A Memory-Efficient Learning Framework for Symbol Level Precoding With Quantized NN Weights

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 trade-off, by quantizing a scalable 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
$\sim 3.46\times $
and
$\sim 2.64\times $
model compression for binary-based and ternary-based SLP-SQDNets, respectively. We also find that our proposals offer
$\sim 20\times $
and
$\sim 10\times $
computational complexity reductions compared to SLP optimization-based and SLP-DNet, respectively.