Ultra-reliable Low-latency Communications Research Articles

Handover optimisation for high-capacity low-latency 5G NR mmWave communication

In millimetre wave (mmWave) networks, due to the limited communication range and to the narrow antenna beams, traditional handover techniques might not fully comply with the requirements of high-capacity Ultra Reliable Low Latency Communications (URLLC) applications, such as autonomous vehicles. In this paper, we propose an optimal solution that sets performance boundaries for handover mechanisms. We compare the Fifth Generation (5G) handover mechanism performance with the proposed optimal solution in terms of number of handovers, ping-pong effect ratio, and delay. Our assessment indicates that the 5G handover can be improved to meet the novel applications’ tight constraints, evidenced by a ping-pong effect average reduction of 61%, a 5% increase in average capacity, and at least 17% and 15% increases in the number of messages received before delay expiration for 1 ms and 3 ms delay tolerance, respectively.

On the design and performance of scheduling policies exploiting spatial diversity for URLLC

In this paper, we study the performance of packet scheduling schemes for Ultra-Reliable Low-Latency Communications (URLLC) services. We exploit spatial diversity (i.e., redundant coverage of users) guaranteed in numerous 5G radio access network scenarios to examine the impact of multi-connectivity. Thus, we consider a set of URLLC users connected to two frequency layers or Radio Access Technologies (RATs) to ensure minimal queuing time. We review four packet scheduling and redundancy schemes, namely Join-the-Shortest-Queue (JSQ), the shortest expected delay (SED), systematic Redundancy (RED), and redundancy with Cancellation upon completion (CAN). We choose the outage probability as a metric, defined as the packet’s probability of arriving after some given target delay. We show that RED performs well at low load, whereas JSQ and SED are better when the load rises. Besides, CAN outperforms all other schemes. We then discuss the trade-off between performance and implementation complexity.

Cooperative vehicular networks over Nakagami-[formula omitted] fading: Joint power control and spectrum scheduling

Facilitated by the rapid development of wireless communication technologies and the broad deployment of cellular networks, vehicular communications have evolved towards ultra-reliable low-latency communications (URLLC). In this paper, we consider a device-to-device (D2D)-enabled vehicular networks, where the uplink from roadside units (RSUs) to the base station (BS) shares spectrum with D2D-enabled vehicle-to-vehicle (V2V) links. The sum capacity of RSUs is maximized by jointly optimizing the transmit power and orthogonal spectrum bands scheduling, with the minimum data rate of RSUs and the reliability of V2V links guaranteed. Specifically, under Nakagami fading channels, the statistical channel state information (CSI) of vehicular communication links is utilized instead of the instantaneous CSI to adapt to the extra overheads resulting from frequent channel estimation. Moreover, user fairness is also considered, through maximizing the minimum ergodic capacity achieved by RSUs. To solve the formulated non-convex problem, we decomposed it into a power allocation subproblem that is analytically solved, and a spectrum bands scheduling subproblem that is tackled by resorting to the Hungarian method. The validity of the proposed methods is confirmed by numerical results.

The Age of Information in Wireless Cellular Systems: Gaps, Open Problems, and Research Challenges.

One of the critical use cases for prospective fifth generation (5G) cellular systems is the delivery of the state of the remote systems to the control center. Such services are relevant for both massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC) services that need to be supported by 5G systems. The recently introduced the age of information (AoI) metric representing the timeliness of the reception of the update at the receiver is nowadays commonly utilized to quantify the performance of such services. However, the metric itself is closely related to the queueing theory, which conventionally requires strict assumptions for analytical tractability. This review paper aims to: (i) identify the gaps between technical wireless systems and queueing models utilized for analysis of the AoI metric; (ii) provide a detailed review of studies that have addressed the AoI metric; and (iii) establish future research challenges in this area. Our major outcome is that the models proposed to date for the AoI performance evaluation and optimization deviate drastically from the technical specifics of modern and future wireless cellular systems, including those proposed for URLLC and mMTC services. Specifically, we identify that the majority of the models considered to date: (i) do not account for service processes of wireless channel that utilize orthogonal frequency division multiple access (OFDMA) technology and are able to serve more than a single packet in a time slot; (ii) neglect the specifics of the multiple access schemes utilized for mMTC communications, specifically, multi-channel random access followed by data transmission; (iii) do not consider special and temporal correlation properties in the set of end systems that may arise naturally in state monitoring applications; and finally, (iv) only few studies have assessed those practical use cases where queuing may happen at more than a single node along the route. Each of these areas requires further advances for performance optimization and integration of modern and future wireless provisioning technologies with mMTC and URLLC services.

Highly-Efficient Low-Latency HARQ Built on NOMA for URLLC: Radio Resource Allocation and Transmission Rate Control Aspects

Hybrid automatic repeat request (HARQ) is an essential technology that efficiently reduces the transmission error rate. However, for ultra-reliable low latency communications (URLLC) in the 5th generation mobile communication systems and beyond, the increase in latency due to retransmission must be minimized in HARQ. In this paper, we propose a highly-efficient low-latency HARQ method built on non-orthogonal multiple access (NOMA) for URLLC while minimizing the performance loss for coexisting services (use cases) such as enhanced mobile broadband (eMBB). The proposed method can be seen as an extension of the conventional link-level non-orthogonal HARQ to the system-level protocol. This mitigates the problems of the conventional link-level non-orthogonal HARQ, which are decoding error under poor channel conditions and an increase in transmission delay due to restrictions in retransmission timing. In the proposed method, delay-sensitive URLLC packets are preferentially multiplexed with best-effort eMBB packets in the same channel using superposition coding to reduce the transmission latency of the URLLC packet while alleviating the throughput loss in eMBB. This is achieved using a weighted channel-aware resource allocator (scheduler). The inter-packet interference multiplexed in the same channel is removed using a successive interference canceller (SIC) at the receiver. Furthermore, the transmission rates for the initial transmission and retransmission are controlled in an appropriate manner for each service in order to deal with decoding errors caused by error in transmission rate control originating from a time varying channel. We show that the proposed method significantly improves the overall performance of a system that simultaneously provides eMBB and URLLC services.

Performance of Ultra Reliable low Latency Communication (URLLC) in 5G Wireless Networks

Performance of Ultra Reliable low Latency Communication (URLLC) in 5G Wireless Networks

Quantum Learning on Structured Code With Computing Traps for Secure URLLC in Industrial IoT Scenarios

Resilient and secure ultra-reliable low-latency communications (URLLC) over radio interface is expected to play a crucial role in next generation Industrial IoT scenarios. However, attacking wireless pilot signals has been a potential easy way to interrupt URLLC services. In this work, we propose a random structured code to encode and decode pilot signals on multidimensional physical resources, and also design a quantum learning framework to make this code secure and reliable. Specifically, the code suggests using random encoding with little structures to disperse the effect of attacks. We find that the decoding process can be modeled as a computing trap if the group spatial channel features are employed. The security problem is therefore transformed as a random computing with redundancy. We employ a quantum algorithm to learn the computing trap model such that the computing redundancy can be removed quickly while the dispersed attack can be eliminated. In this respect, we can prove the existence of the quantum black-box model corresponding to the computing trap, and derive a precise expression of computing performance. Based on the result, we can formulate novel analytical closed-form expressions of system failure probability to characterize the reliability of the URLLC. Numerical results show that the proposed system can maintain ultra-high reliability and low latency against attacks on wireless pilots.

A Novel Adaptive UE Aggregation-Based Transmission Scheme Design for a Hybrid Network with Multi-Connectivity

With the progress of the eras and the development of science and technology, the requirements of device-to-device (D2D) connectivity increased rapidly. As one important service in future systems, ultra-reliable low-latency communication (URLLC) has attracted attention in many applications, especially in the Internet of Things (IoT), smart cities, and other scenarios due to its characteristics of ultra-low latency and ultra-high reliability. However, in order to achieve the requirement of ultra-low latency, energy consumption often increases significantly. The optimization of energy consumption and the latency of the system in the communication field are often in conflict with each other. In this paper, in order to optimize the energy consumption and the latency jointly under different scenarios, and since the detailed requirements for latency and reliability are diverse in different services, we propose an adaptive UE aggregation (AUA)-based transmission scheme that explores the diversity gain of multiple simultaneous paths to reduce the overall latency of data transmission, wherein multiple paths correspond to multiple coordination nodes. Furthermore, it could provide the feasibility of link adaptation by adjusting the path number according to the real transmission environment. Then, unnecessary energy waste could be avoided. To evaluate the performance, the energy-delay product (EDP) is proposed for the latency and energy comparison. The provided simulation results align with the numerical data. Through the analysis, it can be proven that the proposed scheme can achieve a joint optimization of latency and energy consumption to meet different types of URLLC services.

Retrial Queueing System for Analyzing Impact of Priority Ultra-Reliable Low-Latency Communication Transmission on Enhanced Mobile Broadband Quality of Service Degradation in 5G Networks

Fifth generation (5G) networks support ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB). The coexistence of URLLC and eMBB is often organized by non-orthogonal multiple access (NOMA), giving priority to URLLC and resulting in eMBB quality of service (QoS) degradation. In this paper, we address the issue of joint URLLC and eMBB transmission, focusing on the problem from the perspective of delay-tolerant eMBB. Due to the priority given to URLLC, we assume that an eMBB session may be interrupted if there are no free resources available for URLLC or delayed when a new eMBB session arrives. To make the scheme more flexible, we propose that interrupted and delayed eMBB sessions periodically check for free resources, rather than continuously. To analyze this scenario, we propose a retrial queuing system with two retrial buffers (orbits) for interrupted and delayed eMBB sessions. The stationary probability distribution, provided in matrix form by recursive formulas, is presented. The paper concludes with a numerical example showing that the scheme with two buffers, compared to one buffer, practically doubles the average number of active eMBB sessions while keeping the interruption probability below 0.001. We provide an illustration of the configuration of eMBB retrial rates to meet its QoS requirements.

Design of Multi-User Noncoherent Massive SIMO Systems for Scalable URLLC.

This paper develops and optimizes a non-orthogonal and noncoherent multi-user massive single-input multiple-output (SIMO) framework, with the objective of enabling scalable ultra-reliable low-latency communications (sURLLC) in Beyond-5G (B5G)/6G wireless communication systems. In this framework, the huge diversity gain associated with the large-scale antenna array in the massive SIMO system is leveraged to ensure ultra-high reliability. To reduce the overhead and latency induced by the channel estimation process, we advocate for the noncoherent communication technique, which does not need the knowledge of instantaneous channel state information (CSI) but only relies on large-scale fading coefficients for message decoding. To boost the scalability of noncoherent massive SIMO systems, we enable the non-orthogonal channel access of multiple users by devising a new differential modulation scheme to ensure that each transmitted signal matrix can be uniquely determined in the noise-free case and be reliably estimated in noisy cases when the antenna array size is scaled up. The key idea is to make the transmitted signals from multiple geographically separated users be superimposed properly over the air, such that when the sum signal is correctly detected, the signal sent by each individual user can be uniquely determined. To further enhance the average error performance when the array antenna number is large, we propose a max-min Kullback-Leibler (KL) divergence-based design by jointly optimizing the transmitted powers of all users and the sub-constellation assignments among them. The simulation results show that the proposed design significantly outperforms the existing max-min Euclidean distance-based counterpart in terms of error performance. Moreover, our proposed approach also has a better error performance compared to the conventional coherent zero-forcing (ZF) receiver with orthogonal channel training, particularly for cell-edge users.

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches.

The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

Multiple CUAV-Enabled mMTC and URLLC Services: Review of Energy Efficiency and Latency Performance

Cognitive unmanned aerial vehicles (CUAVs) play a vital role in next-generation wireless networks as they assist in massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC) services. This study focuses on multiple CUAV-enabled networks wherein CUAVs are paired with each other. We analyze the data rate, energy efficiency, and latency of such networks by applying the finite information block length theory, wherein mMTC and URLLC information use a non-orthogonal multiple access technique. Furthermore, we formulate an optimization problem to maximize the energy efficiency of paired CUAV devices by jointly optimizing the transmission power of the mMTC and URLLC information to satisfy the latency requirement. The numerical results indicate that our proposed multiple-CUAV-enabled scheme enhances the network performance of CUAV devices in terms of energy efficiency and latency better than the existing scheme.

Efficient Network Slicing for 5G Services in Cloud Fog-RAN Deployment Over WDM Network

Network slicing in fifth generation (5G) radio access network (RAN) enables serving massive network traffic with diverse and stringent quality of service (QoS) requirements. Multiple logical networks can be built using RAN slicing over a single RAN infrastructure. This paper considers three different types of slices that are standardized by third generation partner project (3GPP): ultra-reliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), and massive machine type communication (mMTC). Each slice type has unique requirements for data rate, latency, and reliability. Cloud RAN (C-RAN) was proposed to address the requirements of 5G services, which involves physical separation between the remote radio head (RRH) and baseband unit (BBU) in 2-layers. While C-RAN enables more efficient resource utilization and energy consumption, it limits network scalability. Furthermore, C-RAN is unable to meet the stringent latency demands of URLLC services, as well as the massive fronthaul capacity required for eMBB requests. To address this issue, we propose a novel cloud fog RAN (CF-RAN) over wavelength division multiplexing (WDM) architecture. In CF-RAN over WDM the RAN functions are divided into 3 layers, which include the RRH at layer 1, fog nodes at layer 2, and BBU hotels at layer 3. We employ the emerging fog computing paradigm in optical and wireless networks in this suggested architecture. To facilitate low latency URLLC requests, the fog nodes are located closer to the cell site (CS). We propose an integer linear programming (ILP)-based mathematical model. The model's objective is to decrease the number of active BBU hotels and fog nodes while remaining compliant with practical network restrictions. Furthermore, a low complexity greedy heuristic algorithm is proposed to solve the problem and is compared to the branch & bound (B&B) algorithm, which is assumed to provide an optimal solution with exponential complexity. The proposed 3-layer CF-RAN over WDM architecture achieves a 70% improvement in BBU centralization and a 50% reduction in request blocking when compared to the standard 2-layer architecture.

Towards Critical Industrial Wireless Control: Prototype Implementation and Experimental Evaluation on URLLC

Ultra reliable low latency communication (URLLC) is a promising technology to enable critical industrial wireless control, such as tactile sensing and motion control. This article presents a novel software-defined URLLC prototype and establishes a human-in-loop robotic teleoperation platform for experimental evaluations. First, by studying the control and communication requirements of robotic teleoperation, the key challenges for URLLC-based industrial wireless control are analyzed. Then, the prototype development including protocol stack and hardware architecture design is introduced in detail, and extensive experiments are performed to evaluate the communication latency and control transparency for robotic teleoperation. Experimental results show that the prototype can realize millisecond-level latency with ultra-high reliability, and support wireless teleoperation with similar transparency as wired teleoperation. Finally, challenges and future research issues towards 6G are discussed.

Service Function Chaining and Embedding With Heterogeneous Faults Tolerance in Edge Networks

In the 5G-and-beyond era, ultra-reliable low latency communication (URLLC) services are ubiquitous in edge networks. To enhance the performance metrics and the quality of service (QoS), URLLC services are delivered via a sequence of software-based network functions, also known as a service function chain (SFC). Towards reliable SFC delivery, it is imperative to incorporate fault-tolerance during SFC deployments. However, deploying an SFC with fault-tolerance is challenging because the protection mechanism needs to jointly consider multiple concurrent physical/virtual network failures and hardware/software failures. Considering these concurrent heterogeneous failures, this work investigates how to effectively deliver an SFC in edge networks with the objective of minimizing bandwidth resource consumption. First, we introduce the concept of k-heterogeneous-faults-tolerance and propose an augmented protection graph, called k-connected service function slices layered graph (KC-SLG). Based on the KC-SLG, we formulate a novel problem called k-heterogeneous-faults-tolerant SFC embedding and propose an effective algorithm, called fault-tolerant service function graph embedding (FT-SFGE). FT-SFGE employs two proposed techniques: k-connected network slicing (KC-NS) and k-connected function slicing (KC-FS). Via thorough mathematical proofs, we show that KC-NS is 2-approximate. Extensive simulations show that KC-FS has the best average cost-efficiency when k = 2, and FT-SFGE outperforms the schemes directly extended from the state-of-the-art.

AI-Based Resource Allocation in End-to-End Network Slicing Under Demand and CSI Uncertainties

Network slicing (NwS) is one of the main technologies in the fifth-generation of mobile communication and beyond (5G+). One of the important challenges in the NwS is information uncertainty which mainly involves demand and channel state information (CSI). Demand uncertainty is divided into three types: number of users requests, amount of bandwidth, and requested virtual network functions workloads. Moreover, the CSI uncertainty is modeled by three methods: worst-case, probabilistic, and hybrid. In this paper, our goal is to maximize the utility of the infrastructure provider by exploiting deep reinforcement learning (DRL) algorithms in end-to-end NwS resource allocation under demand and CSI uncertainties. Enhanced mobile broadband (eMBB) requires high data rates. The uncertainties we argued above have a direct negative impact on the data rate and our objective function. Therefore, we focus primarily on eMBB. Additionally, we also consider ultra-reliable low latency communications (uRLLC) and massive machine-type communication (mMTC). The proposed formulation is a non-convex mixed-integer non-linear programming problem. To perform resource allocation in problems that involve uncertainty, we need a history of previous information. To this end, we use a recurrent deterministic policy gradient (RDPG) algorithm, a recurrent and memory-based approach in DRL. Then, we compare the RDPG method in different scenarios with soft actor-critic (SAC), deep deterministic policy gradient (DDPG), distributed, and greedy algorithms. The simulation results show that the SAC method is better than the DDPG, distributed, and greedy methods, respectively. Moreover, the RDPG method out performs the SAC approach on average by 70%.

Dynamic slicing reconfiguration for virtualized 5G networks using ML forecasting of computing capacity

As 5G deployments continue to increase worldwide, new applications can fully leverage the exceptional features of the emerging mobile networks. Ultra-Reliable Low Latency Communications (URLLC) serve as an excellent example of applications highly sensitive to jitter and packet loss. To meet these demanding requirements, 5G relies on network slicing, network virtualization, and software-defined networks. This ecosystem enables the precise allocation of resources for each network slice. However, the applications’ resource demands may vary over time. In this challenging and overwhelming environment, traditional human decision-making for slice reconfiguration is not suitable anymore, due to the multitude of parameters and the need for extremely fast response times. Machine Learning (ML) comes as a tool that can enable better use of the available resources with faster and more intelligent management. This paper introduces an ML model that can predict slices’ traffic and dynamically reconfigure computational capacity. With these forecasting capabilities, the virtualized resources can be fine-tuned to suit the slices’ requirements, guaranteeing their Quality of Service (QoS). By doing so, Mobile Network Operators can make optimized use of the equipment, tailoring their needs to each service while complying with the QoS level. The results obtained demonstrate that the proposed ML model, in combination with a specific set of hysteresis rules, can accurately predict the saturation of virtualized capacity with up to 91% accuracy and proactively adapt it to the network slice requirements.

Sliding Window Network Coding Enables NeXt Generation URLLC Millimeter-Wave Networks

Ultra-reliability and low-latency are pivotal requirements of the emerging 6th generation of communication systems (xURLLC). The transition in millimeter-wave (mmWave) technology, from omni-directional to highly directional antennas, has been seen as an enabler for high bandwidth communications, still susceptible to high loss and high latency variation. Classical error recovery approaches cannot close the rising gap between high throughput and low delay in such systems. In this work, we incorporate effective sliding window network coding solutions in mmWave communications. While legacy systems such as rateless codes improve the delay, cross-layer results show that they do not provide Low Latency Communications (LLC), due to the lossy behaviour of mmWave channel and the lower-layers’ retransmission mechanisms. On the other hand, fixed sliding window random linear network coding (RLNC) is able to achieve LLC, and even better, adaptive sliding window RLNC obtains Ultra-Reliable LLC (URLLC) in mmWave backhaul networks.

On Coordinated Scheduling of Radio and Computing Resources in Cloud-RAN

Cloud Radio Access Network is a promising mobile network architecture based on centralizing the baseband processing of many cellular base stations in a BBU (BaseBand Unit) pool. Such architecture has many advantages. However, computing resources are shared among the base stations connected to the BBU pool. It is challenging to schedule the processing of users’ data, especially on overloaded BBU pools, while respecting the time constraints imposed by the Hybrid Automatic Repeat Request (HARQ) mechanism. Given that the processing time of users’ data and the computing requirement depends on the radio parameters such as the Modulation and Coding Scheme (MCS), we propose to enable the coordination between radio and computing resources schedulers; such coordination makes the selection of MCS dependent on the availability of radio and computing resources and on the ability to process data while respecting the HARQ-deadline. In this context, we propose and evaluate three Integer Linear Programming (ILP)-based schemes and three low-complexity heuristics, demonstrating their ability to reduce the wasted transmission power. Moreover, we evaluate the performance of the coordination under a multi-services scenario consisting of two services having heterogeneous requirements, enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communication (URLLC).

Downlink resource allocation for NOMA-based hybrid spectrum access in cognitive network

To solve the contradiction between limited spectrum resources and increasing communication demand, this paper proposes a wireless resource allocation scheme based on the Deep Q Network (DQN) to allocate radio resources in a downlink multi-user cognitive radio (CR) network with slicing. Secondary users (SUs) are multiplexed using non-orthogonal multiple access (NOMA). The SUs use the hybrid spectrum access mode to improve the spectral efficiency (SE). Considering the demand for multiple services, the enhanced mobile broadband (eMBB) slice and ultrareliable low-latency communication (URLLC) slice were established. The proposed scheme can maximize the SE while ensuring Quality of Service (QoS) for the users. This study established a mapping relationship between resource allocation and the DQN algorithm in the CR-NOMA network. According to the signal-to-interference-plus-noise ratio (SINR) of the primary users (PUs), the proposed scheme can output the optimal channel selection and power allocation. The simulation results reveal that the proposed scheme can converge faster and obtain higher rewards compared with the Q-Learning scheme. Additionally, the proposed scheme has better SE than both the overlay and underlay only modes.