Intelligent Ultra-Reliable and Low Latency Communications: Security and Flexibility

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").

Inspired by the new and dominating traffic services - ultra-reliable and low latency communications (URLLC), finite blocklength coding (FBC) has been developed to support delay and error-rate bounded quality-of-services (QoS) provisioning for time-sensitive wireless applications by using short-packet data communications. On the other hand, the age of information (AoI) has recently emerged as a new dimension of QoS performance metric in terms of the freshness of updated information. Since the status updates normally consist only of a small number of information bits but warrant ultra-low latency, exploring AoI in the finite blocklength regime creates another promising solution for supporting URLLC services. However, how to efficiently integrate and implement the above new techniques for statistical delay and error-rate bounded QoS provisioning in the finite blocklength regime has neither been well understood nor thoroughly studied. To overcome these challenges, we propose the AoI-driven statistical delay and error-rate bounded QoS provisioning schemes which leverage the AoI technique as a key QoS performance metric to efficiently support URLLC in the finite blocklength regime. First, we build up the AoI-metric based modeling frameworks in the finite blocklength regime. Second, we apply the stochastic network calculus (SNC) to characterize the upper-bounded peak AoI violation probability. Third, we jointly optimize the peak AoI violation probability and ∊-effective capacity and characterize their tradeoff in supporting statistical delay and error-rate bounded QoS provisioning for URLLC. Finally, we conduct the extensive simulations to validate and evaluate our developed schemes in the finite blocklength regime.

With the existing Long Term Evolution(LTE) based Cellular Vehicle to Anything(C-V2X) infrastructure and the recent advancements in 5G implemented Ultra Reliable Low Latency Communication(URLLC), comes the need of fast and secure communications in Connected Autonomous Vehicles(CAVs). Physical layer can always be a fast and reliable medium for exploitation while having legit communication as a source for performance and latency optimization besides the enhanced spectrum efficiency depicted by multicasting and its adequate worth. Utilization of CAVs as connected IoT resources for load mitigation is also eminent from research. In this article, we benefit the 5G V2X environment of CAVs for road safety use case where a secure geo-specific locally containerized co-operative aware autonomous driving system would be the prime concern. In the localized V2X server, Roadside units(RSUs) get benefit from physical layer parameters to generate a key for end to end(E2E) communication while the On Board Unit(OBU) containers provide the cryptographic shreds as microservices in real-time as they do while monitoring or location updating. Mininet and RYU as network emulator and controller are used for the scenario implementation and analysis. Beyond RSU, no need of secure communication exists any longer due to the broadcast nature of messages for all involved vehicles in the marked premises so instead of security, we put prime focus on least latency due to highly dynamic environment and we did zero forced sparse channel estimation for evolved multimedia broadcast multicast service(eMBMS) in C-V2X.

The enhanced Mobile Broadband (eMBB) and ultra-Reliable Low Latency Communications (uRLLC) are the two main scenarios of $5^{th}$ generation (5G) mobile communication system networks. There is an obvious difference in service requirements between different scenarios. When multi-scenario services coexist in the 5G networks, exploring optimized resource scheduling and allocation strategies become a critical issue. The 5G New Radio (NR) and numerology technologies have been standardized, which lay the foundation for flexible frame structure and adaptive scheduling. In this paper, we propose the self-adaptive flexible transmission time interval (TTI) scheduling (SAFE-TS) strategy in the eMBB and uRLLC coexistence scenario. Machine learning (ML) is applied to achieve flexible TTI scheduling. Moreover, we design the random forest-based ensemble TTI decision algorithm (RF-ETDA) to accomplish the TTI selection for each service. Compared with the existing ML methods, the proposed algorithm has a performance improvement in selecting TTI, especially for the uRLLC services. Then, the TTI selection results will be the basis of system resource scheduling and allocation. The simulation results prove that the proposed SAFE-TS effectively reduce the delay and packet loss rate of the uRLLC services while guaranteeing the eMBB requirements. Therefore, it is highly recommended that flexible TTI scheduling should be applied in the construction of the 5G networks to achieve superior network performances.

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.

The fifth-generation (5G) New Radio (NR) introduces stringent delay and reliability requirements to support diverse services, including enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC). A major challenge is the efficient coexistence of these heterogeneous services, as eMBB demands high throughput while URLLC requires extreme reliability and minimal latency. This study investigates the coexistence of eMBB and URLLC under time-varying channel conditions and formulates a many-to-many URLLC resource allocation problem. To address this, we propose an opportunistic resource allocation scheme based on a genetic algorithm (GA) that dynamically optimizes resource block (RB) allocation for both services. The GA employs a heuristic fitness function designed to maximize eMBB fairness and throughput while ensuring URLLC reliability. Simulation results demonstrate that the proposed approach significantly improves overall system performance: eMBB fairness exceeds 95%, and the average eMBB data rate increases by approximately 500 Kbps compared to random allocation. Moreover, URLLC users maintain a consistent data rate of 600 Kbps, outperforming benchmark methods while satisfying the 99.999% reliability requirement. The results confirm that the proposed GA-based approach effectively balances throughput, fairness, and reliability, making it a promising solution for future 5G networks with mixed traffic demands.

As the complexity of wireless mobile networks increases significantly, artificial intelligence (AI) and machine learning (ML) have become key enablers for radio resource management and orchestration. In this paper, we propose a multi-agent reinforcement learning (RL) method for allocating radio resources to mobile users under random traffic arrivals, in which Ultra-Reliable Low-Latency Communications (URLLC) and enhanced Mobile Broad-Band (eMBB) services are jointly considered. The proposed system includes hierarchically placed RL agents, where the main-agent residing on the upper hierarchy performs inter-slice resource allocation between the URLLC and eMBB slices. The URLLC and eMBB sub-agents are responsible for the resource allocation within their own slice, where the objective is to maximize the eMBB throughput while satisfying the latency requirements of the URLLC slice. In the RL algorithm, the state space includes the queue occupancy and the channel quality information of mobile users while the action space specifies the resource allocation to the users. For a computationally efficient RL training, the state space is significantly reduced by quantizing the queue occupancy and grouping the users according to their channel qualities. The numerical results for the URLLC show that the proposed RL-based approach provides the average delay results of lower than 1 ms for all experiments while the worst case eMBB throughput degradation is limited to 4%.

In this paper, we study resource allocation for downlink orthogonal frequency division multiple access (OFDMA) systems with the objective to enable ultra-reliable low latency communication (URLLC). To meet the stringent delay requirements of URLLC, the impact of short packet communication is taken into account for resource allocation algorithm design. In particular, the resource allocation design is formulated as a weighted system sum throughput maximization problem with quality-of-service (QoS) constraints for the URLLC users. The formulated problem is non-convex, and hence, finding the global optimum solution entails a high computational complexity. Thus, an algorithm based on successive convex optimization is proposed to find a sub-optimal solution with polynomial time complexity. Simulation results confirm that the proposed algorithm facilities URLLC and significantly improves the system sum throughput compared to two benchmark schemes.

Network slicing envisions the 5th generation (5G) mobile network resource allocation to be based on different requirements for different services, such as Ultra-Reliable Low Latency Communication (URLLC) and Enhanced Mobile Broadband (eMBB). Open Radio Access Network (O-RAN), proposes an open and disaggregated concept of RAN by modulizing the functionalities into independent components. Network slicing for O-RAN can significantly improve performance. Therefore, an advanced resource allocation solution for network slicing in O-RAN is proposed in this study by applying Reinforcement Learning (RL). This research demonstrates an RL compatible simplified edge network simulator with three components, user equipment(UE), Edge O-Cloud, and Regional O-Cloud. This simulator is later used to discover how to improve throughput for targeted network slice(s) by dynamically allocating unused bandwidth from other slices. Increasing the throughput for certain network slicing can also benefit the end users with a higher average data rate, peak rate, or shorter transmission time. The results show that the RL model can provide eMBB traffic with a high peak rate and shorter transmission time for URLLC compared to balanced and eMBB focus baselines.

This paper considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users' number of transmitted bits, packet error probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity resource allocation algorithm to strike a balance between performance and complexity. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon's capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they may violate the users' delay constraints.

This paper investigates the joint design of user association and resource allocation algorithm for multiple intelligent reflecting surfaces (IRSs) aided downlink communication systems, where a base station serves a group of ultra-reliable low-latency communication (URLLC) users. We employ IRSs to enhance the communication channel and increase reliability by creating virtual lines of sight for URLLC users with poorly propagation conditions. To maximize the sum rate of URLLC users, IRS-user's association, the beamforming vectors at the BS and phase shifts at IRSs are jointly optimized while taking into account the quality-of-service (QoS) of the users. The optimization problem is non-convex and finding the globally optimal solution requires high computational complexity which is not desirable for real-time applications. Therefore, we propose an iterative algorithm in which user association and resource allocation are optimized iteratively. The algorithm is guaranteed to obtain a local optimal solution. Our simulation results show that the optimization algorithm proposed in this paper achieves a great gain for the sum of URLLC users' transmission rates.

Ultra-reliable low latency communication (URLLC) is an important scenario for the future sixth-generation communication. To meet the quality of service (QoS) of URLLC users, a rising technology called movable antennas (MAs) is employed to enhance the system performance. This paper investigates a novel MA-aided multiuser multiple-input single-output orthogonal frequency division multiple access (MISO-OFDMA) URLLC system. We formulate a resource allocation problem for maximizing the achievable rate, and optimize the power allocation, subcarrier assignment and positions of MAs under the URLLC requirements. Since the problem is highly non-convex, an efficient optimization algorithm based on the particle swarm optimization (PSO) and successive convex approximation (SCA) method is proposed. Simulation results show that the MA-aided system outperforms the benchmark schemes using fixed-position antennas, which confirms the superiority of MAs in multiuser MISO-OFDMA URLLC systems.

Intelligent Transportation System (ITS) is the focus of many researchers due to the growing of traffic, which ensures complexity, especially in urban scenario. In this regard, Ultra-Reliable Low Latency Communication (URLLC) achievement is the main purpose for ITS. Latency represents a relevant metric for real time exchange of messages and improve the network Quality of Service (QoS). In this contribution, the purpose is delay investigation into Long Term Evolution Vehicle (LTE-V), which is an enabling cellular technology for vehicular connectivity. The idea is RB (Resource Block) allocation for delay minimization through a direct interaction enabled by LTE-V features. We start by a mathematical analysis of a cross-layer structure based on the joint association between physical layer together with a Medium Access Control (MAC) sub layer. Hence, latency minimization is transformed into an optimization problem in which data rate is maximized under the constraint of queue length. Resource allocation resolution is based on Lyapunov policy. Numerical results are obtained according to a Manhattan scenario towards an urban model where the performance criterion is Complementary Cumulative Distribution Function (CCDF) of queue length. The obtained results highlight the efficiency of the proposed resource allocation algorithm.

In this paper, we aim to find the global optimal resource allocation for ultra-reliable and low-latency communications (URLLC), where the blocklength of channel codes is short. The achievable rate in the short blocklength regime is neither convex nor concave in bandwidth and transmit power. Thus, a non-convex constraint is inevitable in optimizing resource allocation for URLLC. We first consider a general resource allocation problem with constraints on the transmission delay and decoding error probability, and prove that a global optimal solution can be found in a convex subset of the original feasible region. Then, we illustrate how to find the global optimal solution for an example problem, where the energy efficiency (EE) is maximized by optimizing antenna configuration, bandwidth allocation, and power control under the latency and reliability constraints. To improve the battery life of devices and EE of communication systems, both uplink and downlink resources are optimized. The simulation and numerical results validate the analysis and show that the circuit power is dominated by the total power consumption when the average inter-arrival time between packets is much larger than the required delay bound. Therefore, optimizing antenna configuration and bandwidth allocation without power control leads to minor EE loss.

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.