Code division multiple access (CDMA) is missing in 4G and 5G systems, albeit it possesses many salient technical advantages over OFDMA, such as its unique multipath diversity gain with RAKE receiver. This work aims to propose a way to vitalize the performance of CDMA systems with M-ary code modulation (i.e., M-ary CDMA). In particular, non-orthogonal multiple access (NOMA) technique is leveraged to augment spectrum efficiency. Built upon the foundation of a high spectrum efficiency of M-ary CDMA systems, NOMA is integrated with M-ary CDMA for enhancing its user capacity further. Specifically, this work showcases the designs of downlink transmitter and receiver structures for a NOMA based M-ary CDMA system, and validates the effectiveness of the proposed scheme in terms of improved user capacity and bit error rate via simulations. Finally, the challenges and limitations faced by the proposed scheme are identified and the directions for future research are given.
The upcoming era of 6G wireless technology will see base stations (BS) employing an immense array of antennas for communications and sensing. A cutting-edge approach to achieving these massive antenna arrays in a cost-effective, power-efficient, and scalable manner involves the use of metasurfaces embedded with radiating metamaterial elements. Traditionally, metasurfaces have been employed as passive reflectors in wireless networks, enhancing the capabilities of standard transceivers via manipulating the propagation environment. Leveraging the tunability and programmability of metamaterials, the reconfigurable intelligent metasurface antenna (RIMSA) array, which consists of many metamaterial-based radiating elements, is emerging as a viable solution to meet those ambitious goals. In this article, we introduce a RIMSA system including hardware structure, design principle, manner of beam control and RIMSA equipped transceiver. We further discuss the applications of RIMSA array for wireless communications and sensing, respectively. Finally, we identify several research challenges and future directions to fully exploit this RIMSA system.
Low Earth orbit (LEO) satellite integration is a key component of 6G non-terrestrial networks, but uplink (UL) access in LEO is fundamentally constrained by long round-trip time (RTT), rapid Doppler variation, and finite beam dwell time. For intermittent session traffic, the dominant cost is no longer a single random-access (RA) collision, but the repeated cold-start re-entry required after idle periods. Existing approaches therefore fail at opposite extremes: contention-based random access suffers from RTT-amplified retries, while terrestrial contention-free schemes assume warm synchronization or persistently reserved resources. This article identifies an intermediate UL access regime for session-oriented intermittent traffic and proposes a connection-lite (CL) mechanism that maintains lightweight access readiness rather than persistent resource reservation. By coupling synchronization refresh with access signaling through a periodic heartbeat, CL enables micro-grant-based UL transmission while reducing repeated RA and idle resource waste. Numerical results show that, for bursty session traffic, CL achieves substantially lower access delay and resource overhead compared to conventional schemes, making it a promising UL-access mode for satellite-enabled 6G networks.
In high-mobility and ultra-dense wireless networks, frequent handovers necessitate continuous beam scanning and measurements, incurring substantial measurement overhead and prolonged handover latency. Existing artificial intelligence (AI)-assisted handover solutions typically rely on lightweight, task-specific models and often generalize poorly across rapidly changing mobility conditions. This paper proposes a large language model (LLM)-empowered wireless channel quality prediction framework that forecasts the temporal evolution of inter-cell reference signal received power (RSRP) to enable proactive handover. Specifically, the framework integrates a beam-adaptive RSRP embedding module with a fine-tuned GPT-2 backbone, effectively bridging numerical RSRP and LLM representations while supporting arbitrary input beam configurations. This architecture enables the effective capture of long-range spatiotemporal dependencies within multi-cell RSRP measurements, achieving high-fidelity prediction accuracy across arbitrary beam configurations. Leveraging these channel quality predictions, we further develop a proactive handover mechanism that significantly reduces latency while enhancing reliability. Simulation results show improved prediction accuracy and generalization over baseline methods, and yield the lowest handover failure rates, indicating the promise of LLM-assisted proactive mobility management.
Satellite constellations equipped with Inter-Satellite Links (ISLs) and onboard packet switching enable real-time Operation and Management (O&M) across globally distributed satellites, but at the same time broaden the attack surface and expose the system to unprecedented cybersecurity threats. Existing efforts have primarily focused on optimizing cryptography for single-satellite, point-to-point links, without considering how a broader notion of security should be applied at the constellation level. To bridge this gap, this article advances in two directions: a network perspective—from individual satellites to constellation-wide architectures; and an expanded security perspective—from isolated cryptography to system-level security that embeds efficiency, resilience, and reliability. Together, these two expansions motivate three fundamental questions: (i) how can efficient security mechanisms be designed for dynamic constellation topologies that require adaptive onboard routing; (ii) how can a constellation O&M resiliently recover to an acceptable operational state under worst-case failures of onboard security functions; and (iii) how can the reliability of onboard security functions be enhanced under stringent onboard resource constraints. To address these challenges, we first construct a constellation-wide hybrid security framework that safeguards content field with high semantic sensitivity using End-to-End (E2E) encryption, while protecting routing-related fields through Moving Target Defense (MTD). Next, we introduce a ciphered-mode and safe-mode management mechanism with an M-delayed fallback that balances recovery timeliness against exploitability. Finally, to enhance reliability of onboard security functions, we propose security-aware routers that manage plaintext/ciphered modes and orchestrate access to a shared pool of onboard cipher modules, thereby enabling redundancy to be shared across multiple endpoints and significantly extending the duration of secure operation in ciphered mode. Overall, these solutions comply with existing standards defined by standardization organizations, including Digital Video Broadcasting (DVB) and the Consultative Committee for Space Data Systems (CCSDS), while translating their conceptual security principles into more practical, system-level mechanisms.
Intelligent reflecting surfaces (IRSs) enhance wireless network performance by intelligently reconfiguring the propagation environment to improve capacity, coverage, and energy efficiency, making them an appealing technology for future sixth-generation (6G) systems. However, conventional IRS design paradigms typically rely on pilot-training-based reflection updates to maintain communication quality in a dynamic environment, which are hindered by significant practical challenges, such as the high implementation overhead caused by frequent channel estimation and reflection reconfiguration, excessive system cost due to base station (BS)-IRS coordination, and lack of robustness against non-ideal channel conditions. To tackle these challenges, this article provides a comprehensive overview of the promising quasi-static IRS reflection design framework for wireless network coverage enhancement, by utilizing easily accessible received signal power measurements at user terminals. Practical design issues and open problems in optimizing this framework are discussed, including long-term network coverage performance characterization, power measurement collection under existing network protocols, and power-measurement-based IRS reflection optimization and robust design strategies. Ray tracing-based simulation results validate the effectiveness of the quasi-static IRS reflection design in practical narrowband and wideband systems. Lastly, promising approaches for IRS deployment and reflection optimization in more general settings are discussed to guide future research.
Industrial automation applications increasingly adopt wireless technologies and mixed wired-wireless networking. However, over a wireless channel, meeting the stringent performance requirements of such applications becomes challenging. That is especially critical for safety communication, i.e., exchange of data used to keep workers and machinery protected. This paper investigates an emerging safety solution, Open Platform Communications Unified Architecture (OPC UA) Safety, and explores how Time-Sensitive Networking’s Frame Replication and Elimination for Reliability (FRER) can enhance communication performance for OPC UA Safety over Wi-Fi networks. Using an emulation-based approach, different redundancy strategies and their impact are analyzed. The results demonstrate that, even under worst-case, high-load network conditions, applying FRER across asymmetrically loaded, independent Wi-Fi paths provides a substantial benefit. For example, round-trip time of safety messages is reduced by up to 92% as compared to single-path Wi-Fi, while communication availability is improved from near 0% to over 85%.
UAV RF surveillance is becoming a critical layer of low-altitude airspace security, yet most fingerprinting pipelines still assume that every signal belongs to a known class. In open-world deployments, where new platforms, protocol updates, and changing propagation conditions are routine, that assumption makes conventional classifiers brittle and often overconfident on unseen emitters. Foundation models offer a compelling alternative because their pretrained priors can support richer sequence understanding beyond task-specific training. We present SkyLLM, a foundation-model-driven framework that aligns RF time-series with the token space of a frozen LLM through patch-based embedding, cross-channel fusion, and prompt conditioning, so that waveform fragments can be interpreted in a representation space better suited to novelty-aware decision making. Experiments on real UAV RF data show that SkyLLM preserves strong discrimination while delivering markedly more reliable unknown rejection than lightweight baselines across increasingly open settings. More broadly, the study suggests that future trusted spectrum intelligence will depend not only on better classifiers, but on representation layers that remain stable as the airspace evolves. SkyLLM therefore offers a practical path toward 6G-era UAV monitoring systems that must combine recognition, uncertainty awareness, and operational trust.
Multi-satellite collaborative communications (MSCC) is emerging as a cornerstone of 6G satellite networks to ensure ubiquitous coverage and high quality of service (QoS). However, the non-stationary channel conditions, limited link resource constraints, highly dynamic topology, and rapidly increasing problem scale pose significant challenges to traditional optimization methods. Deep reinforcement learning (DRL) offers a promising solution, yet current algorithms are tailored to specific problems and lack a holistic perspective. To bridge this gap, this paper presents a comprehensive survey and proposes an intelligent, generalized architecture for multi-satellite collaboration. First, we synthesize a set of algorithmic design principles along the dimensions of problem type, observability, and system scale, providing a systematic guide for algorithm selection. Subsequently, guided by these principles, we establish a cloud-edge-terminal hierarchical federated DRL framework. This architecture leverages geostationary earth orbit (GEO) satellites as intermediate aggregators and global context providers, bridging the gap between the ground cloud’s massive computing power and low earth orbit (LEO) edge’s real-time inference needs. This design effectively mitigates partial observability and feeder link congestion, facilitating the resolution of communication optimization issues spanning from the physical layer to the network layer. We propose a hierarchical multi-satellite collaborative framework where LEO agents, guided by GEO-aggregated global context, employ the multi-agent proximal policy optimization (MAPPO) based algorithm to dynamically optimize semantic modality selection and access control. Simulation results validate that this approach effectively manages multi-level image and text semantic transmission, enhancing system adaptability. Finally, we outline future directions, including communication-computing synergy and embodied intelligence, to guide the evolution of autonomous satellite networks.
Facing the advent of 6G, wireless networks are shifting from connectivity-centric architectures to intelligence-centric systems. However, under fragmented computing-networking coordination, semantic-resource mismatch, and highly dynamic wireless conditions, existing solutions struggle to simultaneously meet the diverse and compound requirements of multimodal sensing, intelligent decision-making, and generative applications. To address these limitations, we propose Service-Generated Networking (SGN), a unified framework that integrates semantic understanding based on Large Artificial Intelligence Models (LAMs), agentic generative decision-making, and a programmable computing-networking substrate. Through this integration, SGN enables the network to understand, generate, and optimize services autonomously. We first review the development trends of Computing-Networking Convergence (CNC) and Generative AI (GAI), identifying the key challenges and the emerging opportunities for 6G. We then present the design motivation, architectural principles, and end-to-end agentic workflow of SGN, followed by an exposition of its core mechanisms in three representative 6G application domains. Using a video-generation orchestration scenario at the wireless edge, we further demonstrate how SGN-generated initial heuristic strategies significantly accelerate hybrid optimizer convergence while improving robustness and fairness. Finally, we outline several open challenges and promising research directions toward realizing fully AI-native and agentic wireless networks.
The rapid development of 6G, Internet of Things, and low-altitude wireless networks has greatly increased the demand for accurate wireless position sensing. This paper focuses on passive position sensing which estimates signal source’s position without active transmission by solely analyzing received signals. We identify and summarize the key factors influencing position sensing accuracy across three dimensions including network configuration, parameter acquisition, and algorithmic processing, and highlight unresolved technical challenges. To systematically address these issues, we propose a novel framework named PCDL integrating four functional modules, Perception, Cognition, Decision, and Learning. We demonstrate effectiveness of the PCDL framework by a case study, validating its ability on improving the localization accuracy for the sources in the blind zone. The PCDL framework structures an integration of artificial intelligence into the position sensing workflow, guiding the design for the future intelligent wireless position sensing system.
Motivated by the transformative potential of integrating uncrewed aerial vehicles (UAVs) and stacked intelligent metasurfaces (SIMs) into low-altitude aerial intelligent networks (LAINs), this article provides a comprehensive investigation of UAV-SIM technologies within integrated sensing and communication (ISAC) systems. By synergizing physical mobility with real-time electromagnetic wavefront manipulation, the UAV-SIM paradigm is poised to significantly enhance system performance across diverse operational scenarios. Despite these promising prospects, deploying UAV-SIM technology in LAINs faces substantial bottlenecks, particularly in the accurate characterization of complex air-to-ground propagation environments and the design of efficient optimization algorithms. To address these challenges, we propose a unified channel model-driven optimization framework tailored to ensure reliable quality of service for UAV-SIM-enabled LAINs. Utilizing the 3GPP TR 38.901 channel model as a realistic benchmark, this framework establishes a rigorous foundation for synthesizing and validating resource allocation strategies, ultimately yielding superior system capacity and reliability. Furthermore, to improve the robustness and efficiency of end-to-end information processing, we develop a deep reinforcement learning (DRL) scheme based on the mixture-of-experts architecture. This specialized approach facilitates the optimal management of network resources, thereby enabling highly efficient signal transmission. Finally, we outline open research directions and emerging trends to stimulate future investigations in the realm of UAV-SIM-enabled LAINs.
The enhanced long-range navigation system (eLoran), a terrestrial navigation system renowned for its strong anti-interference ability and long transmission distance, is internationally acknowledged as the most resilient backup for global navigation satellite systems. In this article, we first elaborate the positioning principle of the eLoran, from which we derive that three modules at the receiver, namely, sky-ground waves recognition, additional secondary factor estimation, and coordinate calculation, are critical to improve positioning accuracy. We then provide an in-depth review on the recent progresses on the design of each module, and rigorously discuss the main challenges imposed on the related algorithms for each module and possible future research directions. Finally, we adopt the practical measurement data to exhibit the state-of-the-art positioning results achieved by the currently most representative algorithms.
Spectrum map is emerging as an effective tool to characterize and visualize the radio environment for covert communications. However, current approaches for spectrum map acquisition still face significant challenges in terms of accuracy and adaptability in highly complex and rapidly changing environments. Generative artificial intelligence (GAI), with its powerful capability in data learning and generation, can provide a promising solution. In this article, we demonstrate a comprehensive study of GAI-based spectrum maps for covert communications. Specifically, we first classify spectrum maps, detail their construction methods from conventional to emerging approaches, and present a comparative analysis. Then, we review how GAI generates spectrum maps, illustrate their applications in covert communications, and highlight research gaps. To this end, we propose a novel hybrid framework with diffusion models to enable the rapid generation of fine-grained spectrum maps. We further present a case study to demonstrate its effectiveness in enhancing the covertness. Finally, three future directions for applying GAI-based spectrum maps to covert communications are outlined to further promote the research progress.
For next-generation wireless communications pursuing high energy efficiency and high throughput, stacked intelligent metasurfaces (SIM) have attracted growing attention due to the signal processing capability in the wave domain. The SIM is composed of multiple layers of programmable transmissive metasurfaces that cooperatively manipulate the amplitude and phase of electromagnetic waves. Currently, most existing studies are conducted under the ideal phase-shift assumption, neglecting the practical modeling based on the electromagnetic response, and often overlook practical hardware non-idealities, including inter-layer misalignment, random unit failures, and manufacturing tolerances, which may significantly degrade the system reliability and overall performance. In this paper, we first establish a element-level modeling framework by integrating the physical attributes of transmissive elements with their equivalent circuit responses, ensuring an electromagnetic characterization that is more consistent with reality. This foundation provides a more practical basis for subsequent system-level algorithm designs. Building on this, various hardware errors encountered in practical deployments are categorized and comprehensively analyzed. Furthermore, we survey the current landscape of element design and explore future research directions and challenges for SIM applications. Finally, potential solutions are provided alongside an outlook on the vision of SIM-aided next-generation communication systems.
Intelligent reflecting surfaces (IRSs) have emerged as a promising technology for enhancing wireless communications by dynamically shaping the propagation environment using controllable passive elements. However, installation angle and position errors must be addressed for real-world IRS implementation, as neglecting these physical misalignments significantly degrades beamforming accuracy and communication quality. This study addresses this challenge by introducing the concept of IRS calibration. Calibration is the process of estimating the incident and reflection angle offsets and correcting them via phase control on the IRS side to recover optimal beamforming. Key design issues are identified by performing calibration under real-world constraints. It is proposed that decoupled angular offsets can be uniquely identified using power measurements at two receiver points. The effectiveness of the proposed method is verified via experiments in the 28 GHz band using actual IRS hardware. The experimental results confirm that the impact of angular errors is successfully reduced across various error scales and reflection directions. The findings of this study highlight the importance and feasibility of calibration as a basis for real-world IRS deployment.
Precise channel state knowledge is crucial in future wireless communication systems, which drives the need for accurate channel prediction without additional pilot overhead. While machine-learning (ML) methods for channel prediction show potential, existing approaches have limitations in their capability to adapt to environmental changes due to their extensive training requirements. In this paper, we introduce the channel prediction approaches in terms of the temporal channel prediction and the environmental adaptation. Then, we elaborate on the use of the advanced ML-based channel prediction to resolve the issues in traditional ML methods. We also analyze the training process, dataset characteristics, and the influence of source tasks and pre-trained models on channel prediction performance, demonstrating their effects under different adaptation samples and environments. Furthermore, we propose practical model selection criteria based on latency constraints, data availability, and environmental dynamics to support effective deployment in wireless communications. Finally, we discuss open challenges and possible future research directions of ML-based channel prediction.
Covert communication plays a crucial role in secure and reliable information transmission by preventing unauthorized detection and jamming attacks. However, its dependence on traditional strategies such as random transmit power, timing shifts, and location changes degrades received signal quality. Moreover, since covert transmission embeds secret data within legitimate signals, the overall data capacity is limited. Integrating covert communication with semantic communication and sensing can overcome these limitations. Semantic communication conserves spectrum under low received signal quality, while sensing technologies enhance wireless environmental awareness. This paper explores how semantic communication and sensing strengthen covert communication. We first review the popular covert strategies and the fundamentals of semantic and sensing techniques, analyzing their potential benefits for covert systems. Then, we propose an unmanned aerial vehicle (UAV)-reconfigurable intelligence surface (RIS)-assisted covert communication model that integrates semantic communication and sensing. Simulation results demonstrate improved energy efficiency and maintained covertness. Finally, potential applications and future research directions for semantic and sensing-enhanced UAV-assisted covert communication are discussed.
The Terahertz (THz) band offers vast spectrum resources that enable ultra-high-rate communication and high-precision sensing, positioning THz integrated sensing and communication (THz-ISAC) as a key technology for future wireless systems. This paper provides a sensing-centric investigation of THz-ISAC, reviewing representative THz sensing applications and clarifying why sensing naturally precedes and supports THz communication. Two complementary THz measurement platforms are introduced, from which sensing-channel data and a large-scale material database are obtained. Building on these datasets, the paper summarizes core signal-processing techniques for THz sensing, including high-resolution parameter estimation, environment reconstruction, and material identification. Leveraging empirical measurements, a hybrid THz sensing channel model is developed, and environment reconstruction results are demonstrated, achieving millimeter-level accuracy. Finally, open challenges and future research directions are discussed.
Reflecting intelligent surface (RIS) is a promising technology for 6G mobile communications. However, identifying the niche of RIS within the mobile networks is a challenging task. To mitigate the escalating system complexity of mobile networks, we propose the concept of Intelligent Reflection as a Service (IRaaS), and discuss its system architecture, enabling technologies, and deployment strategy, respectively. By leveraging technologies such as resource pooling, service based architecture (SBA), cloud infrastructure, and model-free signal processing, IRaaS empowers telecom operators to deliver on-demand intelligent reflection services without a radical update of current communication protocols. In addition, IRaaS brings a novel deployment strategy that creates new opportunities for the vendors of intelligent reflection service and balances the interests of both telecom operators and property owners. IRaaS is expected to speed up the rollout of RIS from both technical perspective and commercial perspective, fostering an authentic smart radio environment for future mobile communications.
