Adaptive Semantic Token Communication for Transformer-based Edge Inference

--This paper presents an adaptive framework for edge inference based on a dynamically configurable transformer-powered deep joint source channel coding (DJSCC) architecture. Motivated by a practical scenario where a resource constrained edge device engages in goal oriented semantic communication, such as selectively transmitting essential features for object detection to an edge server, our approach enables efficient task aware data transmission under varying bandwidth and channel conditions. T o achieve this, input data is tokenized into compact high level semantic representations, refined by a transformer, and transmitted over noisy wireless channels. As part of the DJSCC pipeline, we employ a semantic token selection mechanism that adaptively compresses informative features into a user specified number of tokens per sample. These tokens are then further compressed through the JSCC module, enabling a flexible token communication strategy that adjusts both the number of transmitted tokens and their embedding dimensions. We incorporate a resource allocation algorithm based on Lyapunov stochastic optimization to enhance robustness under dynamic network conditions, effectively balancing compression efficiency and task performance. Experimental results demonstrate that our system consistently outperforms existing baselines, highlighting its potential as a strong foundation for AI native semantic communication in edge intelligence applications. ECENTL Y, there was a surge of interest in semantic and goal-oriented communications, establishing this paradigm as crucial for the development of 6G AI-native communication networks [1], [2]. A. Devoto is with the Department of Computer, Control, and Management Engineering (DIAG) at Sapienza University of Rome, Italy. He is also a member of the Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), Parma, Italy. Jary Pomponi, Paolo Di Lorenzo, and Simone Scardapane are with the Department of Information Engineering, Electronics, and Telecommunications (DIET) at Sapienza University of Rome, Italy, and they are also affiliated with CNIT. Mat-tia Merluzzi is with CEA-Leti, Universit e Grenoble Alpes, located in Grenoble, France. This work has been supported by the SNS JU project 6G-GOALS under the EU's Horizon program Grant Agreement No 101139232, by Sapienza grant RG123188B3EF6A80 (CENTS), and by European Union under the Italian National Recovery and Resilience Plan of NextGenerationEU, partnership on Telecommunications of the Future (PE00000001 - program REST ART).