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Publication Detail
Complexity-Scalable Neural Network Based MIMO Detection With Learnable
Weight Scaling
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Publication Type:Journal article
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Publication Sub Type:Article
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Authors:Mohammad A, Masouros C, Andreopoulos Y
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Publication date:07/07/2020
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Journal:IEEE Transactions on Communications
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Status:Published online
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Keywords:eess.SP, eess.SP, cs.LG
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Author URL:
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Notes:14 pages, 12 figures, journal
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 accuracy-vs.-complexity scalability
during inference. In order to further improve the performance of our proposal,
we introduce a sparsity-inducing regularization constraint in conjunction with
trainable weight-scaling functions. In this way, the network learns to balance
detection accuracy versus complexity while also increasing robustness to
changes in the activation patterns, leading to further improvement in the
detection accuracy and BER performance at the same inference complexity.
Numerical results show that our approach is 10-foldand 100-fold less complex
than classical approaches based on semi-definite relaxation and ML detection,
respectively.
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