Neural-Network Optimized 1-bit Precoding for Massive MU-MIMO
Paper i proceeding, 2019

Base station (BS) architectures for massive multiuser (MU) multiple-input multiple-output (MIMO) wireless systems are equipped with hundreds of antennas to serve tens of users on the same time-frequency channel. The immense number of BS antennas incurs high system costs, power, and interconnect bandwidth. To circumvent these obstacles, sophisticated MU precoding algorithms that enable the use of 1-bit DACs have been proposed. Many of these precoders feature parameters that are, traditionally, tuned manually to optimize their performance. We propose to use deep-learning tools to automatically tune such 1-bit precoders. Specifically, we optimize the biConvex 1-bit PrecOding (C2PO) algorithm using neural networks. Compared to the original C2PO algorithm, our neural-network optimized (NNO-)C2PO achieves the same error-rate performance at 2× lower complexity. Moreover, by training NNO-C2PO for different channel models, we show that 1-bit precoding can be made robust to vastly changing propagation conditions.

Signal processing algorithms

Precoding

Wireless communication

Antennas

Artificial neural networks

Radio frequency

Training

Författare

Alexios Balatsoukas-Stimming

Ecole Polytechnique Federale De Lausanne

Cornell University

Oscar Castañeda

Cornell University

Sven Jacobsson

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

Ericsson AB

Giuseppe Durisi

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

Christoph Studer

Cornell University

IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC

Vol. 2019-July 8815519

20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019
Cannes, France,

Ämneskategorier

Telekommunikation

Kommunikationssystem

Signalbehandling

DOI

10.1109/SPAWC.2019.8815519

Mer information

Senast uppdaterat

2020-07-30