Quantum optimization is poised to play a transformative role in the design of next-generation wireless communication systems by addressing key computational and technological challenges. This article provides an overview of the principles of adiabatic quantum computing, the foundation of quantum optimization, and explores its two primary computational models: quantum annealing and the gate-based quantum approximate optimization algorithm. By highlighting their core features, performance benefits, limitations, and distinctions, we position these methods as promising tools for advancing wireless communication system design. As a case study, we examine the design of passive reconfigurable intelligent surface beamforming with binary phase-shift resolution, supported by experimental results obtained from real-world quantum hardware.

Efficient indoor wireless network (IWN) planning is crucial for providing high-quality 5G in-building services. However, traditional meta-heuristic and artificial intelligence-based planning methods face significant challenges due to the intricate interplay between indoor environments (IEs) and IWN demands. In this article, we present an indoor wireless network Planning with large LANguage models (iPLAN) framework, which integrates multi-modal IE representations into large language model (LLM)-powered optimizers to improve IWN planning. First, we instate the role of LLMs as optimizers, outlining embedding techniques for IEs, and introducing two core applications of iPLAN: (i) IWN planning based on pre-existing IEs and (ii) joint design of IWN and IE for new wireless-friendly buildings. For the former, we embed essential information into LLM optimizers by leveraging indoor descriptions, domain-specific knowledge, and performance-driven perception. For the latter, we conceptualize a multi-agent strategy, where intelligent agents collaboratively address key planning sub-tasks in a step-by-step manner while ensuring optimal trade-offs between the agents. The simulation results demonstrate that iPLAN achieves superior performance in IWN planning tasks and optimizes building wireless performance through the joint design of IEs and IWNs, exemplifying a paradigm shift in IWN planning.

The rise of autonomous mobility has increased the demand for efficient energy management in Connected and Automated Electric Vehicles (CAEVs) and Unmanned Aerial Vehicles (UAVs). Traditional plug-in charging is often impractical due to fixed locations and operational downtime, driving interest in wireless solutions such as laser beaming and magnetic resonance charging. This article explores Deep Reinforcement Learning (DRL)-based wireless charging strategies within Intelligent Transportation Systems (ITS), leveraging Internet of Things (IoT) technologies for realtime energy optimization. DRL enables adaptive charging for ground-based, UAV-to-UAV, and UAV-to-CAEV energy transfer, enhancing efficiency and sustainability. However, challenges such as scalability, fairness, and real-time decision-making remain. Future research directions, including hybrid DRL-optimization models and federated learning, are discussed to improve system reliability and performance. As autonomous mobility evolves, DRL based wireless charging will be essential for seamless, resilient, and sustainable energy solutions in next generation ITS.

International audience

In this article, we present the latest advancements in Ambient IoT (A-IoT) research within 3GPP and explore the challenges of introducing A-IoT technology into 3GPP system. The diverse use cases, device types, design targets, and topologies of A-IoT pose significant design challenges due to factors such as energy harvesting, and the need for low cost, power efficiency, and complexity. Additionally, the mechanisms for A-IoT differ from existing 3GPP technologies. We introduce feasible high-level technical solutions, including waveform, modulation, multiplexing, duty-cycled monitoring, random access, continuous wave (CW) interference cancellation, and proximity determination for current and upcoming 3GPP releases.

Mutual information (MI)-based guidelines have recently proven to be effective for designing task-oriented communication systems, where the ultimate goal is to extract and transmit task-relevant information for downstream tasks. This paper provides a comprehensive overview of MI-empowered task-oriented communication, highlighting how MI-based methods can serve as a unifying design framework in various task-oriented communication scenarios. We begin with the roadmap of MI for designing task-oriented communication systems, and then introduce the roles and applications of MI to guide feature encoding, transmission optimization, and efficient training with two case studies. We further provide a deeper examination of the limitations and challenges associated with MI-based methods. Lastly, we highlight several open issues in MI-based task-oriented communication to inspire future research.