Wireless-powered communication and reconfigurable intelligent surface (RIS) can complement each other for increasing energy utilization and spectrum efficiency by reconfiguring the surrounding radio environment, however, which has not been sufficiently studied by the existing works. In this paper, we propose a joint radio resource and passive beamforming optimization scheme for a downlink RIS-assisted wireless-powered communication network with a harvest-then-transmit protocol to improve system energy efficiency (EE). In the considered model, the single-antenna wireless devices (WDs) harvest wireless energy from a multi-antenna dedicated power station (PS) through the RIS in the downlink and transmit their independent information to a single-antenna receiver in the uplink by a time-division-multiple-access mode. Our goal is to maximize the total EE of all WDs. To make full use of the beamforming gain provided by both the PS and the RIS, we jointly optimize the active beamforming of the PS and the passive beamforming of the RIS. To deal with the challenging non-convex optimization problem with multiple coupled variables, we first consider fixing the passive beamforming, and converting the remaining radio resource allocation problem into an equivalent convex problem which is solved by using Lagrange dual theory. Then, we fix the optimized resource allocation parameters and optimize the passive beamforming of the RIS by using a semidefinite programming method. Simulation results demonstrate that the proposed algorithm achieves higher EE compared to the conventional schemes.

This paper investigates the price-based power control problem in the spectrum sharing cognitive radio networks. The primary user (PU) can admit secondary users (SUs) to access by pricing their interference power under the interference power constraint. We model the interaction between the PU and the SUs as a Stackelberg game. The revenue function of the PU is expressed as a nonconcave function of SU's transmit power by backward induction. Variable substitution is used to transform the nonconcave maximization problem into a concave maximization problem. Based on the equivalent concave maximization problem, a novel algorithm is proposed to find the optimal price for the PU to maximize its revenue. The optimal price of each SU is given as a closed-form expression with one parameter, which can be determined with the complexity of $O(K)$ , where $K$ is the number of the SUs. Furthermore, asymptotic analysis is exploited to derive the number of the admitted SUs at low and high interference-to-noise ratio. Simulation results show the effectiveness of the proposed algorithm in comparison with the nonuniform pricing algorithm.

A novel price-based power allocation algorithm is proposed based on the Stackelberg game for cellular downlink non-orthogonal multiple access networks. First, the closed-form solution is given for two users' case, and shown that it is possible to allocate the transmit power to both users or only the user with the better channel condition. Then, an iterative algorithm is proposed for more than two users' cases based on the algorithm proposed for the two users. Finally, simulation results show that the proposed price-based power allocation algorithm outperforms existing algorithm in terms of the revenue of the base station and the sum rate of the users.

This letter investigates price-based power control problem in the spectrum sharing cognitive radio networks (CRNs). The base station (BS) of primary users (PUs) can admit secondary users (SUs) to access if their interference power is under the interference power constraint (IPC). In order to access the spectrum, the SUs need to pay for their interference power. The BS first decides the price for each SUs to maximize its revenue. Then, each SU controls its transmit power to maximize its revenue based on non-cooperative game. We model the interaction between the BS and the SUs as a Stackelberg game. Using backward induction, the revenue function of the BS is expressed as a function of the transmit power of the SUs. After we depict the property of optimal transmit power of the SUs, we propose a novel price-based power control algorithm to maximize the revenue of the BS. Simulation results show the proposed algorithm improves the revenue of both the BS and SUs compared with the proportionate pricing algorithm.

We investigate a mobile edge computing (MEC) system that supports collaborative task offloading, allowing busy users to offload their tasks to the server and idle users, or perform them locally. However, executing computing tasks consumes device energy, making idle users unwilling to perform other users' tasks due to limited battery capacity. Additionally, the components of the total energy expenditure of the MEC system supporting collaborative task offloading are complex, necessitating appropriate resource allocation and offloading strategies to minimize the system's total energy consumption. To address these challenges, we propose a computing resource sharing auction (CRSA) algorithm to motivate idle users to participate in task offloading. Then, we establish a non-convex mixed-integer nonlinear programming (MINLP) problem to minimize the total energy consumed by the system. By utilizing the McCormick and continuous relaxation (CR) approaches, we develop a low-complexity resource allocation algorithm. Finally, the numerical results demonstrate the effectiveness of the proposed mechanism and resource allocation algorithm.