Mehdi Sattari, Kommunikation, Antenner och Optiska Nätverk

Acquiring accurate channel state information (CSI) is essential for enabling reliable and efficient wireless transmission and reception. However, CSI is inherently stochastic, high-dimensional, and time-varying, which makes its acquisition particularly challenging. Motivated by the success of deep learning (DL) across many data modalities, this thesis explores DL-based solutions for CSI estimation, compression, and prediction.

First, we study CSI estimation in full-duplex (FD) multiple-input multiple-output (MIMO) systems, where strong self-interference (SI) complicates channel acquisition. To reduce the pilot and computational burden of estimating both SI and user channels, we propose a pilot-sharing strategy together with a convolutional neural network that jointly estimates these channels.
We further introduce a neural mapping that enables CSI acquisition at the transmit chain.

Second, we investigate DL–based CSI compression and its limited robustness under distribution shifts. To address this issue, we adopt a full-model fine-tuning while explicitly accounting for model update signaling overhead. Specifically, we employ a spike-and-slab prior to promote sparsity in the model updates and fine-tune the pretrained network using a rate–distortion objective regularized by the update bit rate.

Third, we tackle CSI prediction using a diffusion-based generative framework. The method consists of a temporal encoder that extracts latent features from past CSI and a diffusion generator that synthesizes future CSI. We also study a simplified encoder-free design to reduce latency, compare autoregressive and sequence-to-sequence inference, and explore multiple architectures for both temporal encoding and diffusion generation.