Ultra-reliable Low-latency Communications Research Articles

Joint uplink-downlink user-cell association for ultra-dense NOMA networks

As wireless communication systems evolve, the transition from homogeneous structures to heterogeneous ones intensifies. The need for continuous connectivity, fairness, and spectrum efficiency, such as in massive Internet of Things (IoT) networks and ultra reliable low latency communications (uRLLC), necessitates the utilization of base stations with diverse capabilities and coverage areas. As a result, the need for advanced interference management, user-cell association, and resource allocation techniques increases. This study proposes an innovative solution for the problem of non-orthogonal multiple access (NOMA) user-cell association in multi-tier ultra-dense heterogeneous networks (UD-HetNet). The proposed network constitutes a general framework that can be applied to next generation cellular communications, massive IoT, non-terrestrial and uRLLC type communications. Differing from previous studies that ignore interference and primarily focus on the received signal powers for user-cell association, this investigation advances the subject by considering the interplay between downlink received signal/interference power and uplink inter-cell interference. Due to the non-convex integer programming nature of the problem, it is proposed to employ an innovative relaxation algorithm to obtain a sub-optimal solution for the total signal-to-interference-plus-noise ratio (SINR). As verified through simulation results, the proposed association method permits concurrent communication participation of multiple users while managing the interference, rendering it as an ideal approach for systems emphasizing fairness and continuous connectivity.

Economic Alternatives for the Provision of URLLC and eMBB Services Over a 5G Network

This research work analyzes economic alternatives for the provision of ultra-reliable low latency communication (URLLC) and enhanced mobile broadband (eMBB) services by mobile network operators over the same fifth-generation (5G) network. Two business models are proposed to provide the two services to end users. Concretely, a monopoly is a single operator who offers both services, and a duopoly is two different operators that share network resources and offer one service each. In addition, two types of network scenarios for resource sharing are studied. Specifically, a shared network (SN) is a type of network scenario allowing resources to be shared between the two services without priority. A differentiated network (DN) is a type of network scenario that allows resources to be shared between the two services with a priority to URLLC service using network slicing (NS). Regarding the economic aspects, the incentive is modeled through the user’s utility and the operator’s benefit. At the same time, game theory is used to model the strategic interaction between users and operators, and queuing theory is used to model the interaction between the two services. We conclude that the monopoly social welfare (SW) is closer to the SW of the social optimum than the duopoly SW. In addition, the DN scenario to offer the services through NS is more suitable than the SN scenario since the point of view of service prices, user utilities, and operator benefit.

The symbiotic influence of 5G and IoT on ultra-reliable low latency communications solutions

The rapid advancement of technology has brought about the era of Ultra Reliable Low Latency Communication (URLLC) which holds importance in todays world. This paper explores the significance of URLLC in relation, to the generation of mobile communications technology (5G) and the Internet of Things (IoT) by employing a methodical approach of literature analysis and synthesis. Through analysis, this paper discovered that both 5G and IoT individually play substantial roles in enhancing communication and connectivity, and their combined potential is truly transformative especially when it comes to achieving URLLC across industries. This convergence opens up possibilities for reality virtual reality warehouse automation and other groundbreaking applications. However, there are challenges that must be carefully examined and addressed on the path to achieving an integrated URLLC in the future. These challenges encompass problems related to scheduling and energy efficiency, alongside technical concerns such as economics and regulatory obstacles. By confronting these challenges and promoting a mindset it is possible to fully embrace the opportunity to enter an era where URLLC, 5G and IoT blend harmoniously enabling groundbreaking applications and fueling innovation, in industries.

Proportional-fair uplink resource allocation with statistical QoS provisioning for RAN slicing

This paper investigates the uplink resource allocation issue for radio access network (RAN) slicing with a focus on three primary fifth generation (5G) use cases, namely ultra-reliable low-latency communications (uRLLC), enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC). Most of the existing resource allocation schemes for RAN slicing were designed to address the use cases in downlink communications. However, these schemes are not applicable to the uplink RAN slicing, even though the primary use cases also entail stringent requirements in the uplink. To this end, we aim to develop a new, fairness-aware uplink resource allocation scheme for RAN slicing, catering to the three 5G use cases. Firstly, we formulate a new uplink resource allocation optimization problem that maximizes a sum logarithmic utility for ensuring proportional-fair effective capacity distribution and resource allocation among RAN slices, subject to the distinct quality of service (QoS) requirements of the uRLLC, eMBB and mMTC slices. Then, an efficient, hierarchical resource allocation framework is then proposed to solve the problem deterministically, whereby resource block assignment is first performed at the base station via a demand-oriented greedy algorithm, followed by power allocation using a bisection method at individual user equipment. Results show that the proposed scheme outperforms the baseline schemes with fairness and QoS provisioning performance gains of up to 44.3% and 19.17% respectively.

AI/ML-aided capacity maximization strategies for URLLC in 5G/6G wireless systems: A survey

Ultra-reliable low-latency communication (URLLC) refers to cellular applications in fifth and sixth-generation (5G/6G) networks with specific latency, reliability, and availability demands. Most of the reported 5G/6G applications are focused on URLLC, which necessitates a latency of milliseconds and very high dependability for transmitted data. These systems encounter several obstacles since conventional networks cannot fulfill such demands. According to the standards of the 3rd generation partnership project URLLC, it is predicted that the dependability of a single transmission of a 32-byte packet would be no less than 99.999%, and the latency will not exceed 1 ms. The exceptional degree of dependability and minimal delay will result in the emergence of many novel applications, including smart grids, industrial automation, and intelligent transport systems. This review discusses several methods for maximizing capacity in URLLC, focusing on resource allocation strategies, multi-access approaches, and beamforming with massive MIMO. Furthermore, it explores the requirements and constraints of URLLC and the role of AI/ML in URLLC. Finally, this study examines possible future research areas and obstacles to achieving the URLLC standards.

DDPG 및 연합학습 기반 5G 네트워크 자원 할당과 트래픽 예측

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

Dynamic Node Selection Approach for 6G Heterogeneous Cellular Networks (HCN) to Enhance QoS and Low Latency

A wireless multi-hop network is a group of nodes that can interact directly with one another or across longer distances by using other nodes as relay points and routers. 6G heterogeneous cellular network support several transmission modalities, such as single hop, multi-hop transmission and device-to-any device transmission. These transmission services can greatly increase 6G cellular network capacity, spectral efficiency, power efficiency, Quality of Service (QoS) and Ultra Reliable Low Latency Communication (URLLC). According to current research, Heterogeneous Cellular Networks (HCN) can benefit even more from the integration of demand-driven opportunistic networking into HCN. Devices can search look for the connections between nodes that are most efficient. in Proactive HCN connections by taking advantage of the URLLC acceptability of many mobile data services. Multiple Transmission modes necessitate node selection schemes that can determine the best node for each transmission. Previous proposals for node selection schemes have taken into account the introduction of D2X and HCN. Proactive HCN connections, however, cannot be incorporated into the selection process by current node selection schemes. By implementing outward the first node selection scheme that can incorporate Proactive HCN sharing information within 6G and beyond networks, this research work enhances the existing work. The analysis that was carried out shows the potential for Proactive HCN communications as well as the ability of the suggested node selection scheme to choose the best communication node to enhance QoS and Low Latency.

Cost-efficient RAN slicing for service provisioning in 5G/B5G

Network slicing represents a substantial technological advance in 5G mobile network, greatly expanding the variety and manifoldness of network services to be supported. Additionally, 3GPP 5G New Radio (NR) has introduced novel features such as mixed numerology and mini-slots, which can be harnessed by network slicing to cater to the diverse requirements of 5G services. While however the co-existence of multiple network slices leads to a challenging resource allocation problem, these new features also severely complicate the management of radio resources. As a further point of attention, the virtualization of radio functions may exact a significant toll from the, already limited, computing resources at the network edge. It follows that a cost-efficient resource allocation across all the slices becomes crucial. In this paper, we address the above-mentioned issues by modeling a cost-efficient radio resource management in 5G NR featuring network slicing, named CERS, through a Mixed Integer Quadratically constrained Program (MIQCP). We maximize the profit of all slices simultaneously guaranteeing the target data rate and delay specified in the service level agreements (SLAs) fo the different traffic flows. To reduce the complexity of the MIQCP problem, we decompose it into two sub-problems, namely, the scheduling problem of enhanced Mobile Broadband (eMBB) user equipments (UEs) on a time-slot basis and of Ultra-Reliable Low Latency Communications (uRLLC) UEs on a mini-slot basis, while keeping the objective unchanged. To address the scheduling issue of eMBB UEs, we employ a heuristic technique, and, by leveraging the outcome of this heuristic, we derive an optimal solution for the problem of uRLLC UEs. The significance of the proposed approach over a baseline approach is evaluated through extensive numerical simulations in terms of the number of allocated uRLLC resource blocks (RBs) per mini-slot. We also assess our approach by measuring the impact of the uRLLC slice changes on the eMBB slice, and vice versa, including delay for uRLLC users and data rates for eMBB users.

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.

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.

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.