Management Of Cellular Networks Research Articles

Digital twin enabled cellular network management and prediction

Digital twin (DT) technologies have been increasingly important and useful for wireless communications. In particular, to support soaring wireless traffic with limited frequency spectrum assigned to legacy cellular systems, efficient operation and management of wireless resources as well as preemptively prediction of future spectrum usage are crucially important. For such purpose, DT networks for current fourth-generation (4G) and fifth-generation (5G) networks are constructed in this paper, by simultaneously utilizing measurement data from user equipments (UEs) and geographical information and characteristics of 4G and 5G base stations (BSs) within a specific observation area. Representative case studies are provided to demonstrate the usefulness of DT enabled cellular network management and prediction. As a real or near real time DT application, the impact and benefit of dual connectivity and dynamic spectrum sharing between 4G and 5G networks are analyzed. As a non-real time DT application, long-term improvement of 5G networks such as densification of BSs, implementation of advanced multiple input and multiple output technologies, and assignment of additional spectrum are analyzed and compared.

Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks.

The management of cellular networks, particularly within the environment rapidly advancing to 6G, presents considerable challenges due to the highly dynamic radio environment. Traditional tools such as Radio Environment Maps (REMs) have proven inadequate for real-time network changes, underlining the need for more sophisticated solutions. In response to these challenges, this work introduces a novel approach that harnesses the unprecedented power of state-of-the-art image classifiers for network management. This method involves the generation of Network Synthetic Images (NSIs), which are enriched heat maps that precisely reflect varying cellular network operating states. Created from user location traces linked with Key Performance Indicators (KPIs), NSIs are strategically designed to meet the intricate demands of 6G networks. This research delves deep into a comprehensive analysis of the diverse factors that could potentially impact the successful application of this methodology in the realm of 6G. The results from this investigation, coupled with a comparative assessment against traditional REM usage, emphasize the superior performance of this innovative method. Additionally, a case study involving an automatic network diagnosis scenario validates the effectiveness of this approach. The findings reveal that a generic Convolutional Neural Network (CNN), one of the most powerful tools in the arsenal of modern image classifiers, delivers enhanced performance, even with a reduced demand for positioning accuracy. This contributes significantly to the real-time, robust management of cellular networks as we transition into the era of 6G.

Data-Driven Machine Learning Techniques for Self-Healing in Cellular Wireless Networks: Challenges and Solutions

For enabling automatic deployment and management of cellular networks, the concept of self-organizing network (SON) was introduced. SON capabilities can enhance network performance, improve service quality, and reduce operational and capital expenditure (OPEX/CAPEX). As an important component in SON, self-healing is defined as a paradigm where the faults of target networks are mitigated or recovered by automatically triggering a series of actions such as detection, diagnosis, and compensation. Data-driven machine learning has been recognized as a powerful tool to bring intelligence into networks and to realize self-healing. However, there are major challenges for practical applications of machine learning techniques for self-healing. In this article, we first classify these challenges into five categories: (1) data imbalance, (2) data insufficiency, (3) cost insensitivity, (4) non-real-time response, and (5) multisource data fusion. Then, we provide potential technical solutions to address these challenges. Furthermore, a case study of cost-sensitive fault detection with imbalanced data is provided to illustrate the feasibility and effectiveness of the suggested solutions.

Metaheuristics on quantum computers: Inspiration, simulation and real execution

Quantum-inspired metaheuristics are solvers that incorporate principles inspired from quantum mechanics into classical-approximate algorithms using non-quantum machines. Due to the uniqueness of quantum principles, the inspiration of quantum phenomena and the way it is done in fundamentally different non-quantum systems rather than real or simulated quantum computers raise important questions about these algorithms’ design and the reproducibility of their results in real or simulated quantum devices. Thus, this work’s contribution stands in a first step towards answering those questions as an attempt to identify key findings in the existing literature that should be considered or adapted in order to build hybrid or fully-quantum algorithms that can be used in quantum machines. This is done by proposing and studying four inspired, simulated and real quantum cellular genetic algorithms that, as far as the authors’ knowledge, are the first quantum structured metaheuristics studied in the three quantum realms using a quantum simulator with 32 quantum bits and a real quantum machine employing 15 superconducting quantum bits. The users’ mobility management in cellular networks is taken as a validation problem using 13 real-world instances. The comparisons have been made against 6 diverse algorithms using 9 comparison metrics. Thorough statistical tests and parameters’ sensitivity analysis have been also conducted. The experiments allowed answering several questions, including how quantum hardware influences the studied-algorithms’ search process. They also enabled opening new perspectives in quantum metaheuristics’ design.

A survey of self-coordination in self-organizing network

Self-organizing network (SON) is a well-known approach to reduce the complexity and the cost of cellular network management. It aims at replacing the manual configuration and optimization with the functionalities of self-configuration, self-optimization and self-healing. Due to the important role of SON, the problem of conflicts between SON functions has been seriously considered over the last decade. In order to resolve this problem, 3GPP has introduced the functionality of self-coordination which is responsible for conflict avoidance and resolution. However, the conflict-free execution of SON functions remains a challenge as it requires the coordination mechanisms to address all potential interactions between SON functionalities, anticipate their impact on the network and evaluate their execution results. Self-coordination in SON is therefore considered as an open research field since it directly affects the performance of SON functionalities and as a result affects the network stability. In this paper, we provide a survey of SON conflicts and self-coordination methodologies that can be used for conflicts avoidance and resolution, and review the recent solutions to state-of-the-art, including papers in this research area. Finally, we point out major challenges and research issues to be addressed in the future.

Cellular Network Radio Monitoring and Management through Virtual UE Probes: A Study Case Based on Crowded Events.

Although log processing of network equipment is a common technique in cellular network management, several factors make said task challenging, especially during mass attendance events. The present paper assesses classic methods for cellular network measurement and acquisition, showing how the use of on-the-field user probes can provide relevant capabilities to the analysis of cellular network performance. Therefore, a framework for the deployment of this kind of system is proposed here based on the development of a new hardware virtualization platform with radio frequency capabilities. Accordingly, an analysis of the characteristics and requirements for the use of virtual probes was performed. Moreover, the impact that social events (e.g., sports matches) may have on the service provision was evaluated through a real cellular scenario. For this purpose, a long-term measurement study during crowded events (i.e., football matches) in a stadium has been conducted, and the performances of different services and operators under realistic settings has been evaluated. As a result, several considerations are presented that can be used for better management of future networks.

Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach.

Recent years have seen the proliferation of different techniques for outdoor and, especially, indoor positioning. Still being a field in development, localization is expected to be fully pervasive in the next few years. Although the development of such techniques is driven by the commercialization of location-based services (e.g., navigation), its application to support cellular management is considered to be a key approach for improving its resilience and performance. When different approaches have been defined for integrating location information into the failure management activities, they commonly ignore the increase in the dimensionality of the data as well as their integration into the complete flow of networks failure management. Taking this into account, the present work proposes a complete integrated approach for location-aware failure management, covering the gathering of network and positioning data, the generation of metrics, the reduction in the dimensionality of such data, and the application of inference mechanisms. The proposed scheme is then evaluated by system-level simulation in ultra-dense scenarios, showing the capabilities of the approach to increase the reliability of the supported diagnosis process as well as reducing its computational cost.

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The state-of-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed.

Mobility Load Management in Cellular Networks: A Deep Reinforcement Learning Approach

Balancing traffic among cellular networks is very challenging due to many factors. Nevertheless, the explosive growth of mobile data traffic necessitates addressing this problem. Due to the problem complexity, data-driven self-optimized load balancing techniques are leading contenders. In this work, we propose a comprehensive deep reinforcement learning (RL) framework for steering the cell individual offset (CIO) as a means for mobility load management. The state of the LTE network is represented via a subset of key performance indicators (KPIs), all of which are readily available to network operators. We provide a diverse set of reward functions to satisfy the operators' needs. For a small number of cells, we propose using a deep Q-learning technique. We then introduce various enhancements to the vanilla deep Q-learning to reduce bias and generalization errors. Next, we propose the use of actor-critic RL methods, including Deep Deterministic Policy Gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3) schemes, for optimizing CIOs for a large number of cells. We provide extensive simulation results to assess the efficacy of our methods. Our results show substantial improvements in terms of downlink throughput and non-blocked users at the expense of negligible channel quality degradation.

Maximum bit rate control based on throughput feedback for deadline-aware resource management in cellular networks

Maximum bit rate control based on throughput feedback for deadline-aware resource management in cellular networks

Tracking area list allocation scheme based on overlapping community algorithm

Reducing the singling overhead for tracking and mobile paging devices is a challenging issue in the study of location management of cellular networks. Cellular networks have become massive generators of data, and in the forthcoming years, this data is expected to increase drastically. Big data-based intelligence and analytics can improve network operational efficiency and user service quality. This work proposes to exploit massive handover and paging data from cellular networks to minimize singling due to user mobility. In this paper, we offer a new holistic tracking area lists (TAL) management methodology, considering group user mobility behavior and paging characteristics. Firstly, a series of graphs showing the evolution of user mobility and traffic is built from handover and paging statistics in the network management system (NMS). Then, the TAL allocation problem is formulated as a classical graph partitioning problem, which is then solved by detecting overlapping communities algorithm based on game theory. Results show that the proposed method can effectively reduce the location management singling overhead and improve the TAL configuration efficiency.

A Generative Adversarial Learning-Based Approach for Cell Outage Detection in Self-Organizing Cellular Networks

For enabling automatic deployment and management of cellular networks, Self-Organizing Network (SON) was boosted to enhance network performance, to improve service quality, and to reduce operational and capital expenditure. Cell outage detection is an essential functionality of SON to autonomously detect cells that fail to provide services, due to either software or hardware faults. Machine learning represents an effective tool for such a task. However, traditional classification algorithms for cell outage detection are likely to construct a biased classifier when training samples in one class significantly outnumber other classes. To counter this problem, in this letter, we present a novel method that is able to learn from imbalanced cell outage data in cellular networks, through combining Generative Adversarial Network (GAN) and Adaboost. Specifically, the proposed approach utilizes GAN to change distribution of imbalanced dataset by synthesizing more samples for minority class, and then uses Adaboost to classify the calibrated dataset. Experimental results show significant improvement of classification performance for imbalanced cell outage data, on the basis of several metrics including Receiver Operating Characteristic (ROC), precision, recall rate, and F-value.

Reporting cell planning-based cellular mobility management using a Binary Artificial Bat algorithm

This paper attempts to present a novel application of Binary Artificial Bat algorithm for more effective location management in cellular networks. The location management is a mobility management task, which involves tracking of the mobile stations to locate their exact positions so that an incoming call or data can be routed to the intended mobile user. The location management cost comprises of the costs incurred by two processes, namely location registration and location search. This work focuses on network cost optimization, using Binary Artificial Bat algorithm for reporting cell planning strategy, which has not been reported yet. Results of the proposed algorithm have been compared with that of Binary Particle Swarm Optimization (BPSO) and Binary Differential Evolution (BDE) for some reference and realistic networks. The proposed approach is found to perform as good as other state-of-art techniques reported in the literature in terms of accuracy in solution, but it shows perceptible improvement in convergence speed.

Cell Fault Management Using Machine Learning Techniques

This paper surveys the literature relating to the application of machine learning to fault management in cellular networks from an operational perspective. We summarise the main issues as 5G networks evolve, and their implications for fault management. We describe the relevant machine learning techniques through to deep learning, and survey the progress which has been made in their application, based on the building blocks of a typical fault management system. We review recent work to develop the abilities of deep learning systems to explain and justify their recommendations to network operators. We discuss forthcoming changes in network architecture which are likely to impact fault management and offer a vision of how fault management systems can exploit deep learning in the future. We identify a series of research topics for further study in order to achieve this.

Applying Social Event Data for the Management of Cellular Networks

Internet provides a growing variety of social data sources: calendars, event aggregators, social networks, browsers, etc. Also, the mechanisms to gather information from these sources, such as web services, semantic web and big data techniques have become more accessible and efficient. This allows a detailed prediction of the main expected events and their associated crowds. Due to the increasing requirements for service provision, particularly in urban areas, having information on those events would be extremely useful for Operations, Administration and Maintenance (OAM) tasks, since the social events largely affect the cellular network performance. Therefore, this paper presents a framework for the automatic acquisition and processing of social data, as well as their association with network elements (NEs) and their performance. The main functionalities of this system, which have been devised to directly work in real networks, are defined and developed. Different OAM applications of the proposed approach are analyzed and the system is evaluated in a real deployment.

Measuring Instability of Mobility Management in Cellular Networks

Communication in cellular networks is based on serving cells that provide the basic network service. In the real world, serving cells overlap which means the number of serving cells covering one position is usually more than one. Recently, the instability of mobility management in cellular networks has been studied to monitor and analyze the handoff process in mobile devices. However, the handoff process is actually produced by base stations instead of mobile devices. Hence, it is of great importance to measure the handoff process of mobility management from the base station side. In this article, we present a series of experiments performed using the data obtained by mobile network operators. The contributions of this study are three-fold. We reproduce a handoff process and handoff loop from both the mobile device level and the base station level, and confirm the existence of a handoff loop by measurements from the base station side. Through large-range measurements, we discover that only a small part of serving cells is involved in the handoff process, and in most cases, the number of candidate serving cells is much smaller than the number of cells that cover some position; namely, when a handoff loop occurs, the number of candidate serving cells is quite small, which is in contrast to our assumption. We confirm that the handoff loop often occurs in indoor conditions or when the mobile device has frequent communication with the base station. Finally, we present several comprehensive facts about the handoff process and handoff loop and provide suggestions that can be used to increase the quality of service of cellular networks.

Mobile Demand Forecasting via Deep Graph-Sequence Spatiotemporal Modeling in Cellular Networks

The demand forecasting plays a crucial role in the predictive physical and virtualized network management in cellular networks, which can effectively reduce both the capital and operational expenditures by fully exploiting the network infrastructure. In this paper, we study the per-cell demand forecasting in cellular networks. The success of demand forecasting relies on the effective modeling of both the spatial and temporal aspects of the per-cell demand time series. However, the main challenge of the spatial relevancy modeling in the per-cell demand forecasting is the irregular spatial distribution of cells in a network, where applying grid-based models (e.g., convolutional neural networks) would lead to degradation of spatial granularity. In this paper, we propose to model the spatial relevancy among cells by a dependency graph based on spatial distances among cells without the loss of spatial granularity. Such spatial distance-based graph modeling is confirmed by the spatiotemporal analysis via semivariogram, which suggests that the relevancy between any two cells declines as their spatial distance increases. Hence, the graph convolutional networks and long short-term memory (LSTM) from deep learning are employed to model the spatial and temporal aspects, respectively. In addition, the deep graph-sequence model, graph convolutional LSTM, is further employed to simultaneously characterize both the spatial and temporal aspects of mobile demand forecasting. Experiments demonstrate that our proposed graph-sequence demand forecasting model could achieve a superior forecasting performance compared with the other two proposed models as well as the traditional auto regression integrated moving average time series model.

Wireless big data in cellular networks: the cornerstone of smart cities

The rapid urbanisation has transformed cities to the preferential human settlement and allowed cities to quietly witness all range of human activities. As the key enabler in the information and communications technology industry, cellular networks play a decisive role in delivering communication messages and entertainment content. In particular, cellular network operators respond to human initiated service requests by gradually deploying necessary infrastructure and calibrating transmission protocols. Hence, cellular network records encompass the interesting interaction between human-initiated messages and network-triggered responses. In this study, the authors collect the `big data' in urban cellular networks and try to dig out the human and urban planning properties. Specifically, they focus on the statistical modelling of three representative scenarios like spatial deployment density of base stations, packet length or traffic volume of mobile services, as well as inter-arrival time and dwell time of human mobility. Through extensive data mining, they validate the heavy-tailed feature universally existing in these scenarios. Afterwards, they discuss the implications of this heavy-tailed feature and talk about its fundamental contribution to intelligent resource adjustment, proactive content caching, and enhanced connection management in cellular networks. Finally, they highlight the applications of this feature towards smarter cellular networks and cities.

Reducing the Frequent Switch Over between the Base Stations through Continuous Analysis of Signal Strength

As the increase in the number of mobiles and mobile networks, the existing standards and technologies face several issues and challenges. In the mobility management of cellular networks, HO is the important parameter on which these issues and challenges affect drastically. Several research papers have proposed many ideas and schemes to reduce HO latency. As the increase in the number new technologies and networks, the existing ideas are no longer supported efficiently. So a continuous analysis on handover (HO) procedure is required. Signal strength (SS) and quality of service (QoS) are measured at particular time intervals in cellular networks. Results show that the highest value of signal strength has the best quality of service. In this paper, a continuous analysis on signal strength received by the mobile station (MS) is used to monitor the HO process to reduce the HO delay. In this analysis, the parameters user movement pattern, topological position, signal strength and time are used which will be helpful to forecast the HO in advance. Using HO forecast minimum number of frequent switch over between the base stations (BSs) reduces the HO latency in mobile networks.

User Experience Aware Active Queue Management in Cellular Networks

User Experience Aware Active Queue Management in Cellular Networks