Channel-Adaptive Wireless Image Transmission with OFDM

TL;DR

This paper introduces a novel learning-based, channel-adaptive joint source-channel coding scheme for wireless image transmission that dynamically adjusts to channel conditions using an end-to-end autoencoder with dual-attention and OFDM.

Contribution

It proposes a new adaptive CA-JSCC method with dual-attention mechanism that leverages CSI for optimized resource allocation, outperforming existing schemes.

Findings

Achieves state-of-the-art performance in wireless image transmission

Robust to varying channel conditions

Effectively exploits limited channel resources

Abstract

We present a learning-based channel-adaptive joint source and channel coding (CA-JSCC) scheme for wireless image transmission over multipath fading channels. The proposed method is an end-to-end autoencoder architecture with a dual-attention mechanism employing orthogonal frequency division multiplexing (OFDM) transmission. Unlike the previous works, our approach is adaptive to channel-gain and noise-power variations by exploiting the estimated channel state information (CSI). Specifically, with the proposed dual-attention mechanism, our model can learn to map the features and allocate transmission-power resources judiciously based on the estimated CSI. Extensive numerical experiments verify that CA-JSCC achieves state-of-the-art performance among existing JSCC schemes. In addition, CA-JSCC is robust to varying channel conditions and can better exploit the limited channel resources by transmitting critical features over better subchannels.