Abstracts:Presents the recipients of IEEE Vehicular Technology Society (VTS) awards for 2025.

Abstracts:With the advancement of artificial intelligence (AI) technology, semantic communication is rapidly emerging as one of the next-generation communication technologies. In this article, we first present the architecture to integrate semantic communication with the cloud-network-end (C-N-E) infrastructure. By deploying AI models and domain knowledge on the end and the cloud appropriately, we can efficiently manage, train, and apply AI models to significantly facilitate the extraction, transmission, and inference of semantic information over error-prone physical channels. This architecture realizes the integration of communication and computation. To demonstrate the advantages of this architecture, we design a drone recognition system utilizing the You Only Look Once (YOLO)-based Joint Source-Channel Coding (JSCC). The encoder and decoder are deployed at the detective drone and the cloud, respectively. We transform the optimization of the JSCC into an end-to-end autoencoder task while incorporating the physical channels as an untrainable component. We have also introduced additional convolutional layers to perform data consistency checks and enhance semantic security. The experiment results show that the JSCC effectively mitigates the performance cliff effect commonly observed in traditional communication systems under low signal-to-noise ratio (SNR) conditions. The JSCCs trained in harsh channel conditions have strong robustness against channel fading and severe data impairment.

Abstracts:On the path toward 6G, three trends, including radio access networks (RANs) cloudification, the convergence of communication and computing, as well as the adoption of artificial intelligence (AI), are reshaping the landscape of management and orchestration (M&O) for RAN. The RAN M&O should evolve itself to accommodate the requirements of the trend with intelligent automation for 6G. Toward this end, O-RAN has pioneered at this front with its proposed M&O framework called service management and orchestration (SMO). Building upon O-RAN, this paper further proposed an AI-native RAN SMO framework with the capability to support communication and computing convergence. The effectiveness and the superiority of the proposed framework was demonstrated in a vehicle-to-everything (V2X) scenario. Simulations showed that, thanks to the capability of joint communication and computing resource orchestration from the framework, AI-empowered algorithms could greatly improve the system performance in terms of latency of the computing tasks. In particular, under the arrival rate of 0.4 with a 500-B payload, our approach achieves over 35% latency reduction compared with benchmarks, and the gain increases to about 50% when the payload size varies within [350, 650] B.

Abstracts:We introduce the concept of 1Q, the first wireless generation of integrated classical and quantum communication. 1Q features quantum base stations (QBSs) that support entanglement distribution via free-space optical (FSO) links alongside traditional radio communications. Key new components include quantum cells, quantum user equipment (QUE), and hybrid resource allocation spanning classical time–frequency and quantum entanglement domains. Several application scenarios are discussed and illustrated through system design requirements for quantum key distribution (QKD), blind quantum computing (BQC), and distributed quantum sensing. A range of unique quantum constraints are identified, including decoherence timing, fidelity requirements, and the interplay between quantum and classical error probabilities. Protocol adaptations extend cellular connection management to incorporate entanglement generation, distribution, and handover procedures, expanding the quantum Internet (QI) to cellular wireless.