Neural-Network Optimized 1-bit Precoding for Massive MU-MIMO
Paper in proceedings, 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

Author

Alexios Balatsoukas-Stimming

Ecole Polytechnique Federale De Lausanne

Cornell University

Oscar Castañeda

Cornell University

Sven Jacobsson

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

Ericsson

Giuseppe Durisi

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

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,

Subject Categories

Telecommunications

Communication Systems

Signal Processing

DOI

10.1109/SPAWC.2019.8815519

More information

Latest update

7/30/2020