Toward Robust MIMO and Spread-Spectrum Wireless Image Transmission: Deep Joint Source–Channel Autoencoding with Adaptive Rate Control and Task-Oriented Semantic Attention

Deep joint source–channel coding (Deep JSCC) provides an end-to-end alternative to separated image compression and channel coding, but reconstruction fidelity, channel use, and task utility are distinct design objectives. This study evaluates these objectives in three coordinated experimental tracks. The reconstruction track examines a residual-convolutional Deep JSCC mapping under AWGN and Rayleigh fading; the resource-adaptation track exposes channel use as a controllable variable through SNR-conditioned feature modulation and differentiable rate selection; and the task-oriented track changes the receiver objective from image recovery to classification through semantic feature prioritization. All tracks use CIFAR-10 but are interpreted with objective-specific metrics. At SNR = 10 dB over AWGN, the reconstruction track records 25.54 dB PSNR, compared with 20.07 and 22.34 dB for the two digital operating configurations. The adaptive track exhibits penalty-dependent PSNR–CPP behavior rather than uniform superiority at every SNR. In the classification track, TO-JSCC remains above the compared Deep JSCC classifier across the plotted AWGN operating points, with the largest visual gap occurring in the lower-SNR region. Because the compared task systems are not capacity- and training-matched, the difference is interpreted at the complete-system level rather than attributed solely to attention. These results support bounded conclusions on reconstruction robustness, resource adaptation, and task-level semantic transmission under the reported channel conditions.