Search Results (109)

Low-Density Parity-Check (LDPC) codes have traditionally performed better at longer code lengths. This research develops and rigorously evaluates a layered Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) decoder architecture intended for Ultra-Reliable Low-Latency Communication (URLLC) applications in 5G and subsequent technologies. The paper investigates LDPC codes in short-length contexts through architectural optimization based on a layered decoding approach, which provides improved convergence speed and reduced latency compared with traditional flooding techniques. The short block-length QC-LDPC code ($280,176$) addresses the dual challenge of achieving ultra-low latency while maintaining high performance relative to existing work. For a target BLER of ${10}^{-2}$, the decoder outperforms the shorter reference codes ($60,40$) and ($96,64$), with an $E_b/N_0$ gain of up to $0.9$ dB, part of which is attributable to the longer block length rather than to the decoder alone. The FPGA implementation results demonstrate that the design is hardware-efficient: it operates at a maximum frequency of $218.62$ MHz, resulting in a throughput of $155.77$ Mbps, while maintaining a decoding latency of only $1.129$ µs and a power consumption of $0.201$ W. Full article

This paper investigates vehicle-to-vehicle (V2V) integrated sensing and communication (ISAC) networks under ultra-reliable low-latency communication (URLLC) constraints in the presence of a vehicular eavesdropper (VE). To address the lack of instantaneous eavesdropper channel state information (CSI) in high-mobility scenarios, a vehicular jammer (VJ) is employed to perform radar-based sensing and extended Kalman filter (EKF)-based tracking of the VE’s kinematic state. The estimated state information and its posterior uncertainty are shared with the legitimate transmitter and are used to construct uncertainty-aware spatial covariance matrices for the VE-related channels. Based on these covariance matrices, the VJ transmits artificial noise (AN) in the null space of the legitimate receiver, thereby degrading the VE’s reception while avoiding interference to the intended link. In this context, a finite-blocklength (FBL) secrecy-rate framework is developed together with a two-time-scale optimization strategy, where the sensing resources are optimized at the slot level to enhance EKF tracking accuracy, while the transmit covariance and AN covariance matrices are optimized at the frame level to maximize the average FBL secrecy rate. The resulting non-convex problem is handled through semidefinite relaxation (SDR), alternating optimization (AO), and successive convex approximation (SCA). Simulation results show that the proposed framework improves secrecy robustness against mobility and sensing-induced spatial uncertainty. Full article

The rapid proliferation of Internet of Things (IoT) devices within 5G-enabled smart city environments has introduced unprecedented challenges in communication protocol management across heterogeneous network architectures. With connected IoT devices projected to reach 21.1 billion by the end of 2025 and approximately 39 billion by 2030, existing static protocol selection mechanisms are unable to accommodate the dynamic Quality of Service (QoS) requirements of different smart city applications, such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC). This paper presents APO-DRL (Adaptive Protocol Optimization using Deep Reinforcement Learning), a framework that utilizes a Dueling Double Deep Q-Network (D3QN) combined with a Prioritized Experience Replay mechanism for intelligent, real-time communication protocol selection and parameter optimization in heterogeneous IoT networks. The proposed framework formulates the protocol optimization problem as a Markov Decision Process (MDP), wherein the DRL agent dynamically selects the optimal communication protocol (NB-IoT, LTE-M, LTE Cat-1, or 5G NR) and adaptively tunes transmission parameters based on real-time network conditions. Experimental evaluation in a 3GPP TR 38.901 Urban Macro simulation environment with N = 30 devices demonstrates that APO-DRL achieves a 138.9% improvement in average throughput compared to Static Allocation (60.00 vs. 25.12 Mbps), while simultaneously achieving the highest QoS satisfaction (83.38%) across all methods, albeit with higher energy consumption and packet loss than Static Allocation. Relative to D3QN+PER, APO-DRL exhibits substantially lower cross-seed throughput variance (±0.88 vs. ±11.03 Mbps), confirming that QA-PER produces a more stable and reproducible learned policy. Full article

This paper designs a statistical channel state information-based pinching antenna system for short-packet communication (SPC). To maximize the average maximal achievable rate (MAR) under physical collision-avoidance constraints, we formulate a highly non-convex geometry optimization problem, which is solved by our proposed novel phase-domain proximal policy optimization (PPO) framework. Unlike conventional coordinate-based approaches, the agent operates in a dual-component trigonometric phase domain, and the generated phase actions are mapped to feasible antenna positions via a customized phase-domain action mapping, which fundamentally avoids the $0/2\pi$ phase discontinuity and ensures stable learning. To evaluate the reliability of SPC, we derive a tractable statistical characterization of the received signal-to-noise ratio based on a mixture Gamma approximation over spatially correlated Rician fading channels, leading to a closed-form approximation for the average block error rate (BLER). A bisection search algorithm is further developed to minimize the required blocklength under the target reliability constraint. Simulation results demonstrate that the proposed phase-domain PPO scheme significantly outperforms the conventional algorithms in terms of average MAR, average BLER, and blocklength efficiency, with the performance gain becoming more pronounced as the number of antennas per waveguide increases. Full article

Ultra-reliable low-latency communication (URLLC) is a fundamental technology that plays a crucial role in enabling fifth-generation new radio (5G-NR) communication. URLLC aims to provide a highly reliable connection with strict block error probability requirements and extremely low latency for mission-critical and remote operations. Meanwhile, the advent of sixth-generation (6G) communication, marked by its novel, immersive, and high-stakes control applications, imposes notably more stringent demands on reliability and latency, alongside the added requirements of high data rates, scalability, precision, security, and real-time operation. This scenario introduces unparalleled challenges for both system architecture and the solutions it entails. Several previously proposed solutions, such as retransmission schemes, error correction techniques, and grant-free access, have been insufficient for emerging requirements, as most of these solutions primarily facilitate either low latency or high reliability, but not both. Latency and reliability are conflicting objectives of URLLC. Therefore, an in-depth understanding of the associated issues and careful mitigation of these challenges are essential. This article provides an extensive review of 5G URLLC, emphasizing its technical evolution from 3GPP Release 15 through 19, while also detailing its inherent shortcomings and the potential solutions required for 6G and beyond. We investigate the prerequisites and enabling technologies necessary for URLLC services, exploring related issues across various network components, including frame structure, propagation, processing, retransmission, scheduling, fading, and interference. An important discussion is provided on the fundamental trade-off between latency and reliability, particularly due to retransmission mechanisms. Furthermore, we examine the practical limitations of 5G URLLC when coexisting with other 5G application use cases, such as enhanced mobile broadband (eMBB) and massive machine-type communication (mMTC). Finally, we discuss the future trajectory of URLLC in 6G, identifying key research challenges and opportunities to meet the escalating demands of future mission-critical applications. Full article

Fifth-generation (5G) mobile networks are widely positioned as key enablers of industrial digital transformation. However, despite extensive coverage expansion, the deployment landscape remains dominated by Non-Standalone (NSA) architectures integrated with legacy 4G cores, limiting the practical availability of advanced capabilities such as Ultra-Reliable Low-Latency Communication (URLLC), Massive Machine-Type Communication (mMTC), and network slicing. This has contributed to a disparity between projected 5G functionality and realised industrial utility. This paper investigates the economic and structural factors constraining advanced 5G adoption and examines their implications for emerging sixth-generation (6G) frameworks. We conceptualise the current stagnation as arising from concurrent supply-side and demand-side constraints: elevated Radio Access Network (RAN) capital expenditure relative to previous generations, and limited demonstrable return on investment (ROI) for advanced service capabilities. To evaluate these dynamics empirically, a regional stakeholder study was conducted across industrial and public sector organisations in Ayrshire, Scotland. Data were collected through structured surveys and workshop-based questionnaires involving 34 participants, with proportional sectoral analysis performed to assess representativeness. The results indicate that high initial deployment costs and ROI uncertainty are the primary adoption barriers, with 45.83% of respondents reporting no immediate operational requirement for advanced 5G features. The findings identify an implementation gap in which economic viability, rather than technical feasibility, limits progression beyond basic 5G deployment. The paper argues that unless cost-efficiency and sector-specific value articulation are addressed, similar adoption constraints may extend into 6G development. These results provide empirically grounded insights to inform more economically aligned next-generation network planning. Full article

As mobile networks evolve toward Beyond 5G and 6G architectures, energy efficiency and sustainability have become increasingly critical due to growing traffic volumes, denser base station deployments, and the rising number of connected devices. Supporting Ultra-Reliable Low-Latency Communication (URLLC) services is particularly challenging, as their stringent requirements for both high reliability and minimal latency can lead to a significant increase in energy consumption within the radio access network. This paper examines slot structure mechanisms for concurrently servicing URLLC and enhanced Mobile Broadband (eMBB) traffic within the 5G Advanced framework, with a focus on improving energy efficiency and optimizing radio resource utilization. We propose an adaptive algorithm for managing radio interface time resources, which dynamically allocates sub-slots based on current network load and radio channel conditions. The system model is implemented in Simulink and incorporates URLLC and eMBB traffic generation, signal-to-noise ratio estimation, and a priority-based scheduling mechanism. Simulation results demonstrate that the proposed approach meets URLLC latency and reliability requirements while reducing redundant transmissions and enhancing the energy efficiency of the radio access network. These findings position the proposed method as a promising solution for the design of energy-efficient, next-generation mobile networks. Full article

With the rapid development of 5G communication technology, 5G networks are designed to achieve three major objectives: higher bandwidth, support for a greater number of connected devices, and lower latency. It is necessary to meet the requirements of the three primary 5G application scenarios: Enhanced Mobile Broadband, Massive Machine-Type Communications, and Ultra-Reliable and Low Latency Communications (uRLLC). To meet the stringent requirements for time synchronization and low latency, 5G is being integrated with Ethernet-based Time-Sensitive Networking (TSN) technologies. TSN plays an important role in achieving time determinism in uRLLC scenarios and ensures low-latency and high-reliability Ethernet communication through the transmission of time signals that are also known as the Precision Time Protocol. We applied TSN technology in the Institute of Electrical and Electronics Engineers 802.1Qbv standard and evaluated its transmission delay performance. Modifying the gate control list (GCL) to accommodate varying network traffic ensures low-latency transmission for high-priority traffic. We propose two GCL configurations for TSN that incorporate time-aware shaper to achieve efficient traffic scheduling. Full article

To address the rigorous requirements of ultra-reliable low-latency communication (URLLC) in beyond 5G/6G networks, we propose an innovative architecture combining automatic repeat request (ARQ) protocol with a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) to enhance short-packet non-orthogonal multiple access (NOMA) communications. Specifically, retransmission mechanism provided by ARQ is utilized to mitigate packet errors stemming from practical system imperfections, i.e., imperfect channel state information (ipCSI), imperfect successive interference cancellation (ipSIC), and hardware impairments. Using the analytical foundation provided by finite blocklength (FBL) theory, expressions for two key performance metrics, i.e., the average block error rate (BLER) and effective throughput, are derived for two NOMA users. Simulation results validate the analytical derivations and demonstrate that the ARQ scheme provides significant reliability gains for each user and achieves synergistic gain with STAR-RIS technology. In addition, the effective throughput exhibits a peak at an optimal blocklength, balancing the reliability gain from a longer blocklength against the spectral efficiency loss from a lower coding rate. This optimal blocklength decreases with more STAR-RIS elements, as improved channel conditions reduce the need for long blocklengths. Full article

Ultra-low latency communication is fundamentally required to reduce end-to-end (E2E) latency related to the transportation of time-critical or time-sensitive traffic in 5G networks. Time-sensitive networking has significant prospects in factory automation and Industrial Internet of Things (IIoT) as a key technology that can provide low-latency, highly reliable, and deterministic communications over Ethernet, whereas IETF deterministic networking (DetNet) seeks to provide a complementary network layer to support ultra-low latency communications. DetNet, as standardized in the IETF, provides time-sensitive characteristics that assure extremely low packet loss and latency for ultra-reliable low-latency communications. This study develops a novel framework to enable 3GPP support for DetNet functionalities. First, the proposed framework seeks to support IP-based DetNet traffic and urgent data transmission in the network overload conditions of 3GPP 5G systems. Additionally, the proposed design supports DetNet service connectivity between non-DetNet and DetNet service areas. Based on simulation results, the proposed framework can guarantee deterministic latency requirements and critical data transmission for DetNet compared with conventional approaches. The proposed scheme can achieve more effective performance for moving DetNet devices. 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 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 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

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

Ensuring ultra-low latency and high reliability in 6G network slices remains a significant challenge, as current NFV orchestration approaches are largely reactive and not designed to anticipate performance degradation. The advent of 6G networks brings forth stringent requirements for ultra-reliable low-latency communication (URLLC), necessitating advanced orchestration mechanisms that go beyond reactive policies in traditional NFV environments. In this paper, we propose a latency-aware, AI-driven NFV slice orchestration framework aligned with ETSI MANO architecture to address the needs of time-sensitive 6G applications. Our framework integrates a predictive AI engine into the NFV Orchestrator (NFVO) to forecast latency violations based on real-time telemetry and historical trends. It enables dynamic scaling, intelligent VNF migration, and infrastructure-level isolation to maintain stringent end-to-end (E2E) latency targets. Experimental results indicate up to 30% reduction in average latency, a 42% improvement in SLA compliance, and 25% lower migration overhead compared to traditional reactive orchestration. The framework provides a scalable and intelligent orchestration solution adaptable to future 6G deployments. Full article