# A Comparison of Neural Networks for Wireless Channel Prediction

arXiv: https://arxiv.org/abs/2308.14020 · HTML: https://mlsanity.com/paper/2308.14020/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 27 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning](https://mlsanity.com/paper/2009.09967/) (2020)
- [Time-Varying Channel Prediction for RIS-Assisted MU-MISO Networks via Deep Learning](https://mlsanity.com/paper/2111.04971/) (2021)
- [C-GRBFnet: A Physics-Inspired Generative Deep Neural Network for Channel Representation and Prediction](https://mlsanity.com/paper/2112.02615/) (2021)

## Built on by (most cited first, 8 of 8)

- [LinFormer: A Linear-based Lightweight Transformer Architecture For Time-Aware MIMO Channel Prediction](https://mlsanity.com/paper/2410.21351/) (2024)
- [Improving Wi-Fi Network Performance Prediction with Deep Learning Models](https://mlsanity.com/paper/2507.11168/) (2025)
- [Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning](https://mlsanity.com/paper/2509.18933/) (2025)
- [CSI Prediction Using Diffusion Models](https://mlsanity.com/paper/2510.11214/) (2025)
- [CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction with Comprehensive Robustness and Generalization Testing](https://mlsanity.com/paper/2510.12996/) (2025)
- [On the Prediction of Wi-Fi Performance through Deep Learning](https://mlsanity.com/paper/2512.00211/) (2025)
- [Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks](https://mlsanity.com/paper/2606.05208/) (2026)
- [TRACE-CRC: Trajectory-Adaptive Conformal Risk Control for Multi-Step Channel State Information Prediction](https://mlsanity.com/paper/2608.27124/) (2026)

## Abstract (arXiv)

The performance of modern wireless communications systems depends critically on the quality of the available channel state information (CSI) at the transmitter and receiver. Several previous works have proposed concepts and algorithms that help maintain high quality CSI even in the presence of high mobility and channel aging, such as temporal prediction schemes that employ neural networks. However, it is still unclear which neural network-based scheme provides the best performance in terms of prediction quality, training complexity and practical feasibility. To investigate such a question, this paper first provides an overview of state-of-the-art neural networks applicable to channel prediction and compares their performance in terms of prediction quality. Next, a new comparative analysis is proposed for four promising neural networks with different prediction horizons. The well-known tapped delay channel model recommended by the Third Generation Partnership Program is used for a standardized comparison among the neural networks. Based on this comparative evaluation, the advantages and disadvantages of each neural network are discussed and guidelines for selecting the best-suited neural network in channel prediction applications are given.