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Learning-Based Symbol Level Precoding: A Memory-Efficient Unsupervised Learning Approach

arXiv 2111.08110 ↗First posted 15 Nov 2021eess.SPcited by 0

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Abstract (arXiv)

Symbol level precoding (SLP) has been proven to be an effective means of managing the interference in a multiuser downlink transmission and also enhancing the received signal power. This paper proposes an unsupervised learning based SLP that applies to quantized deep neural networks (DNNs). Rather than simply training a DNN in a supervised mode, our proposal unfolds a power minimization SLP formulation in an imperfect channel scenario using the interior point method (IPM) proximal `log' barrier function. We use binary and ternary quantizations to compress the DNN's weight values. The results show significant memory savings for our proposals compared to the existing full-precision SLP-DNet with significant model compression of ~21x and ~13x for both binary DNN-based SLP (RSLP-BDNet) and ternary DNN-based SLP (RSLP-TDNets), respectively.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 26 references that are in the map. First posted on arXiv 15 Nov 2021.

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References · 7

  1. 2016XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networkscited by 849
  2. 2017Channel Pruning for Accelerating Very Deep Neural Networkscited by 702
  3. 2016Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activationscited by 426
  4. 2016Ternary Neural Networks for Resource-Efficient AI Applicationscited by 33
  5. 2018Deep Unfolding of a Proximal Interior Point Method for Image Restorationcited by 24
  6. 2019Complexity-Scalable Neural Network Based MIMO Detection With Learnable Weight Scalingcited by 3
  7. 2021A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weightscited by 1

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