Low-Latency Task-Oriented Image Transmission with Opportunistic Spectrum Access
This work addresses the need for low-latency task-oriented communication in spectrum-constrained environments, benefiting applications like autonomous driving or remote sensing.
The paper proposes a low-latency task-oriented image transmission framework using opportunistic spectrum access and VQ-VAE compression, achieving 79x and 3.3x latency reductions with only 5.7% and 2.4% accuracy drops compared to conventional coding benchmarks.
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.