Integrated radar sensing and wireless power transfer (ISWPT) is an emerging paradigm that seeks to combine the functionalities of radar sensing and wireless power transfer into a unified system, utilizing a shared hardware platform to maximize resource efficiency. In this paper, we investigate the performance of an ISWPT system that is enhanced by extremely large-scale reconfigurable intelligent surfaces (XL-RIS), which serve to dynamically control the propagation environment and improve both radar sensing and wireless power transfer. Specifically, we consider a system where energy receivers are placed within the near-field region of the XL-RIS and investigate the joint optimization of beamforming at the base station and the XL-RIS reflection phases. The goal is to maximize the efficiency of wireless power transfer while simultaneously enhancing radar sensing performance. The formulated problem, though non-convex in nature, is efficiently addressed through the introduction of an alternating optimization algorithm. Numerical simulations demonstrate the effectiveness of the proposed algorithm, showing substantial improvements in both radar sensing accuracy and wireless power transfer performance compared to existing baseline schemes.

Caching popular contents in the storage of base stations (BSs) has emerged as a promising solution for reducing the transmission latency and providing extra‐high throughput. This study tackles the optimal trade‐off problem between the sum of effective rates and backhaul saving through joint service improvement and content placement in cache‐enabled heterogeneous cellular networks while guaranteeing the quality‐of‐service requirements of all user terminals (UTs) and backhaul traffic constraints of all BSs. However, there exists an intractable issue of mixing the integer nature into the feasible region in the nonlinear optimisation problem. To this end, the authors decompose the optimisation problem into three subproblems by alternately fixing two of three classes of variables (i.e. UT association, power control, and content placement). Aiming at these subproblems, they, respectively, convert them into the tractable forms and propose the corresponding algorithms. By combining them, they propose a three‐tier iterative algorithm for jointly optimising UT association and cache placement. Finally, numerical results have verified the effectiveness of proposed schemes.

This paper considers a proactive eavesdropping problem, in which a full-duplex legitimate monitor aims to eavesdrop on a suspicious communication link between the secondary pairs in a cognitive radio (CR) network via jamming. For such a scenario, the jamming signals would not only disrupt the suspicious receivers, but also influences the interference received at the primary receiver, which both destroy the transmitting rate in the suspicious link. Hence, the design of the beamforming should have good tradeoff between those two affects. We aim to maximize the eavesdropping rate by designing the jamming beamforming under the transmitting power (TP) constraint at the legitimate monitor and the interference temperature (IT) constraint at the primary receiver, which is a non- convex problem. Specifically, several cases are discussed to decompose the original problem and a closed-form solution is finally presented by solving two sub-problems. In particular, some analyses on the main parameters (the maximum power at the legitimate monitor and the interference temperature at the primary receiver) are undertaken to obtain their boundaries corresponding to different modes of the optimal vector, which influence the performance of the system. Numerical results are finally presented to demonstrate the performance of our proposed schemes outperforms the reference solutions.

In this study, the authors address the downlink resource (subchannels and power) allocation problem for device‐to‐device communication with wireless power transfer technique in a cellular network to improve the energy efficiency (EE). The considered problem is formulated as maximising the weighted EE and is solved by leveraging a game‐theoretic learning approach. Specifically, they first prove that an exact potential game applies to the resource allocation problem and there exists the best Nash equilibrium (NE) which is the optimal solution of the optimisation problem. Then, aiming to this optimisation problem with imperfect information, a robust and distributed learning algorithm is proposed and is proved that it can converge to the best NE. Finally, numerical results verify the effectiveness of the proposed scheme.

This paper investigates the joint power control and subchannel allocation problem for device-to-device (D2D) and small cell uplink communications in a cellular network to improve the cellular throughput. For this considered throughput maximization problem, we propose to solve it leveraging a game-theoretic learning approach. However, there is an intractable issue for obtaining the optimal power allocation profile in the continuous space. To this end, we first deduce the optimal power expressions under any given subchannel allocations. Based on the optimal power profile, we then formulate the subchannel allocation problem into a game framework. Next, aiming to this optimization problem, a cloud-assisted learning algorithm with conditioned strategies is proposed to converge to an equilibrium point which maximizes the optimization objective. Finally, numerical results verify the effectiveness of the proposed scheme.

This paper investigates the problem of joint base station selection and resource allocation in an orthogonal frequency division multiple access (OFDMA) heterogeneous cellular network. The original throughput maximization problem is NP-hard and we propose solving it by using game theoretic stochastic learning approaches. To this end, we first transform the original problem into a tractable form, which has a weighted utility function. Then we prove that an exact potential game applies and it exists the best Nash equilibria which is a near optimal solution of the original problem when an efficient solution method of the weights is employed. To obtain the optimal solution, we redesign the utility function by leveraging a state space to formulate the original problem into an ordinal state based potential game, which is proved that it exists a recurrent state equilibrium point that maximizes system throughput. Furthermore, we propose two different variants of Max-logit learning algorithm based on these two games respectively: one is a simultaneous learning algorithm with less information exchange, which achieves the best Nash equilibria point of the exact potential game and the other is an efficient learning algorithm for the ordinal state based potential game, which can converge to the global optimization solution. Finally, numerical results are given to validate that theoretical findings.