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Complexity-Scalable Neural Network Based MIMO Detection With Learnable Weight Scaling

Mohammad, A; Masouros, C; Andreopoulos, Y; (2020) Complexity-Scalable Neural Network Based MIMO Detection With Learnable Weight Scaling. IEEE Transactions on Communications 10.1109/TCOMM.2020.3007622. (In press). Green open access

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Abstract

This paper introduces a framework for systematic complexity scaling of deep neural network (DNN) based MIMO detectors. The model uses a fraction of the DNN inputs by scaling their values through weights that follow monotonically non-increasing functions. This allows for weight scaling across and within the different DNN layers in order to achieve scalable complexity-accuracy results. To reduce complexity further, we introduce a regularization constraint on the layer weights such that, at inference, parts (or the entirety) of network layers can be removed with minimal impact on the detection accuracy. We also introduce trainable weight-scaling functions for increased robustness to changes in the activation patterns and a further improvement in the detection accuracy at the same inference complexity. Numerical results show that our approach is 10 and 100-fold less complex than classical approaches based on semi-definite relaxation and ML detection, respectively.

Type: Article
Title: Complexity-Scalable Neural Network Based MIMO Detection With Learnable Weight Scaling
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TCOMM.2020.3007622
Publisher version: https://doi.org/10.1109/TCOMM.2020.3007622
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: MIMO detection, Deep Neural Networks, DetNet, profile weight coefficients.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10082584
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