Joint Resource Allocation for Maximizing Energy Efficiency in MmWave-Based Wireless-Powered Communication Networks

In this paper, we consider a millimeter-wave (mmWave)-based wireless-powered communication network (WPCN) which consists of a hybrid access point (HAP) and multiple energy-constrained Internet of Things (IoT) devices. In the network, the HAP first transfers radio frequency (RF) energy to multiple IoT devices in the downlink via time division multiple access (TDMA), and then the IoT devices concurrently transmit their individual data to the HAP by using the harvested energy in the uplink via the frequency division multiple access (FDMA). We aim to maximize the energy efficiency (EE) of the considered network by jointly optimizing the grouping strategy of IoT devices and antenna allocation of the HAP for wireless energy transfer (WPT), sub-timeslot allocation of the downlink and uplink transmissions, as well as bandwidth allocation for wireless information transmission (WIT). To address the non-convexity of the formulated optimization problem, a two-stage design method is proposed to obtain the near-optimal solution. In the first stage, by fixing the sub-timeslot of WPT while maximizing the conditional harvested energy of all IoT devices, the stable grouping strategy with the optimal antenna allocation is obtained based on the principle of two-side exchange stability (TES). In the second stage, we derive the optimal sub-timeslot and bandwidth allocations for maximizing the EE by leveraging the Dinkelbach algorithm with the Lagrange dual method. Numerical results reveal that the proposed algorithm can achieve a close-to-optimal performance for energy harvesting in the downlink. In addition, the EE can be significantly enhanced in comparison to the competitive schemes.