Energy Efficiency Maximization for SWIPT Enabled Two-Way DF Relaying

Abstract

This paper focuses on the design of an optimal resource allocation scheme to maximize the energy efficiency (EE) in a simultaneous wireless information and power transfer (SWIPT) enabled two-way decode-and-forward (DF) relay network under a non-linear energy harvesting model. In particular, we formulate an optimization problem by jointly optimizing the transmit powers of two source nodes, the power-splitting (PS) ratios of the relay, and the time for the source-relay transmission, under multiple constraints including the transmit power constraints at sources and the minimum rate requirement. Although the formulated problem is non-convex, an iterative algorithm is developed to obtain the optimal resource allocation. Simulation results verify the proposed algorithm and show that the designed resource allocation scheme is superior to other benchmark schemes in terms of EE.

Secure Information Transmissions in Wireless-Powered Cognitive Radio Networks for Internet of Medical Things

“…A SWIPT scheme for amplify-and-forward (AF) bidirectional relaying network based on OFDM was proposed in [21], where a wireless-powered relay performed information processing and EH by utilizing two disjointed subcarrier groups, respectively. Based on the decode-and-forward (DF) mode, Shi et al [22] designed an optimal resource allocation strategy to maximize the energy efficiency with the nonlinear SWIPT model under a two-way relay network. For cognitive radio networks with energy harvesting in IoTsystems, Zhang et al [23] analyzed the outage probability of a random underlay cognitive network with EH-based assistant relay.…”

Joint energy allocation and multiuser scheduling in SWIPT systems with energy harvesting

“…In the rest parts, we abbreviate J λ, β as J and R λ, β ( ⋅ ) as R( ⋅ ) unless otherwise specified. In (16), the transmission power and scheduling policy depend on the current system state and will influence the next system state. Therefore, this problem is a standard markov decision process (MDP) problem.…”

“…Proof: The proof follows by applying Bellman's equation in [30]. □ Different from the backward method in Lemma 1, we use the PI algorithm to find the optimal solution to the problem (16). Firstly, for a given policy π 1 , we evaluate the state-value function.…”

“…By applying Algorithm 1 (Fig. 2) to problem (16), the optimal policy π 1 * is obtained. Then G sum λ, β , Q sum λ, β and D sum λ, β can be calculated by carrying out policy evaluation of the optimal policy.…”

“…Wang et al [14,15] investigated the application of SWIPT in the full-duplex relay system, in which relays were wireless-powered in the PS mode. Besides ideal linear EH models, some practical non-linear EH models were adopted and analysed in [16][17][18].…”