Ultra-reliable Low-latency Communications Research Articles

Resource-Efficient Multicast URLLC Service in 5G Systems.

Many emerging applications, such as factory automation, electric power distribution, and intelligent transportation systems, require multicast Ultra-Reliable Low-Latency Communications (mURLLC). Since 3GPP Release 17, 5G systems natively support multicast functionality, including multicast Hybrid Automatic Repeat Request and various feedback schemes. Although these features can be promising for mURLLC, the specifications and existing studies fall short in offering guidance on their efficient usage. This paper presents the first comprehensive system-level evaluation of mURLLC, leveraging insights from 3GPP specifications. It points out (i) how mURLLC differs from traditional multicast broadband wireless communications, and (ii) which approaches to provide mURLLC require changing the paradigm compared with the existing solutions. Finally, the paper provides recommendations on how to satisfy strict mURLLC requirements efficiently, i.e., with low channel resource consumption, which increases the capacity of 5G systems for mURLLC. Simulation results show that proper configuration of multicast mechanisms and the corresponding algorithms for mURLLC traffic can reduce resource consumption up to three times compared to the baseline solutions proposed for broadband multicast traffic, which significantly increases the system capacity.

Leveraging 5G Network Capabilities for Smart Grid Communication

This paper presents a comprehensive investigation into the architecture and components of 5G networks, focusing on their suitability for smart grid applications. With the increasing complexity and demands of modern energy infrastructure, there is a growing need for reliable and low-latency communication systems to support critical smart grid operations. This paper explores key features of 5G networks, including network slicing, massive MIMO (Multiple Input Multiple Output), and low-latency communication protocols like URLLC (Ultra-Reliable Low Latency Communication), and examines how these features can be harnessed to address the unique requirements of smart grid environments. Through a detailed analysis of each component, this paper sheds light on the design considerations, deployment strategies, and performance implications of leveraging 5G technology for smart grid communication. Furthermore, practical insights, challenges, and future research areas are discussed to guide the development of next-generation smart grid solutions.

Low‐density algebra‐check codes for ultra reliable low latency communications

AbstractA novel channel code, the low‐density algebra‐check (LDAC) code, is introduced in this letter. LDAC code is constructed by combining sparse matrice with algebraic constraints from two types of subcodes. Detailed explanations for the encoding and decoding process of the LDAC codes are provided in the paper. Simulation results demonstrate that LDAC codes offer advantages over LDPC codes in constructing low rate codes. The proposed ultra‐low rate LDAC code can achieve approximately 0.4 and 0.2 dB improvement in SNR compared to CA‐polar and 5G LDPC, respectively, at the block error rate (BER) over the additive white Gaussian noise (AWGN) channel. LDAC codes demonstrate superior performance over LDPC codes at low rates, due to the enhanced error correction capabilities of the two types of subcodes and reduced rates without introducing short cycles.

Power-aware QoS-centric strategy for ultra reliable low latency communication in 5G beyond wireless networks

Power-aware QoS-centric strategy for ultra reliable low latency communication in 5G beyond wireless networks

Improving the latency for 5G/B5G based smart healthcare connectivity in rural area

Smart hospitals are poised to greatly enhance life quality by offering persistent health monitoring capabilities. Remote healthcare and surgery, which are highly dependent on low latency, have seen a transformative improvement with the advent of 5G technology. This has facilitated a new breed of healthcare services, including monitoring and remote surgical procedures. The enhanced features of 5G, such as Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC), have enabled the development of advanced healthcare systems. These systems reduce the need for direct patient contact in hospitals, which is especially pertinent as 5G becomes more widespread. This research presents novel hybrid detection algorithms, specifically QR decomposition with M-algorithm maximum likelihood-minimum mean square error (QRM-MLD-MMSE) and QRM-MLD-ZF (zero forcing), for use in Massive MIMO (M-MIMO) technology. These methods aim to decrease the latency in MIMO-based Non-Orthogonal Multiple Access (NOMA) waveforms while ensuring optimal bit error rate (BER) performance. We conducted simulations to evaluate parameters like BER and power spectral density (PSD) over Rician and Rayleigh channels using both the proposed hybrid and standard algorithms. The study concludes that our hybrid algorithms significantly enhance BER and PSD with lower complexity, marking a substantial improvement in 5G communication for smart healthcare applications.

Multiple Access Schemes for Wireless Cellular Networks - Survey

The achievement of future development goals in 5G is dependent on the implementation of ITU-published usage scenarios, enhanced Mobile Broadband (eMBB), massive Machine Type Communication (mMTC), and Ultra-reliable Low Latency Communication (URLLC). To support these usage scenarios, an efficient multiple access scheme must be implemented, as classical multiple access schemes used in previous generations will not. Non-orthogonal multiple access schemes are the answer to today's needs for high spectrum efficiency, low latency, accommodating a larger number of users, and providing justice to users who are far from the base station. This survey's condensed overview of various orthogonal and non-orthogonal multiple access schemes is expected to assist researchers in developing new multiple access schemes for 5G and beyond.

Data-driven decision-making framework for optical fronthaul slice resizing in 6G networks

The third-generation partnership introduced three main types of slices: enhanced mobile broadband, massive machine-type communication, and ultrareliable low-latency communication. To accommodate these services, the next generation of mobile networks will require architecture with distinct requirements and network slices. To implement these services on an optical fronthaul, the slices will be hosted using lightpaths. Such lightpaths will have to accommodate latency and bandwidth constraints to keep radio units (RUs) and baseband units (BBUs) synchronized. However, the traffic in a slice may vary, and the resources allocated to a long-established lightpath could be out of date, leading to waste or lack of resources. For example, a lack of bandwidth can cause desynchronization between BBUs and RUs. Therefore, the slice must be resized regularly to meet the variable demands. This work proposes a data-driven decision-making (DDDM) framework to resize the fronthaul slices while mitigating the consequences of a lack of bandwidth. The framework uses long short-term memory to implement its analytical stage and integer linear programming (ILP) to reconfigure the entire network when it is required. The results show that the DDDM-based framework outperforms the state-of-the-art ILP-based heuristic by up to 15% in terms of radio blocking mitigation.

Finite blocklength feedback approach for multi-antenna wireless channels.

Ultra-reliable low-latency communication (URLLC) is a key technology in future wireless communications, and finite blocklength (FBL) coding is the core of the URLLC. In this paper, FBL coding schemes for the wireless multi-antenna channels are proposed, which are based on the classical Schalkwijk-Kailath scheme for the point-to-point additive white Gaussian noise channel with noiseless feedback. Simulation examples show that the proposed feedback-based schemes almost approach the corresponding channel capacities.

Interference aware two-tier fair user-cell association

The deployment of heterogeneous networks (HetNets) incorporating small cells within macrocells has emerged as a predominant strategy to improve the performance of cellular communication systems. This study delves into the intricacies of user-cell association within HetNets and introduces an optimized approach that not only maximizes the sum of downlink signal-to-noise ratio (SNR) but also limits uplink interference within a dual-tier network. The suggested solution is scalable and adaptable to ultra-dense HetNets (UD-HetNets). It also facilitates various communication systems, encompassing ultra-reliable low-latency communications by leveraging a weighted cost function. Assorted simulations illustrate that optimization tools achieve the optimal solution of the proposed association problem. Comparative studies with traditional association methods signify the performance of the proposed algorithm, manifested in increased SNR values and an increased number of active users. It is shown that a trade-off surfaces between increased SNR values and increased fairness. In summary, the research emphasizes a comprehensive methodology for user-cell association, effectively addressing performance enhancement and fairness in diverse communication scenarios.

Digital Revolution in Telecommunications: Harnessing Advanced Electronics for Connectivity and Security

The "Digital Revolution in Telecommunications: Harnessing Advanced Electronics for Connectivity and Security" represents a critical exploration into the profound impact of advanced electronics on the contemporary telecommunications landscape. In this research paper, we navigate the intricate intersection of technology, connectivity, and security to uncover the transformative potential and challenges introduced by the digital revolution. Our investigation begins with an in-depth analysis of how advanced electronics, particularly digital signal processing and sophisticated modulation techniques, have become integral to enhancing connectivity in telecommunication networks. We explore the evolution from traditional communication systems to the era of 5G networks, emphasizing the role of digital electronics in achieving ultra-reliable low latency communications (URLLC) for diverse applications. Security takes center stage as we delve into the cryptographic underpinnings within digital electronics, focusing on encryption protocols, key management, and secure transmission methodologies. This examination aims to unveil the mechanisms safeguarding data integrity and confidentiality in the interconnected digital realm. Furthermore, we address the emergent field of quantum communication and its potential to reshape secure telecommunications. The research scrutinizes the countermeasures developed within the domain of digital electronics to mitigate risks associated with quantum advancements, ensuring the robustness of future telecommunication infrastructures. As we traverse the landscape of the digital revolution in telecommunications, this paper provides a comprehensive understanding of the synergies between advanced electronics, connectivity, and security. The insights gleaned from this research contribute to the ongoing discourse on fortifying and advancing the technological foundations of our interconnected world.

Empirical performance analysis and ML-based modeling of 5G non-standalone networks

Fifth Generation (5G) networks are becoming the norm in the global telecommunications industry, and Mobile Network Operators (MNOs) are currently deploying 5G alongside their existing Fourth Generation (4G) networks. In this paper, we present results and insights from our large-scale measurement study on commercial 5G Non Standalone (NSA) deployments in a European country. We leverage the collected dataset, which covers two MNOs in Rome, Italy, to study network deployment and radio coverage aspects, and explore the performance of two use cases related to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). We further leverage a machine learning (ML)-based approach to model the Dual Connectivity (DC) feature enabled by 5G NSA. Our data-driven analysis shows that 5G NSA can provide higher downlink throughput and slightly lower latency compared to 4G. However, performance is influenced by several factors, including propagation conditions, system configurations, and handovers, ultimately highlighting the need for further system optimization. Moreover, by casting the DC modeling problem into a classification problem, we compare four supervised ML algorithms and show that a high model accuracy (up to 99%) can be achieved, in particular, when several radio coverage indicators from both access networks are used as input. Finally, we conduct analyses towards aiding the explainability of the ML models.

Adaptive contrastive learning based network latency prediction in 5G URLLC scenarios

With the increasing demand for 5G Ultra-Reliable Low Latency Communication (URLLC), accurate network time delay analysis is crucial. However, current network latency prediction models designed for 5G URLLC scenarios overlook the multiple downstream tasks case, such as network intrusion detection, recognition of network operational states, and decision-making for network resource scheduling.Furthermore, the robustness of the models needs to be enhanced. In this paper, we propose a Contrastive Convolutional Structure for Network Delay (CCSND), an adaptive contrastive learning framework that utilizes a temporal representation extraction module for capturing temporal periodicity and statistical features. To improve robustness and adaptability in time-series representation, CCSND designs strategic data augmentation methods, including temporal segmentation and delay obscuration. Experiment results on several real-world 5G URLLC scenario datasets demonstrate the superior performance of the proposed CCSND. It outperforms the current state-of-the-art Informer model by 38.7% and the traditional LSTM model by 64.5% in terms of mean squared error (MSE). Our code can be accessed at https://github.com/MerelyBreeze/CCSND.

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.

Vision-aided Ultra-Reliable Low-Latency Communications for Smart Factory

Vision-aided Ultra-Reliable Low-Latency Communications for Smart Factory

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.