Achievable Degrees of Freedom Analysis and Optimization in Massive MIMO via Characteristic Mode Analysis
Shaohua Yue, Siyu Miao, Shuhao Zeng, Fenghan Lin, Boya Di
TL;DR
This work addresses the problem of quantifying and maximizing the multiplexing DoF in massive MIMO when antenna excitation and radiation EM properties are nonideal. It introduces a characteristic mode analysis based framework that couples CMA derived mode currents and electric fields with a dyadic Green function channel to obtain an achievable DoF DoF_H that is bounded by the number of ports and the number of characteristic modes. The authors derive an explicit CMA based expression for DoF_H and establish its relation to the conventional DoF DoF_G, showing CMA reduces to classical MIMO under certain conditions. They formulate a CMA based DoF maximization problem and propose a CMA guided genetic algorithm to optimize reconfigurable holographic surface RHS antennas, validating the approach with full wave simulations that demonstrate DoF improvements and provide design insights for RHS configuration in near field. The results highlight the practical impact of jointly optimizing antenna EM properties and propagation environment to enable higher spatial multiplexing gains in 6G scale MIMO systems.
Abstract
Massive multiple-input multiple-output (MIMO) is esteemed as a critical technology in 6G communications, providing large degrees of freedom (DoF) to improve multiplexing gain. This paper introduces characteristic mode analysis (CMA) to derive the achievable DoF. Unlike existing works primarily focusing on the DoF of the wireless channel,the excitation and radiation properties of antennas are also involved in our DoF analysis, which influences the number of independent data streams for communication of a MIMO system. Specifically, we model the excitation and radiation properties of transceiver antennas using CMA to analyze the excitation and radiation properties of antennas. The CMA-based DoF analysis framework is established and the achievable DoF is derived. A characteristic mode optimization problem of antennas is then formulated to maximize the achievable DoF. A case study where the reconfigurable holographic surface (RHS) antennas are deployed at the transceiver is investigated, and a CMA-based genetic algorithm is later proposed to solve the above problem. By changing the characteristic modes electric field and surface current distribution of RHS, the achievable DoF is enhanced. Full-wave simulation verifies the theoretical analysis on the the achievable DoF and shows that, via the reconfiguration of RHS based on the proposed algorithm, the achievable DoF is improved.