Channel-Adaptive Wireless Image Transmission With OFDM

• Published in: IEEE wireless communications letters Vol. 11; no. 11; pp. 2400 - 2404
• Main Authors: Wu, Haotian, Shao, Yulin, Mikolajczyk, Krystian, Gunduz, Deniz
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Channel estimation; Decoding; deep neural networks; Image communication; image communications; Image transmission; Joint source channel coding; OFDM; Orthogonal Frequency Division Multiplexing; Resource management; Robustness (mathematics); Symbols; Wireless communication
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2022.3204837

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 to the available subchannels 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.