Uplink OFDM Channel Prediction with Hybrid CNN-LSTM for 6G Non-Terrestrial Networks

TL;DR

This paper introduces a hybrid CNN-LSTM channel prediction model for 6G non-terrestrial networks that reduces pilot overhead and improves throughput by accurately predicting OFDM channel responses in high-mobility conditions.

Contribution

A novel hybrid CNN-LSTM neural network architecture for OFDM channel prediction in 6G NTNs, enabling pilot reduction and enhanced throughput with good generalization.

Findings

Effective CFR prediction with minimal throughput loss

Model generalizes well across different Doppler spreads

Lightweight architecture suitable for real-time implementation

Abstract

Wireless communications are typically subject to complex channel dynamics, requiring the transmission of pilot sequences to estimate and equalize such effects and correctly receive information bits. This is especially true in 6G non-terrestrial networks (NTNs) in low Earth orbit, where one end of the communication link orbits around the Earth at several kilometers per second, and a multi-carrier waveform, such as orthogonal frequency division multiplexing (OFDM), is employed. To minimize the pilot overhead, we remove pilot symbols every other OFDM slot and propose a channel predictor to obtain the channel frequency response (CFR) matrix in absence of pilots. The algorithm employs an encoder-decoder convolutional neural network and a long short-term memory layer, along with skip connections, to predict the CFR matrix on the upcoming slot based on the current one. We demonstrate the effectiveness of the proposed predictor through numerical simulations in tapped delay line channel models, highlighting the effective throughput improvement. We further assess the generalization capabilities of the model, showing minimal throughput degradation when testing under different Doppler spreads and in both line of sight (LoS) and non-LoS propagation conditions. Finally, we discuss computational-complexity-related aspects of the lightweight hybrid CNN-LSTM architecture.