Energy Efficient User Clustering, Hybrid Precoding and Power Optimization in Terahertz MIMO-NOMA Systems

TLDR

Terahertz band communication is being explored to provide ultra‑high capacity, and combining MIMO with NOMA further increases user support and multiplexing gain. This work investigates, for the first time, how to maximize energy efficiency in THz‑NOMA‑MIMO systems. The authors decompose the EE maximization into user clustering, hybrid precoding, and power allocation, using an enhanced K‑means algorithm for fast clustering, a sub‑connection hybrid precoder to reduce power and complexity, and a distributed ADMM scheme for power allocation under fronthaul limits with imperfect SIC. Simulations demonstrate that the proposed clustering converges faster and yields higher EE, the sub‑connection precoder reduces power consumption, and the power optimization further improves EE in the THz cache‑enabled network.

Abstract

Terahertz (THz) band communication has been widely studied to meet the future demand for ultra-high capacity. In addition, multi-input multi-output (MIMO) technique and non-orthogonal multiple access (NOMA) technique with multi-antenna also enable the network to carry more users and provide multiplexing gain. In this paper, we study the maximization of energy efficiency (EE) problem in THz-NOMA-MIMO systems for the first time. And the original optimization problem is divided into user clustering, hybrid precoding and power optimization. Based on channel correlation characteristics, a fast convergence scheme for user clustering in THz-NOMA-MIMO system using enhanced K-means machine learning algorithm is proposed. Considering the power consumption and implementation complexity, the hybrid precoding scheme based on the sub-connection structure is adopted. Considering the fronthaul link capacity constraint, we design a distributed alternating direction method of multipliers (ADMM) algorithm for power allocation to maximize the EE of THz-NOMA cache-enabled system with imperfect successive interference cancellation (SIC). The simulation results show that the proposed user clustering scheme can achieve faster convergence and higher EE, the design of the hybrid precoding of the sub-connection structure can achieve lower power consumption and power optimization can achieve a higher EE for the THz cache-enabled network.