DISTRIBUTED IMAGE TRANSMISSION USING DEEP JOINT SOURCE-CHANNEL CODING

We study the problem of deep joint source-channel coding (D-JSCC) for correlated image sources, where each source is transmitted through an independent noisy channel to the common receiver. In particular, we consider a pair of images captured by two cameras with probably overlapping fields of view transmitted over wireless channels and reconstructed in the center node. The challenging problem locates at designing a practical code to utilize both source and channel correlations to improve the whole transmission efficiency without additional transmission overhead. To tackle this, we need to consider the common information across two stereo images as well as the differences between two transmission channels. In this case, we propose a deep neural networks solution that includes lightweight edge encoders and a powerful center decoder. Besides, in the decoder, we propose a novel channel state information aware cross attention module to highlight the overlapping fields and leverage the relevance between two noisy feature maps. Our results show the impressive improvement of reconstruction quality in both links by exploiting the noisy representations of the other link. Moreover, compared to the separated schemes with capacity-achieving channel codes, the proposed scheme shows competitive results.