Neural network (NN)-based receivers for orthogonal frequency division multiplex (OFDM) excel through promising performance and benefits in their applicability. In this paper we analyze their capabilities when they are imposed with practical constraints that have to be considered when implementing such a receiver on hardware. Specifically, we focus on the effects of uniform linear affine quantization and it is shown which performance can be achieved by utilizing quantization-aware training (QAT) with trainable quantizers. In order to reduce the computational complexity of the NN-based receiver different pruning methods are investigated. We showcase that a reduction of the number of floating-point operations (FLOPs) by more than 50% is possible at the cost of less than 0.25 dB difference. Finally, an intuition on joint pruning and quantization is given.
Beschreibung
On the Implementation of Neural Network-based OFDM Receivers | IEEE Conference Publication | IEEE Xplore
%0 Conference Paper
%1 10683574
%A Fischer, Moritz Benedikt
%A Dörner, Sebastian
%A Shimizu, Takayuki
%A Mahabal, Chinmay
%A Lu, Hongsheng
%A Brink, Stephan ten
%B 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring)
%D 2024
%K myown
%P 1-6
%R 10.1109/VTC2024-Spring62846.2024.10683574
%T On the Implementation of Neural Network-based OFDM Receivers
%X Neural network (NN)-based receivers for orthogonal frequency division multiplex (OFDM) excel through promising performance and benefits in their applicability. In this paper we analyze their capabilities when they are imposed with practical constraints that have to be considered when implementing such a receiver on hardware. Specifically, we focus on the effects of uniform linear affine quantization and it is shown which performance can be achieved by utilizing quantization-aware training (QAT) with trainable quantizers. In order to reduce the computational complexity of the NN-based receiver different pruning methods are investigated. We showcase that a reduction of the number of floating-point operations (FLOPs) by more than 50% is possible at the cost of less than 0.25 dB difference. Finally, an intuition on joint pruning and quantization is given.
@inproceedings{10683574,
abstract = {Neural network (NN)-based receivers for orthogonal frequency division multiplex (OFDM) excel through promising performance and benefits in their applicability. In this paper we analyze their capabilities when they are imposed with practical constraints that have to be considered when implementing such a receiver on hardware. Specifically, we focus on the effects of uniform linear affine quantization and it is shown which performance can be achieved by utilizing quantization-aware training (QAT) with trainable quantizers. In order to reduce the computational complexity of the NN-based receiver different pruning methods are investigated. We showcase that a reduction of the number of floating-point operations (FLOPs) by more than 50% is possible at the cost of less than 0.25 dB difference. Finally, an intuition on joint pruning and quantization is given.},
added-at = {2024-11-28T10:24:14.000+0100},
author = {Fischer, Moritz Benedikt and Dörner, Sebastian and Shimizu, Takayuki and Mahabal, Chinmay and Lu, Hongsheng and Brink, Stephan ten},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/271b902171bc525c70e5faf89425574f4/inue},
booktitle = {2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring)},
description = {On the Implementation of Neural Network-based OFDM Receivers | IEEE Conference Publication | IEEE Xplore},
doi = {10.1109/VTC2024-Spring62846.2024.10683574},
interhash = {1fcdccc907d31eff71387dea084664af},
intrahash = {71b902171bc525c70e5faf89425574f4},
issn = {2577-2465},
keywords = {myown},
month = {June},
pages = {1-6},
timestamp = {2024-11-28T10:24:14.000+0100},
title = {On the Implementation of Neural Network-based OFDM Receivers},
year = 2024
}
