5 papers
Quantized Transformers for Massive MIMO Precoding with Automatic Resolution Tuning
Ghazal Kasalaee, Glodi Sala Mangituka, Ali Hasanzadeh Karkan +2
Deep learning precoders, particularly those based on Transformer architectures, offer superior spectral efficiency for Massive MIMO systems but may incur high computational cost. W…
TensLoRA: Tensor Alternatives for Low-Rank Adaptation
Axel Marmoret, Reda Bensaid, Jonathan Lys +2
Low-Rank Adaptation (LoRA) is widely used to efficiently adapt Transformers by adding trainable low-rank matrices to attention projections. While effective, these matrices are cons…
A Foundation Model for Massive MIMO Precoding with an Adaptive per-User Rate-Power Tradeoff
Jérôme Emery, Ali Hasanzadeh Karkan, Jean-François Frigon +1
Deep learning (DL) has emerged as a solution for precoding in massive multiple-input multiple-output (mMIMO) systems due to its capacity to learn the characteristics of the propaga…
Compression of Site-Specific Deep Neural Networks for Massive MIMO Precoding
Ghazal Kasalaee, Ali Hasanzadeh Karkan, Jean-François Frigon +1
The deployment of deep learning (DL) models for precoding in massive multiple-input multiple-output (mMIMO) systems is often constrained by high computational demands and energy co…
A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites
Ali Hasanzadeh Karkan, Ahmed Ibrahim, Jean-François Frigon +1
Massive multiple-input multiple-output (mMIMO) technology has transformed wireless communication by enhancing spectral efficiency and network capacity. This paper proposes a novel…