Automated Self-Optimization in Heterogeneous Wireless Communications Networks

Lynch, David; Fenton, Michael; Fagan, David; Kučera, Štěpán; Claußen, Holger; O’Neill, Michael

doi:10.1109/tnet.2018.2890547

Cited by 17 publications

(13 citation statements)

References 27 publications

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“…Various mobility techniques and functions have been presented to support mobility. One example would be the techniques used for regulating mobility control parameters, known as handover self-optimization functions [50,51,[53][54][55][56][57][58]. Another example is the mechanisms used for making handover decisions, known as handover decision algorithms [59][60][61][62][63].…”

Section: Figure2 Ultra-dense and Overlapping Hetnets With Multi-typmentioning

confidence: 99%

“…One significant feature that has been introduced to solve mobility problems in 4G and 5G networks is mobility functions under the SON [42,57,84,106,[133][134][135][136][137][138][139][140], which may further be developed and kept as one of the main components in the 6G system as well. The SON feature is one of three main sub-networks that has been introduced under the Self-Organization Network in 4G and 5G networks, as illustrated in Figure 9.…”

Section: A Hpo Modelsmentioning

confidence: 99%

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Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

et al. 2020

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Ensuring a seamless connection during the mobility of various User Equipment (UE) will be one of the major challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. Several key determinants will significantly contribute to numerous mobility challenges. One of the most important determinants is the use of millimeter waves (mm-waves) as it is characterized by high path loss. The inclusion of various types of small coverage Base Stations (BSs), such as Picocell, Femtocell and drone-based BSs is another challenge. Other issues include the use of Dual Connectivity (DC), Carrier Aggregation (CA), the massive growth of mobiles connections, network diversity, the emergence of connected drones (as BS or UE), ultra-dense network, inefficient optimization processes, central optimization operation, partial optimization, complex relation in optimization operations, and the use of inefficient handover decision algorithms. The relationship between these processes and diverse wireless technologies can cause growing concerns in relation to handover associated with mobility. The risk becomes critical with high mobility speed scenarios. Therefore, mobility issues and their determinants must be efficiently addressed. This paper aims to provide an overview of mobility management in 5G networks. The work examines key factors that will significantly contribute to the increase of mobility issues. Furthermore, the innovative, advanced, efficient, and smart handover techniques that have been introduced in 5G networks are discussed. The study also highlights the main challenges facing UEs' mobility as well as future research directions on mobility management in 5G networks and beyond.

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Section: Figure2 Ultra-dense and Overlapping Hetnets With Multi-typmentioning

confidence: 99%

Section: A Hpo Modelsmentioning

confidence: 99%

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

et al. 2020

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“…Previous works have addressed the efficient configuration of eICIC parameters. Some works focused solely on the CRE bias, 14‐16 others focused on the ABS ratio, 17‐21,27 while fewer works 22,23,28 aimed at optimizing both parameters simultaneously, like this work.…”

Section: Related Work and Contributionmentioning

confidence: 99%

“…Regarding the dynamic nature of the network conditions (eg, traffic intensity, number of users, user positions), most previous works 7,8,14‐17,20,21,29‐34 assume static or stationary conditions. While some works consider nonstationary scenarios, 9,18,19,22,28 their approaches are based on optimization or heuristic algorithms over mathematical models of the network , like in References 6‐8,10,20,21,32,34‐37. For mathematical tractability, even the most complete mathematical network models comprise simplifications and assumptions that may limit their application to real operating networks.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Online reinforcement learning for adaptive interference coordination

Alcaraz

Ayala‐Romero

Vales‐Alonso

et al. 2020

Trans Emerging Tel Tech

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Heterogeneous networks (HetNets), in which small cells overlay macro cells, are a cost-effective approach to increase the capacity of cellular networks. However, HetNets have raised new issues related to cell association and interference management. In particular, the optimal configuration of interference coordination (IC) parameters is a challenging task because it depends on multiple stochastic processes such as the locations of the users, the traffic demands, or the strength of the received signals. This work proposes a self-optimization algorithm capable of finding the optimal configuration in an operating network. We address the problem using a reinforcement learning (RL) approach, in which the actions are the IC configurations, whose performances are initially unknown. The main difficulty is that, due to the variable network conditions, the performance of each action may change over time. Our proposal is based on two main elements: the sequential exploration of subsets of actions (exploration regions), and an optimal stopping (OS) strategy for deciding when to end current exploration and start a new one. For our algorithm, referred to as local exploration with optimal stopping (LEOS), we provide theoretical bounds on its long-term regret per sample and its convergence time. We compare LEOS to state-of-the-art learning algorithms based on multiarmed bandits and policy gradient RL. Considering different changing rates in the network conditions, our numerical results show that LEOS outperforms the first alternative by 22%, and the second one by 48% in terms of average regret per sample.

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“…At present, the research of data collection in OppNet mainly focuses on routing strategies which aim to improve the efficiency of data collection by optimizing routing algorithm, such as Direct Transmission Algorithm [7], Epidemic Routing Algorithm [8] and other algorithms between the above two extremes [9], [10]. Unfortunately, in OppNet, it is difficult for nodes to obtain reliable information about the global network topology, and to predict the encounter opportunities between nodes.…”

Section: Introductionmentioning

confidence: 99%

Data Collection Mechanism Based on Wavelet Multi-Resolution for Opportunistic Social Networks

2021

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With the shaping of universal computing concept and the development of microelectronics technology, the mobile terminal devices have the strong functions of computing, storage and communication. The opportunistic social networks composed of a large number of terminal devices can be widely used in various scenarios by deploying them anytime and anywhere. One of its most important tasks is to collect data in order to communicate between people and things. The existing researches mainly focus on routing strategies that aim to improve the performance of data collection by optimizing the routing algorithm. However, the inherent characteristics of the opportunistic social networks such as the intermittent communication opportunities make it difficult to improve routing performance because nodes can not get global network topology information. In order to solve this problem, we should establish an efficient data collection mechanism based on wavelet multi-resolution to improve the efficiency of data collection from the source, which mainly studies the multi-resolution compression storage method of node data, the spatial multi-resolution data hierarchical storage framework, and the multi-resolution data management mechanism of mobile node. The experimental results show that the multi-resolution communication mode based on integer wavelet transform can greatly reduce the amount of data in the network and the energy consumption of nodes, and it is beneficial to the data collection of opportunistic social networks.

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Automated Self-Optimization in Heterogeneous Wireless Communications Networks

Cited by 17 publications

References 27 publications

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

Online reinforcement learning for adaptive interference coordination

Data Collection Mechanism Based on Wavelet Multi-Resolution for Opportunistic Social Networks

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