Uplink Data Detection Analysis of 1-Bit Quantized Massive MIMO

TL;DR

This paper develops an analytical framework for uplink data detection in massive MIMO systems with 1-bit ADCs, providing insights into symbol estimation, error rates, and SNR trade-offs to improve detection performance.

Contribution

It introduces closed-form expressions for symbol estimation statistics and analyzes their asymptotic behavior, enhancing maximum likelihood detection in 1-bit massive MIMO systems.

Findings

Closed-form expressions for expected value and variance of estimated symbols.

Identification of a fundamental SNR trade-off affecting detection accuracy.

Performance evaluation of symbol error rate with respect to antennas, SNR, and pilot length.

Abstract

This paper presents an analytical framework for the data detection in massive multiple-input multiple-output uplink systems with 1-bit analog-to-digital converters (ADCs). Considering the single-user case, we provide closed-form expressions of the expected value and the variance of the estimated symbols when maximum ratio combining is adopted at the base station (BS) along with their asymptotic behavior at high signal-to-noise ratio (SNR). These results are exploited to enhance the performance of maximum likelihood detection by taking into account the dispersion of the estimated symbols about their expected values. The symbol error rate with 1-bit ADCs is evaluated with respect to the number of BS antennas, the SNR, and the pilot length used for the channel estimation. The proposed analysis highlights a fundamental SNR trade-off, according to which operating at the right SNR considerably improves the data detection accuracy.