  This paper introduces an innovative deep joint source-channel coding
(DeepJSCC) approach to image transmission over a cooperative relay channel. The
relay either amplifies and forwards a scaled version of its received signal,
referred to as DeepJSCC-AF, or leverages neural networks to extract relevant
features about the source signal before forwarding it to the destination, which
we call DeepJSCC-PF (Process-and-Forward). In the full-duplex scheme, inspired
by the block Markov coding (BMC) concept, we introduce a novel block
transmission strategy built upon novel vision transformer architecture. In the
proposed scheme, the source transmits information in blocks, and the relay
updates its knowledge about the input signal after each block and generates its
own signal to be conveyed to the destination. To enhance practicality, we
introduce an adaptive transmission model, which allows a single trained
DeepJSCC model to adapt seamlessly to various channel qualities, making it a
versatile solution. Simulation results demonstrate the superior performance of
our proposed DeepJSCC compared to the state-of-the-art BPG image compression
algorithm, even when operating at the maximum achievable rate of conventional
decode-and-forward and compress-and-forward protocols, for both half-duplex and
full-duplex relay scenarios.
