Search Results (193)

Extended reality (XR), encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), is rapidly reshaping the landscape of digital interaction and immersive communication. As XR evolves toward ultra-realistic, real-time, and interactive experiences, it places unprecedented demands on wireless communication systems in terms of bandwidth, latency, and reliability. Conventional RF-based networks, constrained by limited spectrum and interference, struggle to meet these stringent requirements. In contrast, visible light communication (VLC) offers a compelling alternative by exploiting the vast unregulated visible spectrum to deliver high-speed, low-latency, and interference-free data transmission—making it particularly suitable for future XR environments. This paper presents a comprehensive survey on VLC-enabled XR communication systems. We first analyze XR technologies and their diverse quality-of-service (QoS) and quality-of-experience (QoE) requirements, identifying the unique challenges posed to existing wireless infrastructures. Building upon this, we explore the fundamentals, characteristics, and opportunities of VLC systems in supporting immersive XR applications. Furthermore, we elaborate on the key enabling techniques that empower VLC to fulfill XR’s stringent demands, including high-speed transmission technologies, hybrid VLC-RF architectures, dynamic beam control, and visible light sensing capabilities. Finally, we discuss future research directions, emphasizing AI-assisted network intelligence, cross-layer optimization, and collaborative multi-element transmission frameworks as vital enablers for the next-generation VLC–XR ecosystem. Full article

The rapid proliferation of connected vehicles equipped with both Vehicle-to-Vehicle (V2V) sidelink and cellular interfaces creates new opportunities for real-time vehicular applications, yet achieving ultra-reliable communication without prohibitive cellular costs remains challenging. This paper addresses reliable inter-vehicle video streaming for safety-critical applications such as See-Through for Passing and Obstructed View Assist, which require stringent Service Level Objectives (SLOs) of 50 ms latency with 99% reliability. Through measurements in Seoul urban environments, we characterize the complementary nature of V2V and Vehicle-to-Network-to-Vehicle (V2N2V) paths: V2V provides ultra-low latency (mean 2.99 ms) but imperfect reliability (95.77%), while V2N2V achieves perfect reliability but exhibits high latency variability ($P_{99}$: 120.33 ms in centralized routing) that violates target SLOs. We propose a hybrid framework that exploits V2V as the primary path while selectively retransmitting only lost packets via V2N2V. The key innovation is a dual loss detection mechanism combining gap-based and timeout-based triggers leveraging Real-Time Protocol (RTP) headers for both immediate response and comprehensive coverage. Trace-driven simulation demonstrates that the proposed framework achieves a 99.96% packet reception rate and 99.71% frame playback ratio, approaching lossless transmission while maintaining cellular utilization at only 5.54%, which is merely 0.84 percentage points above the V2V loss rate. This represents a $7\times$ cost reduction versus PLR Switching (4.2 GB vs. 28 GB monthly) while reducing video stalls by $10\times$. These results demonstrate that packet-level selective redundancy enables cost-effective ultra-reliable V2X communication at scale. Full article

The rapid growth of wireless devices, the expansion of the Internet of Things, and the aggregate demand for Ultra-Reliable Low-Latency communications (URLLC) are driving the improvement of next-generation wireless systems. One promising emerging technology in this area is cell-free massive Multiple Input Multiple Output (maMIMO) networks. The distributed nature of Access Points presents unique security challenges that must be addressed to unlock their full potential. This paper studies the key security concerns in Cell Free Massive MIMO (CFMM) networks, including eavesdropping, Denial-of-Service attacks, jamming, pilot contamination, and methods for enhancing Physical Layer Security (PLS). We also provide an overview of security solutions specifically designed for CFMM networks and introduce a case study of a Reconfigurable Intelligent Surface (RIS)-aided secure scheme that jointly optimizes the RIS phase shifts with the artificial noise (AN) covariance under power constraints. The non-convex optimization problem is solved via the block coordinate descent (BCD) alternating optimization scheme. The combined RIS, AN, and beamforming configuration achieves a balanced trade-off between security and energy performance, resulting in moderate improvements over the individual schemes. Full article

The advancement of Automotive Industry 4.0 has promoted the development of Vehicle to Vehicle (V2V) and Internet of Vehicles (IoV) communication, which marks the new era for intelligent, connected and automated transportation. Despite the benefits of this metamorphosis in terms of effectiveness and convenience, new obstacles to safety, inter-connectivity, and cybersecurity emerge. The tire pressure monitoring system (TPMS) is one prominent feature that senses tire pressure, which is closely related to vehicle stability, braking performance and fuel efficiency. However, the majority of TPMSs currently in use are based on the use of insecure and proprietary wireless communication links that can be breached by attackers so as to interfere with not only tire pressure readings but also sensor data manipulation. For this purpose, we design a secure TPMS architecture suitable for real-time IoV sensing. The framework is experimentally implemented using a Raspberry Pi 3B+ (Raspberry Pi Ltd., Cambridge, UK) as an independent autonomous control unit (ACU), interfaced with vehicular pressure sensors and a LoRa SX1278 (Semtech Corporation, Camarillo, CA, USA) module to support low-power, long-range communication. The gathered sensor data are encrypted, their integrity checked, source authenticated by lightweight cryptographic algorithms and sent to a secure server locally. To validate this approach, we show a three-node exhibition where Node A (raw data and tampered copy), B (unprotected copy) and C (secure auditor equipped with alerting of tampering and weekly rotation of the ID) realize detection of physical level threats at top speeds. The validated datasets are further enriched in a MATLAB R2024a simulator by replicating the data of one vehicle by 100 virtual vehicles communicating using over 5G, LoRaWAN and LoRa P2P as communication protocols under urban, rural and hill-station scenarios. The presented statistics show that, despite 5G ultra-low latency, LoRa P2P consistently provides better reliability and energy efficiency and is more resistant to attacks in the presence of various terrains. Considering the lack of private vehicular 5G infrastructure and the regulatory restrictions, this work simulated and evaluated the performance of 5G communication, while LoRa-based communication was experimentally validated with a hardware prototype. The results underline the trade-offs among LoRa P2P and an infrastructure-based uplink 5G mode, when under some specific simulation conditions, as opposed to claiming superiority over all 5G modes. In conclusion, the presented Raspberry Pi–MATLAB hybrid solution proves to be an effective and scalable approach to secure TPMS in IoV settings, intersecting real-world sensing with large-scale network simulation, thus enabling safer and smarter next-generation vehicular systems. Full article

This study investigates performance overheads and security threats in 6th Generation Mobile Communication (6G) roaming environments, which are expected to enable services such as autonomous driving, smart cities, and remote healthcare that demand ultra-low latency and high reliability. To bridge the gap between standardization and real-world deployment, we built a realistic roaming testbed by separating the home and visited public land mobile networks (H-PLMN and V-PLMN) and simulating user equipment (UE) interactions. In this environment, we defined and measured roaming cost by comparing non-roaming and roaming procedures, and reproduced two Subscription Concealed Identifier (SUCI)-based denial-of-service (DoS) attacks: random generation and replay. Our experiments showed that intermediary functions such as the Security Edge Protection Proxy (SEPP) and Service Communication Proxy (SCP) introduced CPU/memory overhead and latency, highlighting performance degradation unique to roaming. Moreover, random SUCI generation concentrated load on the Authentication Server Function (AUSF) in the H-PLMN, whereas replay attacks distributed it across both the H-PLMN and the V-PLMN, consistently identifying the AUSF as a bottleneck. These findings demonstrate that roaming enlarges the attack surface and exposes vulnerabilities not fully addressed in current standards. We conclude that secure and reliable 6G roaming requires multi-layered defense strategies with inter-operator cooperation, providing empirical evidence to guide standardization and operational practice. Full article

The Peak Age of Information (PAoI) quantifies the freshness of updates used in cyber-physical systems (CPSs), realized within the Internet of Things (IoT) paradigm, encompassing devices, networks, and control algorithms. Consequently, PAoI is a critical metric for real-time applications enabled by Ultra-Reliable Low Latency Communication (URLLC). While highly useful for system evaluation, the direct analysis of this metric is complicated by the correlation between the random variables constituting the PAoI. Thus, it is often evaluated using only the mean value rather than the full distribution. Furthermore, since CPS communication technologies like Wi-Fi or DECT-2020 involve multiple processing stages, modeling them as tandem queueing systems is essential for accurate PAoI analysis. In this paper, we develop an analytical model for a DECT-2020 network segment represented as a two-phase tandem queueing system, enabling detailed PAoI analysis via Laplace–Stieltjes transforms (LST). We circumvent the dependence between generation and sojourn times by classifying updates into four mutually exclusive groups. This approach allows us to derive the LST of the PAoI and determine the exact Probability Density Function (PDF) for $M\left|M\right|1\to M\left|M\right|1$ system. We also calculate the mean and variance of the PAoIs and validate our results through numerical experiments. Additionally, we evaluate the impact of different service time distributions on PAoI variability. These findings contribute to the theoretical understanding of PAoI in tandem queueing systems and provide practical insights for optimizing DECT-2020-based communication systems. Full article

The rapid proliferation of connected and autonomous vehicles in the 6G era demands ultra-reliable and low-latency computation with intelligent resource coordination. Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) provides a flexible and scalable solution to extend coverage and enhance offloading efficiency for dynamic Internet of Vehicles (IoV) environments. However, jointly optimizing task latency, user fairness, and service priority under time-varying channel conditions remains a fundamental challenge.To address this issue, this paper proposes a novel Multi-Agent Priority-based Fairness Adaptive Delayed Deep Deterministic Policy Gradient (MA-PF-AD3PG) algorithm for UAV-assisted MEC systems. An occlusion-aware dynamic deadline model is first established to capture real-time link blockage and channel fading. Based on this model, a priority–fairness coupled optimization framework is formulated to jointly minimize overall latency and balance service fairness across heterogeneous vehicular tasks. To efficiently solve this NP-hard problem, the proposed MA-PF-AD3PG integrates fairness-aware service preprocessing and an adaptive delayed update mechanism within a multi-agent deep reinforcement learning structure, enabling decentralized yet coordinated UAV decision-making. Extensive simulations demonstrate that MA-PF-AD3PG achieves superior convergence stability, 13–57% higher total rewards, up to 46% lower delay, and nearly perfect fairness compared with state-of-the-art Deep Reinforcement Learning (DRL) and heuristic methods. Full article

Emergency scenarios in the Internet of Vehicles (IoV) face significant challenges due to the stringent requirements for ultra-reliable and low-latency communication under high-mobility conditions. This paper proposes a cooperative transmission framework for semantic communication to address these challenges. We introduce a knowledge graph-based approach to represent information as semantic triples (structured entity-relation-attribute representations), whose importance is quantified using a Zipf distribution, enabling prioritized transmission. At the physical layer, a semantic-aware cooperative communication scheme is proposed to combat fading and enhance transmission reliability. The joint optimization of the number of transmitted triples and node power allocation is formulated as a cross-layer problem. To tackle this Mixed-Integer Nonlinear Programming (MINLP) problem with a hybrid action space, we employ the Multi-Pass Deep Q-Network (MP-DQN) algorithm, which is specifically designed for problems with hybrid discrete-continuous action spaces. Simulation results demonstrate that our framework dynamically adapts to channel states and semantic value, achieving up to 85% end-to-end success rate and improving convergence speed by approximately 40% compared to conventional methods. Full article

In ad hoc networks employing Autonomous Aerial Vehicles (AAVs), the importance of real-time applications and edge computing is steadily increasing. However, existing routing protocols still fail to meet the strict performance requirements under the unique conditions of AAV networks, where the network topology changes continuously, and nodes move at high speed. This paper presents SHARP-AODV (Stability Heuristic Adaptive Routing Protocol—AODV), an enhanced routing protocol specifically developed for AAV networks. SHARP-AODV introduces two key innovations: (1) an intelligent RREQ (Route Request) dissemination mechanism that combines neighbor density control with a multi-parameter probabilistic model, and (2) a multi-criteria path selection mechanism that jointly considers hop count, link quality, and resource state. Simulation results in NS-3 across four distinct mobility models and various numbers of AAV nodes show that SHARP-AODV significantly outperforms standard AODV, improving packet delivery ratio (PDR) by up to 23.9%, increasing throughput by up to 61%, while reducing end-to-end delay by up to 87.8% and jitter by up to 90.6%. The proposed protocol is especially suitable for AAV-enabled applications in Edge Computing and Metaverse ecosystems that require low-latency, highly reliable connectivity with adaptation to dynamic network conditions. Furthermore, SHARP-AODV satisfies 6G network requirements for connection reliability, ultra-low latency, and high device density, unlocking new opportunities for employing AAVs in smart cities, environmental monitoring, and distributed VR/AR systems. Full article

Nowadays, renewable energy sources are gaining importance, yet global energy demand is primarily met by burning fossil fuels. Fluctuations in fossil fuel availability, driven by geopolitical tensions, supply–demand changes, and natural disasters, can lead to sudden energy price spikes or supply shortages, adversely affecting the global economy. Despite its negative impact on carbon emissions and climate change, Heating Oil (HO) offers advantages over other fossil fuels in efficiency, reliability, and availability. Accurate time series prediction models for HO are crucial for stakeholders. This study proposes a novel hybrid model, integrating the Chaotic Adaptive Fitness-Distance Balance-based Stochastic Fractal Search (AFDB-SFS) algorithm with a Bidirectional Long-Short Term Memory (Bi-LSTM) network, for HO close price prediction. The dataset comprises daily observations of five financial time series (close, open, high, low, and volume) over 4260 trading days, yielding a total of 21,300 data points (4260 days × 5 variables). During the feature extraction stage, financial signal processing methods such as Demand Concentration Curve (DCC) and traditional technical indicators are utilized. A total of 189 features are extracted at appropriate intervals for each indicator. Due to the large number of features, the AFDB-SFS algorithm then efficiently identifies the most compatible feature subsets, optimizing the Bi-LSTM model based on three criteria: maximizing R², minimizing RMSE, and minimizing feature count. Experimental results demonstrate the proposed hybrid model’s superior performance, achieving high accuracy (R² of 0.9959 and RMSE of 0.0364), outperforming contemporary models in the literature. Furthermore, the model is successfully implemented on the Jetson Orin Nano Developer Platform, enabling real-time, high-frequency HO price predictions with ultra-low latency (1.01 ms for Bi-LSTM), showcasing its practical utility for edge computing applications in commodity markets. Full article

This paper proposes a transmission-path selection algorithm with joint computation and communication resource allocation for sixth-generation (6G) mobile edge computing (MEC) networks enhanced by helper-assisted device-to-device (D2D) communication and reconfigurable intelligent surfaces (RIS). The novelties of this work lie in the joint design of three key components: a helper-assisted D2D uplink scheme, a packet-partitioning cooperative MEC offloading mechanism, and RIS-assisted downlink transmission and deployment design. These components collectively enable diverse transmission paths under strict latency constraints, helping mitigate overload and reduce delay. To demonstrate its performance advantages, the proposed algorithm is compared with a baseline algorithm without helper-assisted D2D or RIS support, under two representative scheduling policies—modified maximum rate and modified proportional fair. Simulation results in single-base station (BS) and dual-BS environments show that the proposed algorithm consistently achieves a higher effective packet-delivery success percentage, defined as the fraction of packets whose total delay (uplink, MEC computation, and downlink) satisfies service-specific latency thresholds, and a lower average total delay, defined as the mean total delay of all successfully delivered packets, regardless of whether individual delays exceed their thresholds. Both metrics are evaluated separately for ultra-reliable low-latency communications, enhanced mobile broadband, and massive machine-type communications services. These results indicate that the proposed algorithm provides solid performance and robustness in supporting diverse 6G services under stringent latency requirements across different scheduling policies and deployment scenarios. Full article

The growing demand for ultra-reliable and low-latency communication (URLLC) in private 5G environments, such as smart campuses and industrial networks, has highlighted the limitations of conventional Wireline access gateway function (W-AGF) architectures that depend heavily on centralized 5G core (5GC) processing. This paper introduces a novel Centralized Unit (CU)-based Zone-Access Gateway Function (Z-AGF) architecture designed to enhance handover performance and enable Local Breakout (LBO) within Non-Public Networks (NPNs) for non-5G capable (N5GC) devices. The proposed design integrates W-AGF functionalities with the Open Radio Access Network (O-RAN) framework, leveraging the F1 Application Protocol (F1AP) as the primary interface between Z-AGF and CU. By performing local breakout (LBO) locally at the Z-AGF, latency-sensitive traffic is processed closer to the edge, reducing the backhaul load and improving end-to-end latency, throughput, and jitter performance. The experimental results demonstrate that Z-AGF achieves up to 45.6% latency reduction, 69% packet loss improvement, 85.6% reduction of round-trip time (RTT) for local communications under LBO, effective local offloading with quantified throughput compared to conventional W-AGF implementations. This study provides a scalable and interoperable approach for integrating wireline and wireless domains, supporting low-latency, highly reliable services within the O-RAN ecosystem and accelerating the adoption of localized next-generation 5G services. Full article

Among the 15 beamlines in the first phase of the High-Energy Photon Source (HEPS) in China, the maximum peak data generation volume can reach 1 PB per day, with the maximum peak data generation rate reaching 3.2 Tb/s. This poses significant challenges to the underlying network system. To meet the storage, computing, and analysis needs of HEPS scientific data, this paper designed a high-performance and scalable network architecture based on RoCE (RDMA over Converged Ethernet). Test results demonstrate that the RoCE-based HEPS data center network system achieves high bandwidth and ultra-low latency, stably maintains reliable transmission performance during the interaction of scientific data storage, computing, and analysis, and exhibits excellent scalability to adapt to the future expansion of HEPS beamlines. Full article

To support the 6G vision of seamless “space–air–ground-integrated” global coverage, optical satellite networks must enable high-speed, low-latency, and intelligent data transmission. However, conventional inter-satellite laser link-based optical transport networks suffer from inefficient bandwidth utilization and nonlinear latency accumulation caused by multi-hop routing, which severely limits their ability to support ultra-low-latency and real-time applications. To address the critical challenges of high topological complexity and stringent real-time requirements in satellite elastic optical networks, we propose an asynchronous advantage actor–critic-based quality of service-aware routing optimization mechanism for the optical inter-satellite link (OISL-AQROM). By establishing a quantitative model that correlates the optical service unit (OSU) C value with node hop count, the algorithm enhances the performance of latency-sensitive services in dynamic satellite environments. Simulation results conducted on a Walker-type low Earth orbit (LEO) constellation comprising 1152 satellites demonstrate that OISL-AQROM reduces end-to-end latency by 76.3% to 37.6% compared to the traditional heuristic multi-constrained shortest path first (MCSPF) algorithm, while supporting fine-grained dynamic bandwidth adjustment down to a minimum granularity of 2.6 Mbps. Furthermore, OISL-AQROM exhibits strong convergence and robust stability across diverse traffic loads, consistently outperforming MCSPF and deep deterministic policy gradient (DDPG) algorithm in overall efficiency, load adaptability, and operational reliability. The proposed algorithm significantly improves service quality and transmission efficiency in commercial mega-constellation optical satellite networks, demonstrating engineering applicability and potential for practical deployment in future 6G infrastructure. Full article

Ultra-reliable low-latency communication (URLLC) presents significant challenges in simultaneously guaranteeing stringent latency bounds, ultra-high reliability, and efficient resource utilization under dynamic channel conditions. To address these joint constraints, a novel framework that integrates a reconfigurable intelligent surface (RIS) with cooperative non-orthogonal multiple access (NOMA) is proposed for short-packet communications. Two distinct phase configuration designs for the RIS are considered, i.e., a near-user priority strategy (NUPS) and a far-user priority strategy (FUPS). The NUPS configures the RIS to enhance the received signal power for the near user, while the FUPS optimizes the phase shifts to maximize the received power for the far user. Closed-form expressions that characterize the average block error rate (BLER) of the near and far users under the two proposed strategies in the presence of hardware impairments are derived. Specifically, the analysis for the far user considers both selection combining (SC) and maximum ratio combining (MRC) reception schemes. Based on the average BLER, we then derive a closed-form expression for the effective throughput. Simulation findings reveal the following: (1) The far user in the proposed cooperative NOMA achieves a lower average BLER than in the non-cooperative NOMA. (2) When the RIS is deployed in close proximity to the base station (BS), the NUPS can effectively leverage the RIS to enhance the far user’s signal quality through cooperation, without sacrificing the near user’s priority; and (3) SC serves as a low-complexity alternative that achieves near-optimal performance when inter-user channel conditions are favorable. Full article