Simultaneous wireless information and power transfer (SWIPT) has sparked a wave of interest in research, while unmanned aerial vehicles (UAVs) can offer a high level of service for Internet of Things (IoT) due to its deployment flexibly. In this paper, we employ multiple UAVs as transmitters to realize information-transmitting and energy-transferring for ground IoT devices simultaneously to expand the capacity and coverage of the network, where each UAV is associated with multiple ground devices. This paper investigates joint optimization of three-dimensional (3D) locations, user association and power allocation of the UAVs with the aim of maximizing the minimum data rate among multiple dispersed users on the ground while guaranteeing the energy requirement of each user. Meanwhile, the proposed optimization problem contains the transmit power budget of each UAV and constraints on user association. The feasibility analysis ensures that the problem can be solvable. To address the combinatorial optimization problem, nonconvex problems are decomposed into two subproblems. Then they are transformed into a series of convex problems alternately via successive convex optimization technique. Subsequently, we develop a multivariable iterative algorithm to settle the overall problem. Next, the convergence performance of the proposed algorithm is confirmed. In conclusion, simulation results operated under various parameter configurations substantiate the proposed algorithm can achieve a higher data rate compared with other benchmark schemes. INDEX TERMS Simultaneous wireless information and power transfer, unmanned aerial vehicles, 3D locations, user association, power allocation.
With the rapid development of the Internet of Things (IoT) network, research on low-power and energy-saving devices has attracted extensive attention from both academia and the industry. Although the backscatter devices (BDs) that utilize the environmental power to activate circuits and transmit signals are a promising technology to be deployed as IoT nodes, it is challenging to design a flexible data backhaul scheme for massive BDs. Therefore, in this paper, we consider an unmanned-aerial-vehicle (UAV)-assisted backscatter communication network, where BDs are served by multiple full-duplex (FD) UAVs with the non-orthogonal multiple access (NOMA) schemes and modulate their signals on the downlink signals, which are generated by the UAVs to serve the coexisting regular user equipments (UEs). To maximize the sum rate of the considered system, we construct an optimization problem to optimize the reflection coefficient of BDs, the downlink and the backhaul transmission power, and the trajectory of UAVs jointly. Since the formulated problem is a non-convex optimization problem and is difficult to solve directly, we decouple the original problem into three sub-problems and solve them with the successive convex approximation (SCA) method, thereby addressing the original problem by a block coordinate descent (BCD)-based iterative algorithm. The simulation results show that, compared with the benchmark schemes, the proposed algorithm can obtain the highest system sum rate and utilize limited time-frequency resources more efficiently.
This paper investigates the joint relay and channel selection problem using a deep reinforcement learning (DRL) algorithm for cooperative communications in a dynamic jamming environment. The latest types of jammers include the mobile and smart jammer that contains multiple jamming patterns. This new type of jammer poses serious challenges to reliable communications such as huge environment states, tightly coupled joint action selections and real-time decision requirements. To cope with these challenges, a DRLbased relay-assisted cooperative communication scheme is proposed. In this scheme, the joint selection problem is constructed as a Markov decision process (MDP) and a double deep Q network (DDQN) based anti-jamming scheme is proposed to address the unknown and dynamic jamming behaviors. Concretely, a joint decision-making network composed of three sub-networks is designed and the independent learning method of each sub-network is proposed. The simulation results show that the user agent is able to anticipate the jammer behaviors and elude the jamming in advance. Furthermore, compared with the sensing-based algorithm, the Q learning-based algorithm and the existing DRL-based anti-jamming approaches, the proposed algorithm maintains a higher average normalized throughput.
INTRODUCTIONRelay-assisted cooperative communication technology has been considered as a direct technology that increases the system transmission rate and expands the communication coverage [1]. However, cooperative communications are vulnerable to malicious jamming signals, which calls for efficient anti-jamming technologies [2][3][4]. Several techniques such as power control, frequency hopping, backscatter and beamforming have been proposed to combat the malicious jamming [5][6][7][8].Recently, the authors [9, 10] summarize the jammers' behaviors in each domain and name them the jamming patterns. The policy of frequency decisions of the jammer can be regarded as the jamming pattern in frequency domain. As shown in Figure 1(a), in the comb pattern, the jammer transmits jamming signals at fixed frequency points [11]. Figure 1(b) illustrates the sweeping pattern where the jammer hops jamming signals at aThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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