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

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 obtai...

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Bibliographic Details
Main Authors: Abdullahi Mohammad, Christos Masouros, Yiannis Andreopoulos
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Open Journal of the Communications Society
Subjects:
Symbol-level-precoding
constructive interference
power minimization
deep neural networks (DNNs)
stochastic quantization (SQ)
Online Access:https://ieeexplore.ieee.org/document/10153979/

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