TY - GEN
T1 - FedRec
T2 - 21st IEEE Statistical Signal Processing Workshop, SSP 2021
AU - Mashhadi, Mahdi Boloursaz
AU - Shlezinger, Nir
AU - Eldar, Yonina C.
AU - Gunduz, Deniz
N1 - Funding Information:
D. Gündüz received funding from the European Research Council (ERC) through project BEACON under grant No. 677854. Y. C. Eldar received funding from the European Union’s Horizon 2020 research and innovation program under grant No. 646804-ERC-COG-BNYQ, and from the Israel Science Foundation under grant No. 0100101. M. B. Mashhadi and D. Gündüz are with the Dept. of EE, Imperial College, London, UK (email: {m.boloursaz-mashhadi, d.gunduz}@imperial.ac.uk). N. Shlezinger is with the School of ECE, Ben-Gurion University of the Negev, Be’er-Sheva, Israel (e-mail: [email protected]). Y. C. Eldar is with the Faculty of Math and CS, Weizmann Institute, Rehovot, Israel (e-mail: [email protected]).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/11
Y1 - 2021/7/11
N2 - Wireless communications is often subject to channel fading. Various statistical models have been proposed to capture the inherent randomness in fading, and conventional model-based receiver designs rely on accurate knowledge of this underlying distribution, which, in practice, may be complex and intractable. In this work, we propose a neural network-based symbol detection technique for down-link fading channels, which is based on the maximum a-posteriori probability (MAP) detector. To enable training on a diverse ensemble of fading realizations, we propose a federated training scheme, in which multiple users collaborate to jointly learn a universal data-driven detector, hence the name FedRec. The performance of the resulting receiver is shown to approach the MAP performance in diverse channel conditions without requiring knowledge of the fading statistics, while inducing a substantially reduced communication overhead in its training procedure compared to centralized training.
AB - Wireless communications is often subject to channel fading. Various statistical models have been proposed to capture the inherent randomness in fading, and conventional model-based receiver designs rely on accurate knowledge of this underlying distribution, which, in practice, may be complex and intractable. In this work, we propose a neural network-based symbol detection technique for down-link fading channels, which is based on the maximum a-posteriori probability (MAP) detector. To enable training on a diverse ensemble of fading realizations, we propose a federated training scheme, in which multiple users collaborate to jointly learn a universal data-driven detector, hence the name FedRec. The performance of the resulting receiver is shown to approach the MAP performance in diverse channel conditions without requiring knowledge of the fading statistics, while inducing a substantially reduced communication overhead in its training procedure compared to centralized training.
UR - http://www.scopus.com/inward/record.url?scp=85113581978&partnerID=8YFLogxK
U2 - 10.1109/SSP49050.2021.9513736
DO - 10.1109/SSP49050.2021.9513736
M3 - Conference contribution
T3 - IEEE Workshop on Statistical Signal Processing Proceedings
SP - 576
EP - 580
BT - 2021 IEEE Statistical Signal Processing Workshop (SSP)
PB - Institute of Electrical and Electronics Engineers
Y2 - 11 July 2021 through 14 July 2021
ER -