Algorithm and VLSI Design for 1-bit Data Detection in Massive MIMO-OFDM

TL;DR

This paper introduces a quantization-aware data detection and nonlinear channel estimation algorithm for 1-bit massive MIMO-OFDM systems, significantly improving performance and hardware efficiency over linear methods.

Contribution

It presents the first VLSI implementation of nonlinear data detection for 1-bit massive MIMO-OFDM, outperforming linear detectors in error rate and hardware efficiency.

Findings

Significant BER improvement over linear detectors

First FPGA implementation for 1-bit massive MIMO-OFDM

Comparable hardware efficiency with high-resolution systems

Abstract

The use of low-resolution data converters in the radio-frequency (RF) chains of all-digital massive multiple-input multiple-output (MIMO) basestations promises significant reductions in power consumption, hardware costs, and interconnect bandwidth. We propose a quantization-aware data-detection algorithm which mitigates the performance loss of 1-bit quantized massive MIMO orthogonal frequency-division multiplexing (OFDM) systems. Since the system performance heavily depends on the quality of channel estimates, we also develop a nonlinear 1-bit channel estimation algorithm that builds upon the proposed data detection algorithm. We show that the proposed algorithms significantly outperform linear data detectors and channel estimators in terms of bit error rate. For the proposed nonlinear data detection algorithm, we develop a very large scale integration (VLSI) architecture and present implementation results on a Xilinx Virtex-7 field programmable gate array (FPGA). Our implementation results are, to the best of our knowledge, the first for 1-bit massive MU-MIMO-OFDM systems and demonstrate comparable hardware efficiency with respect to state-of-the-art linear data detectors designed for systems with high-resolution data converters, while achieving lower bit error rate.