- Conference Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
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. Show more
Book title2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Pages / Article No.
Organisational unit09695 - Studer, Christoph / Studer, Christoph
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