Ultra-reliable Low-latency Communications Research Articles

Design of Autoconfigurable Random Access NOMA for URLLC Industrial IoT Networking

Low power devices are massively deployed to facilitate real time sensing and data transmission requested in low power wide-area network (LPWAN). However, the dominating signaling, i.e. continuous phase modulation (CPM), barely gains attention in the context of supporting massive connectivity, not to mention ultra-reliable low-latency communications (URLLC), a primary concern in industrial network automation. To this end, auto-configurable nonorthogonal multiple access (AC-NOMA) based on CPM signaling is proposed. The term auto-configuration is used since each device selects a setup from a pool of configurations whenever connecting to the access point in a random and distributive way. It is proven, using ideal power allocation and phase shaping technique, AC-NOMA offers drastically improved user load and near capacity performance even in finite blocklength regime. Moreover, to enable massive yet sporadic access, slotted ALOHA is combined with AC-NOMA. It is proven that the resultant scheme outperforms power-domain NOMA in terms of throughput even with reduced transmit power and simple forward error correction schemes such as repetition and convolutional coding. The throughput is further improved using semi AC-NOMA with slightly increased latency. It is demonstrated that both designs can support very high user load while enabling URLLC in finite blocklength regime, where the packet size is merely 256 bits while the error rate is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-5}$</tex-math></inline-formula> , which are also desirable in a number of applications including satellite communications, visible light communications, etc.

Evaluating the Performance of 5G NR in Indoor Environments: An Experimental Study

The 5G wireless standard has emerged as a trans-formative technology with the potential to revolutionize various industries by providing enhanced connectivity and communication capabilities. This advanced standard offers a diverse range of applications, including Ultra-Reliable Low-Latency Communication (URLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine Type Communication (mMTC). In this Scientific research paper, we present a comprehensive analysis of the performance and capabilities of a deployed indoor 5G network in a controlled laboratory environment. The experimental setup comprises an Amarisoft Callbox, serving as the 5G core, along with a Remote Radio Head (RRH) and user equipment (UEs). Our primary objective is to assess the network's performance by evaluating the downlink, uplink, and joint downlink/uplink transmissions for TCP and UDP protocols between the gNodeB and user equipment (UE). To achieve this, we carefully examine key performance metrics such as latency, power consumption, CPU utilization, and signal quality. Through various configurations that involve MIMO 2 technology and a 30 kHz sub-carrier spacing, we investigate the impact of different NR Bandwidth settings. By establishing TCP and UDP connections for both uplink and downlink scenarios, we meticulously measure and scrutinize the system's performance, thereby providing valuable insights into its efficiency and suitability for diverse application requirements.

Private 5G, “Not As Private As You May Think”

As the need grows for mobile solutions that can offer more data throughput with ultra-reliable low-latency communications and better connection density—albeit at risk—private 5G networks and the shift to Industry 4.0 are gaining pace. As 5G technology develops and enterprises start implementing it, they need to be cautious during this transitional phase to make sure they are cognizant of and control risk as their attack surface changes. Many private 5G deployments use 4G/LTE Core networks in Non-Stand-Alone mode, which keeps many of the same vulnerabilities that have been there for years. As a result, private 5G is frequently not fully 5G pure. In this research paper, a few of the present-day weaknesses in the Stream Control Transmission and GPRS Tunnelling protocols—as well as the Industrial Control System protocols—that businesses need to be aware of and safeguard while using these technologies are discussed and shown.

Market and Sharing Alternatives for the Provision of Massive Machine-Type and Ultra-Reliable Low-Latency Communications Services over a 5G Network

The objective of this paper is to analyze economic alternatives for the provision of ultra-reliable low-latency communication (URLLC) and massive machine-type communication (mMTC) services over a fifth-generation (5G) network. Two business models, a monopoly and a duopoly, are studied and two 5G network scenarios are analyzed: a 5G network where the network resources are shared between the two services without service priority, and a 5G network with network slicing that allows for URLLC traffic to have a higher priority. Microeconomics is used to model the behavior of users and operators, and game theory is used to model the strategic interaction between users and operators. The results show that a monopoly over a 5G network with network slicing is the most efficient way to provide both URLLC and mMTC services.

Evaluation of the QoS policy model of an ordinary 5G smart city cluster with predominant URLLC and eMBB traffic

A typical element of the smart city’s information and communication space is a 5G cluster, which is focused on serving both new and handover requests because it is an open system. In an ordinary 5G smart city cluster, Ultra-Reliable Low-Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB) traffic types prevail. The formation of an effective QoS policy for such an object (taking into account the potentially active slicing technology) is an urgent problem. As a baseline, this research considers a Quality of Service (QoS) policy with constraints for context-defined URLLC and eMBB classes of incoming requests. Evaluating the QoS policy instance defined within the framework of the basic concept requires the formalization of both a complete qualitative metric and a computationally efficient mathematical apparatus for its calculation. The article presents accurate and approximate methods of calculating such quality parameters as the probability of loss of typed requests and the utilization ratio of the communication resource, which depend on the implementation of the estimated QoS policy. At the same time, the original parametric space includes both fixed characteristics (amount of available communication resources, load according to request classes) and controlled characteristics due to the specifics of the implementation of the basic QoS concept. The paper empirically proves the adequacy of the presented mathematical apparatus for evaluating the QoS policy defined within the scope of the research. Also, in the proposed qualitative metric, a comparison of the author’s concept with a parametrically close analogue (the well-known QoS policy scheme, which takes into account the phenomenon of reservation of communication resources), determined taking into account the reservation of communication resources, was made. The results of the comparison testify in favour of the superiority of the author’s approach in the proposed metrics.

An E2E Network Slicing Framework for Slice Creation and Deployment Using Machine Learning

Network slicing shows promise as a means to endow 5G networks with flexible and dynamic features. Network function virtualization (NFV) and software-defined networking (SDN) are the key methods for deploying network slicing, which will enable end-to-end (E2E) isolation services permitting each slice to be customized depending on service requirements. The goal of this investigation is to construct network slices through a machine learning algorithm and allocate resources for the newly created slices using dynamic programming in an efficient manner. A substrate network is constructed with a list of key performance indicators (KPIs) like CPU capacity, bandwidth, delay, link capacity, and security level. After that, network slices are produced by employing multi-layer perceptron (MLP) using the adaptive moment estimation (ADAM) optimization algorithm. For each requested service, the network slices are categorized as massive machine-type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low-latency communications (uRLLC). After network slicing, resources are provided to the services that have been requested. In order to maximize the total user access rate and resource efficiency, Dijkstra’s algorithm is adopted for resource allocation that determines the shortest path between nodes in the substrate network. The simulation output shows that the present model allocates optimum slices to the requested services with high resource efficiency and reduced total bandwidth utilization.

Low complexity physical layer security approach for 5G internet of things

&lt;span lang="EN-US"&gt;Fifth-generation (5G) massive machine-type communication (mMTC) is expected to support the cellular adaptation of internet of things (IoT) applications for massive connectivity. Due to the massive access nature, IoT is prone to high interception probability and the use of conventional cryptographic techniques in these scenarios is not practical considering the limited computational capabilities of the IoT devices and their power budget. This calls for a lightweight physical layer security scheme which will provide security without much computational overhead and/or strengthen the existing security measures. Here a shift based physical layer security approach is proposed which will provide a low complexity security without much changes in baseline orthogonal frequency division multiple access (OFDMA) architecture as per the low power requirements of IoT by systematically rearranging the subcarriers. While the scheme is compatible with most fast Fourier transform (FFT) based waveform contenders which are being proposed in 5G especially in mMTC and ultra-reliable low latency communication (URLLC), it can also add an additional layer of security at physical layer to enhanced mobile broadband (eMBB).&lt;/span&gt;

Spatial modeling and reliability analyzing of reconfigurable intelligent surfaces-assisted Fog-RAN with repulsion

Reconfigurable Intelligent Surface (RIS), fog computing, and Cell-Free (CF) network architecture are three promising technologies for application to the Ultra-Reliable Low Latency Communication (URLLC) scenario in 6G mobile communication systems. This paper considers a RIS-assisted FogRadio Access Network (Fog-RAN) architecture where a) the repulsively distributed Fog-Access Points (F-APs) communicate in a CF manner to suppress inter-cell interference, b) RISs are introduced into the CF network to avoid shadowing and enhance the system performance, and c) fog computing evolved as cloud services providers at the edge of the network and an enabler for constructing a multi-layer computing power RAN. Then, we derive and validate the integral form of the maximum F-AP offloading probability and Successful Delivery Probability (SDP) of this RIS-assisted Fog-RAN over composite Fisher-Snedecor F fading, where the spatial effects are reconsidered with the assumption that the F-APs are modelled as a Beta Ginibre Point Process (β-GPP). The numeric and simulation results indicate that for the investigated RIS-assisted Fog-RAN, the β-GPP-based deployment of F-APs can increase maximum of 8% of the SDP within the repulsion-effective range, compared with the Matern Cluster Process (MCP)-based ones. Also, deploying more RISs per F-AP offers more significant SDP improvements.

Joint Optimization for Secure and Reliable Communications in Finite Blocklength Regime

To realize ultra-reliable low latency communications with high spectral efficiency and security, we investigate a joint optimization problem for downlink communications with multiple users and eavesdroppers in the finite blocklength (FBL) regime. We formulate a multi-objective optimization problem to maximize a sum secrecy rate by developing a secure precoder and to minimize a maximum error probability and information leakage rate. The main challenges arise from the complicated multi-objective problem, non-tractable back-off factors from the FBL assumption, non-convexity and non-smoothness of the secrecy rate, and the intertwined optimization variables. To address these challenges, we adopt an alternating optimization approach by decomposing the problem into two phases: secure precoding design, and maximum error probability and information leakage rate minimization. In the first phase, we obtain a lower bound of the secrecy rate and derive a first-order Karush–Kuhn–Tucker (KKT) condition to identify local optimal solutions with respect to the precoders. Interpreting the condition as a generalized eigenvalue problem, we solve the problem by using a power iteration-based method. In the second phase, we adopt a weighted-sum approach and derive KKT conditions in terms of the error probabilities and leakage rates for given precoders. Simulations validate the proposed algorithm.

Block Orthogonal Sparse Superposition Codes for Ultra-Reliable Low-Latency Communications

Block Orthogonal Sparse Superposition Codes for Ultra-Reliable Low-Latency Communications

Parity Check Codes for Second Order Diversity

Short block length “block” codes are typically not used in wireless standards as soft decision decoding is computationally intensive and hard decision decoding results in performance loss. However, short block lengths are of interest to massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC). In this paper, we propose a diversity preserving enhanced hard-decision decoding scheme for parity check codes (PCC) over Rayleigh fading channels. The proposed flip decoding scheme has linear complexity in the block length. Theoretical analysis and simulation results verify the correctness of the proposed detection scheme.

Path Loss Characterization in an Outdoor Corridor Environment for IoT-5G in a Smart Campus University at 850 MHz and 3.5 GHz Frequency Bands.

The usage scenarios defined in the ITU-M2150-1 recommendation for IMT-2020 systems, including enhanced Mobile Broadband (eMBB), Ultra-reliable Low-latency Communication (URLLC), and massive Machine Type Communication (mMTC), allow the possibility of accessing different services through the set of Radio Interface Technologies (RITs), Long-term Evolution (LTE), and New Radio (NR), which are components of RIT. The potential of the low and medium frequency bands allocated by the Federal Communications Commission (FCC) for the fifth generation of mobile communications (5G) is described. In addition, in the Internet of Things (IoT) applications that will be covered by the case of use of the mMTC are framed. In this sense, a propagation channel measurement campaign was carried out at 850 MHz and 5.9 GHz in a covered corridor environment, located in an open space within the facilities of the Pedagogical and Technological University of Colombia campus. The measurements were carried out in the time domain using a channel sounder based on a Universal Software Radio Peripheral (USRP) to obtain the received signal power levels over a range of separation distances between the transmitter and receiver from 2.00 m to 67.5 m. Then, a link budget was proposed to describe the path loss behavior as a function of these distances to obtain the parameters for the close-in free space reference distance (CI) and the floating intercept (FI) path loss prediction models. These parameters were estimated from the measurements made using the Minimum Mean Square Error (MMSE) approach. The estimated path loss exponent (PLE) values for both the CI and FI path loss models at 850 MHz and 3.5 GHz are in the range of 2.21 to 2.41, respectively. This shows that the multipath effect causes a lack of constructive interference to the received power signal for this type of outdoor corridor scenario. These results can be used in simulation tools to evaluate the path loss behavior and optimize the deployment of device and sensor network infrastructure to enable 5G-IoT connectivity in smart university campus scenarios.

Retraction Note: Ultra-reliable low latency communication technique for agriculture wireless sensor networks

Retraction Note: Ultra-reliable low latency communication technique for agriculture wireless sensor networks

Feature selection based machine learning models for 5G network slicing approximation

The development of 5G networks is expected to introduce novel concepts and challenges in domains such as technology, security, and customer demands due to advancements in mobile internet and communication services. The three main services that the 5G network provides are enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low-latency communication (URLLC). Network slicing is the practice of dividing a single physical network into many virtual networks in order to facilitate the delivery of multiple services across that network. These slices improve the network’s reliability and make it possible to provide more customized services. This article provides an overview of 5G network slicing, discussing its layers and overall architecture. Additionally, the paper proposes a machine learning network slicing model based on feature selection, comprising three main components. Firstly, data collection involves gathering two different datasets from various sources. Secondly, feature selection algorithms are applied to choose the most relevant set of features, as the collected datasets may contain numerous unnecessary attributes and values. Lastly, various classification models are utilized on the selected features to predict the optimal network slice, thereby improving the efficiency of the 5G network. The analytical results and simulation models demonstrate that the proposed machine learning models with feature selection perform exceptionally well and accurately predict 5G network slices.

A Joint Communication and Control System for URLLC in Industrial IoT

Ultra reliable low latency communication (URLLC) is playing an important role in future wireless networks, such as industrial Internet of things (IIoT). Enormous industrial devices are requiring instant and highly-frequent control services, namely delay sensitive control, in order to improve the human safety or the operation accuracy. In this paper, a joint communication and delay sensitive control (JCDSC) system with URLLC is studied, where the control intervals are much shorter than the transmission latency. The control performance, known as the mean square error (MSE) of the plant states, are analysed in theory. The optimal blocklength of the control codewords, the data rate, as well as the minimum required control periods are then obtained, in order to minimize the control latency. Simulation results validate our theoretical analysis, while also demonstrating that an optimal blocklength of codewords should be selected in order to reduce the control latency.

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

With the prosperity of emerging applications, the 6 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>th</i></sup> Generation mobile communication systems (6G) is coming at an unimaginable speed. It is expected to provide more intelligent, flexible, and secure services. As an essential pillar of 6G networks, ultra-Reliable Low Latency Communication (uRLLC) has promoted the vigorous development of intelligent communications. However, the existing networks cannot fully satisfy the strict and various requirements of uRLLC services, including delay, reliability and security. Considering the interaction between the physical layer and the upper layer, we propose a Cross-layer Flexible Security Solution (CFSS), which includes initiative waiting strategy, flexible transmission time interval scheduling strategy, and flexible pre-backup transmission strategy. While considering secure communication, CFSS could flexibly provide customized services to the users through cross-layer parameters configuration and resource allocation. In addition, we extend the Stochastic Network Calculus (SNC) modeling to the security field, and use Finite Blocklength Coding (FBC) to analyze the service process of uRLLC. Two cases of FBC are considered comprehensively, namely, given decoding error probability and given transmission rate. Finally, Experienced Meta-Asynchronous Advantage Actor-Critic (EM-A3C) algorithm is proposed to solve the complex optimization problem, the establishment of experience pool effectively improves the algorithm efficiency.

Preallocation-Based Combinatorial Auction for Efficient Fair Channel Assignments in Multi-Connectivity Networks

We consider a general multi-connectivity framework, intended for ultra-reliable low-latency communications (URLLC) services, and propose a novel, preallocation-based combinatorial auction approach for the efficient allocation of channels. We compare the performance of the proposed method with several other state-of-the-art and alternative channel-allocation algorithms. The two proposed performance metrics are the capacity-based and the utility-based context. In the first case, every unit of additional capacity is regarded as beneficial for any tenant, independent of the already allocated quantity, and the main measure is the total throughput of the system. In the second case, we assume a minimal and maximal required capacity value for each tenant, and consider the implied utility values accordingly. In addition to the total system performance, we also analyze fairness and computational requirements in both contexts. We conclude that at the cost of higher but still plausible computational time, the fairness-enhanced version of the proposed preallocation based combinatorial auction algorithm outperforms every other considered method when one considers total system performance and fairness simultaneously, and performs especially well in the utility context. Therefore, the proposed algorithm may be regarded as candidate scheme for URLLC channel allocation problems, where minimal and maximal capacity requirements have to be considered.

Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication—A Tutorial

Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication—A Tutorial

Embedded Parallel Implementation of LDPC Decoder for Ultra-Reliable Low-Latency Communications

Ultra-reliable low-latency communications, URLLC, are designed for applications such as self-driving cars and telesurgery requiring a response in milliseconds and are very sensitive to transmission errors. To match the computational complexity of LDPC decoding algorithms to URLLC applications on IoT devices having very limited computational resources, this paper presents a new parallel and low-latency software implementation of the LDPC decoder. First, a decoding algorithm optimization and a compact data structure are proposed. Next, a parallel software implementation is performed on ARM multicore platforms in order to evaluate the latency of the proposed optimization. The synthesis results highlight a reduction in the memory size requirement by 50% and a three-time speedup in terms of processing time when compared to previous software decoder implementations. The reached decoding latency on the parallel processing platform is 150 μs for 288 bits with a bit error ratio of 3.410–9.

A three-level slicing algorithm in a multi-slice multi-numerology context

Fifth generation (5G) mobile networks feature a new concept called Bandwidth Part (BWP) that permits applying various multiple Orthogonal Frequency-Division Multiplexing (OFDM) subcarrier spacing, also coined numerologies, in the same band. With flexible numerology, the sought-after purpose is achieving lower latency for the Ultra Reliable Low Latency Communications (URLLC) service. However, the lucrative multiple numerology gains might not stand because of the ensuing Inter-Numerology Interference (INI) problems. In this paper, we assess the feasibility, as well as the profitability, of multi-numerology in a multi-slice setting. In such a context, it is paramount to study the radio resource allocation problem at the Radio Access Network (RAN) level. To that aim, we propose a three-level slicing algorithm that efficiently selects the BWP serving the URLLC users from a set of BWPs using different numerologies, designs a guard band among these BWPs to reduce INI without wasting radio resources, and attributes a bandwidth to each slice depending on multiple parameters such as the users’ Key Performance Indicators (KPIs), channel conditions, INI, and the incurred monetary cost. This work considers the three 5G services, namely the enhanced Mobile BroadBand (eMBB) service, the URLLC service and the massive Machine-Type Communications (mMTC) service. The proposed algorithm combines various tools from Game Theory, Deep-Q learning Networks as well as savvy heuristics. The performance evaluation demonstrates the high efficiency of our solution in terms of latency and throughput compared to well-known approaches in the State-of-the-Art.