Beamspace Channel Estimation for Massive MIMO mmWave Systems: Algorithm and VLSI Design

TL;DR

This paper introduces BEACHES, a low-complexity beamspace channel estimation algorithm for massive MIMO mmWave systems, with a VLSI architecture demonstrating high-throughput hardware implementation.

Contribution

The paper presents a novel adaptive denoising algorithm for beamspace channel estimation and a VLSI design suitable for practical high-speed hardware implementation.

Findings

BEACHES achieves comparable accuracy to state-of-the-art methods.

The algorithm significantly reduces computational complexity.

VLSI implementation demonstrates high throughput in FPGA.

Abstract

Millimeter-wave (mmWave) communication in combination with massive multiuser multiple-input multiple-output (MU-MIMO) enables high-bandwidth data transmission to multiple users in the same time-frequency resource. The strong path loss of wave propagation at such high frequencies necessitates accurate channel state information to ensure reliable data transmission. We propose a novel channel estimation algorithm called BEAmspace CHannel EStimation (BEACHES), which leverages the fact that wave propagation at mmWave frequencies is predominantly directional. BEACHES adaptively denoises the channel vectors in the beamspace domain using an adaptive shrinkage procedure that relies on Stein's unbiased risk estimator (SURE). Simulation results for line-of-sight (LoS) and non-LoS mmWave channels reveal that BEACHES performs on par with state-of-the-art channel estimation methods while requiring orders-of-magnitude lower complexity. To demonstrate the effectiveness of BEACHES in practice, we develop a very large-scale integration (VLSI) architecture and provide field-programmable gate array (FPGA) implementation results. Our results show that adaptive channel denoising can be performed at high throughput and in a hardware-friendly manner for massive MU-MIMO mmWave systems with hundreds of antennas.