Low-altitude economy (LAE) is an emerging business model, which heavily relies on integrated sensing and communications (ISAC), mobile edge computing (MEC), and covert communications. This paper investigates the covert transmission design in MEC-based networked ISAC systems towards LAE, where an MEC server coordinates multiple access points to simultaneously receive computation tasks from multiple unmanned aerial vehicles (UAVs), locate a target in a sensing area, and maintain the UAVs’ covert transmission against multiple wardens. We first derive closed-form expressions for the detection error probability (DEP) at the wardens. Then, we formulate a total energy consumption minimization problem by optimizing communication, sensing, and computation resources as well as UAV trajectories, subject to the requirements on the quality of MEC services, DEP, and the radar signal-to-interference-and-noise ratio, and the causality constraints of UAV trajectories. An alternating optimization-based algorithm is proposed to handle the considered problem, which decomposes it into two subproblems: joint optimization of communication, sensing, and computation resources, and UAV trajectory optimization. The former is addressed by a successive convex approximation-based algorithm, while the latter is solved via a trust-region-based algorithm. Simulations validate the effectiveness of the proposed algorithm compared with various benchmarks, and reveal the trade-offs among communication, sensing, and computation in LAE systems.

This paper investigates the joint transmit and receive beamforming design for a bistatic integrated sensing and communication (ISAC) system, where a transmit base station (BS) and a receive BS are coordinated to simultaneously serve multiple downlink and uplink users as well as estimate target positions. The closed-form expression for the Cramér-Rao bound (CRB) for target positions and reflection coefficients is derived and minimized subject to constraints of the transmit power budget and communication requirements. To address the considered problem, the closed-form expression for the optimal receive beamforming vectors is derived, facilitating the development of a successive convex approximation (SCA)-based algorithm for optimizing the transmit information beamforming vectors and sensing covariance matrix. In addition, a learning-based approach named ISACNet, trained in an unsupervised manner, is proposed to handle the considered problem. The ISACNet incorporates multi-head self-attention and cross-attention mechanisms to significantly enhance its expressive capability. Simulations validate the effectiveness of the proposed SCA-based algorithm and ISACNet. It is observed that the sensing performance is predominantly affected by the downlink communication more than the uplink communication. Furthermore, our ISACNet generates an effective solution in millisecond-level response times with only a marginal performance degradation compared to the SCA-based algorithm.

This paper proposes a graph neural network (GNN)-enabled coordinated beamforming design, termed HSTGNN, for high speed railway (HSR) communication systems, where a high-speed train (HST) and low-speed users (LUs) coexist in multi-cell scenarios. Two transmission schemes with the goal of maximizing quality-of-service (QoS)-constrained sum rate and rate of the HST are formulated and then reformulated using a hybrid maximum ratio transmission and zero-forcing strategy. The HSR communication system is abstracted into a heterogeneous graph, and HSTGNN consists of complex heterogeneous graph attention layers and fully-connected layers. To meet QoS requirements and power budget constraints, we employ constraint-based penalty terms and a numerical scaling operation. HSTGNN is trained via unsupervised learning to solve the two schemes in a unified framework. Numerical results demonstrate that HSTGNN achieves millisecond-level inference speed with an average optimality gap of only 4% relative to traditional optimization algorithm across various scenarios. Moreover, HSTGNN exhibits strong scalability to unseen configurations of both cells and LUs.

Recently, an emerging flexible-antenna technology, termed pinching antennas, has attracted growing academic interest. By inserting discrete dielectric materials, pinching antennas can be activated at arbitrary points along waveguides, allowing for flexible customization of channel conditions. This paper investigates a multi-waveguide pinching-antenna integrated sensing and communications (ISAC) system, where transmit pinching antennas (TPAs) and receive pinching antennas (RPAs) coordinate to simultaneously detect one potential target and serve one downlink user. We formulate a communication rate maximization problem subject to radar signal-to-noise ratio (SNR) requirement, transmit power budget, and the allowable movement region of the TPAs, by jointly optimizing TPA locations and transmit beamforming design. To address the non-convexity of the problem, we propose a novel fine-tuning approximation method to reformulate it into a tractable form, followed by a successive convex approximation (SCA)-based algorithm to obtain the solution efficiently. Furthermore, we derive the closed-form optimal solution for a special multi-waveguide case involving a single TPA. Extensive simulations validate both the system design and the proposed algorithm. Results show that the proposed method achieves near-optimal performance compared with the computational-intensive exhaustive search-based benchmark, and pinching-antenna ISAC systems exhibit a distinct communication-sensing trade-off compared with conventional systems.

Pinching antennas, realized through position-adjustable radiating elements along dielectric waveguides, have emerged as a promising flexible-antenna technology thanks to their ability to dynamically reshape large-scale channel conditions. However, most existing studies focus on idealized LoS-dominated environments, overlooking the stochastic nature of realistic wireless propagation. This paper investigates a more practical multiuser pinching-antenna system under a composite probabilistic channel model that captures distance-dependent LoS blockage and NLoS scattering. To account for both efficiency and reliability aspects of communication, two complementary design metrics are considered: an average signal-to-noise ratio (SNR) metric characterizing long-term link quality and fairness, and an outage-constrained metric ensuring a prescribed reliability level. Based on these metrics, we formulate two optimization problems: the first seeks to maximize the minimum average SNR across users, while the second seeks to maximize a guaranteed SNR threshold subject to per-user outage constraints. Although both problems are inherently nonconvex, we exploit their underlying monotonic structures and develop low-complexity, bisection-based algorithms that achieve globally optimal solutions using only simple scalar evaluations. Extensive simulations validate the effectiveness of the proposed methods and demonstrate that pinching-antenna systems significantly outperform conventional fixed-antenna designs even under random LoS and NLoS channels.

Both the space-air-ground integrated networks (SAGIN) and the integrated sensing and communication (ISAC) are promising technologies in future communication systems. This paper investigates the mobile user (MU) tracking and robust beamforming design by the unmanned aerial vehicle (UAV) in an SAGIN-ISAC system. Two schemes for acquiring the location information of MUs at the UAV are proposed, namely the space-assisted and ISAC-assisted schemes. The former requires the precise location information from the satellite by the space-air transmission, while the latter estimates the location information of MUs via a proposed extended Kalman filter based algorithm. The obtained location information is then utilized to predict the channel distribution of MUs, which can be used to formulate an outage-constrained energy efficiency (EE) maximization problem. The considered problem is first reformulated based on the Bernstein-type inequality to derive computationally tractable forms of the outage probability constraints. Then, the reformulated problem is solved via the semi-definite relaxation (SDR) and successive convex approximation methods, where the tightness of employing SDR is theoretically proved. Numerical results illustrate the trajectories of the UAV for tracking MUs under the space-assisted and ISAC-assisted schemes, and discuss the impact of the space-air transmission on the EE performance. It is observed that there exists a trade-off between space-air transmission overhead and location prediction precision of MUs. By integrating the ISAC in SAGIN, the information demand from the space is reduced compared with traditional SAGIN.

This paper investigates the hybrid beamforming design for the reconfigurable intelligent surface (RIS)-aided full-duplex (FD) cell-free networks, where the access points (APs) connected to the central processing unit serve multiple users cooperatively. The weighted sum rate of uplink and downlink transmissions is maximized by jointly optimizing the digital and analog beamformers, the phase-shift coefficients of RISs and the uplink transmit power while satisfying the power budget constraints of the APs and users. For solving the problem, the objective function is reformulated using Lagrangian dual transform and fractional programming. Then, to tackle the variables coupling, the problem is divided into five subproblems that are solved iteratively via a proposed block coordinate descent (BCD)-based algorithm. To handle the unit-modulus constraint on analog beamformers and phase-shift coefficients, a monotonic fast proximal gradient (mFPG)-based method is proposed under the alternating direction method of multipliers (ADMM) framework. Numerical results demonstrate the effectiveness and efficiency of the proposed algorithm compared to three baseline algorithms. The impacts of the numbers of radio frequency chains and reflecting elements on the uplink and downlink transmissions are presented, respectively. The performance comparison between the FD and half-duplex schemes is provided.

This paper investigates the deep learning based approaches for simultaneous wireless information and power transfer (SWIPT). The quality-of-service (QoS) constrained sumrate maximization problems are, respectively, formulated for power-splitting (PS) receivers and time-switching (TS) receivers and solved by a unified graph neural network (GNN) based model termed SWIPT net (SWIPTNet). To improve the performance of SWIPTNet, we first propose a single-type output method to reduce the learning complexity and facilitate the satisfaction of QoS constraints, and then, utilize the Laplace transform to enhance input features with the structural information. Besides, we adopt the multi-head attention and layer connection to enhance feature extracting. Furthermore, we present the implementation of transfer learning to the SWIPTNet between PS and TS receivers. Ablation studies show the effectiveness of key components in the SWIPTNet. Numerical results also demonstrate the capability of SWIPTNet in achieving nearoptimal performance with millisecond-level inference speed which is much faster than the traditional optimization algorithms. We also show the effectiveness of transfer learning via fast convergence and expressive capability improvement.

This paper investigates the graph neural network (GNN)-enabled beamforming design for interference channels. We propose a model termed interference channel GNN (ICGNN) to solve a quality-of-service constrained energy efficiency maximization problem. The ICGNN is two-stage, where the direction and power parts of beamforming vectors are learned separately but trained jointly via unsupervised learning. By formulating the dimensionality of features independent of the transceiver pairs, the ICGNN is scalable with the number of transceiver pairs. Besides, to improve the performance of the ICGNN, the hybrid maximum ratio transmission and zero-forcing scheme reduces the output ports, the feature enhancement module unifies the two types of links into one type, the subgraph representation enhances the message passing efficiency, and the multi-head attention and residual connection facilitate the feature extracting. Furthermore, we present the over-the-air distributed implementation of the ICGNN. Ablation studies validate the effectiveness of key components in the ICGNN. Numerical results also demonstrate the capability of ICGNN in achieving near-optimal performance with an average inference time less than 0.1 ms. The scalability of ICGNN for unseen problem sizes is evaluated and enhanced by transfer learning with limited fine-tuning cost. The results of the centralized and distributed implementations of ICGNN are illustrated.

Hybrid analog and digital beamforming (HBF) is a cost-efficient technique to achieve high data rates in millimeterwave (mmWave) communication systems. This paper applies the emerging graph neural networks (GNNs) to HBF by leveraging the topological information in wireless networks for better adaptation to dynamic environments. To address the issue of limited feature extraction capability of the existing single-type GNNs, such as node-GNN or edge-GNN, we model the mmWave communication systems as both homogeneous and heterogeneous graphs, and separate the HBF design into node- and edge-level subtasks. Then, the two graphs are presented by two novel models based on homogeneous graph attention network (GAT) and heterogeneous GAT (HGAT), respectively, and mapped to the desired power allocation, radio frequency precoder and baseband precoder. Both the proposed GAT and HGAT are generalizable in the user variation scenarios, while the HGAT is also generalizable in antenna variation scenarios through the incorporation of a complex embedding layer. Furthermore, we introduce a constraint adaptive layer in the GAT and HGAT to ensure feasible outputs. Extensive numerical results based on the public dataset DeepMIMO are provided to evaluate the GAT and HGAT. The proposed approaches generally outperform existing baselines in terms of adaptability to system settings and generalizability to (unseen) problem parameters/sizes, while the HGAT can even achieve faster and better inference than traditional optimization approaches.

This paper investigates the rate outage constrained (ROC) energy efficiency (EE) under multiple-input single-output (MISO) interference channels, where only channel distribution information (CDI) is available at base stations (BSs). An EE maximization problem is formulated under the constraints of tolerable rate outage probability of each user and the power budget of each BS. Due to the computationally intractable form of the considered problem, two equivalent formulations are derived and four algorithms are proposed, where two are based on the block successive upper bound minimization (BSUM) method and the other two are based on the successive convex approximation (SCA) method. Further, the philosophies behind the BSUM-based algorithms and the SCA-based algorithms are analyzed. Numerical results demonstrate the efficacy of the proposed algorithms, while the BSUM-based algorithms are much more computationally efficient than all the state-of-the-art algorithms, as long as all the coupling requirements on quality of service (like ROC transmission) can be fused into EE. Besides, the impacts of power budget, circuit power and tolerable outage probability on the ROC EE are revealed and discussed.

This paper proposes a novel deep learning enabled beamforming design for ultra-dense wireless networks by integrating prior knowledge and graph neural network (GNN), termed model-based GNN. An energy efficiency (EE) maximization problem is first subject to the power budget and quality of service (QoS) requirements, and then reformulated based on the minimum mean square error scheme and the hybrid zero-forcing and maximum ratio transmission scheme. The model-based GNN is designed to realize the mapping from channel state information to beamforming vectors to address the reformulated problems. Particularly, the multi-head attention mechanism and the residual connection are adopted to enhance the feature extracting, and a scheme selection module is designed to improve the adaptability to channel conditions. The unsupervised learning is adopted, and a various-input training strategy is proposed to enhance the stability of the model-based GNN. Numerical results demonstrate that the proposed model-based GNN can realize a millisecond-level inference with limited performance loss, the scalability to different numbers of users and the adaptability to various channel conditions and QoS requirements in ultra-dense wireless networks.

Collaborative Intrusion Detection System (CIDS) is an essential technology that enables vehicular ad hoc networks (VANET) to protect against malicious intrusions. CIDS, however, is unable to prevent accidents if an anomalous vehicle is detected. Detecting anomalies and notifying vehicles in the VANET rapidly is thus essential, considering technical challenges such as communication efficiency, vehicle velocity and privacy. In this paper, we propose a novel two-layer privacy-aware trust evaluation CIDS framework, termed 2PT-CIDS, tailored to VANET. In 2PT-CIDS, vehicles and roadside units (RSUs) cooperate efficiently to enhance anomalous vehicle detection and notification. Considering its potential privacy leakage, we then present two types of game-theoretic information incentive mechanisms. In the case of traffic congestion, the privacy-aware incentive mechanism is presented based on the Stackelberg game. A Barycentric Lagrange interpolation (BLI) based algorithm is then proposed to speedy achieve the Nash equilibrium (NE). In the case of traffic smooth, the varying high velocities of vehicles are involved and a noncooperative game-based mechanism is proposed. The optimal NE decision selection is reconstructed as a Markov decision process (MDP) and the NE point is obtained via the designed novel reward-shaping double duelling deep Q network (D3QN) learning algorithm. Simulation results highlight the superiority of 2PT-CIDS over existing CIDS and potential application algorithms for VANET, effectively enhancing anomaly detection and notification considering communication cost and vehicle privacy.

This paper investigates the energy-efficient coordinated beamforming design for multi-pair multiple-input single-output (MISO) networks with passive eavesdroppers. To be practical, it is assumed that only channel distribution information (CDI) of the network is known by the transmitters/sources, and the dynamic energy consumption model (DECM) is employed. In order to achieve a green network design, an energy efficiency (EE) maximization problem is formulated subjecting to the individual available power constraints, the rate outage probability constraints, and the information leakage probability constraints. To solve the formulated non-convex problem, semidefinite relaxation (SDR) and first-order lower bound are applied to transform the problem, and then an efficient algorithm is proposed based on successive convex approximation (SCA) and Dinkelbach's approaches. The proposed algorithm is theoretically proved to converge to a stationary point of the considered problem. Further, a distributed version of the proposed algorithm is designed, with which each transmitter is able to optimize its own beamforming vector with local CDI. Moreover, the computational complexities and the signaling overheads of the two developed algorithms are analyzed and compared. Simulation results show that both algorithms achieve good EE performance, and the EE performance achieved by the distributed algorithm is very similar to that achieved by the centralized one. Additionally, it is shown that similar to the conventional scenarios without eavesdroppers, the achieved system EE also has a saturation point w.r.t. the available power of the transmitters, and by employing our proposed algorithms, the network security is significantly enhanced.

The advantages of graph neural networks (GNNs) in leveraging the graph topology of wireless networks have drawn increasing attentions. This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the users’ individual data rate requirements and the power budget of the base station (BS). By modeling the MU-MISO network as a graph, a GNN-based architecture named complex residual graph attention network (CRGAT) is proposed to directly map channel state information to beamforming vectors. The attention-enabled aggregation and the residual-assisted combination are adopted to enhance the learning capability and mitigate the oversmoothing issue. Furthermore, a novel activation function is proposed for the constraint due to the limited power budget at the BS. The CRGAT is trained via unsupervised learning with two proposed loss functions. An evaluation method is proposed for the learning-based approaches, based on which the effectiveness of the proposed CRGAT is validated in comparison with several convex optimization and learning based approaches. Numerical results are provided to reveal the advantages of the CRGAT including the millisecond-level response with limited optimality performance loss, the scalability to different number of users and power budgets, and the adaptability to different system settings.

Reconfigurable intelligent surface (RIS) has been proposed as a wireless coverage enhancement enabler. However, due to the passive feature of the RIS, it is challenging to acquire the instantaneous channel state information for RIS-user links. This paper investigates the outage-constrained transmission design for RIS-assisted multi-user multiple-input-single-output (MISO) systems under interference channel based on channel distribution information. The transmission design problem is formulated to maximize the sum transmission rate under constraints of the tolerable outage probability of each user, the power budget of each transmitter and the phase shift coefficient of each reflecting element. To solve the computational intractable problem, a block successive upper bound minimization (BSUM)-based algorithm is proposed where the feasible set is separated w.r.t. variables into several blocks, and for each block, a computationally efficient surrogate subproblem is formulated and solved. Furthermore, the non-decreasing behavior and optimality performance of the proposed algorithms are theoretically analyzed. Numerical results show that the proposed algorithm is more computational efficient than traditional alternative optimization based algorithm, and the proposed the outage-constrained transmission design is able to suppress the average outage rate to a required level as well as maximizing the sum transmission rate.

This paper investigates the system model and the transmit beamforming design for the Cell-Free massive multi-input multi-output (MIMO) integrated sensing and communication (ISAC) system. The impact of the uncertainty of the target locations on the propagation of wireless signals is considered during both uplink and downlink phases, and especially, the main statistics of the MIMO channel estimation error are theoretically derived in the closed-form fashion. A fundamental performance metric, termed communication-sensing (C-S) region, is defined for the considered system via three cases, i.e., the sensing-only case, the communication-only case and the ISAC case. The transmit beamforming design problems for the three cases are respectively carried out through different reformulations, e.g., the Lagrangian dual transform and the quadratic fractional transform, and some combinations of the block coordinate descent method and the successive convex approximation method. Numerical results present a 3-dimensional C-S region with a dynamic number of access points to illustrate the trade-off between communication and radar sensing. The advantage for radar sensing of the Cell-Free massive MIMO system is also studied via a comparison with the traditional cellular system. Finally, the efficacy of the proposed beamforming schemes is validated in comparison with zero-forcing and maximum ratio transmission schemes.

This paper investigates the joint uplink (UL) and downlink (DL) robust transmission design for cell-free networks. The total data amount of the UL and DL transmissions is maximized in the presence of the statistical channel state information error by optimizing the UL digital combiners, UL transmission power, DL transmit beamforming vectors and UL/DL time allocation subject to the tolerable UL/DL outage probability constraints, the UL/DL minimum data amount requirements and the UL/DL power budgets. To tackle the variables coupling in outage probability constraints, the property of perspective function and the quadratic transform are applied. Then, the Bernstein-type inequality is used to derive the computationally tractable forms of the outage probability constraints. The considered problem is further decomposed into four subproblems and solved by the proposed alternating optimization (AO)-based algorithm. Numerical results show the proposed algorithm outperforms three existing AO-based baselines in terms of convergence speed and optimality performance. The impacts of the UL transmission power and tolerable outage probability on the UL/DL transmission and time allocation are revealed. Moreover, the effective APs for each user are defined and illustrated to show the coordination among the APs.

This paper investigates simultaneous wireless information and power transfer (SWIPT)-enabled cell-free massive multiple-input multiple-output (CF-mMIMO) networks with power splitting (PS) receivers and non-orthogonal multiple access (NOMA). By exploiting the conjugated beamforming method, the closed-form expressions of the information rate and the total harvested power at each user equipment (UE) are derived. To improve the system spectral efficiency, a sum rate maximization problem is formulated subjecting to the quality of service requirement at each UE and the power budget constraint at each access point by optimizing the UE clustering, the power control coefficients, and the PS ratios. To solve the formulated non-convex and mixed combinatorial problem, a machine learning-based approach is designed. Particularly, the UE clustering is first optimized by using a K-means based method and then the power control coefficients and the PS ratios are jointly optimized by a proposed multi-agent deep Q-network (MA-DQN) based method. The impact of the discount factor of the MA-DQN based method on the derived result is discussed. It is proved that by setting the discount factor as zero, the performance loss is negligible. Based on this observation, a zero-discount MA-DQN (0-γ MA-DQN) based method is further proposed to improve the computational efficiency. Also, the computational complexity of the proposed machine learning-based approach is analyzed. Simulation results show that the proposed machine learning-based approach outperforms various existing approaches. Moreover, it indicates that CF-mMIMO and NOMA could enhance the propagation performance of SWIPT while the proposed machine learning-based approach could facilitate resource allocation.

This paper investigates the beamforming design in the Cell-Free massive multi-input multi-output (MIMO) integrated sensing and communication (ISAC) system, termed as the CF-ISAC system, in presence of the channel state information (CSI) estimation error. The beamforming design is formulated into a sensing beampattern matching mean square error minimization problem under the constraints of the power budgets of the access points (APs) and the ergodic rate requirements of the users. A computationally tractable lower bound of the ergodic rate over the imperfect CSI is derived based on the Jensen's Inequality, and then a successive convex approximation based algorithm is proposed to solve the considered problem. Numerical results illustrate the beampatterns for different direction of arrival estimations of the targets. The advantage of the CF-ISAC system for radar sensing is revealed based on the relative location between the AP and the target.

This paper investigates the energy-efficient beamforming design in a simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) assisted wireless communication system, where the antenna selection scheme is adopted. An energy efficiency (EE) maximization problem is formulated by optimizing the transmit beamformers and the phase shift vectors subject to the power budget constraint of the base station (BS), the maximum transmit power constraint per antenna and the users' data rate requirements. An alternating optimization-based algorithm is proposed to tackle the coupled variables, and the quadratic transform is used to deal with the fractional formulations. Simulation results demonstrate that the antenna selection scheme can significantly improve the EE performance by suppressing the energy consumption due to massive antennas. With the assistance of the STAR-RIS, the EE performance is further enhanced.

This paper investigates the transmission design of an active reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system. A sensing beampattern matching mean squared error (MSE) minimization problem is formulated under constraints of the power budgets at the base station and the RIS, the amplification factor of the RIS and the information rate requirements of users, by jointly optimizing the transmit beamforming vectors, the covariance matrix of the sensing signal and the reflection coefficients of the RIS. The considered problem is solved in an alternative optimization manner by decomposing the original problem into two sub-problems, where each sub-problem is solved via semi-definite relaxation (SDR) and successive convex approximation (SCA). The tightness of applying SDR is theoretically proved. Simulation results verify the convergence behavior and effectiveness of the proposed algorithm. It is also shown that the active RIS is able to improve the sensing beampattern matching performance by enhancing the information transmission.

Federated learning (FL) is a privacy-preserving distributed learning paradigm relying on but without directly accessing privately owned datasets. However, the ‘‘available but not visible’’ nature of training data in FL leads to security risks. In particular, ‘‘not visible’’ local data can easily become the best targets of poisoning attacks. Although existing data poisoning methods may successfully attack FL systems, they mostly lead to significant data statistical changes and thus can be not hard to detect. In this paper, we propose VagueGAN, a new data poisoning attack model that unconventionally leverages the power of generative adversarial network (GAN) to generate seemingly legitimate vague data with appropriate amounts of poisonous noise. The quality of such vague data can be controlled on demand to achieve a balanced trade-off between attack effectiveness and stealthiness. Extensive experiments show that data poisoning attacks enhanced by our VagueGAN not only better degrade FL outcomes with low efforts but also are generally much less detectable.

The participatory nature of federated learning (FL) makes it attractive for fingerprinting-based indoor localization in multibuilding and multifloor environments. A group of sensing clients can collaboratively leverage their private, local fingerprint data to help their edge server update a location prediction model. However, it is challenging to jointly handle the two involved issues, i.e., building-floor classification (BFC) and latitude–longitude regression (LLR), in a wide 3-D space through enabling FL on decentralized yet heterogeneous data and over an imperfect wireless network. In this article, we confront these challenges and propose an FL framework, FedLoc3D, for both BFC and LLR. Specifically, the former issue is addressed by an FedDSC-BFC approach, which generates a multilabel classification model based on a convolutional neural network with depthwise separable convolutions. The latter issue is addressed by an FedADA-LLR approach, which develops a multitarget regression model based on a deep neural network with autoencoder and data augmentation. Extensive experiments on a real-world data set of WiFi fingerprints are carried out, and our approaches with enhanced capabilities of feature extraction, generalization, and convergence are validated to improve both localization accuracy and learning efficiency under data heterogeneity and network instability.

Unmanned aerial vehicle (UAV) communication systems usually operate in harsh scenarios, which require accurate information about the topology and wireless channel to achieve the desired transmission performance. Therefore, when millimeter-wave (mmWave) communication with its intrinsic Line-of-Sight (LoS) condition is adopted, accurate target localization is essential to determine the spatial relationship between the UAV and the grounded receivers (Rxs). In this article, a computer-vision (CV)-aided jointly optimization scheme of flight trajectory and power allocation is designed for mmWave UAV communication systems by utilizing the visual information captured via cameras equipped at the UAV. Compared with traditional schemes, the implementation cost and overhead can be greatly saved as no radio frequency transmissions are required in the proposed localization scheme. In addition, the transmit power at the UAV is jointly optimized with its flight trajectory in two different cases. Finally, simulation results are presented to demonstrate the efficiency of the proposed schemes.

This paper investigates reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) networks with rate splitting multiple access (RSMA). An energy efficiency (EE) maximization problem is formulated subject to the power budget at the transmitter and the quality of service (QoS) requirements of both information communication and energy harvesting, where the beamforming vectors, the power splitting (PS) ratios, the common message rates, and the discrete phase shifts are jointly optimized. To tackle the non-convex problem with both discrete and continuous variables, a deep reinforcement learning-based approach is proposed with the proximal policy optimization (PPO) framework. Different from traditional optimization approaches which optimizes the beamforming vectors and phase shifts separately and alternatively, our proposed PPO-based approach optimizes all the variables in unison. Besides, to perform beamforming design in action space, the beamforming vectors for the common stream and the private stream are respectively designed based on the maximum-ratio transmission and the zero forcing to enhance both energy and information transmission. To evaluate the performance of the PPO-based approach, a successive convex approximation (SCA) and Dinkelbach’s method based solution scheme (named SCA-D scheme) is also presented. Simulation results show that the system EE obtained by the proposed PPO-based approach is close to that obtained by the SCA-D scheme while outperforming various benchmarks. The RSMA contributes to the EE of the system greatly compared with traditional scheme. As for the case of time-varying channels, the proposed PPO-based approach is with much smaller running time by only sacrificing a slight EE performance compared with the SCA-D scheme.

This paper investigates a multi-user wireless powered fog computing (FC) network, where multiple energy-limited wireless sensor devices (WSDs) first harvest energy from a nearby hybrid access point (HAP), and then compute their tasks locally (i.e., the local computing (LC) mode) or offload the tasks to the HAP (i.e., the FC mode) via a binary offloading policy. In order to pursue the green computing network design, an optimization problem is formulated to minimize the transmit power at the HAP by jointly optimizing the time allocation ratio and the computing mode selection vector, under the energy causality constraints and the WSDs’ computing rate requirements constraints. To efficiently solve the formulated non-convex problem in a distributed manner, it is first transformed into an approximate form, and then an alternating direction method of multipliers (ADMM)-based algorithm is designed to solve the transformed problem, based on which the successive convex approximation (SCA) is adopted to improve the approximating precision in an iterative way. With the proposed ADMM-based distributed algorithm, each WSD is able to optimize its computing mode and offloading time with local channel state information (CSI), which thus is more suitable for large-scale networks. For comparison, a channel-sorting-based (CSB) centralized algorithm with global CSI is also presented, and the computational complexities of the proposed ADMM-based algorithm and the CSB algorithm are analyzed. Simulation results show that the proposed distributed algorithm achieves a comparable performance with the CSB centralized algorithm and the exhaustive search method. It is also observed that to minimize the transmit power at the HAP, the WSDs with the better channel quality are inclined to select the LC mode, which is much different from traditional sum-computation-rate maximization design.

This paper investigates the robust and secure task transmission and computation scheme in multi-antenna unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks, where the UAV is dual-function, i.e., aerial MEC and aerial relay. The channel uncertainty is considered during information offloading and downloading. An energy consumption minimization problem is formulated under some constraints including users’ quality of service and information security requirements and the UAV’s trajectory’s causality, by jointly optimizing the CPU frequency, the offloading time, the beamforming vectors, the artificial noise and the trajectory of the UAV, as well as the CPU frequency, the offloading time and the transmit power of each user. To solve the non-convex problem, a reformulated problem is first derived by a series of convex reformation methods, i.e., semi-definite relaxation, S-Procedure and first-order approximation, and then, solved by a proposed successive convex approximation (SCA)-based algorithm. The convergence performance and computational complexity of the proposed algorithm are analyzed. Numerical results demonstrate that the proposed scheme outperforms existing benchmark schemes. Besides, the proposed SCA-based algorithm is superior to traditional alternative optimization-based algorithm.

This paper studies the cooperative base station switching-off for multiple mobile network operators (MNOs) in multiple-input single-output (MISO) networks. To save the energy consumption of the system and also guarantee MNOs’ profit, we formulate a power minimization problem by jointly optimizing the operation modes of BSs, the connection states between users and BSs, and the beamforming vectors of multi-antenna BSs. To tackle the formulated non-convex problem, a roaming-cost-based BS switching-off scheme is designed to first search the feasible BSs that can be switched off and then optimize the beamforming vectors. Simulation results show that the proposed scheme not only reduces network power consumption but also avoids the profit loss at each MNO. It is also observed that there exists a minimum power consumption and a maximum average profit gain in terms of the rate price. Besides, the proposed scheme has notable capability in improving the profit at the low rate price region.

In multiservice systems, multiple different Age of Information (AoI) penalty functions and corresponding algorithms are required to be deployed, which may result in high deployment complexity. Motivated by this, we propose a universal function$f(t)=\beta e^{\alpha t} -\beta $called$\alpha $-$\beta $AoI penaltyfunction to characterize different nonlinear forms of AoI penalty. With the presented$\alpha $-$\beta $AoI penalty function, we analyze the performance of wireless-powered communication networks (WPCNs), where a sensor first harvests energy from a wireless power station (WPS) and then transmits the generated update to its data collector. The sensor is equipped with a battery of limited energy capacity. When the battery of the sensor node is fully charged, the sensor generates a status update and uses all available energy to transmit it. A closed-form expression of the system average$\alpha $-$\beta $AoI penalty is derived by using some limit methods. In order to minimize the average$\alpha $-$\beta $AoI penalty of the system, an optimization problem is formulated to optimize the battery capacity. Simulation results demonstrate the correctness of our theoretical analysis results and show that there is a unique optimal battery capacity that optimizes the system AoI performance. Moreover, when the system is with the exponential-shape AoI penalty function ($\beta >0$and$\alpha >0$), with the increment of$\alpha $and$\beta $increase, the average$\alpha $-$\beta $AoI penalty also increases. Differently, when the system is with the logarithmic-shape AoI penalty function ($\beta < 0$and$\alpha < 0$), with the increment of$\alpha $and$\beta $, the average$\alpha $-$\beta $AoI penalty decreases.

This article studies the unmanned aerial vehicle (UAV)-assisted wireless powered network, where a UAV is dispatched to wirelessly charge multiple ground nodes (GNs) by using radio frequency (RF) energy transfer and then the GNs use their harvested energy to upload the sensed information to the UAV. At each moment, the UAV is scheduled to charge the GNs or only one GN is scheduled to upload its data. An optimization problem is formulated to minimize the average Age of Information (AoI) of the GNs by jointly optimizing the trajectory of the UAV and the scheduling of information transmission and energy harvesting of GNs. As the problem is a combinational optimization problem with a set of binary variables, it is difficult to be solved. Thus, it is modeled as a Markov problem with large state spaces and a deep${Q}$network (DQN)-based scheme is proposed to find its near-optimal solution on the basis of the deep reinforcement learning (DRL) framework. Two nets are structured with artificial neural network (ANN), where one is for evaluating the reward of the action performed in current state, and the other is for predicting realistic action. The corresponding state spaces, the efficient action spaces, and reward function are designed. Simulation results demonstrate the convergence of the proposed DQN scheme, which also show that the proposed DQN scheme gets much smaller average AoI than the three other known schemes. Moreover, by involving the energy punishment in the reward, the UAV may save its energy but yield higher AoI. Additionally, the effects of the packet size, the transmit power, and the distribution area of GNs on the GNs’ average AoI are also discussed, which are expected to provide some useful insights.

This paper proposes a multi-agent double deep Q network (DDQN)-based approach to jointly optimize the beamforming vectors and power splitting (PS) ratio in multi-user multiple-input single-output (MU-MISO) simultaneous wireless information and power transfer (SWIPT)-enabled heterogeneous networks (HetNets), where a macro base station (MBS) and several femto base stations (FBSs) serve multiple macro user equipments (MUEs) and femto user equipments (FUEs). The PS receiver architecture is deployed at FUEs. An optimization problem is formulated to maximize the achievable sum information rate of FUEs under the constraints of the achievable information rate requirements of MUEs and FUEs and the energy harvesting (EH) requirements of FUEs. Since the optimization problem is challenging to handle due to the high dimension and time-varying environment, an efficient multi-agent DDQN-based algorithm is presented, which is trained in a centralized manner and runs in a distributed manner, where two sets of deep neural network parameters are jointly updated and trained to tackle the problem and avoid overestimation. To facilitate the presented multi-agent DDQN-based algorithm, the action space, the state space and the reward function are designed, where the codebook matrix is employed to deal with the complex transmit beamforming vectors. Simulation results validate the proposed algorithm. Notable performance gains are achieved by the proposed algorithm due to considering the beam directions in the action space and the adaptability to the Doppler frequency shifts. Besides, the proposed algorithm is shown to be superior to other benchmark ones numerically.

This paper studies the robust beamforming design for simultaneous wireless information and power transfer (SWIPT)-enabled networks, where the rate-splitting (RS) scheme and the power-splitting (PS) energy harvesting (EH) receiver are adopted for secure information transfer and EH, respectively. In order to explore the worst-case energy efficiency (EE) performance limit of the system, an EE maximization problem is formulated with the elliptically bounded channel state information error model under the constraints of the quality of service (QoS) requirements of information decoding users, the EH requirements of EH users and the power budget at the transmitter. To tackle the formulated non-convex problem, a sequential minimal optimization-based algorithm is first proposed to construct a mapping table and the optimal PS ratios of the PS EH receiver are found by searching the table. Then, a dual-layer iterative algorithm is designed to obtain the maximal EE based on the Dinkelbach’s method in the inner loop and the successive convex approximation method in the outer loop. To accelerate the convergence of the outer loop, an efficient initialization algorithm is also designed. Simulation results show that the RS scheme contributes to the EE enhancement, and the PS EH receiver enlarges the rate-energy region restricted by the non-linear EH circuit. Moreover, traditional sum-rate maximization design and power minimization design may induce a notable worst-case EE performance loss at the high-power region and the low-QoS requirement region, respectively.

A UAV-aided wireless power transfer and data collection network is studied, where it is assumed that when the harvested energy at the sensor node (SN) cannot surpass its circuit activation threshold or the received data rate at UAV falls below a minimal required rate threshold, the information outage occurs. The closed-form expressions of energy outage probability and rate outage probability are derived at first, and then the overall outage probability and coverage performance of the system are analyzed. Based on which, an optimization problem is formulated to minimize the overall outage probability by optimizing UAV's elevation angle and the time splitting (TS) factor. Since the problem is non-convex and has no known solution, an alternating optimization (AO)-based algorithm with Golden-section (GS) based linear search method is designed to find the global optimal solution. In order to explore the maximum coverage area of the UAV for a given tolerable outage probability, another optimization problem is also formulated to maximize the coverage range by optimizing UAV's elevation angle. By using Karush-Kuhn-Tucker (KKT) conditions, the closed-form solution of the optimal elevation angle for maximizing the coverage area is derived. Monte Carlo simulations verify the accuracy of the derived closed-form expression of the overall outage probability and the semi-closed-form expressions of the optimum UAV's elevation angle and TS factor. It shows that there exist a unique optimum elevation angle and the TS factor to achieve the minimum overall outage probability, and significant performance gain can be obtained by using our proposed optimization scheme. The developed theoretical results can be useful to the design of UAV-aided wireless communication systems with wireless power transfer.

This paper investigates the relay-assisted wireless information and power transfer enabled hybrid visible light communication (VLC)-radio frequency (RF) network, where a light emitting diode (LED) access point (AP) serves multiple information users (IUs) and multiple energy harvesting users (EHUs). IUs are allowed to receive information from the LED AP through time-division-multiple-access (TDMA) manner by either the single-hop VLC-ONLY mode or the relay-assisted dual-hop VLC-RF mode, while EHUs harvest energy via the VLC links. An optimization problem is formulated to maximize the achievable information rate of IUs by jointly optimizing the access mode selection, the direct current (DC) offset at the LED AP, the peak amplitude of the alternating current (AC) component at the LED AP, the electrical power allocated to the LED AP and the power allocation at relay, subject to the energy harvesting (EH) requirement constraints of EHUs. To tackle the non-convex problem with binary variables, we first decompose it into two subproblems in terms of the two access modes. Then, the subproblems are equivalently transformed and solved by the proposed successive convex approximation (SCA)-based algorithms. Simulation results show that significant performance gain can be achieved by optimizing the DC offset. It is also observed that the area where the VLC-ONLY mode is superior to the VLC-RF mode is enlarged with the decrease of the minimal EH requirement. Besides, the achievable information rate of IUs by the VLC-RF mode first increases and then decreases with the increment of the distance between the relay and the LED AP.

This paper studies the secrecy energy efficiency (SEE) for MISO power-splitting (PS) SWIPT networks in the presence of multiple passive eavesdroppers (Eves), where the non-linear energy harvesting (EH) model and the dual-layer PS receiver architecture are employed. With only channel distribution information (CDI) of Eves known and the artificial noise (AN) embedded into the transmit signals at the transmitter, a SEE maximization problem is formulated under constraints of the minimal rate and EH requirements of legitimate receivers and the power budget at the transmitter. To tackle the difficulty caused by the fractional objective function and the probability constraints in solving the considered problem, the second-layer PS ratios are firstly optimized by bisection and sum-of-ratios maximization methods, and then the transmit beamforming vectors, the AN covariance matrix and the first-layer PS ratios are jointly optimized by using successive convex approximation (SCA) and Dinkelbach's methods. The proposed solution approach is theoretically proved to converge to a stationary point of the SDR form of the considered problem, which is further shown to be the optimal one. Numerical results show that our proposed design achieves the highest SEE over traditional power minimization and secrecy rate maximization designs. Moreover, when the rate requirement is larger than a threshold or the available power is less than a threshold, traditional power minimization design or secrecy rate maximization design is able to achieve a similar SEE to our proposed design. Besides, the dual-layer PS receiver architecture is able to improve the EH efficiency and system SEE.

This paper studies the robust energy efficient beamforming design for MISO systems where only channel distribution information (CDI) is assumed to be available at the transmitter. To capture the general relationship between the data transmission and the energy consumption, the dynamic energy consumption model (DECN) is adopted. An optimization problem is formulated to maximize the system energy efficiency under the constraints of rate outage probability and total available power. The problem is difficult to tackle due to the fractional objective function and the information outage constraints. To solve it, the semidefinite relaxation (SDR) is applied at first and then, a solution approach based on the successive convex approximation (SCA) and the Dinklebach's methods is presented. It is proved that our proposed solution approach is able to converge to a stationary point of the formulated optimization problem. Numerical results demonstrate that DECN has a great impact on system EE. It is observed that there is a saturation point on the system EE in term of available power and the required power corresponding to the saturation point of EE highly depends on circuit power. Particularly, higher circuit power leads to a larger required power but a smaller maximal system EE.

This paper investigates the age of information (AoI) for a radio frequency (RF) energy harvesting (EH) enabled network, where a sensor first scavenges energy from a wireless power station and then transmits the collected status update to a sink node. To capture the thirst for the fresh update becoming more and more urgent as time elapsing, urgency-aware AoI (U-AoI) is defined, which increases exponentially with the increment of time between two received updates. Due to EH, a waiting time is required at the sensor before transmitting the status update. An optimization problem is formulated to minimize the long-term average U-AoI under constraint of energy causality. A two-layer algorithm is presented to solve it, where the outer loop is designed based on Dinklebach's method, and the inner loop presents a semi-closed-form expression of the optimal waiting time policy based on Karush-Kuhn-Tucker (KKT) optimality conditions. Numerical results show that our proposed optimal transmission policy outperforms the zero time waiting policy and equal time waiting policy in terms of long-term average U-AoI, especially when the networks are in slight load. It also shows that the system U-AoI first decreases and then keeps unchanged with the increments of EH circuit's saturation level.

This paper considers an MISO simultaneous wireless information and power transfer (SWIPT) system, where one transmitter serves multiple authorized receivers in the presence of several potential eavesdroppers (idle receivers). To prevent the information interception by eavesdroppers, artificial noise (AN) is embedded into the transmit signals. The non-linear energy harvesting (EH) model is adopted and a novel power-splitting (PS) EH receiver architecture is proposed. Stochastic uncertainty channel model (SUM) is considered for the idle receivers due to outdated channel feedback. A global energy efficiency (GEE) maximization problem is formulated by jointly optimizing the transmit beamforming vectors, the AN covariance matrix, and the PS ratios, under the minimal rate and secure transmission constraints of authorized receivers, the EH requirement constraints of idle receivers, and the total available power constraint at the transmitter. Since the problem is non-convex with no known solution, it is solved based on the following solution framework. Firstly, the PS ratios are optimized by using the bisection method and successive convex approximation (SCA), and then, the transmit beamforming vectors and the AN covariance matrix are jointly optimized by using a Dinkelbach's Algorithm based method, where SCA is applied to solve its inner subproblem. It is theoretically proved that by involving AN, the system GEE can be improved. Numerous results show that system GEE first increases and then keeps unchanged with the increment of the total available power, but it first keeps unchanged and then decreases with the increment of the minimal rate requirement. It is also observed that compared with traditional EH receiver architecture and linear EH model, our proposed PS EH receiver architecture is able to achieve higher GEE and avoid false output power at idle receivers.

This paper investigates a simultaneous wireless information and power transfer (SWIPT)-enabled non- orthogonal multiple access (NOMA) network with full- duplex (FD) relaying, where a multi-antenna source transmits information to two users. The nearby user is with multiple antennas, which receives its own information and harvests energy from the signals transmitted by the source and also help forward information to the far-end user. For such a system, an optimization problem is formulated to minimize the required transmit power by jointly optimizing beamforming vectors and power splitting (PS) ratio under the energy harvesting and users' data rate constraints of both users. As the problem is non- convex with unknown solution, a bilevel- optimization method is proposed to solve it via semidefinite relaxation (SDR) and the global optimal solution is achieved with perfect self- interference cancellation. However, since self- interference may not be cancelled perfectly in practice, a successive convex approximation (SCA) based algorithm with low complexity is proposed to obtain a near optimal solution. Numerical results show that integrating NOMA, FD relaying and SWIPT in a single communication system is able to greatly reduce the required transmit power. Besides, the effects of the parameters including the data rate threshold and the energy storage amounts, on the system performance are also discussed.

This paper investigates the artificial noise (AN)-aided multi-cell coordinated beamforming (MCBF) for secure simultaneous wireless information and power transfer in both centralized and distributed manners. The proposed transmit design is formulated into a power-minimization problem to guarantee the authorized users' information and energy harvesting (EH) requirements while avoiding the information interception by unauthorized users. Power splitting receiver architecture and the non-linear EH model are employed. Both perfect and imperfect channel state information (CSI) cases are considered. For the perfect CSI case, the non-robust design is presented by applying semi-definition relaxation (SDR). When no user harvests energy, the global optimum is guaranteed, and when some users harvest energy, approximate global optimum is achieved. For the imperfect CSI case, the worst-case robust design under the deterministic uncertainty channel model is studied, where a solving approach based on SDR and S-procedure is proposed, and the statistically robust design under the stochastic uncertainty channel model is also studied, where an upper bound to the global optimum is obtained by using SDR and Bernstein-type inequality. We further propose a distributed AN-aided MCBF design framework by using an alternating direction method of multipliers for the non-robust, worst-case robust, and statistically robust designs, with which each BS is able to optimize its own transmit design with the local CSI. Simulation results demonstrate our theoretical analysis, which show that our proposed distributed algorithm converges to the optimal results obtained by the centralized one. It also shows that employing the non-linear EH model is able to avoid false output power and save power consumption at the BSs.

This paper investigates the secure transmit design for simultaneous wireless information and power transfer system under the non-linear energy harvesting (EH) model, where a transmitter sends confidential information and transfers energy to multiple information receivers (IRs) and EH receivers (ERs) with the existence of multiple eavesdroppers (Eves). To prevent confidential information leakage, multiple artificial redundant signals (MARSs) are embedded in the transmit signals. The goal is to minimize the total transmit power by jointly optimizing transmit beamforming vectors and the covariance matrixes of MARSs, such that the minimal information rate and EH requirements at IRs and ERs are guaranteed while making the received signal-to-Interference ratio at ERs and Eves lower than their information decoding thresholds. Both the non-robust and the robust designs are studied. For the non-robust design, the optimal solution is derived. For the robust design, an approximate optimal solution is obtained by using Gaussian randomization procedure. Simulation results show that compared with traditional non-MARS-aided beamforming design, our proposed design is superior in terms of the total required transmit power. It also shows that employing the non-linear EH model can avoid false output power at the ERs and/or save power at the transmitter.

This paper investigates the power minimization problem for a simultaneous wireless information and power transfer (SWIPT) system in MISO wiretap networks, where one multiple-antenna transmitter intends to transmit required amount of information and energy to its legitimate receiver while restrict the information leakage to an eavesdropper (Eve). The nonlinear EH model is employed for SWIPT. Two uncertainty MISO channel models are considered for the legitimate receiver, i.e. the deterministic uncertainty model (DUM) and the stochastic uncertainty model (SUM), and the Eve is assumed not to feed back its channel to the transmitter. For the DUM, the worst-case design with global optimum is solved by our proposed method based on semidefinite relaxation (SDR) and S-procedure. For the SUM, the statistically robust design with a tight upper bound to global optimum is obtained by our proposed method based on SDR and Bernstein-type inequality. Numerous simulation results demonstrate the validity and efficiency of our proposed robust transmit design methods. Compared with the traditional linear EH model, employing the nonlinear EH model can avoid false output power at the legitimate receiver or save power consumption at the transmitter as the real circuits are working in the nonlinear output field rather than the linear one.

This paper investigates how to achieve secure information transmission in multi-cell multi-user networks, where artificial noise (AN) aided multi-cell coordinated beamforming (MCBF) is designed to guarantee the authorized users' QoS requirements while avoiding the information being intercepted by unauthorized users. To realize the green communication target, we formulate an optimization problem to minimize the total transmit power by jointly optimizing the beamforming and AN vectors at all BSs. Since the problem is nonconvex and not easy to be solved by using existing solution methods, we then solve it by applying semi-definition relaxation (SDR) and prove that our proposed method can guarantee the global optimal solution under full channel state information (CSI). Moreover, we further design a distributed AN-aided MCBF for the system by using alternating direction method of multipliers (ADMM), with which each BS can calculate the beamforming and AN vectors with its local CSI. Simulation results demonstrate our analysis, which show that our proposed distributed algorithm converges to the optimal results obtained by the centralized one. It is also observed that for the same secure transmission requirement, the total power consumed by our proposed AN-aided MCBF decreases with the increment of transmit antennas, where less part of the power is used by AN and more part is used for information beamforming.

This paper investigates the energy efficient resource allocation for OFDM relay networks, where K users receive information via L helping relays. For such a system, an optimization problem is formulated to maximize the system energy efficiency (EE) by jointly optimizing the relay selection, subcarriers assignment and power allocation under the proportional rate constraints and available power constraint. Since this problem is non-convex with integer variables, which is nontrivial to be solved by using known methods, we design an efficient low- complexity algorithm to solve it. Simulation results show that by using our proposed resource allocation scheme, the approximate optimal results can be achieved. It is also shown that the circuit power (including a rate-dependent part and a constant part) in the consumed power has a great impact on limiting the EE resource allocation to obtain a high spectral efficiency. Besides, the effects of the relay selection, subcarrier assignment and power allocation on the system performance are also discussed via simulations.

Deploying multiple antennas on high speed trains is an effective way to enhance the information transmission performance for high speed railway (HSR) wireless communication systems. However, how to efficiently deploy N (N ≥ 2) antennas on a train has not been studied yet. In this paper, we investigate efficient antenna deployment strategies for HSR communication systems where two multi-antenna deployment strategies, i.e., the equidistant strategy and the fixed-interval strategy, are considered. To evaluate the system performance, mobile service amount and outage time ratio are introduced. Theoretical analysis and numerical results show that, when the length of the train is not very large, for N = 2 case, by increasing the distance of neighboring antennas in a reasonable region, the system performance can be enhanced, and for N> 2 case the two strategies have much difference performance behavior in terms of instantaneous channel capacity, and the fixed-interval strategy may achieve much better performance than the equidistant one in terms of service amount and outage time ratio when the antenna number is much large.

To attain seamless handover and reduce the handover failure probability for high-speed railway (HSR) systems, this paper proposed a remote antenna unit (RAU) selection assisted handover scheme based on two HST antennas and distributed antenna system (DAS) cell architecture. The RAU selection is adopted to provide high quality received signals for trains in DAS cells and the two HST antennas are employed on trains to realize seamless handover. Moreover, to efficiently evaluate the system performance, a new metric termed as handover occurrence probability is define for describing the relation between handover occurrence position and handover failure probability. We derive the expressions of the received signal strength, the handover trigger probability, the handover occurrence probability, the handover failure probability and the communication interruption probability of our proposed method. Numerical experimental results are provided to compare our proposed scheme with traditional handover scheme and some existing ones. It is shown that, our proposed scheme is able to achieve the lowest handover failure probability and communication interruption probability among all schemes.

This paper investigates the energy efficiency (EE) in multiple relay-aided OFDM systems, where decode-and-forward (DF) relay beamforming is employed to help the information transmission. In order to explore the system performance behavior with user fairness for such a system, an optimization problem is formulated to maximize the EE by jointly considering multiple factors, i.e., the transmission mode selection (DF relay beamforming or direct-link transmission), the helping relay set selection, the subcarrier assignment and the power allocation at the source and relays on subcarriers, under nonlinear proportional rate fairness constraints, where both transmit power consumption and linearly rate-dependent circuit power consumption are taken into account. To solve the nonconvex optimization problem, we propose a low-complexity scheme to approximate it. Simulation results demonstrate its effectiveness. The effects of the circuit power consumption on system performance is also studied and it is observed that with either the constant or the linearly rate-dependent circuit power consumption, system EE grows with the increment of system average channel-to-noise ratio (CNR), but the growth rates show different behaviors. For the constant circuit power consumption, system EE increasing rate is an increasing function of the average CNR, while for the linearly rate-dependent one, system EE increasing rate is a decreasing function of the average CNR. This observation is very important, which indicates that by deducing the circuit dynamic power consumption per unit data rate, system EE can be greatly enhanced. Besides, we also discuss the effects of the number of users and subcarriers on the system EE performance.

The heterogeneous vehicular network (HVN) is a novel kind of structure for intelligent transportation systems. It can help to improve the quality of wireless communication and support entertainment service during the trip. HVN is composed of two types of links, vehicle-to-infrastructure (V2I) links and vehicle-to-vehicle (V2V) links. In this paper, we focus on the problem of resource scheduling for HVN. The goal is to investigate how to select the relaying mobile vehicles and how to choose the link to transmit between V2V and V2I to achieve the best system performance. To explore the maximum total information transmission rate with the consideration of fairness among the mobile vehicles, we formulate an optimization problem to maximize the weighted sum-rate for the system. As the problem is difficult to solve, we thus design a new efficient scheduling algorithm on the basis of bipartite graph. Simulation results demonstrated that our proposed algorithm can achieve the optimal solution approximately with low complexity.

In this paper, we propose a semi-blind channel estimation algorithm for amplify-and-forward (AF) relay networks. The algorithm fits well for the recently developed space-time coding (STC) technique in AF relay network that serves for small size terminal and achieve the transmission diversity. Compared to the optimal training based estimators, e.g., maximum likelihood (ML) or linear minimum mean square (MMSE), the proposed semi-blind approach requires less training for successful channel estimation, or it yields better estimates if the same amount of training is used. The channel ambiguity issue as well as its relationship with the traditional semi-blind method is discussed in detail. We then provide various numerical examples to corroborate the proposed studies.