Integrating communication, sensing, and power transfer (ICSPT) is an emerging network paradigm for the sixth-generation (6G) systems, which is able to provide concurrent communication and sensing functions while simultaneously wirelessly powering low-power Internet of Things (IoT) devices with shared spectrum and hardware resources. To enhance the performance of ICSPT in fading channels, the outage probability (OP)constrained robust transmit beamforming design (OP-RTBD) is proposed, and a transmit power minimization problem is formulated with imperfect channel state information (CSI) by jointly optimizing information, sensing, and energy beam vectors at the base station (BS), subject to OP constraints on the communication rate, sensing Cramér-Rao bound, and energy transfer. To solve the non-convex problem, we propose a Bernstein-type inequality (BTI)-based method to conservatively approximate the probabilistic constraints to handle the CSI uncertainty. Then, a semi-positive definite relaxation-based method is proposed to solve the approximated problem. Simulation results show that the proposed OP-RTBD achieves near-optimal performance compared to the exhaustive search method with only less than 4% deviation, and it also significantly reduces the transmit power compared to baselines. Moreover, OP-RTBD exhibits strong robustness, achieving performance very close to that in perfect CSI scenarios, with a deviation of only less than 10%. Besides, the simulation results indicate that the BS’s transmit power should be allocated with priority to communication requirements over sensing and power transfer demands. Additionally, they further demonstrate that to simultaneously meet communication, sensing, and power transfer requirements, our proposed OP-RTBD in ICSPT is more energy-efficient, reducing energy consumption by approximately 10% and 20% compared to SWIPT and ISAC, respectively.

In federated learning (FL), although the original intention of “available but not visible” data is to allay data privacy concerns, it potentially brings new security threats, particularly poisoning attacks that target such “not visible” local data. Intuitively, such data poisoning attacks have great potential in stealthily degrading global FL outcomes, and are expected to be even stealthier if being enhanced by generative models like generative adversarial networks (GANs). However, existing defense methods have not been thoroughly challenged in this regard and generally fail to be aware of a local generation of seemingly legitimate poisoned data. With a growing concern on potentially stealthier attacks, in this paper, a cost-effective defense mechanism named Model Consistency-Based Defense (MCD) is proposed, which offers a comprehensive examination of available local models across multiple feature dimensions, providing an indirect yet effective means of identifying hidden data poisoning attackers. To push the limit of MCD against stealthier attacks, we propose a new GAN-based data poisoning attack model named VagueGAN and an unsupervised variant of it, which can be flexibly deployed to generate seemingly legitimate but noisy poisoned data. The consistency of GAN outputs revealed by VagueGAN helps strengthen MCD to work against stealthier GAN-based attacks as well as other mainstream ones. Extensive experiments on multiple open datasets (MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, and Mini-Imagenet) indicate that our attack method better balances the trade-off between attack effectiveness and stealthiness with low complexity. More importantly, our defense mechanism is shown to be more competent in identifying a variety of poisoned data, particularly stealthier GAN-poisoned ones.

This paper investigates the active reconfigurable intelligent surface (RIS)-assisted integrated sensing, communication, and power transfer (ISCPT) networks, where rate-splitting multiple access (RSMA) scheme is employed to serve multiple downlink communication users. To promote the energy efficiency (EE) of such a system, we formulate an EE maximization problem by jointly optimizing the beamforming matrix, the sensing matrix, the active RIS matrix, the power splitting (PS) ratio vector, and the common rate allocation vector. Due to the non-convexity of the problem, we first design a successive convex approximation scheme with alternating optimization method (named SCA-AO) to solve it. As SCA-AO operates in an iterative manner, which is with relatively high computational complexity, we then design a mixture of experts (MoE)-based deep reinforcement learning (DRL) scheme with smooth clipping function (named MoE-SCF). In comparison, SCA-AO is able to achieve higher solution accuracy, while MOE-SCF has a shorter online execution response time. In order to integrate the advantages of both presented SCA-AO and MoE-SCF simultaneously, we further propose a hybrid MoE (H-MoE) scheme, where both the SCA-AO and the MoE-SCF are employed as expert strategies, and an opportunistic activator (OPA) is designed to dynamically select the best strategy generated by all expert combinations according to the performance evaluation function. Simulation results demonstrate that the proposed H-MoE promotes the system's EE by about 18.14% compared to traditional MoE, with similar response time. Additionally, compared to the SCA-AO, H-MoE significantly decreases the response time by approximately 56.17%, while only marginally compromising the EE performance by less than 3.1%.

Small-cell mobile edge computing (SE-MEC) networks amalgamate the virtues of MEC and small-cell networks, enhancing data processing capabilities of user devices (UDs). Nevertheless, time-varying wireless channels, dynamic UD requirements, and severe interference among UDs make it difficult to fully exploit the limited network resources and stably provide computing services for UDs. Therefore, efficient task offloading and resource allocation (TORA) is essential. Moreover, since multiple small cells are deployed, decentralized TORA schemes are preferred in practice. Thus, this paper aims to design distributed adaptive TORA schemes for SE-MEC networks. In pursuit of an eco-friendly design, an optimization problem is formulated to minimize the total energy consumption (TEC) of UDs subject to delay constraints. To effectively deal with network's dynamic characteristics, the reinforce learning framework is applied, where the TEC minimization problem is first modeled as a partially observable Markov decision process (POMDP), and then an efficient multi-agent proximal policy optimization (MAPPO)-based scheme is presented to solve it. In the presented scheme, each small-cell base station (SBS) serves as an agent and is capable of making TORA decisions only with its own local information. To promote collaboration among multiple agents, a global reward function is designed. A state normalization mechanism is also introduced into the presented scheme for enhancing learning performance. Simulation results show that although the proposed MAPPO-based scheme works in a distributed manner, it achieves very similar performance to the centralized one. In addition, it is demonstrated that the state normalization mechanism has a significant effect on reducing TEC.

In the typical radio frequency (RF)-based wireless power transfer (WPT) system, the wireless power station (WPS) connected to the grid transmits energy to charge low-power sensors via radio signals. Such a system may not be green and also difficult to deploy in some special areas including deserts and mountainous areas, because it depends on the grid. To achieve a green RF WPT system design, this paper considers that the WPS is powered by natural energy sources rather than the grid. To explore the maximal total amount of the energy that can be harvested by the sensors, we focus on the offline setting, so similar to many existing works on offline optimization, we assume that the WPS knows prior knowledge about energy arrivals and channel changes, and then formulate an optimization problem to maximize the total harvested energy via optimizing the WPS’s time-domain transmit power subject to multiple constraints, including the finite battery capacity at the WPS, the causal relationship between the natural energy harvesting and the WPT, and the transmit power budget of the WPS, where for practicality, the nonlinear energy harvesting (EH) model is also taken into account. To solve this non-convex problem, we first equivalently transform it by using the epigraph reformulation and the variable substitution, and then use the first-order Taylor expansion to get an approximate convex version. Then, we present a successive convex approximation (SCA)-based algorithm to improve the accuracy of the obtained solution for approaching the optimal one. For the special case with a single sensor, we further propose a branch and bound (BB)-based algorithm that is able to get a more accurate solution with lower complexity than the SCA-based one. Numerical results demonstrate that the proposed algorithms are able to achieve the near-global optimal solution. As the average recharge rate increases, compared with the other two baselines, i.e., the greedy power (GP) policy and the constant power (CP) policy, the total harvested energy achieved by the SCA-based algorithm is up to about 2.48 times and 1.37 times that of the baselines respectively. For the single-sensor case, the BB-based algorithm always outperforms the SCA-based one in terms of the total harvested energy while reducing the running time required for solving by about 90% on average.

This article investigates rate-splitting multiple access (RSMA)-based visible light communication (VLC) networks with simultaneous lightwave information and power transfer (SLIPT). To effectively enhance the fairness among information decoding users (IDUs), we formulate an optimization problem to maximize the minimum data rate by optimizing the direct current bias vector, the common message rates of RSMA, and the transmit precoding vectors. In the problem, the IDUs’ minimum energy harvesting (EH) requirements, the total power budget of the light-emitting diode (LED) transmitters, and the linear operation region of LEDs are also considered as the system constrains. To solve the formulated nonconvex problem, epigraph reformulation is first employed to transform the nonconvex objective function. Then, a series of transformations is proposed and the semi-definite relaxation (SDR) method is adopted to address the rank-one precoding matrix constraint. After that, an iterative algorithm is proposed to obtain an effective suboptimal solution by applying the successive convex approximation. Extensive simulations show that the max–min rate (MMR) is inversely proportional to the number of IDUs and it decreases as the minimum EH requirement becomes more stringent, especially in the high-EH region. Moreover, the value of the maximum drive current imposes a significant impact on the system performance, particularly, the MMR becomes saturated for a given maximum drive current even if the total power budget is sufficient. Besides, RSMA can contribute to both spectral efficiency and energy efficiency greatly in comparison to the traditional multiple access scheme.

This article investigates the age of information (AoI) performance in a wireless powered communication network (WPCN), where a sensor node (SN) harvests energy from an energy transmitter (ET) and then transmits status information to its data receiver (DR) by using the harvested and accumulated energy. The AoI penalty is used as a performance metric to characterize the nonlinear feature of dissatisfaction with data obsolescence at the DR. We derive the closed-form expressions of the average AoI penalty and the average peak AoI (PAoI) penalty with the Rician fading model. To further explore the system performance limit in terms of AoI penalty, we formulate two optimization problems to minimize the average AoI penalty and the average PAoI penalty with respect to the battery discharge threshold. Particularly, we reveal the conditions for the existence of the average AoI penalty and average PAoI penalty. Simulation results show that there exists a unique optimal battery discharge threshold that minimizes the system’s average AoI penalty and a unique optimal battery discharge threshold that minimizes the system’s average PAoI penalty. Moreover, as expected, the average AoI penalty and the average PAoI penalty decrease with the increment of the Rician$K$-factor, and increase with the increment of the distance between ET and SN. Besides, the average AoI penalty and the average PAoI penalty first decrease with the increment of transmit power of ET and then tend to be flat. Additionally, a smaller data size of SN yields better system performance.

This article studies an intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) network, where the ground users (GUs) desire to receive information from a UAV. Downlink nonorthogonal multiple access (NOMA) is considered typically with two GUs being selected according to whether a Line-of-Sight (LoS) link between GUs and UAV exists. As the accurate channel information of LoS or Non-LoS (NLoS) links for multiple GUs is difficult to acquire, an approximate LoS region-based method is designed to select GUs as an alternative. In order to enhance the communication quality of the far GU, an IRS is deployed to assist the NLoS transmission. For such a system, we evaluate its outage performance in Nakagami-m fading. First, the central limit theorem (CLT) and Laplace transform (LT) are employed to derive the channel statistics of the UAV- IRS-user link. Then, asymptotic closed-form expressions of the outage probabilities are derived for the selected GUs based on Gaussian–Chebyshev quadrature approximation. Monte Carlo simulations validate the validness of our derived outage probabilities. It shows that the approximate LoS region-based scheme provides similar outage performance laws as the accurate LoS region-based one. Moreover, the outage probabilities of selected GUs in terms of NOMA-based protocol and orthogonal multiple access (OMA)-based protocol are analyzed. Simulation results confirm that the proposed NOMA-based protocol is capable of achieving superior performance compared with the OMA-based protocol by setting power allocation factor and targeted acrlong SINR thresholds of near GU and far GU properly. Specifically, when the rate threshold of near GU is relatively large or the rate threshold of far GU is relatively small, the outage performance derived by NOMA-based protocol performs better than OMA-based protocol in most of cases.

This article investigates simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted downlink multiuser multiple-input–single-output (MU-MISO) networks with the rate splitting multiple access (RSMA) scheme. A base station (BS) desires to simultaneously transmit messages to multiple users with the assistance of an STAR-RIS to enhance communication quality as well as extend the coverage of users. An optimization problem is formulated to maximize the achievable sum rate of the networks on the premise of satisfying the constraints on power budget at the BS, total common-stream rate of users, and individual users’ minimum rate requirements, via jointly optimizing the beamforming vectors, the common-stream rate allocation vector, and the transmission and reflection coefficients (TARCs) matrix. Due to the dynamic changes of communication links and the coupling of multiple variables, it is challenging to solve such a nonconvex optimization problem by utilizing traditional methods. Therefore, a proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm is proposed, where the reward function, the action space and the state space are designed properly. A constraint-satisfaction-processing (CSP) method is employed to further adjust the optimized transmit power to make sure that the obtained optimized results satisfy the power budget constraint. Simulation results show that the proposed PPO-based DRL algorithm converges well and achieves much better performance than several baselines, such as the soft actor–critic (SAC), the deep deterministic policy gradient (DDPG), the genetic algorithm (GA), the maximum ratio transmission (MRT), the zero-forcing (ZF), and the random methods. Moreover, it demonstrates that deploying STAR-RIS greatly enhances the system sum rate and user fairness compared to deploying traditional reflecting-only RIS (RO-RIS) and without RIS. Besides, it also shows that adopting the RSMA scheme achieves more notable performance gains than the nonorthogonal multiple access (NOMA) scheme in such a network.

This paper studies the high-speed railway mobile networks (HSRMN), where multiple railway-side sensors (RSs) are deployed along the track to sense environmental data, and multiple train-mounted sensors (TSs) are deployed on the train to collect train data. Both RSs and TSs are scheduled to transmit their sensed data respectively to the ground base station (BS) in a time division multiple access (TDMA) mode. To keep the data received at the BS from the RSs as fresh as possible and also ensure that the TSs complete the given uploading tasks, an optimization problem is established to minimize the average age of information (AoI) of the data gathered from RSs by jointly optimizing sensors’ scheduling and transmission power control constrained by the maximum transmission power budget of RSs and TSs. Since the problem is non-convex and lacks an explicit expression of the objective function and the prior information about future channel state, we present a deep Q-learning network (DQN)-based method to solve it. Particularly, the BS is viewed as the agent, and the action space is constructed by scheduling policy and power control. To further accelerate the convergence speed of the presented DQN-based solution framework, an action space-reduced (ASR) version of the DQN-based method, i.e., the ASR-DQN-based method, is designed by deriving a closed-form solution to the optimal transmission power for a given sensors’ scheduling policy. Numerical simulations show that, compared to the DQN-based method, the ASR-DQN-based method decreases the number of episodes required for convergence by about 23% and reduces the running time by about 41%. Moreover, compared with three baselines, i.e., the random method, the round-robin method, and the deep-Sarsa method, our presented ASR-DQN-based method achieves the lowest average AoI and has the best robustness among these compared methods.

This paper investigates the radio-frequency-energy-harvesting-powered (RF-EH-powered) wireless Industrial Internet of Things (IIoT) networks, where multiple sensor nodes (SNs) are first powered by a wireless power station (WPS), and then collect status updates from the industrial environment and finally transmit the collected data to the monitor with their harvested energy. To enhance the timeliness of data, age of information (AoI) is used as a metric to optimize the system. Particularly, an expected sum AoI (ESA) minimization problem is formulated by optimizing the power adjustment policy for the SNs under multiple practical constraints, including the EH, the minimal signal-to-noise-plus-interference ratio (SINR) and the battery capacity constraints. To solve the non-convex problem with no explicit AoI expression, we transform it into a Markov decision problem (MDP) with continuous state space and action space. Then, inspired by the Soft Actor-Critic (SAC) framework in deep reinforcement learning, a SAC-based age-aware power adjustment (SAPA) method is proposed by modeling the power adjustment as a stochastic strategy. Furthermore, to reduce the communication overhead of SAPA, a multi-agent version of SAPA, i.e., MSAPA, is proposed, with which each SN is able to adjust its transmit power based on its local observations. The communication overhead of SAPA and MSAPA is also analyzed theoretically. Simulation results show that the proposed SAPA and MSAPA converge well with different numbers of SNs. It is also shown that the ESA achieved by the proposed SAPA and MSAPA is lower than that achieved by the baseline methods.

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.

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.

Recently, semantic communication (SC) has been regarded as one of the most potential paradigms of 6G. Current SC frameworks require the physical layer channel state information (CSI) in order to handle the severe signal distortion induced by channel fading. Since practical CSI cannot be obtained accurately and the overhead of channel estimation cannot be neglected, we therefore propose a generative adversarial network (GAN) based SC framework (Ti-GSC) that doesn’t require CSI. In Ti-GSC, there are two main modules, i.e., an autoencoder-based encoder-decoder module (AEDM) and a GAN-based non-CSI signal distortion suppression (SDS) module (GSDSM), where SDS only relies on learning the syntactic distribution and the semantics of the transmitted data, so no prior information such as CSI is needed by GSDSM. In order to measure signal distortion, a novel loss function is proposed where two terms, i.e., a syntactic distortion loss term and a semantic distortion loss term, are newly added, and a differentiable semantic measurement method is designed based on the intermediate layers of the AEDM decoder. To achieve better training results of Ti-GSC, two training schemes, i.e., the joint optimization based training (JOT) and the alternating optimization based training (AOT) are designed for the proposed Ti-GSC. Experimental results show that JOT is more efficient for Ti-GSC, and Ti-GSC outperforms conventional communication frameworks in terms of bilingual evaluation understudy (BLEU) score in both Rician and Rayleigh fading channels. Moreover, without CSI, the BLEU score achieved by Ti-GSC is about 40% and 62% higher than that achieved by existing SC frameworks in Rician and Rayleigh fading, respectively. Besides, each term of the presented loss function has a great impact on the BLEU performance of Ti-GSC, where in Rician fading syntactic learning has the greatest impact, and in Rayleigh fading, the adversarial learning becomes important.

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 age of information (AoI) and energy efficiency of wireless powered industrial Internet of Everything (IIoE) network, where multiple low-power IIoE devices (IIoEDs) are wirelessly charged by a hybrid access point (HAP) to transmit their sensing information to the control nodes. To enhance the system’s information timeliness with high energy efficiency, we define a novel performance metric, i.e., age-energy efficiency (AEE), which depicts the achievable AoI gain per unit energy consumption. Then, an optimization problem is formulated to maximize the system long-term AEE by jointly optimizing the IIoEDs scheduling and the HAP’s transmit power. Due to the non-convexity of the formulated problem and the intractable challenges with discrete binary variables, we first model the problem as a two-stage discrete-time Markov decision process (MDP) with carefully designed state spaces, action spaces, and reward functions. We then propose a deep reinforcement learning (DRL)-based approach to find the effective scheduling strategy and transmit power. To improve the accuracy of the learned policy, we design a dual-layer deep Q-network (DLDQN) algorithm with fast convergence. Simulation results show that our proposed DLDQN algorithm can improve the AEE by at least 25% when the number of IIoEDs exceeds 50 compared with benchmarks. Moreover, with the proposed DLDQN algorithm, the system long-term AEE can be improved with the increase of the number of IIoEDs.

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.

This paper investigates the joint optimization of the task offloading and resource allocation in small cell mobile edge computing (MEC) networks, where multiple small-cell base stations (SBSs) integrating MEC servers provide computing services for user devices (UDs) in their cells. In pursuit of green network design and also saving energy of the UDs, an optimization problem is formulated to minimize the total energy consumption of UDs subjecting to the delay constraints. Since the existing optimization schemes based on traditional optimization theory cannot adapt to the time-varying channel and highly dynamic UD requirements due to their complexity, we propose an efficient learning-enabled joint task offloading and resource allocation scheme based on proximal policy optimization (PPO) framework. Simulation results show that the total energy consumption of UDs is significantly reduced by our proposed PPO-based scheme, and also show the trade-off between the delay constraints satisfaction probability and the total energy consumption.

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.

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 investigates the cache-assisted power allocation in time domain for high-speed railway communications (HSRC), where train users are divided into real-time users (RUs) and non-real-time users (NRUs) from the time-sensitive perspective. RU's real-time data rate demand is ensured by power allocation, and NRU's data amount requirement is guaranteed by releasing cached content. In order to maximize the mobile service amount (MSA) of HSRC, an optimization problem is formulated to find the optimal cache switching time and power distribution under the constraints of total available energy, maximum power, caching and releasing causality, RU's minimal data rate requirement and NRU's minimal data amount requirement. Since the formulated problem is non-convex, a two-stage algorithm is proposed. In the first stage, we fix the cache switching time and transform the problem to be convex, and then use Karush-Kuhn-Tucker (KKT) condition to determine the optimal power distribution. In the second stage, one-dimensional search is employed to find the optimal cache switching time. Simulation results show that our proposed method is able to guarantee the RU's data rate threshold all the time by sacrificing some MSA. Moreover, the increase of data rate threshold and speed lead to a decrease of MSA, while the cache usage rate has relatively weak influence on MSA. © 2022 IEEE.

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 coverage performance of unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) networks under the nonlinear and linear energy harvesting (EH) models in the rich scattering scenarios, including smart farming and smart ranching, where the None-Line-of-Sight (NLoS) links are the dominate component of the wireless channel. Multiple UAVs are equipped with directional antennas to transfer information and energy to ground users (GUs). Power splitting (PS) or time switching (TS) architecture is employed at GUs. In order to evaluate the system performance in fading channels, the information and energy coverage probabilities of the system are discussed, and by using the stochastic geometry approach and the approximate scaling method, the general and lower bound explicit expressions of the coverage probabilities are derived. Numerical results show that the coverage performance of PS-based systems is superior to that of TS-based systems. Moreover, although the linear EH model yields better results than the nonlinear one, as the linear EH model is too ideal, its yielded results may mismatch practical EH circuits, and the ones yielded by the nonlinear EH model is much closer to practice, as the nonlinear EH model is based on real data measurement. Additionally, the nonlinear EH model has a relatively small effect on the harvested energy coverage probability, and the linear EH model introduces greater bias for the TS-based system than that for PS-based one.

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.

Unmanned aerial vehicles (UAVs) with huge-capacity batteries could be employed to wirelessly charge the ground sensor users (GSUs) and enhance the coverage of aerial wireless networks in outdoor Internet of Things (IoT). This paper investigates the information and energy coverage of UAV-enabled simultaneous wireless information and power transfer (SWIPT) networks. Both power splitting (PS) and time switching (TS) receiver architectures are considered. By using stochastic geometry approach, the general and explicit expressions of the information coverage probability (ICP), the energy coverage probability (ECP) and the joint information and energy coverage probability (JIECP) are derived under the nonlinear and linear energy harvesting (EH) models, respectively. Particularly, the Laplace transform and the probability generating functional (PGFL) are used to derive the ICP. And, Campbell’s theorem and the maximum function are applied to obtain the ECP and the JIECP, respectively. To achieve the optimal UAVs’ deployment density, the maximization optimization problems are formulated for the PS-based and TS-based systems, respectively. By using the series expansion of$Q(x)$($Q$-function) with large$x$, the closed-form approximating optimal solutions to the formulated problems are obtained. Monte Carlo simulations validate the correction of our obtained theoretical results, and numerical results show that the performance of the PS-based system is superior to that of the TS-based one. Moreover, when the energy requirement of GSUs or the transmit power of UAVs is relatively large, or when the information requirement of GSUs or the UAV deployment density is relatively small, compared with the nonlinear EH model, the analysis bias caused by traditional linear EH model is relatively large and in these cases, traditional linear EH model cannot be used to replace the nonlinear EH one for the system performance analysis or optimal system design.

This paper investigates the user clustering and power control in the uplink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) networks. A joint optimization problem is formulated to minimize the system transmit power. The formulated optimization problem is prohibitively complicated, especially when the number of users is large. Alternatively, a two-step user clustering and power control algorithm is proposed. First, a K-means-based algorithm is proposed for user clustering, where both channel gain and channel correlation among users are taken into account for the distance measurement to reduce the intra- and inter-cluster interference. Then, a semi-orthogonal user selection (SUS) algorithm is designed, with which the optimal cluster number and cluster centers can be dynamically obtained. Further, the closed-form expression of the optimal intra-cluster power control is derived, and the resulting inter-cluster power control problem is solved by designing an efficient iterative algorithm. Simulation results show that the proposed K-means-based iterative power control scheme outperforms other reference methods, and can approach the optimal performance in terms of power consumption and energy efficiency at a much lower computational complexity.

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.

This paper investigates the user clustering and power control in the uplink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) networks. A joint optimization problem is formulated to minimize the system transmit power. The original optimization problem is prohibitively complicated when the number of users is large. Alternatively, a two-step user clustering and power control algorithm is proposed. First, an im-proved K-means algorithm is proposed for user clustering, where the clustering metric considers both channel gain and channel correlation among users to reduce the intra- and inter-cluster interference. With this basis, the optimal cluster number and cluster centers are dynamically obtained by the semi-orthogonal user selection (SUS) algorithm. Further, the closed-form expression of the optimal intra-cluster power control is derived, and the resulting inter-cluster power control problem is solved iteratively. Simulation results show that the proposed scheme achieves the near-optimal performance in terms of power consumption and energy efficiency with low computational complexity.

This article investigates the unmanned aerial vehicle (UAV)-assisted wireless powered Internet-of-Things system, where a UAV takes off from a data center, flies to each of the ground sensor nodes (SNs) in order to transfer energy and collect data from the SNs, and then returns to the data center. For such a system, an optimization problem is formulated to minimize the average Age of Information (AoI) of the data collected from all ground SNs. Since the average AoI depends on the UAV's trajectory, the time required for energy harvesting (EH) and data collection for each SN, these factors need to be optimized jointly. Moreover, instead of the traditional linear EH model, we employ a nonlinear model because the behavior of the EH circuits is nonlinear by nature. To solve this nonconvex problem, we propose to decompose it into two subproblems, i.e., a joint energy transfer and data collection time allocation problem and a UAV's trajectory planning problem. For the first subproblem, we prove that it is convex and give an optimal solution by using Karush-Kuhn-Tucker (KKT) conditions. This solution is used as the input for the second subproblem, and we solve optimally it by designing dynamic programming (DP) and ant colony (AC) heuristic algorithms. The simulation results show that the DP-based algorithm obtains the minimal average AoI of the system, and the AC-based heuristic finds solutions with near-optimal average AoI. The results also reveal that the average AoI increases as the flying altitude of the UAV increases and linearly with the size of the collected data at each ground SN.

This article investigates a multifog server (FS)-assisted nonorthogonal multiple access (NOMA)-based wireless powered network, where an energy-limited wireless device (WD) first harvests energy from a power transmitter (PT) and multiple helping FSs and then uses the harvested energy to partially offload its computing task to the FSs with NOMA for computing. To explore the WD's performance limit in terms of achievable computation rate, an optimization problem is formulated by jointly optimizing the time assignment, the power allocation, and the computation frequency under multiple system constraints. Since the problem is nonconvex with no known solution, an efficient solution approach is designed to achieve the ε-optimal solution, in which the transmit power vector and the computation frequency are jointly optimized with fixed-time assignment, and then, a golden section search (GSS)-based algorithm is designed to find the optimal time assignment. For the case when the FS is with sufficiently strong computation capability, some semiclosed-form results are derived. Numerous results show that our proposed design achieves much higher computation rate than benchmark schemes. Moreover, with the increment of the helping FSs, the achievable computation rate increases while the increasing rate is decreased. Besides, by employing NOMA, the WD's computation rate is also improved compared with orthogonal multiple access (OMA)-based scheme. Additionally, in such a system, with nonlinear energy harvesting (EH) model adopted, the more the helping FSs are deployed, the more the performance loss caused by the traditional linear EH model can be reduced.

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 a multi-node wireless powered communication network (WPCN), where a hybrid access point (HAP) first charges an Internet of Thing (IoT) device wirelessly with the assistance of multiple selfish wireless nodes (WNs), and then the IoT device uses the harvested energy to transmit real-time status updates to the HAP. Two incentive schemes, i.e., the energy-incentive scheme and the price-incentive scheme, are designed to overcome the selfishness of the WNs and enhance the per-packet AoI performance. For the energy-incentive scheme, an AoI-energy utility function is defined and an optimization problem is formulated to maximize the AoI-energy utility value of the HAP-IoT device pair. By using equality constraint elimination and Lagrangian method, the problem is solved and some closed-form solutions are derived to obtain the optimal solution. For the price-incentive scheme, an AoI-price utility function is defined and a Stackelberg game is established to maximize the utility of the HAP-IoT device pair. By using function transformation and Lagrange method, some semi-closed-form solutions are derived to maximize their own profits of the HAP and WNs in a distributed way. Numerical results show that our proposed two incentive mechanisms are able to achieve higher network utility values than the benchmark scheme. The more the number of WNs, the lower the AoI of each status update packet and the higher utility value of the HAP. It also shows that by positioning WNs closer to the IoT device, the better per-packet AoI performance can be achieved by both incentive mechanisms. Additionally, for the energy-incentive mechanism, its achieved AoI gain and energy gain decrease with the increment of the distance between the HAP and the IoT device. But for the price-incentive mechanism, the opposite phenomenon is observed.

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 investigates the optimal design of wireless powered fog computing networks, where a hybrid access point integrated with fog computing function (F-HAP) first charges an Internet of Things (IoT) device via wireless power transfer (WPT), and then the IoT device uses the harvested energy to compute real-time updates locally or offload the updates to the F-HAP for computing. For such a system, an age-energy aware cost function is defined to evaluate the system performance, based on which, an optimization problem is formulated to explore the minimum age-energy aware cost by jointly optimizing the time assignment, the transmit power, the computing frequency, as well as the computing mode selection, such that the given data processing task can be completed. Since the problem is non-convex with the discrete binary variable, variable substitution and Karush-Kuhn-Tucker (KKT) conditions are applied to solve it and some closed-form results on the optimal solution are derived. Numerical results show that the transmit power has much greater effects on the age-energy performance of the fog offloading mode than on that of the local computing mode. Moreover, when the IoT device is relatively close to the F-HAP, fog offloading is a better choice; Otherwise, local computing should be selected.

This article investigates the unmanned-aerial-vehicle (UAV)-enabled wireless powered cooperative mobile edge computing (MEC) system, where a UAV installed with an energy transmitter (ET) and an MEC server provides both energy and computing services to sensor devices (SDs). The active SDs desire to complete their computing tasks with the assistance of the UAV and their neighboring idle SDs that have no computing task. An optimization problem is formulated to minimize the total required energy of UAV by jointly optimizing the CPU frequencies, the offloading amount, the transmit power, and the UAV's trajectory. To tackle the nonconvex problem, a successive convex approximation (SCA)-based algorithm is designed. Since it may be with relatively high computational complexity, as an alternative, a decomposition and iteration (DAI)-based algorithm is also proposed. The simulation results show that both proposed algorithms converge within several iterations, and the DAI-based algorithm achieve the similar minimal required energy and optimized trajectory with the SCA-based one. Moreover, for a relatively large amount of data, the SCA-based algorithm should be adopted to find an optimal solution, while for a relatively small amount of data, the DAI-based algorithm is a better choice to achieve smaller computing energy consumption. It also shows that the trajectory optimization plays a dominant factor in minimizing the total required energy of the system and optimizing acceleration has a great effect on the required energy of the UAV. Additionally, by jointly optimizing the UAV's CPU frequencies and the amount of bits offloaded to UAV, the minimal required energy for computing can be greatly reduced compared to other schemes and by leveraging the computing resources of idle SDs, the UAV's computing energy can also be greatly reduced.

On-demand deployment of small cells plays a key role in augmenting macro-cell coverage for outdoor hotspots, where user devices are brought together and intensively upload self-generated data. In this paper, we study spectrum sharing among rapidly deployable small cells in the uplink, even without a priori global knowledge. We propose a hybrid multi-agent approach, which allows a leading macro-cell base station (MBS) and multiple following small base stations (SBSs) to take part in a user-centric, online joint optimization of small cell deployment and uplink resource allocation. Specifically, we propose a centralized mechanism for the MBS to solve the first subproblem of small cell deployment stage by stage, based on an adversarial bandit model. Furthermore, we propose a distributed mechanism for the group of SBSs to collectively solve the second subproblem of uplink resource allocation stage by stage, based on a stochastic game model. We prove that our approach is guaranteed to produce a joint strategy, which is built upon a mixed strategy with bounded regret on the first tier and an equilibrium solution on the second tier. Our approach is validated by simulations on the aspects of convergence behavior, strategy correctness, power consumption, and spectral efficiency.

This paper investigates the wireless-powered hierarchical fog-cloud computing networks, where multiple energy-constrained users harvest energy from a hybrid access point (HAP) firstly and then use their harvested energy to offload their computation tasks to fog/cloud servers via the HAP or compute their tasks locally. To pursue multi-user fairness, an optimization problem is formulated to maximize the minimal energy balance among all users by jointly optimizing time assignments, computation central processing unit (CPU) frequencies, and the computing mode selection. Since the problem is mixed-integer combinatorial non-convex, which is intractable, a generalized Benders decomposition (GBD)-based method is proposed, which guarantees the globally optimal solution. To release the high computational complexity of the proposed GBD-based method, a penalized successive convex approximation (P-SCA)-based algorithm is designed as an alternative to obtain a suboptimal solution with low computational complexity. Numerical results show that among different optimizable factors in the system, computing mode selection is the dominant one on affecting the system performance. Moreover, for each user, local computing is a better choice, if it is with relatively poor channel gain and small local computing delay. Otherwise, fog/cloud computing may be a better choice. Additionally, for the users with relatively high channel gains, if their local computing delays are less than those selecting fog computing, cloud computing should be a better choice.

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.

Simultaneous wireless information and power transfer (SWIPT) becomes more and more popular in cognitive radio (CR) networks, as it can increase the resource reuse rate of the system and extend the user’s lifetime. Due to the deployment of energy harvesting nodes, traditional secure beamforming designs are not suitable for SWIPT-enabled CR networks as the power control and energy allocation should be considered. To address this problem, a dedicated green edge power grid is built to realize energy sharing between the primary base stations (PBSs) and cognitive base stations (CBSs) in SWIPT-enabled mobile edge computing (MEC) systems with CR. The energy and computing resource optimal allocation problem is formulated under the constraints of security, energy harvesting, power transfer, and tolerable interference. As the problem is nonconvex with probabilistic constraints, approximations based on generalized Bernstein-type inequalities are adopted to transform the problem into solvable forms. Then, a robust and secure artificial noise- (AN-) aided beamforming algorithm is presented to minimize the total transmit power of the CBS. Simulation results demonstrate that the algorithm achieves a close-to-optimal performance. In addition, the robust and secure AN-aided CR based on SWIPT with green energy sharing is shown to require a lower transmit power compared with traditional systems.

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 optimal design of wireless- powered hierarchical fog-cloud computing networks, where energy-constrained users first harvest energy from a hybrid access point (HAP) and then offload their computation tasks to fog/cloud servers via the HAP or compute the tasks locally by us- ing the harvested energy. An optimization problem is formulated to maximize the minimal energy balance among multiple users by jointly optimizing offloading decisions, communication and computation resource allocations in the system, where computational capacity, processing delay, energy harvesting (EH) and energy consumption constraints are considered. To efficiently solve such a mixed-integer combinatorial non-convex problem, a penalized successive convex approximation (P-SCA)- based algorithm is designed, which is able to converge to a suboptimal solution with the polynomial time computational complexity. Numerical results show that compared to communication and computation resource allocation, the offloading decision is the dominant factor on affecting the system performance. It is also found that local computing is a better choice for users with relatively poor channel gains while fog/cloud computing is a better choice for users with relatively good channel gains. Specifically, cloud computing is preferred if the cloud computational capacity is strong enough and the wired-link data rate is high enough; Otherwise, fog computing is preferred. Besides, more users are served, less max-min energy balance can be obtained.

This paper investigates the efficient cooperation in wireless-powered communication networks, where an access point (AP) and multiple helpers first together charge up a sensor via radio-frequency (RF)-based wireless power transfer (WPT), and then the sensor uses the harvested energy to transmit real-time status updates to the AP. Due to the selfishness of the helpers, payment is provided as an incentive to them by the sensor-AP communication pair. For such a system, a Stackelberg game approach is designed to establish efficient cooperation between the helpers and the sensor-AP communication pair. An Age of Information (AoI)-based utility and a profit-based utility are defined for the sensor-AP pair and the helpers, respectively. Optimization problems are formulated to maximize their utilities. An explicit expression of the optimal transmit power of the helper is derived, with which a Dinkelbach's Programming (DP)-based algorithm is designed to jointly find the optimal payment price and transmit power at the AP, and at the same time, the Stackelberg equilibrium (SE) is achieved. Moreover, a closed-form expression of the minimum AoI of the system is also presented. Numerical results show that the more helpers there exist, the higher payment price the AP should fix, and meanwhile, the lower AoI of the sensor-AP communication pair can be achieved. Besides, it is shown that by increasing the transmit power at the AP, the optimal average AoI could not be reduced evidently.

This paper investigates the information-energy (I-E) region for simultaneous wireless information and power transfer (SWIPT) system in mobility scenarios, where a moving transmitter transmits information and energy to a power splitting (PS)-based receiver. An optimization problem is formulated to explore the system I-E region under the nonlinear energy harvesting (EH) model by jointly optimizing the transmit power at the transmitter and the PS ratio at the receiver. Since the problem is nonconvex, a successive convex approximate-based (SCA-based) algorithm is proposed, which is able to find the sub-optimal solution with low complexity. For comparison, the I-E region of the system under the linear model is also studied, where some closed and semi-closed solutions are derived by using Lagrange dual method and KKT conditions. Numerical results show that compared with the linear EH model, the nonlinear EH model yields a smaller I-E region due to the limitations of EH circuit features. Nevertheless, using the nonlinear EH model avoids the false achievable I-E region for practical mobile SWIPT systems. Besides, it shows that the higher moving speed yields the smaller I-E region. Moreover, with the increment of the required information amount, the harvested energy bias caused by the linear EH model decreases, but the bias ratio increases.

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.

Summary Finding link/node‐disjoint paths between a pair of nodes is capable of providing Quality of Service and reliable routing which is very critical for mesh‐connected Network‐on‐Chip. State‐of‐art works usually aim at random topologies and multiple constraints. Therefore, it is difficult to optimize their time complexity. In this paper, MDPPIM (Manhattan‐distance‐constrained Disjoint Path Pair Problem in Incomplete Mesh) problem is presented based on Network‐on‐Chip application scenarios. Then, PCDP (Path‐Counting Disjoint Path) algorithm is proposed to solve the MDPPIM problem with low time complexity. Compared with previous disjoint path algorithms, PCDP algorithm does not have to use Dijkstra's algorithm to find a shortest path. It is optimized according to the feature of incomplete mesh such as the regularity of mesh and Manhattan‐distance constraint. Therefore, it is with low time complexity. Numerical results demonstrate the proposed PCDP algorithm's effectiveness and low time complexity.

A number of graph-parallel computing abstractions have been proposed to address the needs of solving complex and large-scale graph computing. However, unnecessary and excessive communication and state sharing between nodes in these frameworks not only reduce the network efficiency but may also caus e decrease in runtime performance. In this paper, we propose a mechanism called LightGraph, which reduces the synchronizing communication overhead for distributed graph-parallel computing abstractions. Besides identifying and eliminating the redundant synchronizing communications in existing systems, in order to minimize the required synchronizing communications LightGraph also proposes an edge direction-aware graph partitioning strategy. This new graph partitioning strategy optimally isolates the outgoing edges from the incoming edges of a vertex. We have conducted extensive experiments using real-world data, and our results verified the effectiveness of LightGraph. For example compared to PowerGraph LightGraph can not only reduce up to 31.5% synchronizing communication overhead for intra-graph synchronizations, but also cut up to 16.3% runtime for PageRank running on Livejournal dataset.

This paper investigates the simultaneous wireless information and power transfer (SWIPT) networks with coexisting power-splitting users (PSUs) and time-switching users (TSUs) under the nonlinear energy harvesting (EH) model, where a multiantenna hybrid access point (H-AP) transmits information and power to multiple PSUs and TSUs. For such a network, an optimization problem is formulated to minimize the required transmit power at the H-AP subject to users' information rate and harvested energy constrains by jointly optimizing the H-AP's transmit beamforming vectors, PSUs' power splitting (PS) ratios, and TSUs' time switching (TS) factors. Due to the interferences among PSUs and TSUs, and the nonlinear EH model, the problem is nonconvex and has no known solution method. Thus, a two-layer algorithm is first presented based on semidefinite relaxation (SDR) and it is theoretically proved that the global optimum is achieved. However, since 1-D search is adopted by the two-layer algorithm, which may be too computationally exhaustive, a successive convex approximate-based (SCA-based) algorithm is then proposed as an alternative, which is able to find the near-optimal solution with low complexity by using the first-order approximation. The numerical results show that with the same EH requirements, TSUs are more likely to enter into the saturation region compared with PSUs, but their EH efficiency is higher than that of PSUs. It is also shown that the minimal required transmit power under the nonlinear EH model is much lower than that under the linear one. Although after the saturation point of the nonlinear EH model, the linear one yields a lower required transmit power, it is a fake result, because the linear EH model mismatches the nonlinearity of practical EH circuits.

This paper investigates a fog computing-assisted multiuser simultaneous wireless information and power transfer network, where multiple sensors with power splitting (PS) receiver architectures receive information and harvest energy from a hybrid access point (HAP), and then process the received data by using local computing mode or fog offloading mode. For such a system, an optimization problem is formulated to minimize the sensors' required energy while guaranteeing their required information transmissions and processing rates by jointly optimizing the multiuser scheduling, the time assignment, the sensors' transmit powers, and the PS ratios. Since, the problem is a mixed integer programming problem and cannot be solved with existing solution methods, we solve it by applying problem decomposition, variable substitutions, and theoretical analysis. For a scheduled sensor, the closed-form and semi-closed-form solutions to achieve its minimal required energy are derived, and then an efficient multiuser scheduling scheme is presented, which can achieve the suboptimal user scheduling with low computational complexity. Numerical results demonstrate our obtained theoretical results, which show that for each sensor, when it is located close to the HAP or the fog server, the fog offloading mode is the better choice; otherwise, the local computing mode should be selected. The system performances in a frame-by-frame manner are also simulated, which show that using the energy stored in the batteries and that harvested from the signals transmitted by previous scheduled sensors can further decrease the total required energy of the sensors.

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.

Mobile fog computing (MFC) is an emerging paradigm that extends cloud computing (CC) by adding a new layer between the cloud and its end users.With the cloud-aware MFC, the cloud can pre-push certain important resources to the fog to reduce the networking latency and release the traffic burden over the links.e end user then is able to perform offline computing on the fog layer so that only the important results need to be delivered to and stored in the cloud.Moreover, the dense geographical deployment of fog servers enables the system to be aware of the end user's location.erefore, some location-sensitive applications could be well supported by the fog-aided cloud systems.Note that the cloud-aware MFC is different from the mobile edge computing (MEC), another promising technology for overcoming the shortcomings of CC, since MFC is able to jointly work with the cloud, but MEC is usually defined by the exclusion of CC.Specifically, in MEC, computing applications, data, and services are pushed away from the centralized nodes to the network edge, which enables network edge to run in an isolated environment from the rest of the network and provides access to local resources and data.In contrast, MFC provides not only a systemlevel horizontal architecture but also a new way to distribute, orchestrate, and manage secure resources across the network rather than just performing computing at the network edge.How to design efficient system architectures, transmission strategies, and protocols for MFC and how to efficiently analyze and evaluate the system performance are very important and essential.ese topics have carved out a new area rich in research and innovation potential.is special issue aims to address all these topics and invite contributions from worldwide leading researchers.

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.

In the downlink of high-speed railway communication systems equipped with distributed transmit antennas, high mobility leads to fast time-varying received signal to noise ratios. In this case, dynamic time-domain power allocation and antenna selection could be jointly optimized to improve the system energy efficiency. This paper considers this problem in such a simple way where dynamic switching between multiple-input-multiple-output and single-input-multiple-output is allowed and exclusively utilized, while the sparse scattering terrains and delay-sensitive traffic flows are taken into account. The original optimization problem is a typical mixed integer nonlinear programming (MINLP) problem. Instead of conventional iteration based methods, such as the extended cutting plane method, we propose a low-complexity and direct solution by exploiting the physical nature of original problem, which can be utilized in real time. Theoretical results show that our proposed method can be viewed as the generalization of channel-inversion associated with transmit antenna selection. Also, compared with methods without dynamic antenna selection, our method significantly decreases the average transmit power.

This paper investigates the joint optimal beamforming and power-splitting receiver architecture design for simultaneous information and power transfer (SWIPT) under non-linear energy harvesting (EH) circuit environment, where hybrid access point (H-AP) equipped with multiple antennas simultaneously transmits information and power to multiple single-antenna users. For such a system, in order to achieve green communication design, we formulate an optimization problem to minimize the total transmit power of H-AP subjecting to the required signal-to-interference-plus- noise ratio (SINR) and the harvested power constrains at each user under non-linear EH model. Since the problem is non-convex, the relaxed semidefinite program (SDP) is used to solve it, and it is proved that the semidefinite relaxation (SDR) process is tight and our optimal solution achieves the global optimum. Numerical results show that a considerable gain could be achieved if the SWIPT beamforming vector and the power splitting ratios are jointly designed under the non-linear EH model compared with traditional linear EH model, since the non-linear EH model captures and matches the real EH circuits' non-linear features. Moreover, the feasible region of traditional linear EH model in the non-linear EH circuit environment is also characterized.

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 optimal resource allocation in wireless powered communication network with user cooperation, where two single-antenna users first harvest energy from the signals transmitted by a multi-antenna hybrid access point (H-AP) and then cooperatively send information to the H-AP using their harvested energy. To explore the system information transmission performance limit, an optimization problem is formulated to maximize the weighted sum-rate (WSR) by jointly optimizing energy beamforming vector, time assignment, and power allocation. Besides, another optimization problem is also formulated to minimize the total transmission time for given amount of data required to be transmitted at the two sources. Because both problems are non-convex, we first transform them to be convex by using proper variable substitutions and then apply semi-definite relaxation to solve them. We theoretically prove that our proposed methods guarantee the global optimum of both problems. Simulation results show that system WSR and transmission time can be significantly enhanced by using energy beamforming and user cooperation. It is observed that when the total amount of information of two users is fixed, with the increase of the information amount of the user relatively farther away from the H-AP, the transmission time of the user cooperation scheme decreases while that of the direct transmission increases. Besides, the effects of user position on the system performances are also discussed, which provides some useful insights.

This paper considers a wireless powered communication network (WPCN) with group cooperation, where two communication groups cooperate with each other via wireless power transfer and time sharing to fulfill their expected information delivering and achieve “win-win” collaboration. To explore the system performance limits, we formulate optimization problems to maximize the weighted sum-rate (WSR) and minimize the total consumed power. The time assignment, beamforming vector and power allocation are jointly optimized under available power and quality of service requirement constraints of both the groups. For the WSR-maximization, both fixed and flexible power scenarios are investigated. As all problems are non-convex and have no known solution methods, we solve them by using proper variable substitutions and the semi-definite relaxation. We theoretically prove that our proposed solution method guarantees the global optimum for each problem. Numerical results are presented to show the system performance behaviors, which provide some useful insights for future WPCN design. It shows that in such a group cooperation-aware WPCN, optimal time assignment has the greatest effect on the system performance than other factors.

This paper explores the rate-energy (R-E) region of simultaneous wireless information and power transfer for MIMO broadcasting channel under the nonlinear radio frequency energy harvesting (EH) model. The goal is to characterize the tradeoff between the maximal energy transfer versus information rate. The separated EH and information decoding (ID) receivers and the co-located EH and ID receivers scenarios are considered. For the co-located receivers scenario, both time switching (TS) and power splitting (PS) receiver architectures are investigated. Optimization problems are formulated to derive the boundaries of the R-E region s for the considered systems. As the problems are nonconvex, we first transform them into equivalent ones and derive some semi-closed-form solutions, and then design efficient algorithms to solve them. Numerical results are provided to show the R-E region s of the systems, which provide some interesting insights. It is shown that all practical circuit specifications greatly affect the system R-E region. Compared with the systems under traditional linear EH model, the ones under the nonlinear EH model achieve smaller R-E region s due to the limitations of practical circuit features and also show very different R-E tradeoff behaviors.

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.

This paper presents an analytical model for analyzing the performance of the TDMA-based distributed reservation protocol for wireless multi-hop ad hoc networks. A novel three-way handshake process is presented which considers the hold-off and competition process before the transmission of scheduling messages. An explicit expression of expectation of the average delay of the three-way handshake process is further derived. Regarding throughput, the concepts of forwarding traffic and pseudo-collision traffic are brought up to evaluate the MAC layer effective throughput. The forwarding characteristic of multi-hop ad hoc networks is studied to calculate the forwarding traffic while the pseudo-collision traffic is derived based on the premise that the nodes are not allowed to transmit while their one-hop neighbors receive. An analytical model which jointly considers the impacts of network parameters, scheduling parameters and frame structure design is constructed. The optimal number of control slots that maximizes the effective throughput is further obtained under certain traffic volume, node density and hold-off exponent. Simulation results show the effectiveness of the theoretical model. The upper bound of MAC layer effective throughput under different network settings is presented.

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.

This paper investigates the simultaneous wireless power and information transfer (SWPIT) for network-coded two-way relay transmission from an information theoretical viewpoint, where two sources exchange information via an energy harvesting relay. By considering the time switching (TS) relay receiver architecture, we present the TS-based two-way relaying (TS-TWR) protocol. In order to explore the system throughput limit with data rate fairness, we formulate an optimization problem under total power constraint. To solve the problem, we first derive some explicit results and then design an efficient algorithm. Numerical results show that with the same total available power, TS-TWR has a certain performance loss compared with conventional non-EH two-way relaying due to the path loss effect on energy transfer, where in relatively low and relatively high SNR regimes, the performance losses are relatively small.

Network coding is famous for its capability in significantly improving the throughput of network. The successful decoding of the network coded data relies on some side information of the original data. In that framework, independent data flows are usually decoded first and then network coded by relay nodes. If appropriate signal design is adopted, physical layer network coding is a natural way in wireless networks. In this work, a network coding tree algorithm which enhances the efficiency of the multiple access system (MAS) is presented. For MAS, researchers try to avoid the collisions but collisions happen frequently under heavy load. By introducing network coding into MAS, our proposed algorithm achieves a better trade-off between average delay and system throughput. When multiple users transmit signal in a time slot, the sum signals are saved and used to jointly decode the collided frames after some component frames of the network coded frame are received. Splitting tree structure is extended to our proposed algorithm for collision solving. The system throughput and average delay of frames are presented in a recursive way. Besides, extensive simulations show that network coding tree algorithm enhances the system performance in terms of system throughput and average frame delay compared with other algorithms.

In high speed railways (HSRs) communication system, the wireless channel between the train and base station varies strenuously due to high mobility, which makes it very essential to implement appropriate adaptive algorithms to guarantee the quality-of-service (QoS). What's more, how to evaluate the performance limits in this new scenario must also be considered. To this end, this paper investigates the performance limits of wireless communication in HSRs scenario. Since the information transmitted between train and base station usually has diverse QoS requirements, a QoS-distinguished achievable rate region is utilized to characterize the transmission performance in this paper, which can be regarded as a generalized case of traditional ergodic capacity and outage capacity with unique QoS requirement. The specific adaptive algorithm that can achieve the maximal boundary of achievable rate region is also derived. Compared with conventional strategies, the advantages of the proposed strategy are validated in terms of green communication, namely minimizing average transmit power.

We consider providing services for passengers in a high-speed train and local users (quasi-static users) in a single OFDMA system. For the train, we apply a two-hop architecture, under which, passengers communicate with base stations (BSs) via a mobile relay (MR) installed in the train cabin. With this architecture, all passengers in the train can be represented by the MR. Since the channels of the MR and local users vary differently, we consider allocating system resources (power and subcarriers) over two time-scales for them. We formulate the problem as a capacity optimization problem for the MR subject to the sum capacity constraint of local users. We treat the inter-carrier interference (ICI) at the MR as additive Gaussian noise and derive an explicit expression for the ICI using the two-path Doppler spread model. Then we discuss the optimization problem and propose an optimal power and subcarrier allocation (OPSA) policy. The capacity obtained using OPSA is compared with that of constant power and subcarrier allocation (CPSA) policies. Simulation results justify the optimality of the OPSA. Besides, by comparing the capacity bounds achieved by OPSA with and without ICI, we find that only in specific regions, where the gap between the capacity bounds is large, do practical ICI cancellation methods provide meaningful rate gain.

This paper investigates the simultaneous wireless information and energy transfer for the non-regenerative multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) relaying system. By considering two practical receiver architectures, we present two protocols, time switching-based relaying (TSR) and power splitting-based relaying (PSR). To explore the system performance limits, we formulate two optimization problems to maximize the end-to-end achievable information rate with the full channel state information (CSI) assumption. Since both problems are non-convex and have no known solution method, we firstly derive some explicit results by theoretical analysis and then design effective algorithms for them. Numerical results show that the performances of both protocols are greatly affected by the relay position. Specifically, PSR and TSR show very different behaviors to the variation of relay position. The achievable information rate of PSR monotonically decreases when the relay moves from the source towards the destination, but for TSR, the performance is relatively worse when the relay is placed in the middle of the source and the destination. This is the first time such a phenomenon has been observed. In addition, it is also shown that PSR always outperforms TSR in such a MIMO-OFDM relaying system. Moreover, the effects of the number of antennas and the number of subcarriers are also discussed.

Abstract To provide stable and high data rate wireless access for passengers in the train, it is necessary to properly deploy base stations along the railway. We consider this issue from the perspective of service, which is defined as the integral of the time‐varying instantaneous channel capacity. With large‐scale fading assumption, it will be shown that the total service of each base station is inversely proportional to the velocity of the train. Besides, we find that if the ratio of the service provided by a base station in its service region to its total service is given, the base station interval (i.e., the distance between two adjacent base stations) is a constant regardless of the velocity of the train. On the other hand, if a certain amount of service is required, the interval will increase with the velocity of the train. The aforementioned results apply not only to simple curve rails, like line rail and arc rail, but also to any irregular curve rail, provided that the train is traveling at a constant velocity. Furthermore, the new developed results are applied to analyze the on–off transmission strategy of base stations. Copyright © 2013 John Wiley & Sons, Ltd.

In full-duplex Gaussian relay channels, neither Decode-and-Forward (DF) nor Compress-and-Forward (CF) can achieve a larger rate than the other for all the channel gain combinations. Combining DF and CF strategies, we show that a new achievable rate, which is the concave envelop of the maximal rate achieved by DF and CF with respect to the source power, is achievable. To this end, we actively adjust the transmission power of the source for different time and switch the transmission strategy between DF and CF according to the source power. It is proved that when the signal to noise ratio (SNR) of the source-destination link falls into a certain range, the new achievable rate is strictly larger than that achieved by pure DF and pure CF. The optimal power allocation and corresponding time proportions are also obtained. Numerical results show that the new achievable rate is also competitive with the rate achieved by superposing CF on DF. As strategy switching avoids complex codeword constructions, it is more practical than superposition structures to be implemented in relay systems.

Quality of service (QoS) requirements are usually different from user to user in a multiaccess system, and it is necessary to take the different requirements into account when allocating the shared resources of the system. In this paper, we consider one QoS criterion-average packet delay in a multiaccess system, and we combine information theory with queueing theory in an attempt to analyze whether a multiaccess system can meet the different delay requirements of all users. When the queue state information is not available to the central scheduler, we show that static rate allocation achieves the best performance. Based on this result, we provide a polynomial-time algorithm for deciding whether a system can meet the different delay requirements of all users. In cases where the system cannot meet the needs of all users, we prove that as long as the sum power is larger than a threshold, there is always an approach to adjust the transmission power of each user to make the system delay feasible if power reallocation is available.

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 consider one quality-of-service (QoS) criterion, average packet delay (queueing delay plus service time), in a multiaccess system and investigate the basic problem whether a multiaccess system can meet the different average packet delay requirements of all users by combining information theory with queueing theory. Two different cases of the central scheduler with and without queue state information (QSI) are discussed. If the QSI is not available to the central scheduler, we show that static rate allocation policies (SRAPs) can achieve better average packet delay performance than probabilistic rate allocation policies. Based on this conclusion, the delay feasibility checking process reduces to checking whether the required service rate vector lies in the multiaccess capacity region. We find that for users with equal transmit power, only N inequalities are necessary for the checking process, whereas for users with unequal transmit powers, we provide a polynomial-time algorithm for such a decision. Furthermore, if the system cannot satisfy the average packet delay requirements of all users, we prove that as long as the sum power is larger than a threshold, there is always an approach to adjust the transmit powers of different users to satisfy the average packet delay requirements. On the other hand, if the QSI is available to the central scheduler, we propose two dynamic scheduling algorithms to achieve proportional average packet delay and compare their performances with optimal SRAP by simulations.

Space time network coding (STNC) is a recently proposed time-division multiple-access (TDMA)-based cooperative relaying scheme for multi-relay wireless systems, which can achieve full diversity order with low transmission delay by taking advantage of the concepts of network coding and distributed space time coding. However, STNC does not fully exploit the benefit of the broadcast nature of wireless channels, since it only allows relays to receive signals from the sources. To explore the potential capacity of STNC-based systems, in this paper, we propose a new cooperative relaying scheme, termed space-time network coding with overhearing relays (STNC-OR), by allowing each relay to collect the signals transmitted from not only the sources but also its previous relays. Then, we derive some explicit expressions for the outage probability and symbol error rate (SER) for STNC-OR with decode-and-forward relaying over independent non-identically distributed (i.n.i.d) Rayleigh fading channels. For comparison, we also derive the explicit expression of the outage probability for STNC. To further improve the performance of STNC-OR, we investigate the effect of relay ordering on the performance of STNC-OR and then present the optimal relay ordering algorithm. Further, a suboptimal relay ordering is also designed to reduce the complexity. Extensive simulation and numerical results are presented finally to validate our theoretical analysis. It is shown that the proposed STNC-OR achieves much lower outage probability and SER than STNC and traditional pure TDMA relaying schemes.

ABSTRACT Signal‐time coding (STC) is a newly proposed transmission scheme for half‐duplex relay networks, which is able to achieve higher information flow rate by combining the traditional encoding/modulation mode in the signal domain with the signal pulse phase modulation in the time domain. However, most of the results for STC are only obtained under the ideal assumptions that the signal detections at physical layer are perfect and there are still a lot of fundamental problems to be explored. This paper considers the implementing issues of STC at physical layer in additive white Gaussian noise relay networks. Firstly, a performance evaluation criterion, the reliable information per symbol (RIPS), is proposed to characterize the performance of STC in noisy wireless networks. Secondly, a new construction scheme based on route ID for the codeword of STC is presented, and some structural properties of the codeword of STC are investigated. Thirdly, the error probabilities of STC in both the signal domain and the time domain are discussed. Furthermore, two implementing schemes, that is, the energy detection based STC (ED‐STC) and the symbol detection based STC (SD‐STC), are proposed, and their performance bounds in terms of RIPS are discussed. Numerical analyses show that both ED‐STC and SD‐STC outperform traditional transmission methods in terms of effective information rate even under some practical conditions. Copyright © 2011 John Wiley & Sons, Ltd.

This paper analyzes the outage probability of space-time network coding (STNC) with amplify-and-forward (AF) relays in a cooperative relaying system, where multiple sources transmit their information to a common destination with the help of multiple AF relays in time-division multiple-access (TDMA) mode. We derive an approximate closed-form expression of the outage probability for STNC with an arbitrary number of AF relays for independent but not necessarily identically distributed (i.n.i.d.) Rayleigh fading channels. Numerical results validated our analysis. Moreover, with the developed result, we also discuss the impact of the transmit signal-to-noise ratio (SNR), the outage threshold, the number of relays and the nonorthogonal codes on the system performance.

This paper investigates the resource allocation for two-way relay networks with symmetric data rates from an information theoretic perspective, where a round of information exchange between two sources requiring equal end-to-end transmission rates is considered to be completed by a muti-access (MAC) phase and a broadcast (BC) phase. Decode-and forward (DF) protocol is employed. In this case, we formulate an optimization problem to maximize the sum rate of the system under total available energy. Our goal is to seek the jointly optimized time assignment between the MAC and BC phases and the power allocation among the source and relay nodes. Since the problem is difficult to solve in general, we firstly discuss it in two extreme cases by considering very low and very high system available energy. By doing so, we find an interesting result that in the very high energy case, the optimal ratio of the time assigned for the MAC phase to that assigned for the BC phase is a constant, i.e., 2 : 1. Further, we adopt such constant time assignment to general cases, and derive a closed-form power allocation for two-way relay transmissions. Extensive numerical results vitiated the proposed joint resource allocation and show that the maximum system sum-rate can be approached by our scheme, which obviously excels traditional equal time assignment and equal power distribution schemes.

In this paper, we investigate the joint subcarrier-pairing and resource allocation scheme for multi-relay aided two-way relay OFDM networks, where power allocation, subcarriers assignment and relay selection are taken into account. It is assumed that amplify-and-forward relaying protocol is deployed on all relay nodes to assists the information exchange between two sources via orthogonal subchannels. In this case, we formulate an optimization problem to maximize the total end-to-end transmission rate of the system under individual power constraints at each node. The goal is to seek the jointly optimized subcarrier pairing, subcarrier-pair-to-relay selection and power allocation. To solve the problem, we derive an asymptotically optimal scheme by adopting the dual decomposition approach of mixed-integer programming problems. Finally, simulation results are presented to demonstrate the performance of the proposed scheme.

Multiple input multiple output (MIMO) relay channels has great application potentials in wireless communication systems and therefore attracts both academia and industrial attentions recently. In this paper, we consider the MIMO relay channels where both source node and relay node are equipped with multiple antennas and the destination node has a single antenna. We address the optimal beamforming design when decode-and-forward half-duplex relaying protocol is deployed. Specifically, we develop an efficient algorithm to determine the optimal beamforming vector at the source when transmitting to relay and destination at the same time. Based on the exact channel capacity formulation, the proposed algorithm can achieve arbitrarily high accuracy with low computational complexity.

This paper investigates the resource allocation problem to minimize the downlink transmission delay under power constraints for two-way relay transmission using network coding over OFDM channels, where two sources with unbalanced traffic exchange their information via a relay node with network coding deployed. Since the explicit solution to this optimization problem is hard to obtain and with extremely high computation complexity even for numerical solution, we propose low-complexity suboptimal algorithms for the problem, where subcarrier assignment is carried out by assuming an equal power distribution at first and then optimal power allocation is executed to minimize the transmission delay. By simulations, the proposed resource allocation scheme is shown to achieve less than 1.01 times the optimal delay and outperform the strategies without network coding in overwhelming majority cases.

Opportunistic Network Coding (ONC) has attracted much attention recently. The main improvement of ONC over the traditional network coding is that ONC allows the encoding node to decide whether it employs network coding based on the status of all its input streams. However, due to the nature of fading, wireless links may not always be reliable. Thus, the performance gain of ONC over traditional methods may be diminished due to wireless links in deep fading. Fortunately, some techniques, such as ARQ (Automatic Repeat reQuest) and cooperative diversity etc. can be used to mitigate it. In this paper, we consider the wireless butterfly network topology, a basic component of complex wireless networks. In order to improve the network performance via taking the advantage of ONC, ARQ and cooperative diversity, we propose a new Cooperationbased Opportunistic Network Coding (CP-ONC) protocol and then analyze its performance in terms of network throughput and delay. The advantage of CP-ONC over ONC is that it employs truncated ARQ and cooperative diversity to enhance the reliability of the wireless links. Various simulations show that CP-ONC achieves better gain over ONC in terms of network throughput and delay, especially in low Signal to noise ratio (SNR) region.

Cooperative relaying and network coding have attracting more attention recently. In this paper, we combine these two techniques together and present a Network Coding-aware Cooperative Relaying (NC2R) scheme for downlink cellular networks, in which two relay nodes are used to assist base stations in transmitting signals to cell-edge users. Moreover, we analyze its SINR performance and its spectral efficiency. Extensive simulations show that NC2R greatly improves the downlink transmission performance for users located near celledge regions, and outperforms existing known relaying schemes in terms of blocking probability and spectral efficiency. In addition, the effects of relay position on the performance of NC2R are also discussed.

Signal-Time Coding (STC), a novel transmission mechanism, was proposed recently. It combines the traditional encoding/modulation mode in the signal domain with the signal pulse phase modulation in the time domain and can achieve higher information flow rate in some cases for relay networks. However, there are still many fundamental problems to be investigated. This paper considers the implementing issue of STC in AWGN relay networks. Firstly, an energy detection based STC (ED-STC) scheme is proposed and the error probabilities of ED-STC in both the signal domain and the time domain are given. Secondly, a performance evaluation criterion, the reliable information per symbol (RIPS), is proposed to characterize the performance of STC in noisy wireless networks. Moreover, the performance bounds of the RIPS of ED-STC are derived. Numerical analysis show that ED-STC outperforms traditional transmission method in terms of effective information rate within some practical conditions.