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.

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.

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, 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.

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.

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.

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.

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.

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.

With an extensive increment of computation demands, the aerial multi-access edge computing (MEC), mainly based on unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs), plays significant roles in future network scenarios. In detail, UAVs can be flexibly deployed, while HAPs are characterized with large capacity and stability. Hence, in this paper, we provide a hierarchical model composed of an HAP and multi-UAVs, to provide aerial MEC services. Moreover, considering the errors of channel state information from unpredictable environmental conditions, we formulate the problem to minimize the total energy cost with the chance constraint, which is a mixed-integer nonlinear problem with uncertain parameters and intractable to solve. To tackle this issue, we optimize the UAV deployment via the weighted K-means algorithm. Then, the chance constraint is reformulated via the distributionally robust optimization (DRO). Furthermore, based on the conditional value-at-risk mechanism, we transform the DRO problem into a mixed-integer second order cone programming, which is further decomposed into two subproblems via the primal decomposition. Moreover, to alleviate the complexity of the binary subproblem, we design a binary whale optimization algorithm. Finally, we conduct extensive simulations to verify the effectiveness and robustness of the proposed schemes by comparing with baseline mechanisms.

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.

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.

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 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.

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.

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.

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.

Accurate per-flow counting is beyond the capability of network switches due to the sheer flow number. The conventional divide-and-conquer method by distributing the traffic to multiple servers for software processing is costly. The solution therefore quests for a combination of hardware and software where the hardware with limited resources aims to undertake a part of the job and reduce the workload of software, achieving a desirable balance of cost and performance. To this end we design FASTeller to be deployed on SmartNICs. It is tuned to maximize the counting aggregation level in hardware, leaving the server a much lower workload for accurate per-flow counting and sparing the server capacity for post-counting functions such as network intrusion detection. The novelty lies in the multi-tier hardware caching data structure which is tailored for the flow distribution properties of real traffic. We build an FPGA-based prototype and evaluate the performance of FASTeller. The low-cost implementation achieves the highest performance among the methods in comparison and can easily sustain the accurate perflow counting for 100Gbps traffic with the least software load.

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.

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.

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.

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, 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.

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.