Ultra-reliable And Low-latency Communications Research Articles

Performance Analysis of Distributed Antenna System for Downlink Ultrareliable Low-Latency Communications

In this article, we consider a distributed antenna system to enable the downlink ultrareliable and low-latency communications (URLLC) for the industrial Internet of Things. To satisfy the low latency, the short packet length is exploited and accordingly, the achievable rate under the finite blocklength codes is considered in the analysis of the packet loss probability in downlink URLLC distributed antenna system. Specifically, the packet loss probability is approximated as a sum of the channel OP and the finite blocklength coding error probability. By using a stochastic geometry approach, we analytically derive the packet loss probability in terms of the system parameters (i.e., the densities of the DA ports and the receiving sensor nodes, the number of receive antennas, and the packet size/duration, etc.), when the sensor nodes and the DA ports are randomly distributed in a given area. From the analytic results, we show how to optimize the short blocklength coding error probability to minimize the overall packet loss probability. Furthermore, we also derive the required DA port density to satisfy the packet loss probability constraint for a given distributed antenna system parameters, which gives us a useful insight into the design of downlink distributed antenna system for URLLC.

Ultra-high reliable optimization based on Monte Carlo Tree Search over Nakagami-[formula omitted] Fading

Supporting the ultra-reliable and low-latency communications (URLLCs) has become one of the major goals for future wireless networks. In this paper, we present an analytical reliability model for user equipment (UE) connecting multiple base stations (BSs) with multi-stream carrier aggregation (MSCA) technology. We first derive a closed-form expression for reliability characterization using signal-to-interference-plus-noise (SINR) model over Nakagami-m fading. We then formulate a joint resource allocation problem to maximize reliability, considering UE association, sub-carrier assignment and discrete power allocation. With distributed decision making (DDM) theory, we decouple it into two sub-problems, and each sub-problem is formulated as a separate Markov Decision Problem (MDP). We further propose the Monte Carlo Tree Search (MCTS) method to find the optimal solution for each sub-problem. The iterative joint optimization method based on DDM is also proposed. Our simulation results show that the reliability with MSCA increases with increasing the number of sub-carriers, as well as increasing the power allocation. We also show that our algorithm is an effective way in finding the optimal resource allocation for reliability improvement.

Dynamic Communication QoS Design for Real-Time Wireless Control Systems

In the coming fifth-generation (5G) cellular networks, ultra-reliable and low-latency communication (URLLC) is treated as an indispensable service to enable real-time wireless control systems. However, the extremely high quality-of-service (QoS) in URLLC causes significant wireless resource consumption. Moreover, to obtain good control performance may not always require extremely high communication QoS. In this paper, we propose a communication-control co-design scheme to reduce wireless resource consumption, where we obtain a dynamic communication QoS design method to reduce the energy consumption by jointly using extremely high QoS and a relatively low QoS. In this scheme, we first explore the control process served by different communication QoS levels and find that the whole control process can be divided into two phases, where different QoS levels have their advantages in different phases. Then, we obtain a threshold to decide when the extremely high QoS or relatively low QoS should be provided by communications. Simulation results demonstrate that our method can effectively reduce communication energy consumption while maintaining good control performance.

An ultra-reliable and low latency communications assisted modulation based non-orthogonal multiple access scheme

The technological demand drastically requires Internet of Things (IoT) execution. Certainly, it will bring a paradigm transformation from a local city to a smart city. Besides several other applications, Intelligent transportation system (ITS) will bring a dominant smart society shift. ITS will benefit growing traffic system safety, reliability, demand and security. 5G Ultra-Reliable and Low Latency Communications (URLLC) amalgamation is certain with ITS due to big data and traffic requirements. In this paper we demontrate our Modulation based Non Orthogonal Multiple Access (M-NOMA) technique support for Ultra-Reliable and Low Latencey Communication (URLLC) user case of 5G. Then we present it as a user case for ITS. M-NOMA guarantees reliability and low latency of the system due to its minimum symbol error rate (SER), reduced interference, better data rate and hence the Quality of service (QoS). In this paper, we derive SER for M-NOMA with Additive White Gaussian Noise (AWGN) and fading channel. We also optimized the derived SER, data rate with Lagrange optimization. The proposed system supports the low latency and high reliability requirements. Simulation result confirmed the added benefits offered by our M-NOMA scheme over the traditional schemes.

Joint Frame Design and Resource Allocation for Ultra-Reliable and Low-Latency Vehicular Networks

The rapid development of the fifth generation mobile communication systems accelerates the implementation of vehicle-to-everything communications. Compared with the other types of vehicular communications, vehicle-to-vehicle (V2V) communications mainly focus on the exchange of driving safety information with neighboring vehicles, which requires ultra-reliable and low-latency communications (URLLCs). However, the frame size is significantly shortened in V2V URLLCs because of the rigorous latency requirements, and thus the overhead is no longer negligible compared with the payload information from the perspective of size. In this paper, we investigate the frame design and resource allocation for an urban V2V URLLC system in which the uplink cellular resources are reused at the underlay mode. Specifically, we first analyze the lower bounds of performance for V2V pairs and cellular users based on the regular pilot scheme and superimposed pilot scheme. Then, we propose a frame design algorithm and a semi-persistent scheduling algorithm to achieve the optimal frame design and resource allocation with the reasonable complexity. Finally, our simulation results show that the proposed frame design and resource allocation scheme can greatly satisfy the URLLC requirements of V2V pairs and guarantee the communication quality of cellular users.

Resources Allocation at the Physical Layer for Network Function Virtualization Deployment

Resource Allocation (RA) is one of the important factors in network function virtualization (NFV) deployment. As physical (PHY) layer resources are limited, e.g., transmitted energy and channel uses, the RA problem at the PHY layer for NFV deployment has become a fast-growing problem, especially for supporting ultra-reliable and low-latency communications (URLLC). Moreover, different nodes in NFV have different requirements for end-to-end communication, e.g., a controller has more stringent reliability requirements than does a logical node. There is a need for efficient and robust RA algorithms at the PHY layer for NFV deployment. To illustrate these challenges, we consider an up-link (UL) transmission protocol for NFV deployment, in which wireless transmissions with short packets are considered, and both the packet length and the transmission power are adjustable. Then, for three NFV deployment scenarios, we formulate three RA problems as three optimization problems to obtain the optimal parameters. Since these optimization problems are highly non-convex and they include excessive constraint conditions, the global optimal solutions are hard to obtain and are even infeasible for the conventional heuristic algorithms due to their low convergence efficiency. To address these problems, in this paper, the intelligent scheme of the modified shuffled frog-leaping algorithm (MSFLA) based on improved extremal optimization (EO) is applied to design RA algorithms. Three RA algorithms are designed for three NFV deployment scenarios to evaluate the quality of the solutions produced by the MSFLA-EO scheme. We perform simulations of three proposed RA algorithms in terms of various performance parameters. The experimental results are encouraging and demonstrate the efficiency of the proposed RA algorithms.

Joint Power and Blocklength Optimization for URLLC in a Factory Automation Scenario

Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications defined in the fifth generation new radio (5G NR), and its research is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10−9 packet loss probability) and low latency (say 1 ms) simultaneously. In URLLC, short packet transmission is adopted to reduce latency, such that conventional Shannon’s capacity formula is no longer applicable, and the achievable data rate in finite blocklength becomes a complex expression with respect to the decoding error probability and the blocklength. To provide URLLC service in a factory automation scenario, we consider that the central controller transmits different packets to a robot and an actuator, where the actuator is located far from the controller, and the robot can move between the controller and the actuator. In this scenario, we consider four fundamental downlink transmission schemes, including orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), relay-assisted, and cooperative NOMA (C-NOMA) schemes. For all these transmission schemes, we aim for jointly optimizing the blocklength and power allocation to minimize the decoding error probability of the actuator subject to the reliability requirement of the robot, the total energy constraints, as well as the latency constraints. We further develop low-complexity algorithms to address the optimization problems for each transmission scheme. For the general case with more than two devices, we also develop a low-complexity efficient algorithm for the OMA scheme. Our results show that the relay-assisted transmission significantly outperforms the OMA scheme, while the NOMA scheme performs well when the blocklength is very limited. We further show that the relay-assisted transmission has superior performance over the C-NOMA scheme due to larger feasible region of the former scheme.

Calculation Method of Spectrum Requirement for IMT-2020 eMBB and URLLC With Puncturing Based on M/G/1 Priority Queuing Model

The major use cases for IMT-2020 include enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC). URLLC packets occur sporadically, but each time, available resources with favorable conditions are required for their timely and trusted transmissions. To this end, an overlapping scheme of puncturing a part of resources scheduled to an eMBB packet for URLLC packets is proposed. Such a scheme causes inevitable damages to eMBB packets. Thus, additional delays due to possible retransmissions of damaged packets should be considered in the calculation of spectrum requirement for IMT-2020. So far, the method provided by International Telecommunication Union Radiocommunication Sector (ITU-R) based on an M/G/1 non-preemptive priority queuing model has been widely used in calculating spectrum requirements for mobile broadband services. In this work, we extend it to reflect the puncturing of URLLC on eMBB. We fully consider additional delays due to retransmissions by exploiting the concept of delay cycle. Numerical simulations validate the accuracy of our analytical results. Furthermore, we provide an algorithm to calculate spectrum requirement for IMT-2020 using the same input parameters as in the method of ITU-R.

Resource Management for Millimeter-Wave Ultra-Reliable and Low-Latency Communications

Many mission-critical and latency-sensitive applications require ultra-reliable and low-latency communications (URLLC), which has been listed as a new service category of 5G New Radio (NR). To guarantee stringent latency and reliability constraints, URLLC services always exclusively occupy the spectrum and have priority over enhanced mobile broadband (eMBB) communications in the current coexistence scenario, which will greatly affect the performance of eMBB services and degrade the utilization efficiency of the spectrum resource. On the other hand, millimeter-wave (mmWave) communications can fulfill the enormous throughput requirements of 5G cellular communications. In this paper, we introduce mmWave communications into URLLC systems to provide a more efficient coexistence for eMBB and URLLC. A novel mmWave URLLC system is first developed, where URLLC users are allowed to share the spectrum resources with eMBB users. Besides, multi-connectivity technology, which enables users to access multiple base stations simultaneously, is introduced to the mmWave URLLC system to enhance the reliability. Then, a resource management problem is formulated, which maximizes the throughput of eMBB users while guaranteeing the latency and reliability requirements of URLLC users. To obtain optimal solutions, we first divide it into three subproblems, i.e., power allocation, resource matching, and user paring, and then solve them respectively. Simulation results demonstrate the data rate improvement compared against the traditional coexistence scenario without reusing strategy. Moreover, the multi-connectivity functionality poses a great effect on guaranteeing the latency and reliability requirements for URLLC users.

Towards Enabling Critical mMTC: A Review of URLLC Within mMTC

Massive machine-type communication (mMTC) and ultra-reliable and low-latency communication (URLLC) are two key service types in the fifth-generation (5G) communication systems, pursuing scalability and reliability with low-latency, respectively. These two extreme services are envisaged to agglomerate together into critical mMTC shortly with emerging use cases (e.g., wide-area disaster monitoring, wireless factory automation), creating new challenges to designing wireless systems beyond 5G. While conventional network slicing is effective in supporting a simple mixture of mMTC and URLLC, it is difficult to simultaneously guarantee the reliability, latency, and scalability requirements of critical mMTC (e.g., i 4ms latency, 10 6 devices/km 2 for factory automation) with limited radio resources. Furthermore, recently proposed solutions to scalable URLLC (e.g., machine learning aided URLLC for driverless vehicles) are ill-suited to critical mMTC whose machine type users have minimal energy budget and computing capability that should be (tightly) optimized for given tasks. To this end, our paper aims to characterize promising use cases of critical mMTC and search for their possible solutions. To this end, we first review the state-of-the-art (SOTA) technologies for separate mMTC and URLLC services and then identify key challenges from conflicting SOTA requirements, followed by potential approaches to prospective critical mMTC solutions at different layers.

Contract-Based Scheduling of URLLC Packets in Incumbent EMBB Traffic

Recently, the coexistence of ultra-reliable and low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services on the same licensed spectrum has gained a lot of attention from both academia and industry. However, the coexistence of these services is not trivial due to the diverse multiple access protocols, contrasting frame distributions in the existing network, and the distinct quality of service requirements posed by these services. Therefore, such coexistence drives towards a challenging resource scheduling problem. To address this problem, in this paper, we first investigate the possibilities of scheduling URLLC packets in incumbent eMBB traffic. In this regard, we formulate an optimization problem for coexistence by dynamically adopting a superposition or puncturing scheme. In particular, the aim is to provide spectrum access to the URLLC users while reducing the intervention on incumbent eMBB users. Next, we apply the one-to-one matching game to find stable URLLC-eMBB pairs that can coexist on the same spectrum. Then, we apply the contract theory framework to design contracts for URLLC users to adopt the superposition scheme. Simulation results reveal that the proposed contract-based scheduling scheme achieves up to 63% of the eMBB rate for the “No URLLC” case compared to the “Puncturing” scheme.

Online Radio Pattern Optimization Based on Dual Reinforcement-Learning Approach for 5G URLLC Networks

The fifth generation (5G) radio access technology is designed to support highly delay-sensitive applications, i.e., ultra-reliable and low-latency communications (URLLC). For dynamic time division duplex (TDD) systems, the real-time optimization of the radio pattern selection becomes of a vital significance in achieving decent URLLC outage latency. In this study, a dual reinforcement machine learning (RML) approach is developed for online pattern optimization in 5G new radio TDD deployments. The proposed solution seeks to minimizing the maximum URLLC tail latency, i.e., min-max problem, by introducing nested RML instances. The directional and real-time traffic statistics are monitored and given to the primary RML layer to estimate the sufficient number of downlink (DL) and uplink (UL) symbols across the upcoming radio pattern. The secondary RML sub-networks determine the DL and UL symbol structure which best minimizes the URLLC outage latency. The proposed solution is evaluated by extensive and highly-detailed system level simulations, where our results demonstrate a considerable URLLC outage latency improvement with the proposed scheme, compared to the state-of-the-art dynamic-TDD proposals.

Twin-Timescale Radio Resource Management for Ultra-Reliable and Low-Latency Vehicular Networks

To efficiently support safety-related vehicular applications, the ultra-reliable and low-latency communication (URLLC) concept has become an indispensable component of vehicular networks (VNETs). Due to the high mobility of VNETs, exchanging near-instantaneous channel state information (CSI) and making reliable resource allocation decisions based on such short-term CSI evaluations are not practical. In this paper, we consider the downlink of a vehicle-to-infrastructure (V2I) system conceived for URLLC based on idealized perfect and realistic imperfect CSI. By exploiting the benefits of the massive MIMO concept, a two-stage radio resource allocation problem is formulated based on a novel twin-timescale perspective for avoiding the frequent exchange of near-instantaneous CSI. Specifically, based on the prevalent road-traffic density, Stage 1 is constructed for minimizing the worst-case transmission latency on a long-term timescale. In Stage 2, the base station allocates the total power at a short-term timescale according to the large-scale fading CSI encountered for minimizing the maximum transmission latency across all vehicular users. Then, a primary algorithm and a secondary algorithm are conceived for our V2I URLLC system to find the optimal solution of the twin-timescale resource allocation problem, with special emphasis on the complexity imposed. Finally, our simulation results show that the proposed resource allocation scheme significantly reduces the maximum transmission latency, and it is not sensitive to the fluctuation of road-traffic density.

A RAN Resource Slicing Mechanism for Multiplexing of eMBB and URLLC Services in OFDMA Based 5G Wireless Networks

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two main expected services in the next generation of wireless networks. Accommodation of these two services on the same wireless infrastructure leads to a challenging resource allocation problem due to their heterogeneous specifications. To address this problem, slicing has emerged as an architecture that enables a logical network with specific radio access functionality to each of the supported services on the same network infrastructure. The allocation of radio resources to each slice according to their requirements is a fundamental part of the network slicing that is usually executed at the radio access network (RAN). In this work, we formulate the RAN resource allocation problem as a sum-rate maximization problem subject to the orthogonality constraint (i.e., service isolation), latency-related constraint and minimum rate constraint while maintaining the reliability constraint with the incorporation of adaptive modulation and coding (AMC). However, the formulated problem is not mathematically tractable due to the presence of a step-wise function associated with the AMC and a binary assignment variable. Therefore, to solve the proposed optimization problem, first, we relax the mathematical intractability of AMC by using an approximation of the non-linear AMC achievable throughput, and next, the binary constraint is relaxed to a box constraint by using the penalized reformulation of the problem. The result of the above two-step procedure provides a close-to-optimal solution to the original optimization problem. Furthermore, to ease the complexity of the optimization-based scheduling algorithm, a low-complexity heuristic scheduling scheme is proposed for the efficient multiplexing of URLLC and eMBB services. Finally, the effectiveness of the proposed optimization and heuristic schemes is illustrated through extensive numerical simulations.

Energy-Efficient Edge Offloading in Heterogeneous Industrial IoT Networks for Factory of Future

The ultra-reliable and low latency communication (URLLC) and massive machine type communication (mMTC) in 5G are envisioned to support intelligent automation in the heterogeneous Factory of Future (FoF) networks, and Mobile-edge computing (MEC) is considered to be a promising system for enabling real-time task processing at the edge of the network. In the future factory, production machines, and environmental monitoring devices will be endowed with the wireless connecting for mobility. These devices are deployed for running complicated real-time tasks. To make such mission-critical tasks being processed in time, parts of the tasks should be completed with the assistance of the edge server or even the cloud. In this work, we jointly investigate the partial task offloading, computation, and communication (licensed and unlicensed) resource allocation problem in the trade-off between overall power consumption and quality of service (QoS) satisfaction. A 2-tier MEC-cloud framework is provided, wherein the IoT mobile devices (MDs) are able to partition the tasks into segments and offload them to the MEC and the cloud server. Considering the limits of communication and computation resources, we proposed a mechanism call 5G and NR-U opportunity-cost-based offloading algorithm (5G/NR-U OCBOA) to optimize resource allocation. Within the mechanism, there are two proposed algorithms, 5G OCBOA is for the licensed-only case, and NR-U OCBOA dedicates on unlicensed one. We iteratively perform the two algorithms to get the final solution. The simulation results show that our low-complexity algorithms almost outperform the other benchmark greedy algorithms. The proposed algorithm is up to 59.3% MD blocking probability less, up to 58.7% power saving gain, and up to 47.6% more QoS gain.

Power Saving Techniques for 5G and Beyond

Energy efficiency is one of the key performance indicators in 5G New Radio (NR) networks targeted to support diversified use cases including enhanced mobile broadband (eMBB), massive machine type communications (mMTC) and ultra-reliable and low latency communications (URLLC). Trade-offs have to be carefully considered between energy efficiency and other performance aspects such as latency, throughput, connection densities and reliability. Energy efficiency is important for both user equipment (UE) side and base station side. On UE side, UE battery life has great impact on user experience. It is challenging to improve UE experience in other performance aspects without affecting battery life of 5G handsets. On the base station side, efficient network implementation is critical in both environmental and operation cost standpoints. To adapt different requirements and trade-offs, the 5G NR standard is designed to have great flexibility on network operation modes. This paper provides an overview on power saving techniques supported by 5G NR standards according to the current 5G standardization progress. It provides the 5G evolution path of the power saving techniques from the first release of 5G standard to the future beyond-5G releases. In addition to the existing standardized techniques, some major development trends of green communication and the future potential enhancements expected in the beyond-5G standards are discussed.

LTE-indoor: a novel PHY layer design for future 5G indoor femtocell networks

Small cell technology such as femtocell plays an important role in 5G heterogeneous network especially in the indoor environments. However, it is unreasonable that the design of the PHY layer in femtocell networks follows that of the macrocell since the channel model used in indoor environments is entirely different from that used in urban or rural areas. To boost the PHY throughput of femtocell network, LTE-indoor (LTE-I), a new PHY layer frame structure, is proposed to accommodate more symbols in a slot time as well as to adopt higher order modulations and narrower guard bands. Furthermore, through carrier aggregation (CA) techniques, the PHY layer throughput can be further booted as high as 18.2 Gbps. The proposed LTE-I frame structure also supports the ultra-reliable and low latency communications (URLLC) by making the subcarrier spacing wider to increase the symbol rate with almost no throughput loss.

Network Function Parallelization for High Reliability and Low Latency Services

In 5G-and-beyond wireless communication systems, Network Function Virtualization (NFV) has been widely acknowledged as an important network architecture solution to meet diverse service requirements in various scenarios. However, with the increase of network functions, the introduction of NFV may significantly increase the delay of traffic flows, which is much undesired, especially for Ultra Reliable and Low Latency Communication (URLLC) service. Network Function Parallelism (NFP) architecture has been recently proposed as an effective technique to address the bottleneck of NFV technology. NFP can potentially improve the reliability and reduce the delay of service function chains (SFCs). In this paper, we propose a learning based SFC deployment strategy under NFP architecture with aim to improve the service reliability while reducing the end-to-end service delay. Specifically, service reliability is improved by deploying back-up virtual network function (VNF) nodes, while the flow delay is reduced via parallel network function processing. We formulate the VNF deployment as an integer-programming problem with objective of minimizing the reserved computing and bandwidth resources, while guaranteeing the service reliability and end-to-end delay. Considering the hardness and properties of the problem, we transform it as a Markov Decision Process (MDP), and employ a reinforcement-learning algorithm to solve it. We conduct simulations and the numerical results demonstrate that the proposed strategy can significantly improve the service reliability and delay performance, which are crucial for URLLC service.

Joint Power and Resource Block Allocation for Mixed-Numerology-Based 5G Downlink Under Imperfect CSI

Fifth-generation (5G) of wireless networks are expected to accommodate different services with contrasting quality of service (QoS) requirements within a common physical infrastructure in an efficient way. In this article, we address the radio access network (RAN) slicing problem and focus on the three 5G primary services, namely, enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC) and massive machine-type communications (mMTC). In particular, we formulate the joint allocation of power and resource blocks to the heterogeneous users in the downlink targeting the transmit power minimization and by considering mixed numerology-based frame structures. Most importantly, the proposed scheme does not only consider the heterogeneous QoS requirements of each service, but also the queue status of each user during the scheduling of resource blocks. In addition, imperfect Channel State Information (CSI) is considered by including an outage probabilistic constraint into the formulation. The resulting non-convex problem is converted to a more tractable problem by exploiting Big-M formulation, probabilistic to non-probabilistic transformation, binary relaxation and successive convex approximation (SCA). The proposed solution is evaluated for different mixed-numerology resource grids within the context of strict slice-isolation and slice-aware radio resource management schemes via extensive numerical simulations.

Side Channel Attack-Aware Resource Allocation for URLLC and eMBB Slices in 5G RAN

Network slicing is a key enabling technology to realize the provisioning of customized services in 5G paradigm. Due to logical isolation instead of physical isolation, network slicing is facing a series of security issues. Side Channel Attack (SCA) is a typical attack for slices that share resources in the same hardware. Considering the risk of SCA among slices, this paper investigates how to effectively allocate heterogeneous resources for the slices under their different security requirements. Then, a SCA-aware Resource Allocation (SCA-RA) algorithm is proposed for Ultra-reliable and Low-latency Communications (URLLC) and Enhanced Mobile Broadband (eMBB) slices in 5G RAN. The objective is to maximize the number of slices accommodated in 5G RAN. With dynamic slice requests, simulation is conducted to evaluate the performance of the proposed algorithm in two different network scenarios. Simulation results indicate that compared with benchmark, SCA-RA algorithm can effectively reduce blocking probability of slice requests. In addition, the usage of IT and transport resources is also optimized.