Self-optimizing Networks Research Articles

AI Telco Research: Advancements in Telecommunications Scientific Discovery

This article explores the transformative impact of Artificial Intelligence (AI) on telecommunications research and development. It examines how the integration of AI technologies is revolutionizing various aspects of the industry, including network optimization, spectrum management, and scientific discovery. The article highlights the synergy between human expertise and AI capabilities in enhancing research processes, improving data analysis, and driving innovation. Key areas of focus include AI-powered data analysis, network behavior simulation, signal processing enhancements, and the evolving nature of human-AI collaboration in telco research. The article also addresses challenges such as data privacy and algorithmic bias, while emphasizing the potential of AI to unlock new possibilities in telecommunications, from self-optimizing networks to quantum communication systems.

Research on 5G Heterogeneous Network Switching Mechanism and Self-optimizing Network Switching Algorithm

Research on 5G Heterogeneous Network Switching Mechanism and Self-optimizing Network Switching Algorithm

6G Networks and the AI Revolution-Exploring Technologies, Applications, and Emerging Challenges.

In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. This paper presents a hierarchical exploration of 6G networks, poised at the forefront of the next revolution in wireless technology. This study delves into the technological advancements that underpin the need for 6G, examining its key features, benefits, and key enabling technologies. We dissect the intricacies of cutting-edge innovations like terahertz communication, ultra-massive MIMO, artificial intelligence (AI), machine learning (ML), quantum communication, and reconfigurable intelligent surfaces. Through a meticulous analysis, we evaluate the strengths, weaknesses, and state-of-the-art research in these areas, offering a wider view of the current progress and potential applications of 6G networks. Central to our discussion is the transformative role of AI in shaping the future of 6G networks. By integrating AI and ML, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems. This integration heralds a new era of intelligent, self-optimizing networks that promise to redefine the parameters of connectivity and digital interaction. We also address critical challenges in the deployment of 6G, from technological hurdles to regulatory concerns, providing a holistic assessment of potential barriers. By highlighting the interplay between 6G and AI technologies, this study maps out the current landscape and lights the path forward in this rapidly evolving domain. This paper aims to be a cornerstone resource, providing essential insights, addressing unresolved research questions, and stimulating further investigation into the multifaceted realm of 6G networks. By highlighting the synergy between 6G and AI technologies, we aim to illuminate the path forward in this rapidly evolving field.

Predicting Network Hardware Faults through Layered Treatment of Alarms Logs.

Maintaining and managing ever more complex telecommunication networks is an increasingly difficult task, which often challenges the capabilities of human experts. There is a consensus both in academia and in the industry on the need to enhance human capabilities with sophisticated algorithmic tools for decision-making, with the aim of transitioning towards more autonomous, self-optimizing networks. We aimed to contribute to this larger project. We tackled the problem of detecting and predicting the occurrence of faults in hardware components in a radio access network, leveraging the alarm logs produced by the network elements. We defined an end-to-end method for data collection, preparation, labelling, and fault prediction. We proposed a layered approach to fault prediction: we first detected the base station that is going to be faulty and at a second stage, and using a different algorithm, we detected the component of the base station that is going to be faulty. We designed a range of algorithmic solutions and tested them on real data collected from a major telecommunication operator. We concluded that we are able to predict the failure of a network component with satisfying precision and recall.

Intelligence and Learning in O-RAN for Data-Driven NextG Cellular Networks

Next Generation (NextG) cellular networks will be natively cloud-based and built upon programmable, virtualized, and disaggregated architectures. The separation of control functions from the hardware fabric and the introduction of standardized control interfaces will enable the definition of custom closed-control loops, which will ultimately enable embedded intelligence and real-time analytics, thus effectively realizing the vision of autonomous and self-optimizing networks. This article explores the disaggregated network architecture proposed by the O-RAN Alliance as a key enabler of NextG networks. Within this architectural context, we discuss the potential, the challenges, and the limitations of data-driven optimization approaches to network control over different timescales. We also present the first large-scale integration of O-RAN-compliant software components with an open-source full-stack softwarized cellular network. Experiments conducted on Colosseum, the world's largest wireless network emulator, demonstrate closed-loop integration of real-time analytics and control through deep reinforcement learning agents. We also show the feasibility of Radio Access Network (RAN) control through xApps running on the near real-time RAN Intelligent Controller, to optimize the scheduling policies of co-existing network slices, leveraging the O-RAN open interfaces to collect data at the edge of the network.

A Robust Self-Optimization Algorithm Based on Idiosyncratic Adaptation of Handover Parameters for Mobility Management in LTE-A Heterogeneous Networks

Mobility robustness optimization (MRO) is a fundamental issue regarding self-optimization network (SON). In long-term evolution-advanced (LTE-A), handover (HO) optimization in heterogeneous networks (HetNets) is an urgent need to improve system performance. This improvement is in terms of immunity against unnecessary HO (UHO) such as ping-pong HO (PPHO) and against failed HOs in the sense of radio link failure (RLF) such as too-early HO (TEHO) and too-late HO (TLHO), that is, RLF HO. The occurrence of these undesired HOs increases the consumption of network resources and decreases the quality of service (QoS). In this study, we propose a robust algorithm to reduce the number of PPHO, TLHO, and TEHO events to a minimum by an innovative mechanism that adaptively sets the HO control parameters (HCPs). This reduction is obtained without the need for unjustified techniques that assume certain thresholds for the ratio of PPHOs or the ratio of RLF HOs relative to the total number of HOs, as proposed in the most recent literature. We also present the importance of thinking about the existence of a direct relationship between the hysteresis margin (HM) and time-to-trigger (TTT). We invest this relationship in determining adaptive HCPs. Simulation results show that RLF HOs and PPHOs are minimal, almost zero, compared to the literature and the classical method. This study opens a new avenue for research on mobility management. It reorients research axes toward thinking about the possibility of a correlation between HM and TTT. This is what the research community has not yet realized.

Performance Tuning of 5G Networks Using AI and Machine Learning Algorithms

As the demand for faster and more reliable mobile networks intensifies, the deployment of 5G has emerged as a transformative solution to meet the growing needs of connectivity. However, to fully leverage the potential of 5G networks, it is crucial to optimize their performance. This paper explores the application of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in the performance tuning of 5G networks, focusing on areas such as network resource allocation, traffic prediction, and real-time decision-making. By analyzing vast datasets generated by 5G infrastructures, AI and ML enable dynamic adjustments, thereby improving network efficiency, reducing latency, and enhancing overall user experience. The integration of these advanced technologies allows for self-optimizing networks that adapt in real time, minimizing human intervention and operational costs. This research highlights the key algorithms and techniques used for performance optimization, discusses the challenges of implementing AI in real-world 5G networks, and outlines the future directions for achieving fully autonomous network management. Ultimately, the study illustrates how AI and ML are pivotal in driving the future of telecommunications through intelligent 5G network tuning.

Self-Optimizing Wireless Networks on Structures

This brief proposes half-duplex wireless networks that use acoustic guided waves traveling on or through structures for physically-secure long-range power and data telemetry. It also develops a protocol for such networks to optimize their power and data delivery to compensate for slowly-varying factors like temperature and structural damage. Specifically, near-maximum power point tracking (nMPPT) is implemented by searching for the optimal transmission frequency in complex acoustic channels. Binary frequency shift keying (BPSK) modulated on the power waveform is proposed for downlink. A power-efficient fast uplink is achieved by using Non Return to Zero - Inverted (NRZ-I) data encoding. Data security is naturally ensured by the unique channel responses defined by the transceivers’ physical locations. Miniature smart sensor nodes with a quiescent power of 4.8 $\mu \text{W}$ and a peak power of $\sim 200~\mu \text{W}$ have been developed for such sparse networks. The proposed half-duplex telemetry approach is experimentally evaluated on a structural health monitoring (SHM) test bed and achieves a power transfer of $\sim 181.8~\mu \text{W}$ at 0.5 m and a BER of 10−4 for a 80 bps downlink driven at 20 $\text{V}_{pp}$ , and a BER of 10−4 for a 2 Mbps uplink driven at $\sim 2~\text{V}_{pp}$ .

Complex Event Processing for Self-Optimizing Cellular Networks.

In a cellular network, signaling and data messages exchanged between network elements are an extremely valuable information for network optimization. The consideration of different types of information allows to improve the optimization results. However, the huge amount of information has made it very difficult for operators to process all the available information. To cope with this issue, in this paper, a methodology for processing cell and user connection traces to optimize a live cellular network is presented. The aim is to generate new performance indicators different from those supplied by manufacturers, taking advantage of the ability of complex event processing tools to correlate events of different nature. For illustrative purposes, an example of how a new performance indicator is created from real traces by complex event processing is given.

Self-Optimizing Optical Network With Cloud-Edge Collaboration: Architecture and Application

As an important bearer network of the fifth generation (5G) mobile communication technology, the optical transport network (OTN) needs to have high-quality network performance and management capabilities. Proof by facts, the combination of artificial intelligence (AI) technology and software-defined networking (SDN) can improve significant optimization effects and management for optical transport networks. However, how to properly deploy AI in optical networks is still an open issue. The training process of AI models depends on a large amount of computing resources and training data, which undoubtedly increases the carrying burden and operating costs of the centralized network controller. With the continuous upgrading of functions and performance, small AI-based chips can be used in optical networks as on-board AI. The emergence of edge computing technology can effectively relieve the computation load of network controllers and provide high-quality AI-based networks optimization functions. In this paper, we describe an architecture called self-optimizing optical network (SOON) with cloud-edge collaboration, which introduces control-layer AI and on-board AI to achieve intelligent network management. In addition, this paper introduces several cloud-edge collaborative strategies and reviews some AI-based network optimization applications to improve the overall network performance.

Patient-Centric HetNets Powered by Machine Learning and Big Data Analytics for 6G Networks

Having a cognitive and self-optimizing network that proactively adapts not only to channel conditions, but also according to its users' needs can be one of the highest forthcoming priorities of future 6G Heterogeneous Networks (HetNets). In this paper, we introduce an interdisciplinary approach linking the concepts of e-healthcare, priority, big data analytics (BDA) and radio resource optimization in a multi-tier 5G network. We employ three machine learning (ML) algorithms, namely, naive Bayesian (NB) classifier, logistic regression (LR), and decision tree (DT), working as an ensemble system to analyze historical medical records of stroke out-patients (OPs) and readings from body-attached internet-of-things (IoT) sensors to predict the likelihood of an imminent stroke. We convert the stroke likelihood into a risk factor functioning as a priority in a mixed integer linear programming (MILP) optimization model. Hence, the task is to optimally allocate physical resource blocks (PRBs) to HetNet users while prioritizing OPs by granting them high gain PRBs according to the severity of their medical state. Thus, empowering the OPs to send their critical data to their healthcare provider with minimized delay. To that end, two optimization approaches are proposed, a weighted sum rate maximization (WSRMax) approach and a proportional fairness (PF) approach. The proposed approaches increased the OPs' average signal to interference plus noise (SINR) by 57% and 95%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, nevertheless, the PF approach yielded higher SINRs for the OPs, better fairness and a lower margin of error.

The Relationship between Artificial Intelligence and Internet of Things: A quick review

Internet Of Things (IOT) is a network of various devices that are connected over the internet, and they can collect and exchange data with each other. These IOT devices generate a lot of data that needs to be collected and mined for actionable results through use artificial intelligence (AI) to manage huge data flows and storage in the IOT network. In this paper we briefly discussed about what IOT is, what AI is, Algorithm of AI, Challenge AI with IOT, application of artificial intelligence system in the IOT. The self-optimizing network and software defined network are parts of the important parameters in the Artificial Intelligence IoT System. This paper provides a general discussion about importance of the IoT in different applications. The paper covers different applications of IoT and shows the relationship between AI and IoT. The role of the AI in IoT applications is extensively discussed. In the future work, we are planning to work on improving the performance of IoT applications using advanced AI methods and algorithms such as Machine Learning and Deep Learning.

Improved Network Analytics with novel feedback quantities for Self-Optimized Networks

With the ever-increasing Capital Expenditure (CAPEX) required for newer technologies like New Radio (NR) to meet 5th Generation (5G) requirements, it is imperative for the operators to look at reduced Operation and Maintenance (O&M), as a way to optimize the Return on Investment (RoI) over several y

Theory of Algorithm Suitability on Managing Radio Resources in Next Generation Mobile Networks

Beyond 2020, wireless networking model will be radically changed and oriented to business-driven concept as foreseen by the next generation mobile network (NGMN) alliance. As the available spectrum granted to a given operator is physically limited, new radio resource management techniques are required to ensure massive connectivity for wireless devices. Given this situation, we investigate in this paper how the key network functionalities as self-optimizing network (SON) must be thought to meet NGMN requirements. We propose therefore, algorithm suitability theory (AST) combined to the notion of network operator infrastructure convergence. The approach is based on software-defined networking (SDN) principle that allows an adaptability of the load balance algorithm to the dynamic network status. Besides, we use the concept of network function virtualization (NFV) that alleviates the constraint of confining the wireless devices to their home network operator only. Relying on these two technologies, we build AST through a lexicographic optimality criterion based on SPC (Status, Performance, and Complexity) order. Numerical results demonstrate a better network coverage verified by the improvement of metrics such as call blocking rate, spectrum efficiency, energy efficiency and load balance index.

A cluster based load balancing in long term evaluation self optimization network

Load Balancing (lb) is an important use case in long-term evaluation (lte) self-optimizing networks (sons). To overcome the potential ping-pong load transfer and low-convergence issues, we propose a clustering algorithm in lte son to solve the asymmetry traffic distribution among multiple cells .The proposed algorithm, which is referred to as clustering algorithm which optimally redistributes the load of cells to the multiple cells on clustering. Thus the clustered nodes formed selects the cluster head based upon the efficiency of the node. The cluster head is responsible for gathering the information about all the nodes within and outside the cluster, thus the speed gets increased and convergence time gets reduced by this method the hidden node problem gets avoided by implementing the enhanced protocol.

Energy Saving Through Traffic Profiling in Self-Optimizing Optical Networks

An increasing fraction of the electrical energy produced in western countries is being consumed by Internet infrastructure; reducing its energy footprint is therefore of utmost importance for the scalability of the Internet. We address optical transport backbones and propose a novel method to reduce the energy consumed by dynamically adjusting the number of active optical carriers to support the short-term load of the network with a small and controllable margin. This is achieved in a nondisruptive manner that does not interact with routing strategies and does not rely on any specific control plane, but exploits automated traffic profiling and prediction of the well-known circadian traffic cycle. The proposed approach works with both fixed and flexible grid optical networks. We describe a method to automatically learn these patterns and multiple techniques to predict incoming traffic. Furthermore, we present an algorithm that tunes the parameters of the proposed system in order to achieve a target a posteriori probability of causing traffic losses. The behavior of the system is studied, using simulations, under a variety of conditions. Results show that the proposed prediction algorithms can significantly reduce the number of active optical carriers, even in nonoptimal scenarios, while guaranteeing low traffic losses.

Wi-Fi Self-Organizing Networks: Challenges and Use Cases

Wireless customers expect to have a guaranteed quality of experience at all times, at any location, and through different devices. Wi-Fi has become an access network of preference for service/ network providers and customers as well for public and private access. This sets a challenging requirement for next-generation Wi-Fi technology to provide seamless and uniform network quality of service). Consequently, the necessity for self-optimization of network and radio frequency segments becomes critical. This article surveys challenges and use cases of Wi-Fi self-optimizing networks (Wi-SONs) that have not been presented to date. We address technology and design challenges that shape Wi-SON as a very complex problem.

Modelling Load Balancing Mechanisms in Self-Optimising 4G Mobile Networks with Elastic and Adaptive Traffic

Modelling Load Balancing Mechanisms in Self-Optimising 4G Mobile Networks with Elastic and Adaptive Traffic

On a Way to the Self-Optimizing LTE Networks

Main achievements in load balancing algorithm in a long-term-evolution (LTE) mobile communication system are presented. As result a self-optimizing system, that could be implement in the real networks. Basic target functions, such as the signal-to-noise ratio distribution, bit error rate, energy efficiency are derived as a figure of merit. The framework is exemplified by basic investigations on load balancing technology.

Zone-Based Load Balancing in LTE Self-Optimizing Networks: A Game-Theoretic Approach

Mobility load balancing (MLB) is an important use case in long-term evaluation (LTE) self-optimizing networks (SONs). To combat the potential ping-pong load transfer and low-convergence issues, we propose a game-theoretic solution to the SON MLB to solve the asymmetry traffic distribution among multiple cells and the potential hidden-cell case. The proposed algorithm, which is referred to as zone-based mobility load balancing (ZLB), optimally redistributes the load of all cells within a zone. We model the multiple cells' ZLB as a Cournot game, where multiple cell pairs carry out load balancing (LB) simultaneously under the constraints of load distribution of hidden cells. Simulation results show that the ZLB algorithm can overcome the ping-pong LB problem and that the convergence time of LB is also dramatically reduced. Meanwhile, the load distribution of ZLB is more balancing than that of MLB, and the performance of average blocking probability and the number of unsatisfied users of ZLB are significantly improved.