Deep Learning Based Uplink Multi-User SIMO Beamforming Design
Cemil Vahapoglu Timothy J. O'Shea Tamoghna Roy Sennur Ulukus
The advancement of fifth generation (5G) wireless communication networks has
created a greater demand for wireless resource management solutions that offer
high data rates, extensive coverage, minimal latency and energy-efficient
performance. Nonetheless, traditional approaches have shortcomings when it
comes to computational complexity and their ability to adapt to dynamic
conditions, creating a gap between theoretical analysis and the practical
execution of algorithmic solutions for managing wireless resources. Deep
learning-based techniques offer promising solutions for bridging this gap with
their substantial representation capabilities. We propose a novel unsupervised
deep learning framework, which is called NNBF, for the design of uplink receive
multi-user single input multiple output (MU-SIMO) beamforming. The primary
objective is to enhance the throughput by focusing on maximizing the sum-rate
while also offering computationally efficient solution, in contrast to
established conventional methods. We conduct experiments for several antenna
configurations. Our experimental results demonstrate that NNBF exhibits
superior performance compared to our baseline methods, namely, zero-forcing
beamforming (ZFBF) and minimum mean square error (MMSE) equalizer.
Additionally, NNBF is scalable to the number of single-antenna user equipments
(UEs) while baseline methods have significant computational burden due to
matrix pseudo-inverse operation.