Energy-Efficient Resource Allocation in Massive MIMO-NOMA Networks with Wireless Power Transfer: A Distributed ADMM Approach

TL;DR

This paper presents a distributed ADMM-based resource allocation scheme for energy-efficient multicell massive MIMO-NOMA networks with wireless power transfer, considering perfect and imperfect CSI, and optimizing multiple parameters under QoS constraints.

Contribution

It introduces a novel joint resource allocation method using ADMM for energy efficiency in massive MIMO-NOMA networks with wireless power transfer, handling non-convex optimization.

Findings

The proposed algorithm converges quickly within fewer iterations.

It achieves higher energy efficiency compared to alternative methods.

The scheme effectively balances energy harvesting and data transmission.

Abstract

In multicell massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks, base stations (BSs) with multiple antennas deliver their radio frequency energy in the downlink, and Internet-of-Things (IoT) devices use their harvested energy to support uplink data transmission. This paper investigates the energy efficiency (EE) problem for multicell massive MIMO NOMA networks with wireless power transfer (WPT). To maximize the EE of the network, we propose a novel joint power, time, antenna selection, and subcarrier resource allocation scheme, which can properly allocate the time for energy harvesting and data transmission. Both perfect and imperfect channel state information (CSI) are considered, and their corresponding EE performance is analyzed. Under quality-of-service (QoS) requirements, an EE maximization problem is formulated, which is non-trivial due to non-convexity. We first adopt nonlinear fraction programming methods to convert the problem to be convex, and then, develop a distributed alternating direction method of multipliers (ADMM)- based approach to solve the problem. Simulation results demonstrate that compared to alternative methods, the proposed algorithm can converge quickly within fewer iterations, and can achieve better EE performance.