  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.
