Efficient Adaptive Subarrays in Millimeter-Wave MIMO Systems With Hybrid RF/Baseband Precoding/Combining Design

Abstract

This paper concentrates on the computationally efficient sub-connected mapping strategies from radio frequency (RF) chains to antennas in millimeter wave (mmWave) massive multiple input multiple output (MIMO) systems with hybrid RF/baseband precoding/combining design. Such an adaptive subarray architecture offers a tradeoff between hardware complexity and spectral efficiency in massive MIMO networks at mmWave frequencies. Three proposed low-complexity dynamically connected structures comprise the Manhattan metric (MM) with/without overlapping, contiguous subarray correlation (CSC), and equally neighboring correlation design techniques. On the basis of the achievable sum-rate (ASR) maximization, the proposed adaptive RF chain-antenna planning methodologies are used to reconfigure effectively RF-chain associated subarrays. Remarkably, both the nonoverlapped MM and the CSC dynamically connected configurations possess excellent advantages of ASR performance and computational complexity simultaneously at a fixed hardware requirement in terms of phase shifters. Finally, simulation results demonstrate that the proposed adaptive partially connected networks are capable of accomplishing an ASR capacity approaching the fully digital and fully connected design with an alleviated demand on both computation and hardware complexities. Furthermore, with the same amount of phase shifters, all the proposed dynamically constructed subarrays outperform the partially connected but fixed architectures.