An Optimal Low-Complexity Energy-Efficient ADC Bit Allocation for Massive MIMO

TL;DR

This paper proposes an optimal bit allocation algorithm for variable-resolution ADCs in massive MIMO systems, enhancing energy efficiency while reducing computational complexity compared to brute-force methods.

Contribution

It introduces a novel EE-maximizing bit allocation method for VR ADCs in Ma-MIMO, with a derived optimality condition and a low-complexity heuristic approach.

Findings

Optimal bit allocation matches brute-force results

Significant reduction in computational complexity

Heuristic algorithm achieves near-optimal EE performance

Abstract

Fixed low-resolution Analog to Digital Converters (ADC) help reduce the power consumption in millimeter-wave Massive Multiple-Input Multiple-Output (Ma-MIMO) receivers operating at large bandwidths. However, they do not guarantee optimal Energy Efficiency (EE). It has been shown that adopting variable-resolution (VR) ADCs in Ma-MIMO receivers can improve performance with Mean Squared Error (MSE) and throughput while providing better EE. In this paper, we present an optimal energy-efficient bit allocation (BA) algorithm for Ma-MIMO receivers equipped with VR ADCs under a power constraint. We derive an expression for EE as a function of the Cramer-Rao Lower Bound on the MSE of the received, combined, and quantized signal. An optimal BA condition is derived by maximizing EE under a power constraint. We show that the optimal BA thus obtained is exactly the same as that obtained using the brute-force BA with a significant reduction in computational complexity. We also study the EE performance and computational complexity of a heuristic algorithm that yields a near-optimal solution.