problem, we propose an iterative algorithm to obtain a suboptimal solution by utilizing the Lagrange duality as well as penalty-based optimization methods. Simulation results are provided to validate the performance of the proposed algorithm.

In this article, we consider a cellular-based healthcare Internet of Things (IoT) system with imperfect channel state information (CSI), where a healthcare IoT device first receives radio frequency (RF) energy from the small cell base station (SBS) and then transmits physiological status updates to the corresponding SBS as timely as possible. A newly proposed metric, named Age of Information (AoI), will be introduced to characterize the data freshness, which is determined by the status updates generation probability and the information transmission outage probability. To minimize the average AoI, we formulate a distributionally robust optimization problem under an energy harvesting probability (chance) constraint and an information transmission probability constraint. Since the distributionally robust probability constraints are nonconvex, we use the conditional value-at-risk (CVaR)-based method to express constraint specifications related to distributional ambiguity. To tackle the NP-hard problem efficiently, we decompose the AoI minimization problem into two subproblems and propose a low-complexity iterative algorithm to obtain a suboptimal solution. Simulation results show that there exists an AoI-energy tradeoff in the considered healthcare IoT, and the CVaR-based method can achieve a better performance than the nonrobust method.

Unmanned aerial vehicle (UAV)-assisted wireless powered sensor networks (WPSNs) have emerged as a promising paradigm for charging sensor nodes' batteries in remote areas. However, the sum-throughput of overall sensor nodes can dramatically decrease due to their long-distance transmission to the UAV. In this paper, we propose a joint distributed beamforming and backscatter cooperation (BC) scheme to enhance the sum-throughput of UAV-assisted WPSNs with various types of sensor nodes. In particular, we consider the BC mechanism which leverages other types sensor nodes with constructive multi-path signals to enhance the long-distance transmission of same-type sensor nodes. We maximize the sum-throughput by jointly optimizing the distributed backscattering, distributed beamforming and time allocation. The sum-throughput maximization problem is difficult to be solved directly due to the coupling among optimizing variables. We decompose the problem into a BC subproblem and a time allocation subproblem, and propose a two-step scheme to solve them. First, for the BC subproblem, we derive closed-form low-complexity distributed beamforming solutions and distributed backscattering solutions to maximize the signal-to-noise ratios of the same-type sensor nodes. Second, for the time allocation subproblem, we derive the closed-form solutions according to KKT conditions. Simulation results are provided to demonstrate that the proposed joint distributed beamforming and BC scheme can increase the sum-throughput as compared to conventional distributed beamforming schemes.

In this paper, we consider a real-time E-Health Internet of Things (IoT) system with the uncertainty of channel state information (CSI), in which a wearable device collects radio frequency (RF) energy from a Personal Digital Assistant (PDA), and then transmits healthcare data status updates to the corresponding PDA promptly. The Peak Age of Information (PAoI) is considered as a parameter to measure the freshness of information. Our goal is to minimize the average PAoI under non-convex constraints related to an uncertain CSI mismatch model. Only mean and variance information is specified in the distributional ambiguity set. This distributionally robust optimization problem is transformed into a tractable semi-definite programming (SDP) problem using the Conditional Value-at-Risk (CVaR) based method. To solve this NP-hard problem effectively, we decompose the PAoI minimization problem into two subproblems, and propose a low complexity iterative algorithm to derive a suboptimal solution. Simulation results show an average PAoI-energy tradeoff in the considered healthcare IoT, and the CVaR based method can achieve a better performance than a non-robust method.

In this article, a classified system with a relay is investigated in wireless body area networks (WBANs), where some on-body sensors are divided into several sensor groups on the basis of different body parts. Not only can the network detect basic vital signs but also the physiological signals of special diseases. All groups of sensors and the relay harvest energy by the radio-frequency (RF) source broadcasting, then the same group of sensors simultaneously sends common information through cooperation to the source. Since the classified system is a confined system, we maximize the system throughput with a joint power allocation on maximum ratio combining (JPA-MRC) protocol, where the transmission power allocation at the relay is unequal for each relaying subslot. The optimal problem is solved by the Lagrange multiplier method and the Karush-Kuhn-Tucker (KKT) optimal conditions. To further the obtain optimal solution, we simplify co-optimization of relay power and sensor power to the optimization of single power by the equivalent channel gains. Analytical and simulation results show that our proposed optimal method can greatly improve the information throughput compared to the conventional mean power allocation method. In addition, the protocol based on the classified scheme outperforms than that of the conventional unclassified scheme. The impacts of various important system parameters are analyzed, which provide useful design insights under different cases.

In this letter, the order recursive least squares (ORLS) is applied to the azimuth-only ULA to probe the DoA estimation under the unknown number of external sources. In the ORLS-based method, the iterative measured matrix equations are feasibly constructed by combining the two spatial modified Yule-Walker (MYW) systems of linear equations and are required to remain Hankel-block-matrix structure of augmented matrices after arrangement order of the unknowns and the maximum number of detectable sources are preset. Under the stationary assumption on source signals and noises, the remarkable advantage of the proposed method lies in that it can theoretically provide the zero LS error as iteration times is equal to the number of sources. The zero LS error is an evident mark to judge the number of sources, especially for the scenario of the unknown noise variances. The proposed method is free of uncorrelated and coherent signals and the corresponding various mixtures and remains computationally efficient in estimation owing to no eigenvalue decomposition (EVD) and matrix inversion operation. The effectiveness of the determination of the number and azimuth angles of sources versus the different SNRs and numbers of snapshots is numerically confirmed.

Implementing wireless body area networks (WBANs) is very challenging, due to limited power supply, inadequate computation capability, and imperfect channel state information (CSI). In this paper, we propose a hybrid offloading scheme with backscatter communication (BackCom) under imperfect CSI, where each sensor firstly receives radio frequency (RF) energy and then offloads body data task via low-power BackCom to the access point (AP) for edge computing. Aiming to minimize the end-to-end system latency, we jointly optimize the computation speed of AP for processing computation tasks, the power of the signal transmitted by the AP, and the power reflection coefficient under energy and data rate chance constraints. To solve the proposed distributionally robust chance-constrained optimization problem, we approximate chance constraints by the Bernstein-type-inequality (BTI) method and Conditional value-at-risk (CVaR) method in the Gaussian distribution and arbitrary distribution of channel estimation errors, respectively. To tackle the NP-hard problem efficiently, the original problem can be decomposed into two subproblems, which are solved by successive linear programming and iterative algorithm, respectively. Simulation results show that the CVaR method outperforms the other methods for the non-Gaussian CSI mismatch, and the Bernstein method is more suitable for the Gaussian distribution of CSI errors.

In this paper, we propose a distributionally robust chance-constrained design for the backscatter communication-aided computation offloading scheme in wireless body area networks (WBANs), where each sensor firstly receives radio frequency (RF) energy and then offloads body physiological computation tasks via low-power BackCom to the access point (AP) for edge computing. Specifically, only rough first and second-order moment statistics are obtained for the estimation errors of CSI. Based on all the possible distributions of CSI errors, we aim to minimize the end-to-end system latency by jointly optimizing the power of the signal transmitted by the AP and the power reflection coefficient with energy chance restrictions and throughput requirement constraints. In order to solve the proposed non-convex chance-constrained optimization problem, we approximate chance constraints by the conditional value-at-risk (CVaR), and apply an efficient block coordinate descent (BCD) algorithm to solve it. Simulation results are provided to corroborate that the proposed method outperforms other methods for the non-Gaussian mismatch.

In this paper, we focus on a trade-off strategy between significance and fairness of sensors in a wireless body area network (WBAN). Due to different types and functions of sensors, it makes sense to classify sensors according to significance and fairness in WBAN, and we can get better performance of the entire communication system with the balance between significance and fairness of sensors. Based on a multi-input multi-output (MIMO) system, which contains an access point (AP) with multi-antenna and many sensors placed on a human body, the two classification methods for significance and fairness are combined with the model, and we propose a trade-off scheme by maximizing the sum-throughput. In this paper, simultaneous information and power transfer (SWIPT) model is considered. We divide the protocol into energy transfer phase and information transfer phase. Maximal ratio transmission (MRT) and zero-forcing (ZF) technology are applied to the two parts respectively. In order to ensure the reliability of information without harming human health, we use the transmit power and signal to interference plus noise ratio (SINR) threshold as constraints, and jointly optimize the time and power allocation to achieve the total throughput maximization. Finally, simulation results demonstrate the reliability of the optimal trade-off scheme.

In this paper, a classified system with a relay is investigated in wireless body area network (WBAN), where several sensors are divided into several groups. All types of sensors and the relay harvest energy by the radio frequency (RF) source broadcasting, then the same type of sensors simultaneously send common information through cooperation to the source. Since the classified system is a confined system, based on power allocation and maximum ratio combining (MRC) in a point-to-point WBAN, we propose a joint power allocation on MRC (JPA-MRC) protocol, where the transmission power allocation at relay is unequal for different types of sensors and interacts with the sensors power allocation. In order to achieve the maximum sum-throughput at the source by joint power allocation at the relay and at sensors, the optimal solutions for joint-objective linear programming methods are proposed. Analytical and simulation results show that, our proposed optimal method can greatly improve the information throughput compared to direct transmission (DT) and relay transmission (RT), respectively. In addition, the protocol based on classified scheme outperforms than that of conventional unclassified scheme. The impacts of various important system parameters are analyzed, which provide useful design insights under different cases.

Seamless outdoor-indoor positioning plays a critical role in many emerging applications, e.g., large-coverage user navigation in cities, smart buildings, and analytics of user spatial location big data. It is still challenging to construct a large-scale seamless outdoor-indoor positioning system due to the limited coverage of indoor positioning. In this paper, we propose a seamless outdoor-indoor crowdsensing positioning (SoiCP) system in which a radio map is automatically constructed based on crowdsourcing pedestrian dead reckoning (PDR) traces without professional site surveying. The constructed radio map is robust to inaccurate PDR traces and does not rely on prior knowledge of floor plans. In SoiCP, the crowdsensed radio map is obtained by a proposed three-step trace matching algorithm. This algorithm leverages building gates and WiFi fingerprints as landmarks to merge the noisy crowdsourcing traces and accurately construct the user walking paths. Moreover, following the crowdsensed radio map, SoiCP uses an enhanced particle filter to fuse PDR, GPS, and WiFi fingerprinting for seamless outdoor-indoor positioning with high accuracy. The comprehensive real-world experiments in two large-scale shopping malls demonstrate that SoiCP can effectively crowdsense the walking paths and track moving users with high accuracy.

In this paper, we investigate optimal schemes to manage time scheduling of multiple modules, including spectrum sensing, radio frequency (RF) energy harvesting (RFH) and ambient backscatter communication (ABCom) by maximizing data transmission rate in Internet of Things networks. We first detect ambient RF signals with high signal power as the RF resource of RFH and ABCom by using spectrum sensing with energy detection techniques. Specifically, compressive sensing (CS) is adopted to detect the wideband RF signals with improving spectrum sensing efficiency at the same time. We formulate a joint optimization problem to manage time scheduling parameter and power allocation ratio. In addition, we propose to find the threshold of spectrum sensing for ABCom communications by analyzing the outage probability of backscatter communications. Numerical results demonstrate that the optimal schemes using spectrum sensing are achieved with better transmission rates. The designed time scheduling scheme with CS is confirmed to be more efficient, and the superiorities become more obvious with the increase of network operation time. Moreover, the optimal scheduling parameters and power allocation ratios are obtained. Simulations illustrate that the threshold of spectrum sensing for backscatter communications is obtained by analyzing the outage probability of backscatter communications.

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.

Crowdsensing indoor walking paths based on crowdsourcing traces collected from normal users has recently become an emerging topic for indoor positioning, which can reduce the labor effort of building radio maps and improve the positioning accuracy when a floor plan is unavailable. In this work, we design an indoor walking path crowdsensing system with massive noisy crowdsourcing traces. In this system, we propose a robust iterative trace merging algorithm based on WiFi access points as markers (named 'WiFi-RITA') to merge massive noisy traces. The algorithm formulates the trace merging problem as an optimization problem in which each trace is controlled to translate and rotate to minimize the limitation of distances among traces defined by WiFi access points as markers. WiFi-RITA is robust to the rotation errors and uncertain absolute locations of user traces, and can efficiently work for a large number of user traces. We further adopt a landmark matching algorithm to match the merged traces to the target building and adopt a 2-dimensional histogram approach to remove outlier traces. With such procedures, we generate walking paths of a large-scale building with a mean accuracy of 2.1m.

In this paper, a relay selection protocol for energy harvesting wireless body area network with buffer is proposed, that considers both the energy of relay nodes and channel state information. The relay node harvests energy from the received radio-frequency signals, then the relay node consumes the harvested energy to transmit information to the destination node. In the proposed relay selection protocol, the relay with the most energy is selected to receive information, the relay with the best relay-destination channel is selected from candidate relays for transmission. The proposed protocol can mitigate the risk of inappropriate relay selection due to channel mismatch problem and avoid the overuse of the same relay. The outage probability is used to measure the system performance. We investigate the outage probability of the optimal relay selection protocol in the time switching and power splitting schemes, respectively. Numerical results show that the proposed protocol significantly outperforms conventional relay selection protocols.

Reliability is a critical design parameter in wireless body area networks (WBANs). In this study, the authors quantify the reliability of WBAN based on the network lifetime, and derive a general formula of the reliability in terms of the number of sensors and group characteristic parameter (GCP). Upper and lower bounds of the reliability are derived for an arbitrary WBAN. Based on the monotonicity property of reliability, an algorithm by using average GCP is presented to calculate the minimum and maximum number of sensors for a given network lifetime, and the optimal number of sensors for a given reliability. The performance of reliability is evaluated through numerical results.

This paper studies a simultaneous wireless information and power transfer system with a helping relay in wireless body area network, where the relay harvests energy from the radio-frequency signals sent by other nodes, then the relay uses the harvested energy to help transmit energy to the destination and forward information to the source, respectively. Compared with the existing protocols, we propose the dynamic time allocation strategy in this paper. First, based on power splitting (PS) and time switching (TS) transmission protocols, we propose two new transmission protocols, where the transmission time slots are unequal allocation. Then the optimal strategy to achieve the maximum information throughput by solving nonlinear programming problems is presented. And by changing the relay position, the optimal time and power ratios for the best system performance are presented. Finally, the fitting curves of the optimal solutions for different relay positions are plotted. Numerical results show that our proposed optimal strategy can achieve the best throughput performance and the protocol based on TS outperforms slightly than the protocol based on PS.

Wireless body area network (WBAN) is emerging as a powerful tool for health management which is characterized by a modest number of sensors placed on or around human bodies. Since sensor nodes are generally battery-powered devices, reducing sensor node power consumption to improve the efficiency and save sensor usage costs is crucial. In this paper, a convex optimization method based beam-pattern synthesis with sensor selection is proposed for circular sparse arrays, which can be regarded as the model of the head of human. Our goal is two-fold: minimize the number of sensors while achieving the best peak level of side-lobe. The method can solve uniformly spaced circular arrays with inter-element spacings about half- wavelength in order to satisfy the performance of the beam-pattern. Based on the one circular ring array, we propose two different configuration modes, one is uniform circular array and the other is concentric circular array. Simulations are shown using up to a few hundred sensors to illustrate the practicality of the proposed algorithm.

This paper investigates power allocation problems for orthogonal frequency division multiplexing (OFDM)-based cognitive radio networks operating in licensed frequency bands. Considering imperfect spectrum sensing, a new power allocation algorithm is proposed to minimize the total interference introduced to primary user under a minimum capacity constraint of secondary user (SU) and a total transmit power constraint of the SU. The numerical results demonstrate that the proposed power allocation scheme can not only keep the rate requirement of SU under spectrum sensing errors by comparison with traditional method, but also fully make use of the limited spectrum resource.

This study concerns sensor selection problem in wireless body area network (WBAN), which can reduce the burden of the human body and improve the energy efficiency. The authors highlight the sparse sensor array synthesis algorithm for different shapes of sensor arrays via convex optimisation to solve this problem. For simplicity, one regular spherical sensor array and five regular cuboid sensor arrays are considered to simulate the sensor distribution around the human body. As a comparison, the conventional method with one objective method is used to synthesise the sensor array first. Then the proposed algorithm which includes two objective variables: the l 1 ‐norm of weight vector and the peak side‐lobe level is used. Simulations demonstrate that the proposed sparse sensor array synthesis algorithm achieves array sparsity with lower side lobe and more concentrated main lobe when operated in spherical and cuboid sensor array. Hence, the sensor selection problem is achieved by the sparse sensor array synthesis.

In this paper, we consider the multiple antenna based spectrum sensing problem for a cognitive radio (CR) network. Different from conventional CR, we here consider a more practice-matching scenario when the primary user (PU) could work under more than one transmit power levels, depending on the communication environments. Consequently the spectrum sensing at the secondary user (SU), besides checking the on/off status of PU, could also identify PU's transmit power level if it is “detected”, making the overall sensing problem much more challenging. We design the optimal spectrum sensing algorithm when the channel information is known and partially known. New performance metrics under this new multiple primary transmit power (MPTP) scenario are defined to better evaluate the proposed algorithm, and quite a number of closed-form results are derived. Finally simulation results are presented to verify the proposed studies.

In this paper we consider multi-input multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM)-based systems under unknown narrow-band interference (NBI). We propose an iterative receiver to jointly estimate the channel information, which consists of channel coefficients and noise-plus-interference variances of each sub-carrier, and detect the transmitted signals. The simulation results show that our proposed receiver provides a close bit-error-rate (BER) to that of the case where perfect channel information is available at the receiver. Besides, Cramér-Rao lower bound (CRLB) of interested parameters are also derived. The mean-square-error (MSE) of the estimated parameters given by our proposed algorithm reaches the CRLB.

This paper presents un approach for beamforming based on third-order cyclic statistics. The approach has fairly well accuracy. Further, a new scheme joint beamforming and space-time block coding is suggested that multiple antenna arrays transmit at base station and multi-antenna elements receive at mobile terminal. This scheme improves the receiving error performance of mobile terminal and increases the capacity of the entire wireless communication system. The computer simulation validates that the aforementioned scheme is superior and effective.