Ultra-reliable And Low-latency Communications Research Articles

An Applied Analysis of Securing 5G/6G Core Networks with Post-Quantum Key Encapsulation Methods

Fifth Generation (5G) cellular networks have been adopted worldwide since the rollout began around 2019. It brought with it many innovations and new services, such as Enhanced Mobile Broadband (eMBB), Ultra Reliable and Low-Latency Communications (URLLC), and Massive Internet of Things (mIoT). Furthermore, 5G introduced a more scalable approach to network operations using fully software-based Virtualized Network Functions (VNF) in Core Networks (CN) rather than the prior hardware-based approach. However, while this shift towards a fully software-based system design provides numerous significant benefits, such as increased interoperability, scalability, and cost-effectiveness, it also brings with it an increased cybersecurity risk. Security is crucial to maintaining trust between vendors, operators, and consumers. Cyberattacks are rapidly increasing in number and sophistication, and we are seeing a shift towards zero-trust approaches. This means that even communications between VNFs inside a 5G core must be scrutinized and hardened against attacks, especially with the advent of quantum computers. The National Institute of Standards and Technology (NIST), over the past 10 years, has led efforts to standardize post-quantum cryptography (PQC) to protect against quantum attacks. This paper covers a custom implementation of the open-source free5GC CN, to expand its HTTPS capabilities for VNFs by introducing PQC Key Encapsulation Methods (KEM) for Transport Layer Security (TLS) v1.3. This paper provides the details of this integration with a focus on the latency of different PQC KEMs in initial handshakes between VNFs, on packet size, and the implications in a 5G environment. This work also conducts a security comparison between the PQC-equipped free5GC and other open-source 5G CNs. The presented results indicate a negligible increase in UE connection setup duration and a small increase in connection setup data requirements, strongly indicating that PQC KEM’s benefits far outweigh any downsides when integrated into 5G and 6G core services. To the best of our knowledge, this is the first work incorporating PQC into an open-source 5G core. Furthermore, the results from this effort demonstrate that employing PQC ciphers for securing VNF communications results in only a negligible impact on latency and bandwidth usage, thus demonstrating significant benefits to 5G cybersecurity.

Optimizing data transmission in 6G software defined networks using deep reinforcement learning for next generation of virtual environments

Data transmission of Virtual Reality (VR) plays an important role in delivering a powerful VR experience. This increasing demand on both high bandwidth and low latency. 6G emerging technologies like Software Defined Network (SDN) and resource slicing are acting as promising technologies for addressing the transmission requirements of VR users. Efficient resource management becomes dominant to ensure a satisfactory user experience. The integration of Deep Reinforcement Learning (DRL) allows for dynamic network resource balancing, minimizing communication latency and maximizing data transmission rates wirelessly. Employing slicing techniques further aids in managing distributed resources across the network for different services as enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC). The proposed VR-based SDN system model for 6G cellular networks facilitates centralized administration of resources, enhancing communication between VR users. This innovative solution seeks to contribute to the effective and streamlined resource management essential for VR video transmission in 6G cellular networks. The utilization of Deep Reinforcement Learning (DRL) approaches, is presented as an alternative solution, showcasing significant performance and feature distinctions through comparative results. Our results show that implementing strategies based on DRL leads to a considerable improvement in the resource management process as well as in the achievable data rate and a reduction in the necessary latency in dynamic and large scale networks.

Ultra-Reliable and Low-Latency Wireless Hierarchical Federated Learning: Performance Analysis.

Wireless hierarchical federated learning (WHFL) is an implementation of wireless federated Learning (WFL) on a cloud-edge-client hierarchical architecture that accelerates model training and achieves more favorable trade-offs between communication and computation. However, due to the broadcast nature of wireless communication, the WHFL is susceptible to eavesdropping during the training process. Apart from this, recently ultra-reliable and low-latency communication (URLLC) has received much attention since it serves as a critical communication service in current 5G and upcoming 6G, and this motivates us to study the URLLC-WHFL in the presence of physical layer security (PLS) issue. In this paper, we propose a secure finite block-length (FBL) approach for the multi-antenna URLLC-WHFL, and characterize the relationship between privacy, utility, and PLS of the proposed scheme. Simulation results show that when the eavesdropper's CSI is perfectly known by the edge server, our proposed FBL approach not only almost achieves perfect secrecy but also does not affect learning performance, and further shows the robustness of our schemes against imperfect CSI of the eavesdropper's channel. This paper provides a new method for the URLLC-WHFL in the presence of PLS.

Analysis of symbiotic backscatter empowered wireless sensors network with short-packet communications.

Recent progress studies in light of wireless communication systems mainly centred around two focuses: zero-energy consumption and ultra-reliable and low-latency communication (URLLC). Among various cutting-edge areas, exploiting ambient backscatter communication (Backcom) has recently been devised as one of the foremost solutions for achieving zero energy consumption through the viability of ambient radio frequency. Meanwhile, using short-packet communication (SPC) is the cheapest way to reach the goal of URLLCs. Upon these benefits, we investigate the feasibility of Backcom and SPC for symbiotic wireless sensor networks by analyzing the system performance. Specifically, we provide a highly approximated mathematical framework for evaluating the block-error rate (BLER) performance, followed by some useful asymptotic results. These results provide insights into the level of diversity and coding gain, as well as how packet design impacts BLER performance. Numerical results confirm the efficacy of the developed framework and the correctness of key insights gleaned from the asymptotic analyses.

Secure beamforming design for MISO URLLC networks in IoT applications

This paper investigates joint beamforming and artificial noise (AN) design for secure multiple-input single-output (MISO) ultra-reliable and low latency communication (URLLC) networks in Internet of Things (IoT) applications. In considered system, a base station (BS) transmits confidential information for individual IoT users using the short-packet communication technique under the wiretap of eavesdroppers. To enhance physical layer security, the BS injects additional dedicated AN symbols to degrade the information retrieval ability at eavesdroppers. In this paper, we aim to joint design the transmit beamforming and AN symbol to maximize the minimum URLLC secrecy rate of IoT user subject to the power budget of the BS. The optimization design problem is highly nonconvex due to coupled variables in the URLLC secrecy rate and channel dispersion expressions, and thus it is mathematically challenging to solve this problem directly. To overcome this issue, we first introduce various convex inner approximations to convexify the nonconvex terms, and then we develop an efficient iterative algorithm based on the sequential convex programming approach. Extensive numerical simulation results are conducted to investigate the URLLC secrecy rate region. In conclusion, the two new URLLC parameters, i.e., the transmit packet blocklength and block error probability, will cause the considerable degradation on the URLLC secrecy rate region when comparing to that of the traditional beamforming design based on the Shannon capacity.

Blocklength Allocation and Power Control in UAV-Assisted URLLC System via Multi-agent Deep Reinforcement Learning

Integration of unmanned aerial vehicles (UAVs) with ultra-reliable and low-latency communication (URLLC) systems can improve the real-time communication performance for various industrial internet of things (IIoT) applications. Designing an intelligent resource allocation system is one of the challenges posed by an energy-constrained UAV communication system. Therefore, we formulate a sum rate maximization problem, subject to the UAVs’ energy by optimizing the blocklength allocation and the power control jointly in the uplink UAV-assisted URLLC systems, in which the probabilistic channel model between UAV and users is adopted. The problem is difficult to solve due to the non-convex objective function and the energy constraints, and also challenging to make fast decision in the complex communication environment. Thus, we propose a deep reinforcement learning (DRL)-based scheme to optimize the blocklength allocation and power control jointly. First, transform the original problem into the multi-agent reinforcement learning process, where each subcarrier is regarded as the agent that optimizes its individual blocklength allocation and power control. Then, each agent makes the intelligent decision to obtain the maximum reward value depending on the weighted segmented reward function, which is related to the UAV energy consumption and user rates to improve the rate performance. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes and has the stable convergence in different settings, such as the learning rate, the error probability, the subcarrier spacing, and the number of users.

DRL-Based URLLC-Constraint and Energy-Efficient Task Offloading for Internet of Health Things.

Internet of Health Things (IoHT) is a promising e-Health paradigm that involves offloading numerous computational-intensive and delay-sensitive tasks from locally limited IoHT points to edge servers (ESs) with abundant computational resources in close proximity. However, existing computation offloading techniques struggle to meet the burgeoning health demands in ultra-reliable and low-latency communication (URLLC), one of the 5G application scenarios. This article proposes a Multi-Agent Soft-Actor-Critic-discrete based URLLC-constrained task offloading and resource allocation (MASACDUA) scheme to maximize throughput while minimizing power consumption on the remote side, considering the long-term URLLC constraints. The URLLC constraint conditions are formulated using extreme value theory, and Lyapunov optimization is employed to divide the problem into task offloading and computation resource allocation. MASAC-discrete and a queue backlog-aware algorithm are utilized to approach task offloading and computation resource allocation, respectively. Extensive simulation results demonstrate that MASACDUA outperforms traditional DRL algorithms under different IoHT points and data arrival rate intervals and achieves superior performance in delay, bound violation probability, and other characteristics related to URLLC.

Fairness‐aware eMBB/URLLC spectrum resource multiplexing to ensure reliability in vehicular networks

AbstractSpectrum resource multiplexing of enhanced Mobile BroadBand (eMBB) and Ultra‐Reliable and Low Latency Communications (URLLC) via puncturing in vehicular networks has become an important research direction. The main issue in this direction is how to minimize the adverse impact of preemptive puncturing on eMBB traffic. However, the available eMBB/URLLC coexisting schemes lack considerations on the quality of service of a single eMBB user and fairness between users. In view of this, we formulate the eMBB/URLLC multiplexing problem to maximize eMBB reliability and fairness under the premise of meeting the requirements of URLLC as a nonlinear integer programming (NIP) in this article. To balance the performance and computational complexity, we decompose this NIP into two subproblems that be solved efficiently. For the eMBB spectrum resource allocation subproblem of maximizing the reliability and fairness under resource constraints, we prove that this problem is NP‐hard, and design a Deep Q‐Network (DQN)‐based spectrum resource allocation algorithm and a two‐stage heuristic spectrum resource allocation algorithm, respectively. For the URLLC preemption subproblem, we further propose bandwidth‐sensitive URLLC spectrum resource preemption algorithm and time‐sensitive URLLC spectrum resource preemption algorithm, respectively. Simulation results show that the proposed scheme can guarantee better fairness and reliability for eMBB users compared with the comparison schemes. Besides, for the eMBB resource allocation problem, the proposed scheme can achieve near‐optimal performance with low complexity.

Maximizing the Downlink Data Rates in Massive Multiple Input Multiple Output with Frequency Division Duplex Transmission Mode Using Power Allocation Optimization Method with Limited Coherence Time

The expected development of the future generation of wireless communications systems such as 6G aims to achieve an ultrareliable and low-latency communications (URLLCs) while maximizing the data rates. These requirements push research into developing new advanced technologies. To this end, massive multiple input multiple output (MMIMO) is introduced as a promising transmission approach to fulfill these requirements. However, maximizing the downlink-achievable sum rate (DASR) in MMIMO with a frequency division duplex (FDD) transmission mode and limited coherence time (LCT) is very challenging. To address this challenge, this paper proposes a DASR maximization approach using a feasible power allocation optimization method. The proposed approach is based on smartly allocating the total transmit power between the data transmission and training sequence transmission for channel estimation. This can be achieved by allocating more energy to the training signal than the data transmission during the channel estimation process to improve the quality of channel estimation without compromising more training sequence length, thus maximizing the DASR. Additionally, the theory of random matrix approach is exploited to derive an asymptotic closed-form expression for the DASR with a regularized zero-forcing precoder (RZFP), which allows the power optimization process to be achieved without the need for computationally complex Monte Carlo simulations. The results provided in this paper indicate that a considerable enhancement in the DASR performance is achieved using the proposed power allocation method in comparison with the conventional uniform power allocation method.

Delay-sensitive resource allocation for IoT systems in 5G O-RAN networks

The rapid advancement in sensors and communications has led to the expansion of the Internet of Things (IoT) services, where many devices need access to the transport network using fixed or wireless access technologies and mobile Radio Access Networks (RAN). However, supporting IoT in RAN is challenging as IoT services may produce many short and variable sessions, impacting the performance of mobile users sharing the same RAN. To address this issue, network slicing is a promising solution to support heterogeneous service segments sharing the same RAN, which is a crucial requirement of the upcoming fifth-generation (5G) mobile network. This paper proposes a two-level network slicing mechanism for enhanced mobile broadband (eMBB) and Ultra-Reliable and Low Latency communications (URLLC) in order to provide end-to-end slicing at the core and edge of the network with the aim of reducing latency for IoT services and mobile users sharing the same core and RAN using the O-RAN architecture. The problem is modeled at both levels as a Markov decision process (MDP) and solved using hierarchical reinforcement learning. At a high level, an SDN controller using an agent that has been trained by a Double Deep Q-network (DDQN) allocates radio resources to gNodeBs (next-generation NodeB, a 5G base station) based on the requirements of eMBB and URLLC services. At a low level, each gNodeB using an agent that has been trained by a DDQN allocates its pre-allocated resources to its end-users. The proposed approach has been demonstrated and validated through a real testbed. Notably, it surpasses the prevalent approaches in terms of end-to-end latency.

Small attenuation negative group delay of spoof surface plasmon polaritons

Spoof surface plasmon polariton (SSPP) waveguides can be used to effectively construct on-chip microwave systems due to the single-side conductor structure. SSPP waveguides usually exhibit normal dispersion, which can introduce large group delays to transmission signal and impair waveform shapes. However, the negative group velocity (NGV) effect may occur in the split modes of coupled surface plasmon polariton waveguides. In this regard, the synthesis wave model of forward and backward transmission waves is applied, which generates a quadrupole vortex wave transmission. In this paper, the SSPP unit structure with folded multi-split rings is proposed to achieve anomalous dispersion with NGV feature in the fundamental mode. The SSPP unit can be equivalent to an epsilon-negative medium, and a maximum negative group delay (NGD) can be reached at the frequency where the NGV begins to appear. For the corresponding SSPP waveguide, a NGD band with lower attenuation can be achieved at the stopband edge. While the SSPP unit structures are loaded as dispersive materials in a microstip line, the similar NGD performance can be obtained. Clearly, the SSPP NGD with lower attenuation provides a new delay equalization method for ultra reliable and low latency communications (URLLC), and can also be used as an effective dispersion compensation means.

An efficient frame preemption algorithm for time-sensitive networks using enhanced graph convolutional network with particle swarm optimization

Numerous time-sensitive applications are being made possible by the Internet of Things (IoT) phenomenon, which calls for real-time transmission to provide communication services. Applications that depend on low latency have been made possible because of the 5G Ultra-Reliable and Low-Latency Communication (URLLC) scenario. Widely regarded as a possible paradigm for achieving the deterministic transmission guarantees for 5G, Time-Sensitive Networking (TSN) has gained much attention. TSN, on the other hand, is a hybrid traffic system that combines best-effort and time-sensitive traffic, necessitating efficient routing and frame preemption to provide a predictable and limited latency. On the other hand, time-sensitive and non-time-sensitive traffic optimization significantly broaden the issue area and increase the difficulty of establishing a solution. This research presents Enhanced Graph Convolutional Network-based Deep Reinforcement Learning (EGCN-based DRL) solutions for the joint optimization problem in real-world communication contexts. The EGCN is included in deep reinforcement learning to obtain the network's spatial dependency and enhance the generalization performance of the suggested technique (DRL). Specifically, the EGCN approximates the graph convolution kernel using the first-order Chebyshev polynomial, which reduces the complexity of the algorithm and improves the feasibility of the task. Particle Swarm Optimization (PSO) is also employed to hasten the convergence of the model training process. The objective of TSN is to provide deterministic and time-critical communication capabilities over Ethernet networks. TSN aims to enable reliable, low-latency, and synchronized communication between devices, ensuring that time-sensitive data and control messages are delivered with high precision and determinism. The suggested EGCN-based DRL algorithm surpasses cutting-edge techniques in terms of average end-to-end latency, and it also converges quickly, according to numerical simulations.

Secure short-packet transmission in uplink massive MU-MIMO assisted URLLC under imperfect CSI

Ultra-reliable and low-latency communication (URLLC) is still in the early stage of research due to its two strict and conflicting requirements, i.e., ultra-low latency and ultra-high reliability, and its impact on security performance is still unclear. Specifically, short-packet communication is expected to meet the delay requirement of URLLC, while the degradation of reliability caused by it makes traditional physical-layer security metrics not applicable. In this paper, we investigate the secure short-packet transmission in uplink massive multiuser multiple-input-multiple-output (MU-MIMO) system under imperfect channel state information (CSI). We propose an artificial noise scheme to improve the security performance of the system and use the system average secrecy throughput (AST) as the analysis metric. We derive the approximate closed-form expression of the system AST and further analyze the system asymptotic performance in two regimes. Furthermore, a one-dimensional search method is used to optimize the maximum system AST for a given pilot length. Numerical results verify the correctness of theoretical analysis, and show that there are some parameters that affect the tradeoff between security and latency. Moreover, appropriately increasing the number of antennas at the base station (BS) and transmission power at user devices (UDs) can increase the system AST to achieve the required threshold.

Short-packet communication for MIMO NOMA relay networks: BLER and AoI analysis

This work considers a multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) dual-hop relay system in which the size of signal packets is finite and the propagation environment follows the Nakagami-m distribution. Based on an approximation of the Gaussian Q-function, Riemann integral, and incomplete Gamma function, we derive the closed-form expressions of the block error rate (BLER), age of information (AoI), and latency of the proposed system in both exact and asymptotic cases. On the other hand, the automatic repeat request (ARQ) protocol and acknowledgment (ACK) are applied to confirm the accuracy of the receiving data packet process. Moreover, the maximal-ratio transmission (MRT), and maximal-ratio combining (MRC) are used to improve the BLER, AoI, and latency of the proposed system. The accuracy of mathematical analysis frameworks is confirmed by simulation results. The calculation results show that the considered system satisfies the requirement of ultra-reliable and low-latency communications (URLLC). The results also represent that the MIMO-NOMA system outperforms the MIMO-OMA system in terms of BLER performance. The results also provide insight into the optimizations of the power allocation coefficient and the number of transmission bits that minimize the BLER and AoI of the considered system.

Enabling Grant-Free URLLC for AoI Minimization in RAN-Coordinated 5G Health Monitoring System

Age of information (AoI) is used to evaluate the performance of 5G health monitoring systems because stale data can be fatal for patients with serious illness. Recently, grant-free ultra-reliable and low latency communications (URLLC) have shown greater potential of minimizing AoI than conventional grant-based approaches; however, existing grant-free schedulers cannot provide guaranteed performance in 5G health monitoring systems because they involve two fundamental problems in time and frequency domains, namely the joint scheduling problem and physical resource block (PRB) allocation. In this study, we investigate two resource allocation problems for the first time, aiming to enable grant-free URLLC to minimize AoI in 5G health monitoring systems. Specifically, we propose two adaptive solutions based on an open radio access network-coordinated wireless system: 1) a joint scheduling algorithm and 2) an adaptive PRB allocation algorithm. To verify the effectiveness of the proposed solutions, we built a simulation environment similar to a real health monitoring system and captured the performance variations under realistic deployment scenarios.

On performance of short-packet ultra-reliable and low-latency communications NOMA MIMO relay networks

This paper proposes and analyzes the performance of a multi-antenna NOMA relay system over the Nakagami-m fading channel, in which the size of signal packets is finite. Based on the application of the Gaussian–Chebyshev quadrature, Riemann integral and incomplete Gamma function, the exact and asymptotic closed-form expressions of the block error rate (BLER), throughput, goodput of the proposed finite block-length multi-antenna NOMA relay system are derived. Moreover, in order to improve the BLER, throughput, and goodput of the proposed system, maximal-ratio combining and maximal-ratio transmission are applied to receivers and transmitters, respectively. The provided analysis and simulation results indicate that the proposed system meets the requirements of ultra-reliable and low-latency communications (URLLC). Especially, the reliability is higher than 99.99%, i.e., the BLER is less than 10−4. The accuracy of analysis results is verified by simulation results. Furthermore, the number of transmission bits and power allocation coefficients are optimized to maximize the throughput, and goodput and minimize the BLER.

Online deep learning based energy efficient optimization for IRS-assisted eMBB and URLLC services

In this paper, we consider a downlink multiple-input single-output (MISO) system for enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services assisted by intelligent reflecting surface (IRS). We formulate an optimization problem which aims at maximizing energy efficiency (EE) by jointly optimizing the beamforming vectors at the BS, IRS reflection coefficients matrix and resource allocation while satisfying the requirement of quality of service (QoS). The EE maximization problem is non-convex and contains various constraints. In this paper, a primal–dual online deep learning (PDODL) algorithm is proposed to tackle this issue. Specifically, we transform the original problem into the primal–dual problem by integrating constraints into the objective function. Then, online deep learning (DL) algorithm is adopted where the optimization variables are viewed as the network parameters which are further expressed in the form of unconstrained counterparts. The PDODL algorithm takes the objective function of the primal–dual problem as loss function because its decrease through network training is equal to the solving process of the optimization problem. Simulation results verify the effectiveness of the proposed algorithm and IRS can significantly improve the EE performance of the system.

Hybrid Transmission Scheme for Improving Link Reliability in mmWave URLLC Communications

Ultra-Reliable and Low-Latency Communication (URLLC) represents a key ingredient of current 5G and future 6G mobile communication networks. The demanding reliability requirement set for URLLC claims for novel techniques that can deliver the necessary level of reliability without sacrificing the overall system capacity. In this context, this work presents and analyses a hybrid transmission scheme for improved reliability in millimetre wave bands with adaptive diversity combining. The proposed scheme is based on a hybrid approach that combines two links, one in the FR1 band (characterised by lower capacity but higher reliability due to more favourable propagation) and one in the FR2 band (offering higher capacity but experiencing a less reliable connectivity). The proposed scheme dynamically adapts the usage of both links in order to exploit the complementary characteristics of both bands (reliability of FR1 bands and capacity of FR2 bands) by switching between FR2-only and joint FR1-FR2 transmission according to the instantaneous channel quality in the main FR2 link. The performance is evaluated under two popular and well-known diversity combining techniques, namely Selection Combining (SC) and Maximal Ratio Combining (MRC). The obtained results demonstrate that the proposed scheme can achieve the same level of reliability as a continuous dual-link transmission scheme but with a much lower level of links usage and without sacrificing (and indeed enhancing) the capacity, thus making it a suitable candidate to deliver URLLC services in a resource-efficient manner.

Beamforming Design in Short-Packet Transmission for URLLC in Cell-Free Massive MIMO System

Low latency, high reliability, and high data rate are three performance indicators of Ultra-reliable and low latency communication (URLLC) systems, but they are not able to be met simultaneously. Based on the determined latency and reliability requirements, this paper investigates the problem of the weighted sum rate (WSR) in cell-free massive multi-input multi-output (MIMO) system with multiple access points (APs). User-centric access point selection is considered to further reduce the computational complexity for AP used with acceptable performance loss. The transmitting beamforming is designed to mitigate inter-user interference so as to maximize the WSR under the backhaul and transmitting power constraints. Since the proposed problem is non-convex, two algorithms are proposed to solve the problem under the general SINR regime and the high SINR regime, respectively. For the former, we first approximate the objective function with the difference of convex (DC) programming and reformulate the problem into a semi-definite relaxation (SDR) form. Afterward, auxiliary variables are introduced in accordance with the successive convex approximation (SCA) method to further transform the nonconvex problem into a convex one. In the end, the sub-optimal WSR is obtained by iteratively solving a final convex problem. For the latter, the well-known WMMSE method is applied to address the WSR problem. Numerical results demonstrate the efficiency of our proposed algorithm, e.g., the maximum performance gain of the proposed algorithm is about 25% compared to the classical MMSE method. And the user-centric approach achieves 8 times computational complexity reduction with 4% performance loss.

Context-Aware Mobile Edge Resource Allocation in OFDMA Downlink System

Advanced sensing, data analysis, and communication techniques have promoted the emergence and tremendous development of the Intelligent Industrial Internet of Things (Intelligent IIoT). Intelligent IIoT-enabled 5G communication networks improve overall efficiency and open up a new market opportunity and economic growth era. In particular, for the ultra-reliable and low-latency communication (URLLC) scenario, The system requires a lightweight computing algorithm to ensure transmission reliability while providing rapid radio resource allocation. A proactive downlink system framework, supported by the reinforcement learning-based online model-free algorithm, is proposed to meet the upcoming challenge. The proactive task data transmission problem is decomposed into three sub-problems. With the help of anticipatory mobility management and virtual cell, the system gains high reliability through multipath diversity. The anchor node forwards the task data to multiple access points and manages radio resource allocation among and under the access points. The simulation justifies that the proposed framework handles the URLLC scenario well.