High-Bandwidth Spatial Equalization for mmWave Massive MU-MIMO with Processing-In-Memory

TL;DR

This paper proposes and compares two hardware architectures for finite-alphabet equalization in mmWave massive MU-MIMO systems, demonstrating significant reductions in power and area for processing-in-memory solutions.

Contribution

It introduces two integrated hardware implementations of finite-alphabet equalization, highlighting the advantages of processing-in-memory architecture over traditional MAC arrays.

Findings

Bit-serial PIM reduces area and power consumption by up to 2x and 3x respectively.

All-digital VLSI implementation achieves high throughput at mmWave frequencies.

Finite-alphabet equalization enables efficient high-bandwidth processing in massive MIMO systems.

Abstract

All-digital basestation (BS) architectures enable superior spectral efficiency compared to hybrid solutions in massive multi-user MIMO systems. However, supporting large bandwidths with all-digital architectures at mmWave frequencies is challenging as traditional baseband processing would result in excessively high power consumption and large silicon area. The recently-proposed concept of finite-alphabet equalization is able to address both of these issues by using equalization matrices that contain low-resolution entries to lower the power and complexity of high-throughput matrix-vector products in hardware. In this paper, we explore two different finite-alphabet equalization hardware implementations that tightly integrate the memory and processing elements: (i) a parallel array of multiply-accumulate (MAC) units and (ii) a bit-serial processing-in-memory (PIM) architecture. Our all-digital VLSI implementation results in 28nm CMOS show that the bit-serial PIM architecture reduces the area and power consumption up to a factor of 2x and 3x, respectively, when compared to a parallel MAC array that operates at the same throughput.