Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.24400 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- Time division duplexing (TDD) has become the dominant duplexing mode in 5G and beyond due to its ability to exploit channel reciprocity for efficient downlink channel state information (CSI) acquisition. However, channel aging caused by user mobility and processing delays degrades the accuracy of CSI, leading to suboptimal link adaptation and loss of performance. To address this issue, we propose a learning-based CSI prediction framework that leverages temporal correlations in wireless channels to forecast future signal to interference plus noise ratio (SINR) values. The prediction operates in the effective SINR domain, obtained via exponential effective SINR mapping (EESM), ensuring full compatibility with existing 5G standards without requiring continuous pilot signaling. Two models are considered: a fully connected deep neural network (DNN) and an long short-term memory (LSTM)-based network. The simulation results show that the LSTM predictor achieves an improvement of up to 2 dB in normalized mean squared error (NMSE) and a gain of up to 1.2 Mbps throughput over a baseline without prediction under moderate Doppler conditions. These results confirm the potential of lightweight AI-based CSI prediction to effectively mitigate channel aging and enhance link adaptation in TDD 5G systems.