Reconstruction of Clipped Signals in Quantized Uplink Massive MIMO Systems
Journal article, 2020

This paper considers the uplink of a single-cell multiuser massive multiple-input multiple-output system. Each receiver antenna of the base station (BS) is assumed to be equipped with a pair of analog-to-digital converters to quantize the real and imaginary part of the received signal. We propose a novel clipping-aware receiver (CA-MMSE), which performs minimum mean square error (MMSE) reconstruction only on the clipped received samples, while the granular samples are left unchanged after the quantization. On this basis, we present an iterative algorithm to implement the CA-MMSE receiver and derive a sufficient condition for its geometrical convergence to a fixed point. We show that as long as the number of BS antennas or the quantization resolution is sufficiently high, then, the performance of the CA-MMSE is as good as the optimal MMSE receiver which reconstructs all quantized received symbols. Additionally, we propose a novel Bussgang-based weighted zero-forcing (B-WZF) receiver which distinguishes the clipping and granular distortion and it is shown that as long as the received training symbols per antenna are correlated, the CA-MMSE brings significant improvements compared to conventional receivers in the literature while for users that do not experience deep large-scale fading the simpler B-WZF is near to the CA-MMSE for sufficiently high signal-to-noise ratio and quantization resolution.

channel estimation

clipping

Massive multi-user multiple-input multiple-output (MIMO)

signal reconstruction

minimum mean-square error (MMSE)

Bussgang's theorem

analog-to-digital converter (ADC)

Author

Nikolaos Kolomvakis

Ericsson

Thomas Eriksson

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Mikael Coldrey

Ericsson

Mats Viberg

Blekinge Tekniska Högskola, BTH

IEEE Transactions on Communications

00906778 (ISSN)

Vol. 68 5 2891-2905 8984303

Subject Categories

Telecommunications

Communication Systems

Signal Processing

DOI

10.1109/TCOMM.2020.2971975

More information

Latest update

12/21/2020