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 letter studies the optimization of a UAV-aided vehicular communication system where a rotary-wing UAV serves a moving vehicle. Considering the propulsion energy of the UAV, our goal is to maximize the energy efficiency (EE) of the system by optimizing UAV trajectory. Due to the causality of the vehicle location information (VLI), the formulated optimization problem is challenging to solve. To address the VLI causality issue, a vehicle's trajectory prediction framework is proposed, in which a short-term accurate trajectory prediction model and a long-term coarse trajectory prediction model are integrated. Based on the proposed vehicle's trajectory prediction framework, a novel receding horizon optimization method is proposed to transform the EE maximization problem into a sequence of optimization problems, in which a time window of the short-term vehicle's trajectory prediction is successively moved along the vehicle's trip. By solving the sequence of optimization problems, an energy-efficient UAV's trajectory optimization algorithm is obtained. Simulation results verify that our proposed scheme can achieve about 20%-40% EE performance gains over existing baseline schemes.

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 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.

In this paper, we investigate a joint beamforming and relay selection design problem in amplify-and-forward two-way relay networks with energy transfer. In the relay network, multiple relays are responsible for not only information exchange of two sources, but also energy transfer to two sources. Two sources use the power-splitting scheme to harvest energy. The relay selection constraint, included in the aforementioned problem, causes it to be nonconvex. We prove that the relay selection constraint can be replaced by a convex constraint and a concave constraint. Furthermore, we propose to use a sequential parametric convex approximation-based algorithm to solve the problem. Simulation results demonstrate the effectiveness of our proposed scheme.

In this paper, a dual-function intelligent reflecting surfaces (IRS) assisted cognitive radio (CR) non-orthogonal 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 stations (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 non-convex 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 non-convex 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 Pmax≥ 15dBm, the proposed active IRS scheme is superior to the proposed passive IRS scheme.