Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication

Background. Ultra-Reliable Low Latency Communication (URLLC) as a service offered by fifth and sixth generation (5G/6G) wireless systems is a technological response to the needs of various mission-critical applications that require reliable data transmission with low latency. These applications also include Intelligent Transportation Systems services, which, among other things, provide connectivity and autonomous vehicle control. The combination of high reliability and low latency requirements in URLLC usage scenarios creates a security problem for URLLC data transmission that cannot be solved using conventional complex cryptographic methods based on a secret key. The article discusses in detail the approach to using physical layer security mechanisms (physical layer security - PLS) as a powerful alternative to classical cryptographic security methods for URLLC, and also proposes the application of the wire-tap channel concept in URLLC with an analysis of the efficiency that can be achieved for physical layer security. Objective. The aim of the article is to provide an overview of information security solutions in URLLC usage scenarios, as well as to propose a constructive method for information protection for reliable data transmission with low latency without the use of cryptographic mechanisms based on a secret key. Methods. Theoretical research in the field of the branch channel concept was used to create solutions that allow data protection with information-theoretic stability in URLLC usage scenarios for providing IoT, connected car and autonomous driving services. Results. The article examines in detail the data security issues in ultra-reliable low latency communication (URLLC) as a service offered by fifth and sixth generation (5G/6G) wireless systems. It is determined that URLLC is a technological response to the needs of various critical applications that require reliable signal transmission with low latency, and among these applications are Intelligent Transportation Systems services, which, among other things, provide connectivity and autonomous vehicle control. It is shown that the combination of high reliability and low latency requirements in URLLC scenarios creates a security problem for URLLC data transmission that cannot be solved using conventional complex cryptographic methods based on a secret key. The feasibility of using physical layer security mechanisms (PLS) as a powerful alternative to classical cryptographic security methods for URLLC is substantiated. The approach to applying the concept of a wire-tap channel in URLLC is considered in detail, as well as the results that can be achieved for physical layer security, and the influence of code parameters for probabilistic cryptographic transformations in accordance with the concept of a wire-tap channel on PLS URLLC. Estimates of the effectiveness of PLS URLLC for finite block length codes are provided. Conclusions. An effective way to ensure data security for ultra-reliable low-latency physical layer link (PLS URLLC) of fifth-generation 5G wireless systems in the field of connected cars and vehicles of 4-5 levels of automation can be approaches based on the concept of a tapped channel ("wire-tap channel").

Ultra reliable low latency communication (URLLC) in vehicular networks is crucial for safety-related vehicular applications. Mini-slot with a short packet that carries only a few symbols is used to reduce the transmission time interval and enable quick scheduling for URLLC that requires extremely low latency. However, a single air interface transmission of URLLC packets may fail due to the high mobility of vehicles. Leveraging multi-connectivity technologies, the real-time reliability of URLLC can be greatly enhanced without relying on packet retransmission. In this paper, we propose a multi-connectivity URLLC downlink transmission scheme for vehicular networks, where the URLLC packet is duplicated and transmitted over multiple independent wireless links to improve packet reliability. Specifically, we design a multi-agent cooperative deep reinforcement learning algorithm, called transformer associated proximal policy optimization (TAPPO), to achieve real-time robust power allocation for multi-connectivity URLLC with imperfect channel state information (CSI). The transformer neural network architecture is employed to share the information among multiple links serving the same URLLC user and choose appropriate transmit powers, enabling cooperation to ensure reliability while minimizing inter-cell interference and energy consumption. Extensive simulation results validate the effectiveness of multi-connectivity packet duplication for URLLC and proposed TAPPO for power allocation.

Ultra-reliable low latency communication (URLLC) is one of the key services offered by the 5G wireless networks. This service type is characterized by stringent high reliability and low latency criteria, thus require efficient multiple access techniques to facilitate various mission-critical services. Non-orthogonal multiple access (NOMA) effectively enhances spectral efficiency and ensures fairness by exploiting the power allocation at receivers compared to orthogonal multiple access (OMA). Therefore, it becomes suitable for facilitating URLLC service. The addition of the cooperative relaying technique to NOMA can enhance the reliability of the data transmission. This motivated us to analyze the performance of cooperative NOMA (C-NOMA) for short packet URLLC data transmission. Hence, we have proposed a C-NOMA based system using the decode and forward (DF) relaying technique and successive interference cancellation (SIC) at the receiver to minimize the overall error probability for URLLC. The minimization of overall outage probability has been achieved by optimizing power allocation at the receiver considering the decoding error and error due to queuing delay violation while ensuring fair throughput among users. The simulation study has shown that the proposed C-NOMA based short packet transmission improves overall outage probability, energy efficiency, and ensures fair throughput among users in comparison to only NOMA and OMA based techniques.

Traditional link adaptation (LA) schemes in cellular network must be revised for networks beyond the fifth generation (b5G), to guarantee the strict latency and reliability requirements advocated by ultra reliable low latency communications (URLLC). In particular, a poor error rate prediction potentially increases retransmissions, which in turn increase latency and reduce reliability. In this paper, we present an interference prediction method to enhance LA for URLLC. To develop our prediction method, we propose a kernel based probability density estimation algorithm, and provide an in depth analysis of its statistical performance. We also provide a low complexity version, suitable for practical scenarios. The proposed scheme is compared with state-of-the-art LA solutions over fully compliant 3rd generation partnership project (3GPP) calibrated channels, showing the validity of our proposal.

This paper proposes a low-complexity low-density parity-check (LDPC) decoder architecture for 5G ultra-reliable low latency communication (URLLC). In order to reduce the hardware utilization and the influence of data dependency problem. Proposed low-complexity decoder reduces the number of multi-size shift modules of the conventional single-layer decoder from two to one by improve the method in [15]. Due to the reduction of multi-size shift modules, we also construct new matrices that generate the shift value in proposed low-complexity decoder. Using the FPGA platform of Xilinx Virtex 7, experimental results show our proposed low-complexity decoder improves the resources utilization and throughput of conventional architectures. The improvement in percentage is about 20.1% for LUTs and 6.8% for throughput. The experimental result show that our method is more efficient than [15].

A three-level slicing algorithm in a multi-slice multi-numerology context

This paper considers the coexistence of Ultra Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB) services in the uplink of Cloud Radio Access Network (C-RAN) architecture based on the relaying of radio signals over analog fronthaul links. While Orthogonal Multiple Access (OMA) to the radio resources enables the isolation and the separate design of different 5G services, Non-Orthogonal Multiple Access (NOMA) can enhance the system performance by sharing wireless and fronthaul resources. This paper provides an information-theoretic perspective in the performance of URLLC and eMBB traffic under both OMA and NOMA. The analysis focuses on standard cellular models with additive Gaussian noise links and a finite inter-cell interference span, and it accounts for different decoding strategies such as puncturing, Treating Interference as Noise (TIN) and Successive Interference Cancellation (SIC). Numerical results demonstrate that, for the considered analog fronthauling C-RAN architecture, NOMA achieves higher eMBB rates with respect to OMA, while guaranteeing reliable low-rate URLLC communication with minimal access latency. Moreover, NOMA under SIC is seen to achieve the best performance, while, unlike the case with digital capacity-constrained fronthaul links, TIN always outperforms puncturing.

This paper presents solutions for efficient multiplexing of ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) traffic on a shared channel. This scenario presents multiple challenges in terms of radio resource scheduling, link adaptation, and inter-cell interference, which are identified and addressed throughout this paper. We propose a joint link adaptation and resource allocation policy that dynamically adjusts the block error probability of URLLC small payload transmissions in accordance with the instantaneous experienced load per cell. Extensive system-level simulations of the downlink performance show promising gains of this technique, reducing the URLLC latency from 1.3 to 1 ms at the 99.999% percentile, with less than 10% degradation of the eMBB throughput performance as compared with conventional scheduling policies. Moreover, an exhaustive sensitivity analysis is conducted to determine the URLLC and eMBB performance under different offered loads, URLLC payload sizes, and link adaptation and scheduling strategies. The presented results give valuable insights on the maximum URLLC offered traffic load that can be tolerated while still satisfying the URLLC requirements, as well as what conditions are more appropriate for dynamic multiplexing of URLLC and eMBB traffic in the upcoming 5G systems.

The Ultra-Reliable Low-Latency Communication (URLLC) is expected to be an important feature of 5G and beyond networks. Supporting URLLC in a resource-efficient manner demands optimal Modulation and Coding Scheme (MCS) selection and spectrum allocation. This paper presents a study on MCS selection and spectrum allocation to support URLLC. The essential idea is to establish an analytical connection between the delay and reliability requirements of URLLC data transmission and the underlying MCS selection and spectrum allocation. In particular, the connection factors in fundamental aspects of wireless data communication include channel quality, coding and modulation, spectrum allocation and data traffic characteristics. With this connection, MCS selection and spectrum allocation can be efficiently performed based on the delay and reliability requirements of URLLC. Theoretical results in the scenario of a 5G New Radio system are presented, where the Signal-to-Noise Ratio (SNR) thresholds for adaptive MCS selection, data-transmission rate and delay, as well as spectrum allocation under different configurations, including data duplication, are discussed. Simulation results are also obtained and compared with the theoretical results, which validate the analysis and its efficiency.

Future 5G systems will need to support ultra-reliable low-latency communications scenarios. From a latency-reliability viewpoint, it is inefficient to rely on average utility-based system design. Therefore, we introduce the notion of guaranteeable delay which is the average delay plus three standard deviations of the mean. We investigate the trade-off between guaranteeable delay and throughput for the point-to-point wireless erasure links with unreliable and delayed feedback, by bringing together signal flow techniques to the area of coding. We use tiny codes, i.e., sliding window by coding with just 2 packets, and design three variations of selective-repeat ARQ protocols, by building on the baseline scheme, i.e., uncoded ARQ, developed by Ausavapattanakun and Nosratinia: (i) Hybrid ARQ with soft combining at the receiver; (ii) cumulative feedback-based ARQ without rate adaptation; and (iii) coded ARQ with rate adaptation based on the cumulative feedback. Contrasting the performance of these protocols with uncoded ARQ, we demonstrate that the HARQ performs only slightly better, the cumulative feedback-based ARQ does not provide significant throughput while it has a better average delay, and the Coded ARQ can provide gains up to about 40% in terms of throughput. The Coded ARQ also provides delay guarantees, and is robust to various challenges such as imperfect and delayed feedback, burst erasures, and round-trip time fluctuations. This feature may be preferable for meeting the strict end-to-end latency and reliability requirements of the future use cases of ultra-reliable low-latency communications in 5G, such as mission-critical communications and industrial control for critical control messaging.

Fifth-generation (5G) networks are already available in major urban areas and are expected to bring a major transformation to citizens’ lives. 5G services, such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), and massive machine-type communications (mMTC), require a network infrastructure capable of supporting stringent requirements in terms of latency and bandwidth demands; as such, it must be highly dynamic and flexible. Network slicing is a key enabler technology that can provide dynamic and flexible characteristics to 5G network architecture. A network slice (NS) can be defined as a partition of network and IT resources, that is, network links and nodes capacity dedicated to a specific set of service demands. As a result, different NSs can coexist over the same physical infrastructure network and can be used to dynamically and flexibly deploy the aforementioned 5G services. However, to efficiently implement NSs with different requirements, communication service providers (CSPs) that own the physical infrastructure network must adopt sophisticated techniques for admission control and resource allocation of NSs. In this paper, we present a novel framework for admission control and resource allocation of 5G NSs in metro-core networks. Specifically, our framework is based on a deep reinforcement learning (DRL) algorithm called Advantage Actor Critic (A2C), which performs admission control, i.e. it is capable of learning which slice to admit based on the availability of the physical network resources. Then, given the diversity of requirements for each 5G service, we propose different resource allocation algorithms based on integer linear programming (ILP) and heuristics to treat each service accordingly. Results show that our proposed framework can increase the number of admitted NSs with respect to the case in which the admission control is disabled by improving the resource allocation performance.

Using the stochastic geometry framework, we study a millimeter wave (mmWave) Device-to-Device (D2D) network dedicated to Ultra-Reliable Low Latency Communications (URLLC), where users employ multiple antennas to perform beamforming. We leverage the notion of meta-distribution in order to capture the reliability requirement of URLLC. The packet transmission process is divided into two phases: a beam training phase, during which exhaustive beam sweeping is adopted, and a data transmission phase. The paper investigates the misalignment error distribution resulting from an imperfect training phase, due to the finite codebooks resolution and the fast variation of the channel. For the data transmission phase, closed-form expressions for all the moments of the conditional rate coverage probability are derived, and the meta-distribution is approximated using the beta approximation. The study evaluates the overall network performance through the effective rate meta-distribution, which accounts for the training overhead and beam misalignment errors. The results show the detrimental impact of misalignment errors when URLLC requirements are stringent and highlight the trade-off between the training overhead and the gain brought by multiple antennas. Insights are provided for optimally and jointly choosing the codebook size and tbe number of antennas.

The train-to-ground communications for Hyperloop encounter numerous challenges compared to the previous systems as a result of ultrahigh velocity, metal confined tube, severe penetration attenuation, frequent handover, etc. Ultrareliable low latency communication (uRLLC) services are extremely pivotal to the operation of Hyperloops since they are responsible for the transmission of safety-related messages like train control system (TCS) information. Furthermore, the sporadic generated uRLLC traffic is strictly latency-sensitive and requires stringent transmission reliability, which is usually realized by multiplexing with the enhanced mobile broadband (eMBB) traffic when coexisting with heterogeneous traffic. Different from current multiplexing schemes which handle all uRLLC traffic indiscriminately, we focus on the detailed demands of different uRLLC services for Hyperloops, including the end-to-end transmission latency and bit error rate (BER). Then, we classify the safety-related uRLLC traffic into 5 levels according to the metrics and relax the multiplexing latency properly. On this basis, an optimization problem incorporating minimizing uRLLC power and maximizing the eMBB throughput is formulated. Then, a particle swarm optimization (PSO) algorithm is proposed to cope with this nondeterministic polynomial- (NP-) hard problem. Simulation results imply that our proposed service-aware latency-relaxed concept enables a flexible multiplexing of the uRLLC traffic, and the proposed PSO algorithm can obtain the suboptimal solutions with the constraints of BER and time-frequency range, which can cater to the demands of uRLLC and improve the passengers’ in-journey communication quality of services to some degree.

Ultra-Reliable Low-Latency Communications (URLLC) is a novel feature of 5G cellular systems. To satisfy strict URLLC requirements for uplink data transmission, the specifications of 5G systems introduce the grant-free channel access method. According to this method, a User Equipment (UE) performs packet transmission without requesting channel resources from a base station (gNB). With the grant-free channel access, the gNB configures the uplink transmission parameters in a long-term time scale. Since the channel quality can significantly change in time and frequency domains, the gNB should select robust transmission parameters to satisfy the URLLC requirements. Many existing studies consider fixed robust uplink transmission parameter selection that allows satisfying the requirements even for UEs with poor channel conditions. However, the more robust transmission parameters are selected, the lower is the network capacity. In this paper, we propose an adaptive algorithm that selects the transmission parameters depending on the channel quality based on the signal-to-noise ratio statistics analysis at the gNB. Simulation results obtained with NS-3 show that the algorithm allows meeting the URLLC latency and reliability requirements while reducing the channel resource consumption more than twice in comparison with the fixed transmission parameters selection.

BackgroundGeneral wards rely on intermittent vital-sign checks, creating blind spots for deterioration detection under staffing constraints. While wearable sensors promise continuous ward-scale monitoring, real-world feasibility remains uncertain because prior studies emphasized signal accuracy rather than the timeliness and low-latency continuous monitoring system. This study addresses that gap by quantifying transmission latency and signal acquisition rate (SAR) of a ring-based system in routine surgical-ward care.MethodsWe conducted a single-center retrospective analysis of 128 adult postoperative inpatients monitored with a finger-worn smart ring (CART-1 plus) from 23 December 2024 to 20 July 2025. Signals streamed via BLE to a dedicated receiver and hospital Wi-Fi. Primary endpoints were (i) transmission latency (device timestamp→server receipt) and (ii) SAR. Cached burst uploads at 0–1-s intervals were excluded from latency analyses. Prespecified acceptability thresholds were ≤10 min for BP latency, ≤5 min for SpO2 latency, and ≥80% for SAR. Associations with patient/environmental factors (mobility status, room type, time of day, network environment) were modeled.ResultsMean transmission latency was 7.23 min (BP) and 4.35 min (SpO2), both within predefined thresholds (10 and 5 min, respectively; p < 0.001). The mean SAR was 90.17% for BP and 90.62% for SpO2, significantly exceeding the 80% threshold (p < 0.001). Transmission latency was significantly lower in patients requiring no or full assistance with mobility, in those residing in single or double rooms (vs. quad), and during daytime monitoring (p < 0.001). For SpO2, latency was affected by mobility and change in network environment but not by room type or monitoring time. SAR was not significantly influenced by clinical variables but was higher after change in network environment (p < 0.05).ConclusionIn real-world surgical wards, a finger-worn ring achieved clinically acceptable transmission latency and high continuity, supporting feasibility for a low-latency continuous monitoring system. Findings identify modifiable environmental and patient-level factors that influence latency and can guide scale-up and integration with early-warning workflows.