This article investigates the uplink of a reconfigurable intelligent surface (RIS)-assisted wireless communication system. In this uplink, the RIS is powered by the energy harvested from ambient energy sources, and assists multiple users in uploading data to a multi-antenna base station (BS). A hybrid architecture is proposed for the RIS, so that each reflecting unit (RU) at the RIS is enabled to select its working mode among passive, active and deactivated modes. In this way, the RIS can schedule the energy in a fined-grained manner. Meanwhile, a new protocol is proposed to enable the RIS to schedule the harvested energy with a forward-looking approach. Under the hybrid RIS architecture and the newly proposed protocol, an optimisation problem is formulated to jointly optimise the working modes of RUs, the amplitude coefficient of active RUs, the receive beamforming at the BS, the power allocation at the users, with the goal to maximize the long-term system throughput by considering a minimum-rate-requirements constraint at each user and an energy scheduling constraint at the RIS. The formulated problem is an intractable dynamic and mixed-integer nonlinear programming. To solve this problem, a hierarchical deep reinforcement learning based framework is proposed. Simulation results show that, by using hybrid RISs, our self-powered wireless system can achieve up to 12 (5) times of the throughput than the throughput achieved by a self-powered wireless system with just active (passive) RISs and myopic energy scheduling.

This article studies the design of vehicular edge computing networks (VECNs) with multiple moving vehicles and roadside units (RSUs). Uniquely, our study reflects a pragmatic situation where wireless channels are time-varying in the duration of task offloading and vehicles can travel with inconstant speeds in a real-world scenario. By jointly optimizing transmit power and time allocation for task offloading as well as computation task partition in the VECN, our goal is to minimize the cost at the vehicles for energy consumption on task offloading and computing, and rent on task computing service at RSUs. However, solving the formulated optimization problem directly is impossible due to the requirement of noncausal vehicular position information (VPI) and noncausal channel state information (CSI) between vehicles and RSUs. To address this issue, a path prediction model is adopted to predict the noncausal VPI, based on which the noncausal CSI can be estimated. Then, a novel receding horizon optimization method is proposed to transform the original problem into a sequence of tractable problems. Despite this, the problems remain complex due to the computationally prohibitive task of identifying the optimal task offloading duration at each vehicle in a centralized manner. To overcome this difficulty, the consensus alternating directions method of multipliers is proposed to solve the problem in a distributed manner with low computational complexity. Numerical results show that our proposed scheme can save at most 30% of monetary cost as compared with existing baseline schemes.

In this article, a dual-function intelligent reflecting surface (IRS) assisted cognitive radio (CR) nonorthogonal multiple access (NOMA) networks for secure transmission is studied. Considering the scenario of both internal and multiple external Eves in the system, our goal is to maximize the sum achievable secrecy rate through jointly optimizing the transmit beamforming of cognitive base station (CBS), the mode selection of each IRS element and the phase vector of IRS, while both the passive dual-function IRS and active dual-function IRS are investigated. For passive dual-function IRS setup, we decouple the proposed nonconvex optimization problem into two subproblems, the transmit beamforming subproblem and the phase vector subproblem. For transmit beamforming subproblem, we first use arithmetic geometric mean (AGM) inequality and linear matrix inequality (LMI) to deal with the nonconvex fraction form in the objective function and in constraints. Then, successive convex approximation (SCA) is applied to iteratively solve it. For the phase vector subproblem, we utilize the penalty function to handle the rank one constraint and the binary constraint. By alternately optimizing these two subproblems, we obtain a local optimal solution. Then, we extend the proposed algorithm to the secure transmission scheme with active dual-function IRS. The simulation results demonstrate that the proposed scheme with the help of a dual-function IRS effectively enhances the security of the CR-NOMA network compared to other benchmark schemes. Moreover, when the maximum transmitting power$P_{\max }\ge 15$dBm, the proposed active IRS scheme is superior to the proposed passive IRS scheme.

This article studies the design of an intelligent reflecting surface (IRS)-assisted mobile-edge computing (MEC) system, which consists of a mobile user, an IRS equipped with radio-frequency (RF) energy harvesting (EH) circuits, and a hybrid access point (HAP) connected with an MEC server. The IRS is deployed to reflect the user’s task offloading signals to enhance the received power at HAP, and it needs to harvest energy from the RF signals emitted by the HAP before reflecting signals. To save energy consumption at the user, we first propose a novel MEC protocol, in which the system is enabled to operate in three modes, namely, an EH mode, an IRS-assisted task offloading mode, and an IRS-inactive task offloading mode, so that the energy at IRS and the tasks generated at user can be flexibly scheduled within a finite-time horizon, depending on channel conditions, IRS energy states, and user’s task queue states. Under the protocol, we optimize the operation mode selection and resource allocation in each mode with a task execution delay constraint. Due to the randomness of wireless channels and task arrivals, the optimization problem is a stochastic programming. To solve this problem, we first transform it into a deterministic one by assuming that noncausal channel state information (CSI) and task state information (TSI) are available, and then derive a practical algorithm where only causal CSI and TSI are required. Simulation results verify that our proposed design can save at most 80% energy consumption as compared with the existing baseline schemes.

In this article, a wireless multirelay network in which the relays are energy constrained is studied. Especially, in order to consume the harvested energy efficiently at the relays so as to improve the network throughput, a new hybrid relaying protocol is first proposed. In the proposed protocol, each relay can flexibly switch its operation among energy harvesting (EH), information receiving (IR), active information transmission (IT), and two passive backscatter communication (BC) modes according to the channel states as well as its data buffer states and energy states in each transmission block, by which the harvested energy can be efficiently utilized and superior throughput performance can be achieved. However, under the hybrid relaying protocol, it is challenging to achieve a strategy to optimally determine the operation mode for each relay, and the energy and information scheduling at the relays that operate in the IR, IT, and BC modes. To address this issue, the involved optimization problem is formulated as a stochastic optimization problem, which cannot be tackled directly. To make it tractable, the stochastic optimization problem is transformed into a Markov decision process (MDP) with finite state and action spaces. By solving the MDP via a dynamic programming (DP) algorithm, the optimal strategy for the multirelay network is achieved. Furthermore, to reduce the computational complexity in the DP algorithm, an efficient algorithm with low complexity is developed by using a Lyapunov optimization framework. Numerical simulations show that our proposed hybrid relaying strategy can achieve superior throughput performance in wireless multirelay networks.

In this article, the joint design for user scheduling and analog beam selection in a downlink multiuser millimeter-wave (mmWave) system is studied. Our objective is to maximize the achievable sum rate under the user scheduling constraint, the analog beam selection constraint, and the resource capacity constraint. This problem is nonconvex and NP hard. We first propose a whale optimization algorithm (WOA)-based scheme to obtain a near-global-optimal solution with fast convergence and low complexity. Since the joint optimization user scheduling and beam selection problem is a constrained integer programming problem, the binary version of WOA is applied to deal with integer variables and the penalty method is used to handle the constraints. Besides, a nonlinear convergence factor is introduced to enhance the optimal solutions. For real-time use, we also propose a low-complexity machine-learning (ML)-based scheme. In the ML-based scheme, we decompose the original optimization problem into two subproblems: 1) user classification subproblem and 2) the analog beam selection subproblem. The user classification subproblem is solved based on the$k$-means algorithm, where the users are clustered according to channel correlation. To solve the analog beam selection subproblem, we reformulate this subproblem as a multiclass classification problem. Considering the imbalance nature of the data set of the subproblem, we train the multiclass classifiers via the biased-SVM algorithm. Finally, the simulation results of the WOA-based scheme and the proposed ML-based scheme against the state-of-the-art schemes have shown the advantages of our proposed schemes.

This article studies a mobile-edge computing (MEC) system, where an access point (AP) and a relay node serve a user terminal over multicarrier subchannels. In the MEC system, the relay can assist not only task offloading but also task computation. Aiming at minimizing total energy consumption at the user terminal and the relay, the resource allocations, such as subcarrier allocation, power allocation, task partition, and offloading time and computation time allocation, are to be optimized, subject to a given task computation delay constraint. To achieve this goal, a novel cooperative MEC protocol is designed, where multicarrier subchannels are utilized for parallel task offloading by integrating the rateless coding technique. Then, under the newly designed protocol, the resource allocation optimization problem is formulated as a mixed-integer programming (MIP) problem that is challenging to solve. To tackle this MIP problem, continuous relaxation and algebraic transformation techniques are applied to transform it into a convex problem in order to reveal the lower bound of energy consumption performance. After that, by equivalently rewriting the integer subcarrier allocation constraint in the original optimization problem as the intersection of a convex set and a d.c. (difference of two convex sets) set, the problem is solved by the successive convex approximation to achieve a practical and efficient resource allocation scheme. Simulation results show that the proposed jointly cooperative task offloading and computation scheme can significantly reduce the energy consumption as compared to the baseline schemes, where the relay only assists the task offloading or task computation.

This article studies a network where a transmitter communicates with a receiver by hybrid communications that consist of passive information transmission (IT) via backscatter communication (BC) and active IT via wireless-powered communication (WPC). Because the circuit energy consumption in the passive IT of BC is much lower than that in the active IT of WPC, BC usually achieves a higher data transmission rate than WPC. Thus, it was suggested in the literature that the network throughput performance could not be improved by hybrid communications. However, our work in this article demonstrates that the throughput can be enhanced by a newly designed hybrid communication strategy. To demonstrate this, we develop a novel protocol that enables the transmitter to adaptively switch its operation between BC, active IT, and energy harvesting in one time block while scheduling energy consumption flexibly among multiple time blocks. Under the developed protocol, we formulate an optimization problem to jointly optimize the operation mode and resource allocation at the transmitter. The formulated problem is difficult to solve because the energy scheduling at the transmitter is coupled across multiple time blocks, and noncausal channel state information (CSI) is required. To address this problem, we first solve a simplified optimization problem via dynamic programming (DP) and a layered optimization method by assuming that the noncausal CSI is known. Then, we employ an approximate DP approach to solve the original problem with causal CSI. Finally, we verify by simulations that the proposed scheme can achieve superior throughput performance.

This paper considers the distributed beamforming design for a simultaneous wireless information and power transfer (SWIPT) in two-way relay network, which consists of two sources, K relay nodes and one energy harvesting (EH) node. For such a network, assuming perfect channel state information (CSI) is available, and we study two different beamforming design schemes. As the first scheme, we design the beamformer through minimization of the average mean squared error (MSE) subject to the total transmit power constraint at the relays and the energy harvesting constraint at the EH receiver. Due to the intractable expression of the objective function, an upper bound of MSE is derived via the approximation of the signal-to-noise ratio (SNR). Based on the minimization of this upper bound, this problem can be turned into a convex feasibility semidefinite programming (SDP) and, therefore, can be efficiently solved using interior point method. To reduce the computational complexity, a suboptimal beamforming scheme is proposed in the second scheme, for which the optimization problem could be recast to the form of the Rayleigh–Ritz ratio and a closed-form solution is obtained. Numerical results are provided and analyzed to demonstrate the efficiency of our proposed beamforming schemes.

Energy harvesting (EH) is a promising technique for cognitive relaying transmission (CRT) where secondary users (SUs) and relaying nodes do not have a constant power supply each. Unlike conventional CRT where the end‐to‐end data rate is usually maximised without taking into account the energy consumption at the source and relay, in this study, the energy consumption is characterised by means of energy efficiency, defined as the achievable data rate per Joule. In particular, the energy states at each node (either at a SU or a relay) is modelled as a finite‐state Markov chain and the transmit power at a node is optimally allocated by jointly accounting for the interference threshold prescribed by primary users (PUs), the maximum allowable transmit power and the harvested energy at the node. To maximise the energy efficiency, a best relay selection criterion is proposed and the subsequent optimal transmit power allocation is initially formulated as a non‐linear fractional programming problem and, then, equivalently transformed into a parametric programming problem and, finally, solved analytically by using the classic Karush–Kuhn–Tucker conditions. With extensive Monte‐Carlo simulation results, the effectiveness of the proposed relay selection algorithm and corresponding optimal power allocation strategy are corroborated.

Employing energy harvesting (EH) at a relay node with simultaneous wireless information and energy transfer (SWIET) is promising to prolong the lifetime of energy-constrained relay systems. In this paper, we investigate the optimal resource allocation in an orthogonal-frequency-division multiplexing (OFDM) decode-and-forward (DF) relay system that implements SWIET at the source-to-relay link in order to maximize the end-to-end achievable rate. We first propose an optimal energy-transfer power allocation (EPA) policy which can utilize the frequency diversity provided by OFDM modulation. We then validate that the ordered-SNR subcarrier pairing is globally optimum. To optimize EH time and information-transmission power allocation (IPA), we formulate the involved problem as a non-convex programming problem, and then transform it into a quasi-convex optimization problem. By solving this quasi-convex optimization problem with the bisection search method, we propose an algorithm which can jointly optimize the EH time and IPA. By analytical analysis, we validate that our proposed resource allocation scheme has much lower computational complexity than the peer studies in the literature. Finally, our simulations demonstrate the optimality of our proposed resource allocation scheme.

Employing energy harvesting (EH) at the relay with simultaneous wireless information and power transfer is promising to prolong lifetime of energy-constrained relay networks. In this letter, considering multicarrier decode-and-forward (DF) relay network with time-switching (TS) based relaying, we investigate the optimization problem which jointly designs TS ratios of EH and information-decoding at the relay, TS ratio of signal forwarding from relay to destination as well as power allocation (PA) over all subcarriers at source and relay. Our objective is to maximize end-to-end achievable rate of DF relay networks subject to transmit power constraint at source and EH constraint at relay. We propose to decouple the optimization problem into a convex problem and a quasi-convex problem. For the quasi-convex problem, we propose to solve it by bisection search. Simulation results demonstrate that our proposed joint TS and PA optimization scheme outperforms the scheme with fixed TS ratio of EH.