Mobile-data traffic exponentially increases day by day due to the rapid development of smart devices and mobile internet services. Thus, the cellular network suffers from various problems, like traffic congestion and load imbalance, which might decrease end-user quality of service. This work compensates for the problem of offloading in the cellular network by forming device-to-device (D2D) links. A game scenario is formulated where D2D-link pairs compete for network resources. In a D2D-link pair, the data of a user equipment (UE) is offloaded to another UE with an offload coefficient, i.e., the proportion of requested data that can be delivered via D2D links. Each link acts as a player in a cooperative game, with the optimal solution for the game found using the Nash bargaining solution (NBS). The proposed solution aims to present a strategy to control different parameters of the UE, including harvested energy which is stored in a rechargeable battery with a finite capacity and the offload coefficients of the D2D-link pairs, to optimize the performance of the network in terms of throughput and energy efficiency (EE) while considering fairness among links in the network. Simulation results show that the proposed game scheme can effectively offload mobile data, achieve better EE and improve the throughput while maintaining high fairness, compared to an offloading scheme based on a maximized fairness index (MFI) and to a no-offload scheme.

Recently, the demand for spectral and energy efficiency has significantly been increased along with new breakthroughs in programmable meta-material techniques. The integration of an intelligent reflecting surface (IRS) into simultaneous wireless information and power transfer (SWIPT) systems has attracted much attention from operators in advanced wireless communication networks (WCNs) such as fifth-generation (5G) and sixth-generation (6G) networks. In addition, an IRS-assisted SWIPT system faces many security risks that can easily be compromised by eavesdroppers. In this paper, we investigate the physical-layer secure and transmission optimization problem in an IRS-assisted SWIPT system where a power-splitting (PS) scheme is installed in the user equipment (UE). In particular, our purpose is to maximize the system secrecy rate by jointly finding optimal solutions for transmitter power, PS factor of UE, and phase shifts matrix of IRS under the required minimum harvested energy and maximum transmitter power. We propose the alternating optimization (AO)-based scheme to obtain optimal solutions. The proposed AO-based scheme can effectively solve both convex and non-convex problems; however, applying them in practice still poses some difficulties due to the complexity and long computation time. This is because many mathematical transformations are used and the optimal solution needs a number of iterations to achieve convergence. Therefore, we also propose 5 types of data and DNN structures to potentially achieve efficiency in computations by using a deep learning (DL)-based approach. The simulation results indicate that the proposed IRS scheme provides an improvement in terms of the average secrecy rate (ASR) by up to 38.91% when the number of reflecting elements is high (30 elements) compared to a scheme without an IRS. We also observe that the DL-based approach not only provides similar performance to the AO-based scheme but it also significantly reduces computation time.

The cognitive radio network (CRN) is vulnerable to various newly-arising attacks targeting the weaknesses of cognitive radio (CR) communication and networking. In this paper, we focus on improving the secrecy performance of CR communications in a decentralized, multiple-channel manner while various eavesdroppers (EVs) try to listen to their private information. By choosing the best channel, the secondary user (SU) aims at mitigating the effects of eavesdropping and other SUs that compete for the same channel. Accordingly, the problem of finding the best channel that maximizes the secrecy rate for the SU is formulated as the framework of multiple repeated games where both the SU and the EVs try to maximize their own performance. In this case, the secrecy rate of an SU is defined based on the expected rewards of the SUs and the EVs. In the paper, we propose a repeated games-based scheme that can provide the best channel for the SU to avoid eavesdropping attacks and also minimize interference from other SUs that compete for the same channel. The simulation results demonstrate that the proposed scheme can combat a physical layer attack from EVs quite well and can provide much better performance, in comparison with other conventional channel selection schemes.

Recently, simultaneous wireless information and power transfer (SWIPT) systems, which can supply efficiently throughput and energy, have emerged as a potential research area in fifth-generation (5G) system. In this paper, we study SWIPT with multi-user, single-input single-output (SISO) system. First, we solve the transmit power optimization problem, which provides the optimal strategy for getting minimum power while satisfying sufficient signal-to-noise ratio (SINR) and harvested energy requirements to ensure receiver circuits work in SWIPT systems where receivers are equipped with a power-splitting structure. Although optimization algorithms are able to achieve relatively high performance, they often entail a significant number of iterations, which raises many issues in computation costs and time for real-time applications. Therefore, we aim at providing a deep learning-based approach, which is a promising solution to address this challenging issue. Deep learning architectures used in this paper include a type of Deep Neural Network (DNN): the Feed-Forward Neural Network (FFNN) and three types of Recurrent Neural Network (RNN): the Layer Recurrent Network (LRN), the Nonlinear AutoRegressive network with eXogenous inputs (NARX), and Long Short-Term Memory (LSTM). Through simulations, we show that the deep learning approaches can approximate a complex optimization algorithm that optimizes transmit power in SWIPT systems with much less computation time.

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