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Citation key | AwanBoo2020 |
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Author | D. A. Awan and R.L.G. Cavalcante and M. Yukawa and S. Stanczak |
Title of Book | Machine Learning for Future Wireless Communications |
Pages | 15 |
Year | 2020 |
DOI | 10.1002/9781119562306.ch11 |
Location | New York, United States |
Month | December |
Editor | Wiley & IEEE Press |
Publisher | Wiley & IEEE Press |
Chapter | 11 |
Abstract | This chapter introduces a novel machine learning algorithm for symbol detection in multiuser environments. It considers a challenging multiuser uplink scenario in which the number of antennas available at the base station may be smaller than the number of active users. More specifically, the proposed method is an adaptive (nonlinear) receive filter that learns to detect symbols from data directly, without performing any intermediate estimation tasks (e.g. channel estimation). Furthermore, the method is robust against abrupt changes of the wireless environment. The proposed algorithms for symbol detection are based on the theory of reproducing kernel Hilbert spaces, which have been extensively used in diverse fields such as statistics, probability, signal processing, and machine learning, among others. It also discusses the adaptive learning method for symbol detection in multiuser environments. |