Spectral Efficiency of Mixed-ADC Massive MIMO

TL;DR

This paper analyzes the spectral efficiency of a mixed-ADC massive MIMO system, proposing methods to mitigate quantization errors and optimize ADC usage, with analytical and numerical results comparing different architectures and training schemes.

Contribution

It introduces an extended round-robin training scheme for mixed-ADC systems and analyzes the impact of channel estimation errors on spectral efficiency.

Findings

Mixed-ADC architectures can mitigate SE loss due to coarse quantization.

High-resolution ADCs improve channel estimation and spectral efficiency.

Performance varies with ADC assignment and training schemes.

Abstract

We study the spectral efficiency (SE) of a mixed-ADC massive MIMO system in which K single-antenna users communicate with a base station (BS) equipped with M antennas connected to N high-resolution ADCs and M-N one-bit ADCs. This architecture has been proposed as an approach for realizing massive MIMO systems with reasonable power consumption. First, we investigate the effectiveness of mixed-ADC architectures in overcoming the channel estimation error caused by coarse quantization. For the channel estimation phase, we study to what extent one can combat the SE loss by exploiting just N << M pairs of high-resolution ADCs. We extend the round-robin training scheme for mixed-ADC systems to include both high-resolution and one-bit quantized observations. Then, we analyze the impact of the resulting channel estimation error in the data detection phase. We consider random high-resolution ADC assignment and also analyze a simple antenna selection scheme to increase the SE. Analytical expressions are derived for the SE for maximum ratio combining (MRC) and numerical results are presented for zero-forcing (ZF) detection. Performance comparisons are made against systems with uniform ADC resolution and against mixed-ADC systems without round-robin training to illustrate under what conditions each approach provides the greatest benefit.