High-Efficient Near-Field Channel Characteristics Analysis for Large-Scale MIMO Communication Systems

TL;DR

This paper introduces a subarray decomposition scheme for large-scale MIMO near-field channel modeling, balancing accuracy and computational efficiency, and analyzes how various factors influence channel statistics.

Contribution

It proposes a novel subarray decomposition method for near-field large-scale MIMO channel modeling, reducing complexity while maintaining accuracy.

Findings

The scheme effectively balances modeling precision and computational complexity.

Channel statistics are significantly influenced by BS height, MR motion, and antenna configurations.

Numerical results validate the model's accuracy and efficiency tradeoff.

Abstract

Large-scale multiple-input multiple-output (MIMO) holds great promise for the fifth-generation (5G) and future communication systems. In near-field scenarios, the spherical wavefront model is commonly utilized to accurately depict the propagation characteristics of large-scale MIMO communication channels. However, employing this modeling method necessitates the computation of angle and distance parameters for each antenna element, resulting in challenges regarding computational complexity. To solve this problem, we introduce a subarray decomposition scheme with the purpose of dividing the whole large-scale antenna array into several smaller subarrays. This scheme is implemented in the near-field channel modeling for large-scale MIMO communications between the base stations (BS) and the mobile receiver (MR). Essential channel propagation statistics, such as spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency correlation functions (CFs), and channel capacities, are derived and discussed. A comprehensive analysis is conducted to investigate the influence of the height of the BS, motion characteristics of the MR, and antenna configurations on the channel statistics. The proposed channel model criterions, such as the modeling precision and computational complexity, are also theoretically compared. Numerical results demonstrate the effectiveness of the presented communication model in obtaining a good tradeoff between modeling precision and computational complexity.