  Efficient data transmission across mobile multi-hop networks that connect
edge devices to core servers presents significant challenges, particularly due
to the variability in link qualities between wireless and wired segments. This
variability necessitates a robust transmission scheme that transcends the
limitations of existing deep joint source-channel coding (DeepJSCC) strategies,
which often struggle at the intersection of analog and digital methods.
Addressing this need, this paper introduces a novel hybrid DeepJSCC framework,
h-DJSCC, tailored for effective image transmission from edge devices through a
network architecture that includes initial wireless transmission followed by
multiple wired hops. Our approach harnesses the strengths of DeepJSCC for the
initial, variable-quality wireless link to avoid the cliff effect inherent in
purely digital schemes. For the subsequent wired hops, which feature more
stable and high-capacity connections, we implement digital compression and
forwarding techniques to prevent noise accumulation. This dual-mode strategy is
adaptable even in scenarios with limited knowledge of the image distribution,
enhancing the framework's robustness and utility. Extensive numerical
simulations demonstrate that our hybrid solution outperforms traditional fully
digital approaches by effectively managing transitions between different
network segments and optimizing for variable signal-to-noise ratios (SNRs). We
also introduce a fully adaptive h-DJSCC architecture capable of adjusting to
different network conditions and achieving diverse rate-distortion objectives,
thereby reducing the memory requirements on network nodes.
