Neural-Network Optimized 1-bit Precoding for Massive MU-MIMO
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Author / Producer
Date
2019-07
Publication Type
Conference Paper
ETH Bibliography
no
Citations
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OPEN ACCESS
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Rights / License
Abstract
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 2x 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.
Publication status
published
Editor
Book title
2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Journal / series
Volume
Pages / Article No.
1 - 5
Publisher
IEEE
Event
20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2019)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09695 - Studer, Christoph / Studer, Christoph