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Accelerated Learning-Based MIMO Detection through Weighted Neural Network Design

Mohammad, A; Masouros, C; Andreopoulos, Y; (2020) Accelerated Learning-Based MIMO Detection through Weighted Neural Network Design. In: ICC 2020 - 2020 IEEE International Conference on Communications (ICC). IEEE: Dublin, Ireland. Green open access

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Abstract

In this paper, we introduce a framework for a systematic acceleration of deep neural network (DNN) design for MIMO detection. A monotonically non-increasing function is used to scale the values of the layer weights such that only a certain fraction of the inputs is used for feedforward computation. This enables a dynamic weight scaling across and within the network layers, and it is termed as weight-scaling neural network-based MIMO detector (WeSNet). To increase the robustness against the changes in the activation patterns and additional enhancement in the detection accuracy for the same inference complexity, we introduce trainable weight-scaling functions. Experimental results show the superiority of our proposed method over the benchmark model (DetNet) and classical approaches based on semi-definite relaxation in terms of detection accuracy and computational efficiency.

Type: Proceedings paper
Title: Accelerated Learning-Based MIMO Detection through Weighted Neural Network Design
Event: 2020 IEEE International Conference on Communications (ICC)
ISBN-13: 9781728150895
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICC40277.2020.9148726
Publisher version: http://dx.doi.org/10.1109/ICC40277.2020.9148726
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, multilayer perceptron, Deep Neural Networks, DetNet, WesNet, 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/10111934
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