{"ID":5675254,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-07T01:06:03.009715918Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01921","arxiv_id":"2607.01921","title":"Low-Latency Task-Oriented Image Transmission with Opportunistic Spectrum Access","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2607.01921","url_pdf":"https://arxiv.org/pdf/2607.01921v1","authors":"[\"João Henrique Inacio de Souza\",\"Mattia Merluzzi\",\"Mateus P. Mota\",\"Beatriz Soret\",\"Petar Popovski\"]","published":"2026-07-02T09:18:24Z","proceeding":"cs.IT","tasks":"[\"cs.IT\",\"cs.AI\"]","methods":"[\"Variational Autoencoder\"]","has_code":false}