5 papers
A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights
Abdullahi Mohammad, Christos Masouros, Yiannis Andreopoulos
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 a…
An Unsupervised Deep Unfolding Framework for robust Symbol Level Precoding
Abdullahi Mohammad, Christos Masouros, Yiannis Andreopoulos
Symbol Level Precoding (SLP) has attracted significant research interest due to its ability to exploit interference for energy-efficient transmission. This paper proposes an unsupe…
Learning-Based Symbol Level Precoding: A Memory-Efficient Unsupervised Learning Approach
Abdullahi Mohammad, Christos Masouros, Yiannis Andreopoulos
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…
An Unsupervised Learning-Based Approach for Symbol-Level-Precoding
Abdullahi Mohammad, Christos Masouros, Yiannis Andreopoulos
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) pro…
Complexity-Scalable Neural Network Based MIMO Detection With Learnable Weight Scaling
Abdullahi Mohammad, Christos Masouros, Yiannis Andreopoulos
This paper introduces a framework for systematic complexity scaling of deep neural network(DNN) based MIMO detectors. The model uses a fraction of the DNN inputs by scaling their v…