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Found 6 papers, 1 papers with code
Enhancing Physical Layer Security with Deep SIMO Auto-Encoder and RF Impairments Modeling
This paper presents a novel approach to achieving secure wireless communication by leveraging the inherent characteristics of wireless channels through end-to-end learning using a single-input-multiple-output (SIMO) autoencoder (AE).
An Unsupervised Deep Unfolding Framework for robust Symbol Level Precoding
Symbol Level Precoding (SLP) has attracted significant research interest due to its ability to exploit interference for energy-efficient transmission.
Learning-Based Symbol Level Precoding: A Memory-Efficient Unsupervised Learning Approach
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.
A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights
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.
An Unsupervised Learning-Based Approach for Symbol-Level-Precoding
This paper proposes an unsupervised learning-based precoding framework that trains deep neural networks (DNNs) with no target labels by unfolding an interior point method (IPM) proximal `log' barrier function.
Complexity-Scalable Neural Network Based MIMO Detection With Learnable Weight Scaling
This paper introduces a framework for systematic complexity scaling of deep neural network(DNN) based MIMO detectors.
