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Publication Detail
A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights
  • Publication Type:
    Journal article
  • Authors:
    Mohammad A, Masouros C, Andreopoulos Y
  • Publication date:
  • Keywords:
    eess.SP, eess.SP
  • Notes:
    13 pages, 10 figures, Journal
This paper proposes a memory-efficient deep neural network (DNN) framework-based symbol level precoding (SLP). We focus on a DNN with realistic finite precision weights and adopt an unsupervised deep learning (DL) based SLP model (SLP-DNet). We apply a stochastic quantization (SQ) technique to obtain its corresponding quantized version called SLP-SQDNet. The proposed scheme offers a scalable performance vs memory tradeoff, by quantizing a scale-able percentage of the DNN weights, and we explore binary and ternary quantizations. Our results show that while SLP-DNet provides near-optimal performance, its quantized versions through SQ yield 3.46x and 2.64x model compression for binary-based and ternary-based SLP-SQDNets, respectively. We also find that our proposals offer 20x and 10x computational complexity reductions compared to SLP optimization-based and SLP-DNet, respectively.
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