[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83620-en":3,"doc-seo-83620-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83620,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Low-Latency Task-Oriented Image Transmission with Opportunistic Spectrum Access","Communication systems designed for reliable data reconstruction often suffer from high latency when spectrum availability is limited and fading is present, especially when source and channel coding are handled separately. A transmission framework with opportunistic spectrum access is proposed, where a transmitter sends discrete latent representations learned via a VQ-VAE over idle licensed channels using standard digital modulation. An AI receiver reconstructs task-relevant information from heavily compressed data, while a cross-layer latency model accounts for compression, block errors, retransmissions, and stochastic access. Results show 79× and 3.3× latency reductions with only 5.7% and 2.4% accuracy drops in classification compared to benchmarks.","Low-Latency Task-Oriented Image Transmission with Opportunistic Spectrum Access  \nJoão Henrique Inacio de Souza∗ , Mattia Merluzzi†, Mateus P. Mota†, Beatriz Soret‡∗ , Petar Popovski∗  \n∗ Department of Electronic Systems, Aalborg University, Aalborg, Denmark. E-mail: {jhids,[petarp}@es.aau.dk](petarp}@es.aau.dk)[ ](petarp}@es.aau.dk)†CEA-Leti, Université Grenoble Alpes, F-38000 Grenoble, France. E-mail: {mattia.merluzzi,[mateus.pontesmota}@cea.fr](mateus.pontesmota}@cea.fr)[ ](mateus.pontesmota}@cea.fr)‡ Telecommunications Research Institute, Universidad de Málaga, Málaga, Spain. E-mail: [bsa@uma.es](bsa@uma.es)  \narXiv :2607 .0 192 1v 1 [ cs .IT] 2 Jul 2026  \nAbstract—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.  \nIndex Terms—Task-oriented communication, cognitive radio, remote inference, variational inference, vector quantization.  \nI. INTRODUCTION  \nThe convergence of wireless communications and artificial intelligence (AI) has led to the paradigm of task-oriented communication, where the objective is to efficiently support downstream tasks rather than to reliably reconstruct transmitted data [1] . In this context, performance is measured through metrics that jointly capture task accuracy and communication efficiency, which is particularly relevant for computer vision applications such as image classification, object detection, and scene understanding.  \nFor many emerging applications, communication must support latency-sensitive tasks, where end-to-end delay is the primary performance metric. Achieving low latency requires reducing the communication payload while preserving taskrelevant information, which challenges conventional transmission schemes employing separate source and channel coding. Recent works on joint source channel coding (JSCC) have shown that learned representations can provide compact and robust data compression tailored to downstream tasks [2]–[4] . In particular, vector-quantized variational autoencoder (VQVAE) [5] enables compression into a discrete latent space,  \nThis work was supported by the SNS JU project 6G-GOALS under the EU’s Horizon Europe program under Grant Agreement No 101139232 . The work by J. H. Inacio de Souza and P. Popovski was also supported by the Villum Investigator Grant “WATER” from the Velux Foundation, Denmark.  \nFigure 1 . System model for low-latency image transmission with opportunistic spectrum access for AI-based remote inference.  \nallowing the resulting representations to be transmitted using standard digital modulation schemes and integrated into link adaptation frameworks. Moreover, such representations have been shown to preserve task-relevant features, enabling competitive classification performance even under compression and channel-induced distortions [2] .  \nMost existing studies assume dedicated or continuously available communication resources. However, in cognitiv","cbCaibGkYF9t3EiX","https://ap.wps.com/l/cbCaibGkYF9t3EiX","pdf",6449158,1,7,"English","en",105,"# Introduction\n# System Model\n# Proposed Framework\n# Communication Latency Analysis\n# Numerical Results\n# Conclusion","[{\"question\":\"What problem does the paper target in communication latency?\",\"answer\":\"It targets high end-to-end latency caused by separate source/channel coding and limited spectrum availability with fading, which is critical for latency-sensitive downstream tasks like image understanding.\"},{\"question\":\"How does the proposed method transmit images under opportunistic spectrum access?\",\"answer\":\"It converts images into discrete latent representations using a vector-quantized variational autoencoder (VQ-VAE) and transmits them over idle licensed channels using standard digital modulation, enabling remote reconstruction of task-related information.\"},{\"question\":\"Which factors are included in the cross-layer latency model?\",\"answer\":\"The model jointly accounts for compression effects, block errors, retransmissions, and the stochastic availability of spectrum due to opportunistic channel 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