Machine Learning-Based Adaptive Receive Filtering: Proof-of-Concept on an SDR Platform
Abstract
Conventional multiuser detection techniques either require a large number of antennas at the receiver for a desired performance, or they are too complex for practical implementation. Moreover, many of these techniques, such as successive interference cancellation (SIC), suffer from errors in parameter estimation (user channels, covariance matrix, noise variance, etc.) that is performed before detection of user data symbols. As an alternative to conventional methods, this paper proposes and demonstrates a low-complexity practical Machine Learning (ML) based receiver that achieves similar (and at times better) performance to the SIC receiver. The proposed receiver does not require parameter estimation; instead it uses supervised learning to detect the user modulation symbols directly. We perform comparisons with minimum mean square error (MMSE) and SIC receivers in terms of symbol error rate (SER) and complexity.
Cite
@article{arxiv.1911.04291,
title = {Machine Learning-Based Adaptive Receive Filtering: Proof-of-Concept on an SDR Platform},
author = {Matthias Mehlhose and Daniyal Amir Awan and Renato L. G. Cavalcante and Martin Kurras and Slawomir Stanczak},
journal= {arXiv preprint arXiv:1911.04291},
year = {2022}
}
Comments
submitted to ICC 2020