This article proposes a deep reinforcement learning-based hybrid optical–RF communication framework for Ultra-Reliable Low-Latency Communication (URLLC) networks under dynamic wireless and atmospheric channel conditions. The proposed system integrates OptiSystem-based optical channel modeling, MATLAB R2019b Simulink RF subsystem implementation, and a Deep Q-Network (DQN)-based adaptive transmission controller for intelligent hybrid link selection. The adaptive transmission problem is formulated as a Markov Decision Process (MDP), enabling dynamic selection among RF-only, FSO-only, and cooperative hybrid transmission modes according to real-time RF/FSO channel quality, queue status, and URLLC latency requirements. Performance evaluation is conducted using an integrated OptiSystem–MATLAB co-simulation framework with 100 Monte Carlo simulation runs under varying atmospheric attenuation and RF fading conditions. In addition, a laboratory-scale experimental hybrid optical–RF testbed operating at 1550 nm and 28 GHz is implemented to validate the practical feasibility and real-time operational performance of the proposed framework under realistic operating conditions. Simulation results demonstrate that the proposed DRL-based framework achieves an average latency reduction of approximately 28–35% compared to conventional RF-only transmission while improving throughput performance under favorable optical channel conditions. The adaptive hybrid framework maintains reliability approaching URLLC-grade communication performance across varying channel environments through intelligent adaptive transmission control. Experimental validation demonstrates close agreement with simulation predictions, with average deviation remaining below 6% across evaluated latency, reliability, and throughput metrics. The obtained results demonstrate that the proposed reinforcement learning-driven hybrid optical–RF communication framework can approach stringent URLLC latency and reliability targets under varying wireless and atmospheric channel conditions, supporting its application in mission-critical 5G and emerging 6G communication systems.
Indoor users served by outdoor macro base stations often experience degraded radio performance due to penetration loss, which leads to inefficient spectrum utilization. In a neutral-host deployment, an MNO subscriber entering an indoor venue should be able to offload seamlessly onto the neutral-host infrastructure and return to macro service upon leaving. However, this requires automated procedures for subscriber authentication across operator boundaries, session-level authorization that governs which subscribers may use the neutral-host radio resources, and a settlement mechanism that accounts for the volume and quality of service consumed during the offloading period. In practice, such inter-operator procedures remain largely ad hoc, which limits the adoption of neutral-host models despite their benefits. Settlement between MNOs and neutral-host operators is typically governed by fixed monthly fees, flat-rate infrastructure access charges, or coarse traffic-volume accounting that does not differentiate between high-throughput and degraded service. While such arrangements may account for the volume of data consumed, they do not reflect the radio-layer quality actually experienced by offloaded users. A neutral-host operator (NHO) that delivers consistently high throughput is compensated identically to one whose indoor coverage is impaired by cross-tier interference or contention, weakening the incentive to maintain service quality and making it difficult to resolve disputes when performance falls short of expectations. This paper proposes a blockchain-enabled settlement framework for indoor neutral-host Open RAN deployments that explicitly links radio-layer quality of service (QoS) to economic charging. The framework enables an MNO subscriber to temporarily offload onto a neutral-host network with smart contracts automating and enforcing agreements between participating entities (e.g., MNOs and the neutral host) to ensure secure and controlled use of third-party infrastructure. Throughput gains achieved via neutral-host service are quantified through off-chain radio performance evaluation and translated into reward–penalty pricing adjustments, while settlement is enforced on-chain to provide transparency and auditability. By coupling radio performance with economic settlement, the proposed approach introduces accountability into neutral-host operation and aligns financial incentives with spectrum utilization efficiency. The framework is evaluated on Ethereum using the Sepolia test network.
Cooperative cognitive radio networks (CCRANs) not only improve spectrum utilization efficiency by sharing spectrum resources between primary and secondary systems but also expand wireless coverage by relay cooperative communications, while UAV communication is a crucial supporting technology for the low-altitude economy. Conventional single-antenna rate-splitting multiple access (RSMA)-enabled CCRAN can only benefit from the power resource allocation and the associated system performance is limited. Against this background, a multi-antenna RSMA and UAV-enabled CCRAN is examined to improve the network transmission capability, and a two-phase communication protocol with scalable transmission duration is established to coordinate the information transmission between the primary and secondary systems. Consequently, two additional degrees of freedom (DoFs) of space and time resources are utilized in the proposed system as compared with conventional single-antenna CCRANs. To accomplish the system design, we formulate a non-convex optimization problem and derive the associated iterative algorithm based on successive convex approximation. Simulation results show that the proposed scheme can significantly enhance the system’s transmission capability.
Our primary focus in this study is on the potential benefits to military communications from integrating Non-Terrestrial Networks (NTN) into the existing 5G terrestrial network architecture. Despite current 3rd Generation Partnership Project (3GPP) standardization efforts, considerable impediments exist to the full adoption and adaptation of 5G Low Earth Orbit (LEO) satellite integration to satisfy the special demands and requirements of military communication. Military communications, unlike civilian networks, require architecture that can withstand complex and changing environments. This paper provides a complete review of the requirements for military communications. The requirements are based on actual military application scenarios, as well as standards developed by organizations such as the North Atlantic Treaty Organization (NATO), the United States (U.S.) Department of Defense (DoD), the European Defense Agency (EDA) and the China Central Military Commission (CMC). Furthermore, in this work, we propose a novel 5G-LEO NTN backhaul architecture that utilizes multi-link LEO aggregation and Virtual Private Network (VPN) tunneling. The proposed architecture is evaluated in a defense-oriented scenario, focusing on security, resilience and high-throughput communication.
As the railway industry undergoes digital transformation driven by AI, big data, and 5G/6G, secure data transmission for IoT-oriented railway operations has become a critical challenge in high-speed railway (HSR) wireless communications. Covert communication, which conceals wireless transmissions from eavesdroppers, together with reconfigurable intelligent surfaces (RISs), MIMO, and millimeter-wave technologies, offers a promising solution for enhancing security in such dynamic environments. This paper presents a comprehensive review of RIS-assisted covert communication for mmWave MIMO channels in HSR IoT networks. We examine security threats and the limitations of existing schemes, introduce the system architecture and channel modeling approaches, and summarize Doppler shift compensation techniques for high-mobility scenarios. In particular, this review introduces a service-priority-based covert constraint design framework as an original analytical perspective, rather than a fully validated engineering solution, to improve adaptability in diverse HSR operational contexts. We further provide a critical comparative review of optimization algorithms, including convex relaxation, heuristic, and deep reinforcement learning approaches, with a focus on their applicability and complexity trade-offs, and outline future research directions. Beyond summarizing the existing literature, this review integrates new analytical perspectives and simulation-based illustrations derived from our recent publications, offering both a systematic reference and forward-looking insights for researchers in this emerging field.
