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This paper introduces a novel transmission framework that leverages opportunistic spectrum access to enhance low-latency task-oriented image communication. By utilizing discrete latent representations from a vector-quantized variational autoencoder (VQ-VAE) and standard digital modulation, the system effectively reduces latency while maintaining acceptable levels of classification accuracy. The results demonstrate significant latency reductions—79-fold and 3.3-fold—while only incurring minor drops in accuracy, highlighting the framework's potential for reliable communication in constrained environments.
Achieving up to 79-fold latency reductions in task-oriented image transmission without sacrificing classification accuracy could revolutionize communication under limited spectrum conditions.
Communication systems designed for reliable data reconstruction, rather than task-oriented communication, typically rely on separate source and channel coding and incur high latency under limited spectrum availability and fading channels. To address this, we propose a transmission framework with opportunistic spectrum access, in which the transmitter sends discrete latent representations learned via a vector-quantized variational autoencoder (VQ-VAE) over idle licensed channels using standard digital modulation. The AI-powered receiver is still able to reconstruct task-related information from the heavily compressed data. We develop a cross-layer latency model that accounts for compression, block errors, retransmissions, and stochastic channel access. Results on latency-accuracy trade-offs show that the proposed scheme achieves at least 79- and 3.3-fold latency reductions with only 5.7% and 2.4% drops in classification accuracy compared to benchmarks using conventional source and channel coding. The framework enables low-latency communication and reliable task execution even under limited spectrum availability and challenging channel conditions.
