Ultra-reliable And Low-latency Communications Services Research Articles

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.

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.

Energy-Efficient Optimization in Distributed Massive MIMO Systems for Slicing eMBB and URLLC Services

The fifth generation (5G) communication system aims to support diverse services such as enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC). In order to enable the coexistence of eMBB and URLLC services on a common physical infrastructure, network slicing incorporated in the distributed massive multiple-input multiple-output (DM-MIMO) system is regarded as a promising solution. In this paper, a non-orthogonal slicing scheme is proposed to improve the spectral efficiency, and the short packet transmission is adopted for URLLC to meet the low latency requirement. Through a joint optimization of beamforming and remote radio unit (RRU) selection, we investigate the energy efficiency (EE) maximization problem for the downlink DM-MIMO system. The formulated problem is a mixed integer nonlinear program (MINLP) that we solve by applying some efficient approaches, such as the Dinkelbach method, the reweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> -norm and difference of convex (DC) programming. By combining these approaches in one iteration, a double-loop procedure based on semi-definite relaxation (SDR) and successive convex approximation (SCA) is employed to find the suboptimal solution of the problem. Simulation results show that the proposed scheme significantly improves the EE of the DM-MIMO system and efficiently manages resource allocation.

Resource Allocation for Cell-Free Massive MIMO-Enabled URLLC Downlink Systems

Ultra-Reliable and low-latency communication (URLLC) is a pivotal technique for enabling the wireless control over industrial Internet-of-Things (IIoT) devices. By deploying distributed access points (APs), cell-free massive multiple-input and multiple-output (CF mMIMO) has great potential to provide URLLC services for IIoT devices. In this paper, we investigate CF mMIMO-enabled URLLC in a smart factory. Lower bounds (LBs) of downlink ergodic data rate under finite channel blocklength (FCBL) with imperfect channel state information (CSI) are derived for maximum-ratio transmission (MRT), full-pilot zero-forcing (FZF), and local zero-forcing (LZF) precoding schemes. Meanwhile, the weighted sum rate is maximized by jointly optimizing the pilot power and transmission power based on the derived LBs. Specifically, we first provide the globally optimal solution of the pilot power, and then introduce some approximations to transform the original problems into a series of subproblems, which can be expressed in a geometric programming (GP) form that can be readily solved. Finally, an iterative algorithm is proposed to optimize the power allocation based on various precoding schemes. Simulation results demonstrate that the proposed algorithm is superior to the existing algorithms, and that the quality of URLLC services will benefit by deploying more APs, except for the FZF precoding scheme.

Machine Learning for 6G Enhanced Ultra-Reliable and Low-Latency Services

Ultra-reliable and low-latency communications (URLLC), as one of the major communication services of the fifth-generation (5G) and the sixth-generation (6G) cellular networks, is critical to supporting a variety of emerging mission-critical applications. However, the modern mobile networks could not satisfy the latency and reliability requirements, as well as other Quality of Service (QoS) requirements, including spectrum efficiency, energy efficiency, capacity, jitter, round-trip delay, network coverage, etc. To fulfill diverse QoS requirements for various URLLC applications, machine learning (ML) solutions are promising for future 6G networks. In this article, we first categorize the 6G URLLC vision into three connectivity characteristics, including ubiquitous connectivity, deep connectivity, and holographic connectivity, with their corresponding unique QoS requirements. We then identify potential challenges in meeting these connectivity requirements, and investigate promising ML solutions to achieve the intelligent connectivity for the 6G URLLC service. We further discuss how to implement the ML algorithms to guarantee the QoS requirements for different URLLC scenarios, including mobility URLLC, massive URLLC, and broadband URLLC. Finally, we present a case study of downlink URLLC channel access problems, solved by centralized deep reinforcement learning (CDRL) and federated DRL (FDRL), respectively, which validates the effectiveness of machine learning for URLLC services.

Improved Grant-Free Access for URLLC via Multi-Tier-Driven Computing: Network-Load Learning, Prediction, and Resource Allocation

Grant-Free (GF) access has been recognized as a promising candidate for Ultra-Reliable and Low-Latency Communications (URLLC). However, even with GF access, URLLC still may not effectively gain high reliability and millimeter-level latency, simultaneously. This is because the network load is typically time-varying and not known to the base station (BS), and thus, the resource allocated for GF access cannot well adapt to variations of the network load, resulting in low resource utilization efficiency under light network load and leading to severe collisions under heavy network load. To tackle this problem, we propose a multi-tier-driven computing framework and the associated algorithms for URLLC to support users with different QoS requirements. Especially, we concentrate on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K $ </tex-math></inline-formula> - repetition GF access in light of its simplicity and well-balanced performance for practical systems. In particular, our framework consists of three tiers of computation, namely network-load learning, network-load prediction, and adaptive resource allocation. In the first tier, the BS can learn the network-load information from the states (success, collision, and idle) of random-access resources in terms of resource blocks (RB) and time slots. In the second tier, the network-load variation is effectively predicted based on estimation results from the first tier. Finally, in the third tier, by deriving and weighing the failure probabilities of different groups of users, their QoS requirements, and the predicted network loads, the BS is able to dynamically allocate sufficient resources accommodating the varying network loads. Simulation results show that our proposed approach can estimate the network load more accurately compared with the baseline schemes. Moreover, with the assistance of network-load prediction, our adaptive resource allocation offers an effective way to enhance the QoS for different URLLC services, simultaneously.

Coexistence of URLLC and eMBB Services in MIMO-NOMA Systems

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are two major expected services in the fifth-generation mobile communication systems (5G). Specifically, eMBB applications support extremely high data rate communications, while URLLC services aim to provide stringent latency with high reliability communications. Due to their differentiated quality-of-service (QoS) requirements, the spectrum sharing between URLLC and eMBB services becomes a challenging scheduling issue. In this paper, we aim to investigate the URLLC and eMBB co-scheduling/coexistence problem under a puncturing technique in multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. The objective function is formulated to maximize the data rate of eMBB users while satisfying the latency requirements of URLLC users through joint user selection and power allocation scheduling. To solve this problem, we first introduce an eMBB user clustering mechanism to balance the system throughput and computational complexity. Thereafter, we decompose the original problem into two subproblems, namely the scheduling problem of user selection and power allocation. We introduce a Gale-Shapley (GS) theory to solve the user selection problem, and a successive convex approximation (SCA) and a difference of convex (DC) programming to deal with the power allocation problem. Finally, an iterative algorithm is utilized to find the global solution with low computational complexity. Numerical results show the effectiveness of the proposed algorithms, and verify the proposed approach outperforms other baseline methods.

Quasi-Optimization of Resource Allocation and Positioning for Solar-Powered UAVs

Unmanned Aerial Vehicles (UAVs) will be an integral part of future smart cities to provide applications such as traffic management, environment monitoring and data collection. UAVs offer flexible deployment, dynamic mobility, and Ultra-Reliable and Low Latency Communications (URLLC). However, UAVs are power-hungry devices, and their limited battery capacity cannot support their flight and communication operations for a long period. Additionally, multi-carrier (MC) techniques will be vital for supporting futuristic multi-user communication systems. To overcome these issues, we propose a solar-powered UAV MC system to support URLLC services for multi-users. In this regard, we aim to maximize the system sum throughput and we jointly optimize UAV positioning and sub-carrier allocation. To solve the optimization problem, we propose the low-complexity coordinate descent approximation algorithm (CDAA). Lastly, we show the proposed algorithm converges quickly and simultaneously yields superior performance than fixed benchmark schemes for two simulated environments.

Resource Allocation in MU-MISO Rate-Splitting Multiple Access With SIC Errors for URLLC Services

Rate-splitting multiple access (RSMA) is promising to be developed as a key enabling technology for 6G. This paper investigates the resource allocation problem in a downlink multi-user and multiple-input single-output (MU-MISO) RSMA system with successive interference cancellation (SIC) errors for ultra-reliable and low-latency communications (URLLC). The single-carrier RSMA (SC-RSMA) resource allocation scheme with URLLC is first given, where the beamforming vector and the transmission rate are optimized to maximize the effective throughput (ET). To solve the non-convex optimization problem formulated in the SC-RSMA scheme, an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) is proposed to alternately optimize the beamforming factor and the transmission rate. Furthermore, the multicarrier RSMA (MC-RSMA) scheme is developed for the URLLC access of large-scale users. Based on the results of the SC-RSMA scheme, a low-complexity three-step optimization algorithm is proposed to solve the resource allocation problem formulated in the MC-RSMA scheme. Finally, the simulation results show that the RSMA scheme and MC-RSMA scheme can respectively achieve higher ET than the non-orthogonal multiple access (NOMA) scheme and multi-carrier NOMA (MC-NOMA) scheme in the URLLC scenario, and verify that RSMA can reduce the latency and improve the reliability of the system.

Intercell Interference Coordination for UAV Enabled URLLC With Perfect/Imperfect CSI Using Cognitive Radio

Ultra-reliable and low latency communications (URLLC) will be the backbone of the upcoming sixth-generation (6G) systems and will facilitate mission-critical scenarios. A design accounting for stringent reliability and latency requirements for URLLC systems poses a challenge for both industry and academia. Recently, unmanned aerial vehicles (UAV) have emerged as a potential candidate to support communications in futuristic wireless systems due to providing favourable channel gains thanks to Line-of-Sight (LoS) communications. However, usage of UAV in cellular infrastructure increases interference in aerial and terrestrial user equipment (UE) limiting the performance gain of UAV-assisted cellular systems. To resolve these issues, we propose low-complexity algorithms for intercell interference coordination (ICIC) using cognitive radio when single and multi-UAVs are deployed in a cellular environment to facilitate URLLC services. Moreover, we model BS-to-UAV (B2U) interference in downlink communication, whereas in uplink we model UAV-to-BS (U2B), UAV-to-UAV (U2U), and UE-to-UAV (UE2U) interference under perfect/imperfect channel state information (CSI). Results demonstrate that the proposed perfect ICIC accounts for fairness among UAV especially in downlink communications compared to conventional ICIC algorithms. Furthermore, in general, the proposed UAV-sensing assisted ICIC and perfect ICIC algorithms yield better performance when compared to conventional ICIC for both uplink and downlink for the single and multi-UAV frameworks.

Off-site protection against service function forwarder failures in NFV

Many internet applications in the emerging 5G/6G networks require ultra-reliable and low-latency communications (URLLC) services. To deliver URLLC services flexibly and efficiently, network function virtualization (NFV) is employed to enable network slicing. NFV deploys service function chains (SFCs) consisting of service functions (SFs) and service function forwarders (SFFs) to deliver services. It is possible for SFFs in an SFC to fail when forwarding traffic to specified SF instances. Protecting SFF failures in NFV is challenging since multiple SF instances may fail simultaneously as a result of a single SFF failure. This work investigates how to protect the SF instances against the SFF failures while minimizing the cost of backup computing resources. We formulate a new problem called SFF-driven multi-instance failures and protection (SMFP), and prove its NP-hardness. We propose an efficient heuristic algorithm, namely, SFF-centralized resource optimization (FCRO), which is based on the proposed techniques of backup auxiliary transferring, backup cost-effectiveness selection, and adaptive fit backup. Our experimental results demonstrate that the proposed FCRO is effective and significantly outperforms the schemes directly extended from the existing work.

RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC

Enhanced mobile broadband (eMBB) and Ultra-Reliable and Low-Latency Communications (URLLC) are the two main services in the fifth-generation mobile network. URLLC services have stringent latency and reliability requirements, while eMBB services are designed to provide extremely high data rates for content delivery. The differentiated Quality of Service (QoS) requirements of the two services make their coexistence on the same bandwidth resources a challenging issue. To meet this challenge, we introduce reconfigurable intelligent surface (RIS) technology to assist in solving the resource allocation problem. The problem is formulated as two optimization problems. For the eMBB allocation problem, we jointly optimize the resource block allocation, power allocation, and RIS phase shift matrix and propose a two-stage alternating iterative algorithm to maximize the total traversal capacity of eMBB users. For the URLLC allocation problem, we maximize the URLLC packet reception rate and minimize the amount of eMBB rate loss while ensuring the quality of service for eMBB and URLLC. A heuristic URLLC allocation algorithm based on pre-configured RIS is proposed. Simulation results show that the proposed algorithm can meet the strict delay requirements of URLLC even when the URLLC packet reception rate is nearly 95.5%. Meanwhile, the total loss rate of eMBB service caused by the reuse of URLLC service is less than 6%. Thus, the applicability of the proposed solution to the coexistence problem is demonstrated.

Coordinated Resource Allocations for eMBB and URLLC in 5G Communication Networks

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two core services of the emerging fifth-generation (5G) systems. URLLC services require lower latency and high reliability, whereas eMBB services expect maximum data rates. Since 5G new radio (NR) aims to support heterogeneous services, the 3rd generation partnership project (3GPP) supports a highly flexible frame structure by introducing mixed numerology and mini-slot approaches. In these systems, the time slot is further divided into mini-slots. The coexistence of these two heterogeneous services leads to a challenging resource allocation problem. In this paper, we study the joint resource allocation problem of eMBB and URLLC schedulers to maximize the minimum expected achieved rate (MEAR) of eMBB users while satisfying URLLC users’ quality of service (QoS) constraints. The resource allocation of eMBB traffic occurs at time slot boundaries. In contrast, URLLC traffic is overlapped at the mini-slot timescale by adopting the non-orthogonal multiple access (NOMA) superposition technique. The suitable pairs of eMBB, URLLC users are evaluated using the one-one matching theory by inculcating fairness among eMBB users while satisfying the QoS parameters of URLCC users. The significance of the proposed approach over the baseline approaches is evaluated through numerical simulations in terms of the MEAR, mixed numerologies, and fairness scores among eMBB users.

Dynamic resource allocation schemes for eMBB and URLLC services in 5G wireless networks

The fifth generation (5G) of wireless networks features three core use cases, namely ultra-reliable and low latency communications (URLLC), massive machine type communications (mMTC), and enhanced mobile broadband (eMBB). These use cases co-exist in many practical scenarios and compete for the same set of time and frequency resources, resulting in a natural trade-off in their performance. In this paper, a network supporting both URLLC and eMBB modes of operation is studied. To guarantee the ultra low latency requirement of URLLC, a dynamic resource allocation scheme indicated by a two-dimensional bitmap is proposed. This approach is capable to achieve finer granularity as well as lower false cancellation rate compared to the state-of-the-art methods. A novel power control and indication method is also proposed to dynamically provide different power control parameters to the user equipment (UE), while guaranteeing the reliability requirement of URLLC and minimizing the impact to eMBB. In addition, we devise a dynamic selection mechanism (DSM) to accommodate diverse scenarios, which is empowered with load prediction to become more intelligent. Our extensive system-level simulation results for eMBB-URLLC co-existence scenarios showcase that the perceived throughput of eMBB UEs is increased by 45.3%, while about 13.3% more UEs are enjoying URLLC services with at most 84% transmit power savings compared to the state-of-the-art methods.

URLLC service-oriented reliable network deployment over MEC enabled TDM-PON based cloud radio access network

New emerging ultra-reliable and low latency communications (URLLC) services have imposed challenged stringent low latency and reliability requirements on fifth generation and beyond (5G/B5G) cloud radio access network (CRAN). However, limited research has been discussed service-oriented reliable network deployment in the mobile edge computing (MEC)-enabled time-division multiplexing-passive optical network (TDM-PON) based CRAN. Moreover, different from most previous protection studies, both optical transport and functional processing resources in multiple domains should be considered in the network deployment of a converged network. To mitigate these limitation, we present a mixed integer linear programming (MILP) for URLLC service-oriented reliable network deployment under the physical and management constraints in MEC-enabled TDM-PON based CRAN. Given the model’s complexity, a heuristic algorithm called “Time-efficient completely isolated protection scheme (TECIPS)” is proposed, which can completely guarantee protection through establishing both primary network topology and strong isolated backup network topology against multiple network failures, while reducing redundant transmission latency and backup recovery latency for URLLC services. Simulation results indicate that our proposed algorithm significantly reduces total latency compared to the benchmark algorithm and receives the near-optimal solution of MILP in both sparse and dense networks with an acceptable time complexity. In addition, the impact of key parameters on the consumption of total transmission latency and backup recovery latency is also evaluated.

Joint Uplink and Downlink Resource Allocation in NOMA for End-to-End URLLC Services

Non-orthogonal multiple access (NOMA) has great potential to increase spectral efficiency and reduce latency. However, reliability problems of NOMA limit its applications in ultra-reliable and low-latency communications (URLLC). For the first time in this letter, the optimal resource allocation scheme is investigated for uplink (UL) and downlink (DL) NOMA in URLLC communications. Specifically, we consider an UL and DL NOMA system with two-pair of users, and try to minimize the overall error probability under stringent URLLC requirements. Then, an optimal scheme for both UL and DL resources allocation is proposed, and a global optimal solution for both power control and channel blocklength allocation is obtained. Finally, simulation results show that NOMA significantly outperforms the orthogonal multiple access (OMA) subjected to the same power and channel blocklength levels and demonstrate the availability of NOMA for end-to-end (E2E) URLLC services.

A Downlink Puncturing Scheme for Simultaneous Transmission of URLLC and eMBB Traffic by Exploiting Data Similarity

Ultra Reliable and Low Latency Communications (URLLC) is deemed to be an essential service in 5G systems and beyond (also called 6G) to accommodate a wide range of emerging applications with stringent latency and reliability requirements. Coexistence of URLLC alongside other service categories calls for developing spectrally efficient multiplexing techniques. Specifically, coupling URLLC and conventional enhanced Mobile BroadBand (eMBB) through superposition/puncturing naturally arises as a promising option due to the tolerance of the latter in terms of latency and reliability. The idea here is to transmit URLLC packets (typically sporadic and of short size) over resources occupied by ongoing eMBB transmissions while minimizing the impact on the eMBB transmissions. In this paper, we propose a novel downlink URLLC-eMBB multiplexing technique that exploits possible similarities among URLLC and eMBB symbols, with the objective of reducing the size of the punctured eMBB symbols. We propose that the base station (BS) scans the eMBB traffic symbol sequences and punctures those that have the highest symbol similarity with that of the URLLC users to be served. As the eMBB and URLLC may use different constellation sizes, we introduce the concept of symbol region similarity to accommodate the different constellations. We assess the performance of the proposed scheme analytically, where we derive closed-form expressions for the symbol error rate (SER) of the eMBB and URLLC services. We also derive an expression for the eMBB loss function due to puncturing in terms of the eMBB SER. We demonstrate through numerical and simulation results the efficacy of the proposed scheme where we show that 1) the eMBB spectral efficiency is improved by puncturing fewer symbols, 2) the SER and reliability performance of eMBB are improved, and 3) the URLLC data is accommodated within the specified delay constraint while maintaining its reliability, 4) and the proposed strategy has polynomial time complexity making it an efficient solution to be used in practice.

Modelling and Analysis of 5G Networks Based on MEC-NFV for URLLC Services

Ultra-Reliable and Low Latency Communications (URLLC) is a Fifth Generation (5G) or Beyond(B) 5G of Mobile Networks service that presents strict reliability and latency requirements. Multi-Access Edge Computing (MEC) and Network Function Virtualization (NFV) emerge as essential solutions since they allow network applications/functions to be virtualized and hosted closer to the end-user, improving reliability and latency. However, they may incur delay overheads (e.g., resource setup delay, failure events) to the dynamic resource provisioning that may impair the URLLC, which must be considered in the MECNFV node setting. This work addresses the resource provisioning for URLLC services in MEC-NFV-based networks, considering the Virtualized Network Function (VNF) setup time, failure occurrence and resource pre-initialization. A queue-based model is proposed to analyze the MEC-NFV node configuration in terms of average response time, blocking probability, and average number of active resources, under different service arrival and resource setup rates, maximum system capacity, number of resources and pre-initialized ones are carried out. The results show that the resource pre-initialization may mitigate the negative effect of the lower VNF setup rate.

Two-Level Soft RAN Slicing for Customized Services in 5G-and-Beyond Wireless Communications

In this article, a two-level soft-slicing scheme is proposed for 5G-and-beyond radio access networks to support ultrareliable and low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services with delay/reliability and throughput requirements, respectively. At the network level, we first determine the number of radio resources required for eMBB services and analyze the delay violation probability for URLLC services. Then, an integer nonlinear program is formulated for the network-level resource preallocation. Since the formulated problem is NP-complete, a low-complexity heuristic algorithm is proposed to obtain near-optimal solutions. Given the preallocated resources at each gNodeB (gNB), a gNB-level resource scheduling scheme is designed to enable real-time resource sharing among URLLC services considering the reliability and delay requirements. Simulation results show that the proposed soft-slicing scheme meets stringent quality-of-service requirements for both URLLC and eMBB services and achieves high resource utilization efficiency when compared with conventional hard resource slicing schemes.