The rapid scaling of massive multiple-input multiple-output (MIMO) systems toward future gigantic MIMO arrays imposes prohibitive power and hardware bottlenecks on conventional digital beamforming designs. To overcome these limitations, this paper introduces the microwave linear analog computer (MiLAC) into the front-end of massive MIMO systems, establishing a comprehensive cross-layer framework that unifies algorithmic design with physical hardware implementation. We first formulate the system model, detailing a systematic mapping from the target digital-domain combining matrix to the tunable electromagnetic admittance network, thereby realizing sophisticated beamforming directly in wave domains. Furthermore, to compensate for the inevitable non-idealities of practical analog components, we propose a novel hardware-in-the-loop adaptive calibration mechanism. Specifically, driven by gradient back-propagation, this closed-loop scheme iteratively mitigates admittance programming errors via pilot probing and error feedback. Case studies demonstrate that this strategy consistently minimizes the hardware-to-algorithm mismatch, translating enhanced matrix-level precision into substantial system-level sum-rate gains. Finally, we present several key challenges and research directions for future MiLAC-enabled wireless networks.
Collaborations among distributed on-device Large Language Models (LLMs) can overcome the limited capability of a single model, yet prevailing token-level exchanges suffer from additional pre-fill computation, and inherent information bottleneck. To address these challenges, this article advocates a paradigm shift from discrete tokens to KV caches, the rich continuous-space attention states that LLMs already maintain, as the native communication medium for cross-model collaboration. We systematically analyze why the token interface is insufficient on its own for efficient federated LLM inference and present FedCacheInf, a cache communication-enabled framework that encompasses cache projection for cross-model compatibility, flexible cache integration strategies, and compression techniques. Diverse numerical results demonstrate the superiority of cache communications over token-based approaches in accuracy and efficiency, while identifying the regimes in which token exchange remains preferable. We further outline open research directions to advance cache-centric LLM collaboration toward next-generation AI-native networks. By exploring the LLM-native communications, we wish to provide a new paradigm for this broad area.
Maritime Internet of Things (IoT) deployments increasingly rely on the space-air-ground-sea integrated network (SAGSIN) to connect underwater sensors with terrestrial and space backbones. However, the heterogeneous links along this path, ranging from bandwidth-limited and energy-hungry underwater acoustic channels to high-capacity optical links above the sea, make the transport of massive raw sensing data costly in both latency and energy, and difficult to sustain for unattended, battery-powered nodes. This article presents an edge-computing paradigm for SAGSIN built on two coupled ideas: a Multi-Layer Network Architecture (MLNA) that organizes the underwater, surface, aerial, and ground/space tiers, and Multi-Level Information Processing (MLIP) that progressively refines data from raw measurements toward compact, event-level representations as they ascend the network. We characterize the computation, transmission, and storage energy at each tier and show how distributing refinement across layers trades local processing cost against transmission and storage savings. We then discuss how MLNA-MLIP reduces latency, strengthens data privacy, improves service reliability, and manages energy to prolong network lifetime. A case study on offshore monitoring quantifies the resulting lifetime gains and identifies the optimal processing depth. Open challenges and future directions are outlined.
The rapid evolution of smart high-speed railway (HSR) systems has driven a significant surge in data traffic. However, existing HSR communication systems still face severe challenges due to scarce radio resources, severe Doppler effects, and rapidly time-varying channels, as traditional communication methods based on precise bit-level transmission struggle to achieve further notable improvements in capacity and spectral efficiency. To address this, this article presents an HSR semantic communication (SemCom) paradigm and proposes a tailored framework for it. Leveraging the advantages of SemCom in semantic compression, context awareness, and error-tolerant inference, the proposed HSR SemCom framework aims to enhance the system’s effective capacity, task adaptability, and service reliability, providing technical support for efficient and intelligent next-generation railway communication. Particularly, we first outline the challenges facing HSR communication systems and then propose an HSR SemCom framework, detailing how the sensing layer, network access layer, semantic layer, and application layer coordinate with each other. Moreover, we systematically analyze the benefits of HSR SemCom in typical HSR scenarios, including in-train (ITC), train-to-ground (T2G), train-to-train (T2T), and train-to-satellite (T2S) communications. A Swin Transformer-based T2G SemCom image-transmission case study is provided to demonstrate the potential of SemCom in improving transmission efficiency over HSR T2G channels. Finally, promising research directions are identified to guide future work.
This article conceives an electromagnetic-compliant architecture for practical deployments of holographic multiple-input multiple-output (HMIMO) systems, addressing an intractable dilemma: “How to avoid mutual coupling under sub-half-wavelength antenna spacing?” To answer this question, we pivot from conductor-based antennas to Rydberg atomic receivers and present design principles of mutual coupling-free HMIMO. In re-examining the fundamental limitations of conductor-based antennas, we demonstrate the inherent superiorities of Rydberg atomic receivers (RARs) from an electromagnetic-compliant perspective. Subsequently, a thorough comparison of the signal detection mechanism between two types of RAR is provided, facilitating a unified review on the operating principles of RAR. Furthermore, we provide a comprehensive exposition on the proposed RAR-based holographic MIMO architecture, along with crucial engineering considerations in practical deployments.
