This paper applies the wireless powered communication network (WPCN) to an indoor communication system with two energy harvesting (EH) enabled users. Unlike the existing WPCN works designed for outdoor communications, where each user harvests energy only from the signals transmitted by a dedicatedly deployed hybrid access point (H-AP), due to the short device-to-device distance in the indoor scenario, each user additionally harvests a sufficient amount of energy from the information signals transmitted by the H-AP and the other user. First, the joint downlink and uplink throughput are maximized for the wireless powered indoor communication system. This problem is non-convex. Thus, the authors manage to transform this problem into a convex one and solve it using convex optimization techniques. The solutions reveal that the total throughput increases largely over a reduced device-to-device distance due to the resultant harvested energy from both energy and information signals at each user. However, an unfair downlink versus uplink rate allocation phenomenon is observed. Thus, considering the importance of uplink communication quality for various indoor applications, a new problem is further proposed to maximize the uplink sum-throughput over both users with an additional constraint to ensure a sufficient downlink rate. This problem is also shown to be non-convex and is solved optimally by using a method similar to that in the first problem. Numerical results demonstrate the effectiveness of the proposed approach for improving the downlink versus uplink rate allocation fairness in the indoor wireless-powered communication system.

In this article, we investigate two-tier non-orthogonal multiple access (NOMA)-based wireless powered communication networks. The considered network consists of a hybrid access point and two groups of users, namely near and far users. A near user and a far user are selected to form a NOMA pair, which harvest energy during the downlink phase and transmit information during the uplink phase. Specifically, we consider two cases of energy-transfer time and analyze the corresponding performance. We first analyze the performance of the case with fixed energy-transfer time and derive the exact expression for the ergodic rate. Due to the doubly near-far effect, the far user experiences poor performance. To address this issue, an adaptive energy-transfer time scheme is designed by maximizing the far user's data rate, and the optimal solution is derived. To reduce the system complexity, a suboptimal solution is proposed, and the ergodic rate's exact expression is derived. Simulation results reveal that the suboptimal solution achieves near-optimal performance and has a lower computational complexity. Moreover, a one-tier NOMA network is set to be a benchmark, and numerical results show that the two-tier system outperforms the one-tier system significantly under the appropriate radius setting.

To develop sustainable fifth generation (5G) wireless networks and utilize the unused spectrum, this paper focuses on cognitive radio (CR) based wireless information and energy transmissions from an unmanned aerial vehicle (UAV) to multiple low-power ground terminals (GTs). By practically considering the location-dependent air-to-ground (A2G) channel states and the non-linear energy harvesting (EH), we propose a dynamic fly-hover-transmit scheme, where the UAV successively flies between GTs, and hovers close to each GT for efficient wireless energy transfer (WET) or wireless information transfer (WIT) when the primary user (PU) is idle. By causally and optimally determining the UAV's mobility and transmit power for each selected transmission mode (WIT, WET, or being silent), we formulate the UAV's sum-throughput maximization over all GTs as a constrained Markov decision process (MDP) problem with battery energy constraints at all GTs and the UAV. Due to the infinitely large MDP system state space, this problem is difficult to solve. We then decompose this problem into two subproblems, by first deciding the UAV's transmission mode and power above a given GT, and then optimizing the UAV movement policy over multiple GTs. In the first subproblem, we propose an approximate to the complicated MDP value function of low complexity in closed-form, and then analytically derive the threshold-based suboptimal transmission policies. In the second subproblem, we optimally solve a simple-but-fundamental two-GT case, and then extend the general location-dependent GT weight design to an efficient suboptimal UAV movement policy. Simulation results show the significantly improved system performance under the proposed suboptimal policies over various benchmarks in dynamic networks.

This paper considered a buffer-aided relaying system with multiple source-destination pairs and a relay node (RN) with energy harvesting (EH) capability. The RN harvests energy from the ambient environment and uses the harvested energy to forward the sources' information packet to the corresponding destinations. It is assumed that information on the EH and channel gain processes is unavailable. Thus, the model free deep reinforcement learning (DRL) method, specifically the deep Q-learning, is applied to learn an optimal link selection policy directly from historical experience to maximize the system utility. In addition, by taking advantage of the structural features of the considered system, a pretraining scheme is proposed to accelerate the training convergence of the deep Q-network. Experiment results show that the proposed pretraining method can significantly reduce the training time required. Moreover, the performance of the transmission policy obtained by using deep Q-learning is compared with that of several conventional transmission schemes. It is shown that the transmission policy obtained by using our proposed model can achieve better performance.

In this paper, we investigate the uplink transmission strategy of a cloud radio access network (C-RAN) based millimeter-wave (mmWave) communication system and propose to use a space-domain index modulation (SDIM) scheme to improve the system reliability. The key idea of the proposed SDIM scheme is to enable the user (UE) to use different beam directions to carry part of information. We first introduce the SDIM scheme into C-RAN based mmWave communication system and model the signal design as an optimization problem. Then, two detection methods, namely the centralized detection and distributed detection, are proposed to demodulate the received signal. Moreover, we propose a power allocation algorithm to minimize the system symbol error rate (SER). Following this, by ignoring the side lobe of the UE, the condition which ensures the superiority performance of mmWave SDIM against the conventional communication scheme is derived. Finally, we model the position of the remote radio heads (RHHs) of the network as a homogeneous Poisson point process (PPP) and derive the probability that the proposed SDIM scheme outperforms the conventional communication scheme to evaluate its performance. Analytical and numerical results show that the proposed SDIM scheme can effectively enhance the system reliability.
© 1967-2012 IEEE.

In this correspondence, we investigate the transmit signal design for millimeter-wave (mmWave) communication systems with distributed antennas. To be specific, by taking advantage of the directional communication property of mmWave communication, we first propose a generalized space domain index modulation (GSDIM) scheme to improve the system reliability. In the proposed GSDIM scheme, the user equipment (UE) uses different beam direction combinations to carry additional information bits, i.e., using multi-domain modulations to optimize transmission. Then, we derive the symbol error rate (SER) of the proposed GSDIM, and design digital precoding matrices to minimize the SER. It is shown that the SER minimization problem is a non-convex problem, and we propose an iterative method to solve it. Numerical results show that the proposed GSDIM scheme can achieve a lower system SER as compared with the existing counterpart.
© 1967-2012 IEEE.