18 results on '"handover margin"'
Search Results
2. A comparative study of machine learning-based load balancing in high-speed
- Author
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Emre Gures, Ibrahim Yazici, Ibraheem Shayea, Muntasir Sheikh, Mustafa Ergen, and Ayman A. El-Saleh
- Subjects
Load balancing ,Machine learning ,High-speed railways ,Regression ,Handover margin ,Time-to-Trigger ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
With the rapid developments of fifth generation (5G) mobile communication networks in recent years, different use cases can now significantly benefit from 5G networks. One such example is high-speed trains found in several countries across the world. Due to the dense deployment of 5G millimetre wave (mmWave) base stations (BSs) and the high speed of moving trains, frequent handovers (HOs) occur which adversely affect the Quality-of-Service (QoS) of mobile users. User association for load balancing is also a key issue in 5G networks. Therefore, HO optimisation and resource allocation are major challenges in the mobility management of high-speed train systems. Handover Margin (HOM) and Time-to-Trigger (TTT) parameters are crucial for the HO process since they affect the key performance indicators (KPIs) of high-speed train systems in 5G networks. To manage system performance from the aspect of predictive analytics, we have modelled system performance of mobility management through machine learning (ML). First, the HO management process of a high-speed train scenario is framed as a supervised ML problem. The inputs for the problem are regression task, HOM and TTT and the outputs are key performance indicators (KPIs). Second, data processing is accomplished after generating a simulation dataset. Several methods are employed for the dataset, such as Adaptive Boosting (AdaBoost), Gradient Boosting Regression (GBR), CatBoost Regression (CBR), Support Vector Regression (SVR), Multi-layer Perceptron (MLP), Kernel Ridge Regression (KRR) and K-Nearest Neighbour Regression (KNNR). Tenfold cross validation is then applied for choosing the best hyperparameters. Finally, the deployed methods are compared in terms of the Mean Absolute Error (MAE), Mean Square Error (MSE), Maximum Error (Max E), and R2 score metrics. From the MAE results, CBR achieves the best outcomes for load level and throughput KPIs with 0.003 and 0.0144, respectively. On the other hand, GBR achieves the best results for call dropping ratio (CDR), radio link failure (RLF) and spectral efficiency KPIs with 0.354, 0.082 and 0.354, respectively. CBR also follows GBR for the three KPIs with 0.356, 0.082 and 0.357, respectively. Only a slight difference in estimations is present. MLP achieves the best results for HO ping-pong (HOPP) and HO probability (HOP) KPIs with 0.0045 and 0.011, respectively. This is followed by GBR and CBR. From the MSE outcomes, CBR and GBR exhibit the best results for load level and throughput KPIs with 2e-5 and 3e-5, respectively. GBR attains the best results for CDR, RLF and spectral efficiency KPIs with 0.25, 0.011 and 0.025, respectively. Accordingly, CBR follows GBR with slightly different errors for the three KPI estimations. MLP achieves the best results for HOPP and HOP KPIs with 5e-5 and 3.6e-5, respectively. Again, this is followed by GBR and CBR for the estimation of these results. This indicates that CBR and GBR can capture relationships between inputs and KPIs for the dataset used in this study, outperforming all other methods generally used for solving this problem. more...
- Published
- 2023
- Full Text
- View/download PDF
Catalog
3. A comparative study of machine learning-based load balancing in high-speed train system.
- Author
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Gures, Emre, Yazici, Ibrahim, Shayea, Ibraheem, Sheikh, Muntasir, Ergen, Mustafa, and El-Saleh, Ayman A.
- Subjects
5G networks ,HIGH speed trains ,KEY performance indicators (Management) ,LOADERS (Machines) ,ROAMING (Telecommunication) ,TELECOMMUNICATION systems ,MACHINE learning - Abstract
With the rapid developments of fifth generation (5G) mobile communication networks in recent years, different use cases can now significantly benefit from 5G networks. One such example is high-speed trains found in several countries across the world. Due to the dense deployment of 5G millimetre wave (mmWave) base stations (BSs) and the high speed of moving trains, frequent handovers (HOs) occur which adversely affect the Quality-of-Service (QoS) of mobile users. User association for load balancing is also a key issue in 5G networks. Therefore, HO optimisation and resource allocation are major challenges in the mobility management of high-speed train systems. Handover Margin (HOM) and Time-to-Trigger (TTT) parameters are crucial for the HO process since they affect the key performance indicators (KPIs) of high-speed train systems in 5G networks. To manage system performance from the aspect of predictive analytics, we have modelled system performance of mobility management through machine learning (ML). First, the HO management process of a high-speed train scenario is framed as a supervised ML problem. The inputs for the problem are regression task, HOM and TTT and the outputs are key performance indicators (KPIs). Second, data processing is accomplished after generating a simulation dataset. Several methods are employed for the dataset, such as Adaptive Boosting (AdaBoost), Gradient Boosting Regression (GBR), CatBoost Regression (CBR), Support Vector Regression (SVR), Multi-layer Perceptron (MLP), Kernel Ridge Regression (KRR) and K-Nearest Neighbour Regression (KNNR). Tenfold cross validation is then applied for choosing the best hyperparameters. Finally, the deployed methods are compared in terms of the Mean Absolute Error (MAE), Mean Square Error (MSE), Maximum Error (Max E), and R
2 score metrics. From the MAE results, CBR achieves the best outcomes for load level and throughput KPIs with 0.003 and 0.0144, respectively. On the other hand, GBR achieves the best results for call dropping ratio (CDR), radio link failure (RLF) and spectral efficiency KPIs with 0.354, 0.082 and 0.354, respectively. CBR also follows GBR for the three KPIs with 0.356, 0.082 and 0.357, respectively. Only a slight difference in estimations is present. MLP achieves the best results for HO ping-pong (HOPP) and HO probability (HOP) KPIs with 0.0045 and 0.011, respectively. This is followed by GBR and CBR. From the MSE outcomes, CBR and GBR exhibit the best results for load level and throughput KPIs with 2e-5 and 3e-5, respectively. GBR attains the best results for CDR, RLF and spectral efficiency KPIs with 0.25, 0.011 and 0.025, respectively. Accordingly, CBR follows GBR with slightly different errors for the three KPI estimations. MLP achieves the best results for HOPP and HOP KPIs with 5e-5 and 3.6e-5, respectively. Again, this is followed by GBR and CBR for the estimation of these results. This indicates that CBR and GBR can capture relationships between inputs and KPIs for the dataset used in this study, outperforming all other methods generally used for solving this problem. [ABSTRACT FROM AUTHOR] more...- Published
- 2023
- Full Text
- View/download PDF
4. A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions.
- Author
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Rehman, Aziz Ur, Roslee, Mardeni Bin, and Jun Jiat, Tiang
- Subjects
ROAMING (Telecommunication) ,5G networks ,ALGORITHMS - Abstract
With the rapid growth of data traffic and mobile devices, it is imperative to provide reliable and stable services during mobility. Heterogeneous Networks (HetNets) and dense networks have been identified as potential solutions to address the upcoming capacity crunch, but they also pose significant challenges related to handover optimization. This paper presents a comprehensive review of recent handover decision algorithms in HetNets, categorizing them based on their decision techniques and summarizing their input parameters, techniques, and performance evaluations. Our study highlights the technical challenges and opportunities related to handovers in HetNets and dense cellular networks and provides key findings from recent studies. The significance of this survey is to provide a comprehensive overview of handover decision algorithms in HetNets and dense cellular networks, which can aid in the development of more advanced handover optimization approaches. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
- Full Text
- View/download PDF
5. Advanced Mobility Robustness Optimization Models in Future Mobile Networks Based on Machine Learning Solutions
- Author
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Waheeb Tashan, Ibraheem Shayea, Sultan Aldirmaz-Colak, Omar Abdul Aziz, Abdulraqeb Alhammadi, and Yousef Ibrahim Daradkeh
- Subjects
Machine learning ,handover ,self-optimization ,mobility robustness optimization ,handover margin ,time-to-trigger ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Ultra-dense heterogeneous networks (HetNets) are deployment scenarios in the advent of fifth generation (5G) and beyond network generations. A massive number of small base stations (SBSs) and connected devices have been exponentially increasing. This has subsequently led to a rise of several mobility management issues which require optimization techniques to avoid performance degradation. Machine learning (ML) is a promising approach for future mobile communication networks (5G and beyond). It has the ability of improving the efficiency of complicated heterogeneous and decentralized networks. ML has proven to be significant in the mobility management field since it optimizes handover control parameters (HCPs) over various dynamic environments. To the best of the authors’ knowledge, no comprehensive survey deeply discussing a state-of-the-art ML algorithms in mobility robustness optimization (MRO) functions. However, each summarized algorithm in this study includes deployment scenario, ML type, methodology used, criteria, HCPs, key performance indicators (KPIs), simulators, and achievements which can assist researchers for future investigations in MRO functions. In addition, this study serves as a guide in the selection of proper optimization algorithms according to the outcomes of each algorithm. Furthermore, this study presented the common types of ML and the techniques used from each type to optimize the HCPs of the MRO functions. Moreover, high-mobility-aware and network topologies are presented in MRO function for further system enhancements. Besides, the survey further highlights several potential problems for upcoming research and provides future directions to address the issues of next generation wireless networks. more...
- Published
- 2022
- Full Text
- View/download PDF
6. Mobility Robustness Optimization in Future Mobile Heterogeneous Networks: A Survey
- Author
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Waheeb Tashan, Ibraheem Shayea, Sultan Aldirmaz-Colak, Mustafa Ergen, Marwan Hadri Azmi, and Abdulraqeb Alhammadi
- Subjects
Handover ,handover control parameter ,handover margin ,handover parameter optimization ,handover self-optimization ,heterogeneous networks ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Ensuring reliable and stable communication during the movements of mobile users is one of the key issues in mobile networks. In the recent years, several studies have been conducted to address the issues related to Handover (HO) self-optimization in Heterogeneous Networks (HetNets) for Fourth Generation (4G) and Fifth Generation (5G) mobile networks. Various solutions have been developed to determine or estimating the optimum and ideal settings of Handover Control Parameters (HCPs), such as Time-To-Trigger (TTT) and Handover Margin (HOM). However, the complexity, high requirements, and the upcoming structure of ultra-dense HetNets require more advanced HO self-optimization techniques for future implementation. This paper studies HO self-optimization techniques that may implemented in the next-generation mobile HetNets by reviewing state-of-the-art algorithms. The solutions discussed in this survey are more focus on Mobility Robustness Optimization (MRO), which is a significant self-optimization function in 4G and 5G mobile networks. The applied solutions will preserve the continuous connection between the User Equipment (UE) and eNBs during UE mobility, thereby enhancing connection quality. The various algorithms and techniques applied to HO have revealed different outcomes. This paper discusses the pros and cons of these techniques, and further examines HO self-optimization challenges and solutions. New future directions for the implementation of HO self-optimization are also identified. This survey will contribute to the understanding of the issues related to mobility management, particularly in relation to the self-optimization of HO control parameters in future mobile HetNets. more...
- Published
- 2022
- Full Text
- View/download PDF
7. Individualistic Dynamic Handover Parameter Self-Optimization Algorithm for 5G Networks Based on Automatic Weight Function
- Author
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Ibraheem Shayea, Mustafa Ergen, Azizul Azizan, Mahamod Ismail, and Yousef Ibrahim Daradkeh
- Subjects
Handover parameter optimization ,mobility robustness optimization ,self-optimization algorithm ,handover control parameters ,Hysteresis ,handover margin ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Ensuring a reliable and stable communication throughout the mobility of User Equipment (UE) is one of the key challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. One of the main issues is the use of suboptimal Handover Control Parameters (HCPs) settings, which are configured manually or generated automatically by certain self-optimization functions. This issue becomes more critical with the massive deployment of small base stations and connected mobile users. This will essentially require an individual handover self-optimization technique for each user individually instead of a unified and centrally configured setting for all users in the cell. In this paper, an Individualistic Dynamic Handover Parameter Optimization algorithm based on an Automatic Weight Function (IDHPO-AWF) is proposed for 5G networks. This algorithm dynamically estimates the HCPs settings for each individual UE based on UE's experiences. The algorithm mainly depends on three bounded functions and their Automatic Weights levels. First, the bounded functions are evaluated, independently, as a function of the UE's Signal-to-Interference-plus-Noise-Ratio (SINR), cells' load and UE's speed. Next, the outputs of the three bounded functions are used as inputs in a new proposed Automatic Weight Function (AWF) to estimate the weight of each output bounded function. After that, the final output is used as an indicator for optimizing HCPs settings automatically for a specific user. The algorithm is validated throughout various mobility conditions in the 5G network. The performance of the analytical HCPs estimation method is investigated and compared with other handover algorithms from the literature. The evaluation comparisons are performed in terms of Reference Signal Received Power (RSRP), Handover Probability (HOP), Handover Ping-Pong Probability (HPPP), and Radio Link Failure (RLF). The simulation results show that the proposed algorithm provides noticeable enhancements for various mobile speed scenarios as compared to the existing Handover Parameter Self-Optimization (HPSO) algorithms. more...
- Published
- 2020
- Full Text
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8. An Enhanced Mobility State Estimation Based Handover Optimization Algorithm in LTE-A Self-organizing Network.
- Author
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Nie, Shiwen, Wu, Di, Zhao, Ming, Gu, Xinyu, Zhang, Lin, and Lu, Liyang
- Subjects
ESTIMATION theory ,MATHEMATICAL optimization ,ALGORITHMS ,SELF-organizing maps ,LONG-Term Evolution (Telecommunications) - Abstract
Heterogeneous network (HetNet) is considered as a prime way to solve the limits of system capacity and broadband service coverage in traditional network. However, the deployments of small cells with varied sizes make the network topology more complicated. Self-organizing network (SON) technology, aiming to reduce the operational costs, is a significant technology in HetNet. One of the common use cases is to improve handover performance. In this paper, a handover optimization algorithm based on enhanced mobility state estimation (EMSE) is proposed. Considering both user equipment (UE) speed and handover types, the optimization algorithm based on EMSE combines selective Time-to-Trigger (TTT) and dynamic handover margin (HM)-adjusting in SON. Furthermore, the algorithm performance is compared with two different reference cases. Simulation results show that total handover failure has an obvious decline with our self-optimizing algorithm. Therefore, handover performance gets improved and UEs have better mobility robustness in HetNet through our algorithm. [ABSTRACT FROM AUTHOR] more...
- Published
- 2015
- Full Text
- View/download PDF
9. Handover self-optimization mechanism based on velocity for cellular networks.
- Author
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Yang Yang, Peng Yu, and Wenjing Li
- Abstract
Quality of Handover (QoH) is a crucially important performance indicator for cellular networks. With the increasing requirement of users, the velocity of users should be highly valued, because it has great effect on handover process and user experience. In this paper, we propose Handover Self-optimization Mechanism based on Velocity (HSMV), a novel handover mechanism which can improve the performance: firstly, some original data should be extracted from the mobile devices and a mathematic model will be proposed to estimate the velocity of users using Received Signal Strength (RSS); secondly, the relation between handover margin (HM) and the estimated velocity is concluded based on the model, then the value of HM will be adjusted accordingly; finally, the assessment method to estimate velocity as well as tackle with some handover problems will be validated. Simulation results show that it can not only gain relatively accurate velocity but also decrease the call drop rate significantly. [ABSTRACT FROM PUBLISHER] more...
- Published
- 2012
- Full Text
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10. Self Organizing Networks: A Reinforcement Learning approach for self-optimization of LTE Mobility parameters.
- Author
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Tiwana, Moazzam Islam
- Subjects
ROAMING (Telecommunication) ,LONG-Term Evolution (Telecommunications) ,SELF-organizing systems ,REINFORCEMENT learning ,FUZZY systems ,MULTIAGENT systems - Abstract
Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...
- Published
- 2014
- Full Text
- View/download PDF
11. A Novel Framework of Automated RRM for LTE SON Using Data Mining: Application to LTE Mobility.
- Author
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Tiwana, Moazzam and Tiwana, Mohsin
- Subjects
- *
DATA mining , *LONG-Term Evolution (Telecommunications) , *RADIO resource management , *REGRESSION analysis , *COMPUTER network monitoring - Abstract
With the evolution of broadband mobile networks towards LTE and beyond, the support for the internet and internet based services is growing. However, the size and operational costs of mobile networks are also growing. Self Organizing Networks (SON) are introduced as a part of the specifications of the LTE standard with the purpose of reducing the Operation and Maintenance costs of the mobile networks. This paper introduces a novel framework for automated Radio Resource Management (RRM) in LTE SON. This framework deals with the self-optimization and self-healing features of SON. The data mining technique of linear regression has been used to derive the functional relationship, known as model, between Key Performance Indicators and RRM parameters. The proposed framework uses this model in two ways: first, for network monitoring, which is the first step of the self-healing procedure and secondly, to devise a handover auto-tuning algorithm as part of the self-optimization procedure. The detailed results obtained for the finished case studies, demonstrate the effectiveness and usefulness of this approach. [ABSTRACT FROM AUTHOR] more...
- Published
- 2014
- Full Text
- View/download PDF
12. An Enhanced Mobility State Estimation Based Handover Optimization Algorithm in LTE-A Self-organizing Network
- Author
-
Xinyu Gu, Di Wu, Lin Zhang, Shiwen Nie, Liyang Lu, and Ming Zhao
- Subjects
HetNet ,Computer science ,business.industry ,handover margin ,Self-organizing network ,Network topology ,LTE Advanced ,EMSE ,Handover ,User equipment ,Broadband ,self-organizing network ,General Earth and Planetary Sciences ,business ,handover optimization ,Heterogeneous network ,General Environmental Science ,Computer network - Abstract
Heterogeneous network (HetNet) is considered as a prime way to solve the limits of system capacity and broadband service coverage in traditional network. However, the deployments of small cells with varied sizes make the network topology more complicated. Self-organizing network (SON) technology, aiming to reduce the operational costs, is a significant technology in HetNet. One of the common use cases is to improve handover performance. In this paper, a handover optimization algorithm based on enhanced mobility state estimation (EMSE) is proposed. Considering both user equipment (UE) speed and handover types, the optimization algorithm based on EMSE combines selective Time-to-Trigger (TTT) and dynamic handover margin (HM)-adjusting in SON. Furthermore, the algorithm performance is compared with two different reference cases. Simulation results show that total handover failure has an obvious decline with our self-optimizing algorithm. Therefore, handover performance gets improved and UEs have better mobility robustness in HetNet through our algorithm. more...
- Published
- 2015
- Full Text
- View/download PDF
13. Traffic steering by self-tuning controllers in enterprise LTE femtocells
- Author
-
Ruiz-Avilés, Jose Maria, Luna-Ramírez, Salvador, Toril, Matias, and Ruiz, Fernando
- Published
- 2012
- Full Text
- View/download PDF
14. Self Organizing Networks: A Reinforcement Learning approach for self-optimization of LTE Mobility parameters
- Author
-
Moazzam Islam Tiwana
- Subjects
Handover Margin ,LTE ,Reinforcement Learning ,Fuzzy Q-Learning ,SON ,margina primopredaje ,podržano učenje ,neizrazito Q-učenje ,samoorganizirajuće mreže - Abstract
With the evolution of broadband mobile networks towards LTE and beyond, the support for the Internet and Internet based services is growing. Self Organizing Network (SON) functionalities intend to optimize the network performance for the improved user experience while at the same time reducing the network operational cost. This paper proposes a Reinforcement Learning (RL) based framework to improve throughput of the mobile users. The problem of spectral efficiency maximization is modeled as co-operative Multi-Agent control problem between the neighbouring eNodeBs (eNBs). Each eNB has an associated agent that dynamically changes the outgoing Handover Margin (HM) to its neighbouring cells. The agent uses the RL technique of Fuzzy Q-Learning (FQL) to learn the optimal mobility parameter i.e., HM value. The learning framework is designed to operate in an environment with the variations in traffic, user positions and propagation conditions. Simulation results have shown the proposed approach improves the network capacity and user experiences in terms of throughput., Razvoj širokopojasne mobilne mreže prema LTE mrežama uvjetuje pojačani rast internetskih servisa i usluga. Samoorganizirajuće mreže namijenjene su optimizaciji performansi mreže s ciljem poboljšanja korisnikovog zadovoljstva i smanjenja troškova rada. U radu se predlaže pristup zasnovan na podržanom učenju kako bi se popravila propusnost mobilnog korisnika. Problem maksimizacije spektralne učinkovitosti modelira se kao kooperativni više agentski problem upravljanje između susjednih čvorova (eNBs). Svaki čvor ima pridruženog agenta koji dinamički mijenja marginu primopredaje prema susjednim ćelijama. Agent koristi tehniku neizrazitog Q učenja (FQL) kako bi naučio optimizirati parametre mreže. Učenje je organizirano za rad u uvjetima raznovrsnog prometa, korisničkih položaja i uvjeta propagacije. Simulacijski rezultati pokazuju kako predloženi pristup poboljšava kapacitet mreže i korisnički doživljaj u smislu propusnosti mreže. more...
- Published
- 2014
15. Particle Swarm Optimization for Mobility Load Balancing SON in LTE Networks
- Author
-
Altman, Zwi, Sallem, Soumaya, Nasri, Ridha, Sayrac, Berna, Clerc, Maurice, Orange Labs [Issy les Moulineaux], France Télécom, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), REP/REM, France Télécom-France Télécom, and Chercheur indépendant more...
- Subjects
LTE ,[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] ,Self-Organizing Networks ,Mobility Load Balancing ,Particle Swarm Optimization ,handover margin ,SON - Abstract
This paper presents a self-optimizing solution for Mobility Load Balancing (MLB). The MLB-SON is performed in two phases. In the first, a MLB controller is designed using Multi-Objective Particle Swarm Optimization (MO-PSO) which incorporates a priori expert knowledge to considerably reduce the search space and optimization time. The dynamicity of the optimization phase is addressed. In the second phase, the controller is pushed into the base stations to implement the MLB SON. The method is applied to dynamically adapt Handover Margin parameters of a large scale LTE network in order to balance traffic of the network eNodeBs. Numerical results illustrate the benefits of the proposed solution. more...
- Published
- 2013
16. Statistical learning-based automated healing : application to mobility in 3G LTE networks
- Author
-
Berna Sayrac, Tijani Chahed, Moazzam Islam Tiwana, Zwi Altman, Orange Labs [Issy les Moulineaux], France Télécom, Méthodes et modèles pour les réseaux (METHODES-SAMOVAR), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Département Réseaux et Services de Télécommunications (RST), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), and Centre National de la Recherche Scientifique (CNRS) more...
- Subjects
Automated troubleshooting ,Computer science ,Logistic regression ,050801 communication & media studies ,02 engineering and technology ,Troubleshooting ,Machine learning ,computer.software_genre ,3G LTE ,[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] ,0508 media and communications ,Margin (machine learning) ,0202 electrical engineering, electronic engineering, information engineering ,Radio resource management ,Hidden Markov model ,Handover margin ,Mobility ,business.industry ,Wireless network ,05 social sciences ,ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS ,020206 networking & telecommunications ,Statistical learning ,Network management ,Handover ,Artificial intelligence ,Performance indicator ,business ,computer - Abstract
International audience; Troubleshooting of wireless networks is a challenging network management task. We have developed, in a previous work, a new troubleshooting methodology, which we named Statistical Learning Automated Healing (SLAH). This methodology uses statistical learning, in particular logistic regression, to extract the functional relationships between the noisy Key Performance Indicators (KPIs) and Radio Resource Management (RRM) parameters. These relationships are then processed by an optimization engine so as to calculate the optimized RRM parameters which improve the KPIs of a degraded cell. The process is iterative and converges to the optimum RRM parameter value in few iterations, which makes it suitable for wireless networks. The present work focuses on the adaptation of SLAH for troubleshooting the mobility parameter, namely the handover margin, in 3G Long Term Evolution (LTE) networks. The simulation results, which we obtain for a practical use case, show the advantage of this new, automated troubleshooting methodology more...
- Published
- 2010
- Full Text
- View/download PDF
17. Soft Handover and Power Control in Loaded UMTS Networks
- Author
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Zrno, Damir, Vasilić, Zoran, Šimunić, Dina, and World Wireless Research Forum
- Subjects
UMTS ,soft handover ,handover margin ,power control ,loaded network - Abstract
This paper presents results in soft handover and uplink power control for UMTS obtained by simulations. The simulations were done using created map that covers all major propagation environments. The objective was to determine the behaviour of uplink power control and the influence of soft handover on network performance in a loaded network depending on the soft handover margin. Simulations were done without soft handover, and for 3 dB and 6 dB margins, with various network loading. It was found that the 3 dB handover margin provides optimum. more...
- Published
- 2003
18. Individualistic Dynamic Handover Parameter Self-Optimization Algorithm for 5G Networks Based on Automatic Weight Function
- Author
-
Azizul Azizan, Mustafa Ergen, Ibraheem Shayea, Yousef Ibrahim Daradkeh, and Mahamod Ismail
- Subjects
Weight function ,self-optimization algorithm ,General Computer Science ,Computer science ,Hysteresis ,General Engineering ,020206 networking & telecommunications ,020302 automobile design & engineering ,handover margin ,02 engineering and technology ,mobility robustness optimization ,Self-optimization ,Hop (networking) ,Handover parameter optimization ,Base station ,0203 mechanical engineering ,User equipment ,Handover ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Algorithm ,lcsh:TK1-9971 ,5G ,handover control parameters - Abstract
Ensuring a reliable and stable communication throughout the mobility of User Equipment (UE) is one of the key challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. One of the main issues is the use of suboptimal Handover Control Parameters (HCPs) settings, which are configured manually or generated automatically by certain self-optimization functions. This issue becomes more critical with the massive deployment of small base stations and connected mobile users. This will essentially require an individual handover self-optimization technique for each user individually instead of a unified and centrally configured setting for all users in the cell. In this paper, an Individualistic Dynamic Handover Parameter Optimization algorithm based on an Automatic Weight Function (IDHPO-AWF) is proposed for 5G networks. This algorithm dynamically estimates the HCPs settings for each individual UE based on UE’s experiences. The algorithm mainly depends on three bounded functions and their Automatic Weights levels. First, the bounded functions are evaluated, independently, as a function of the UE’s Signal-to-Interference-plus-Noise-Ratio (SINR), cells’ load and UE’s speed. Next, the outputs of the three bounded functions are used as inputs in a new proposed Automatic Weight Function (AWF) to estimate the weight of each output bounded function. After that, the final output is used as an indicator for optimizing HCPs settings automatically for a specific user. The algorithm is validated throughout various mobility conditions in the 5G network. The performance of the analytical HCPs estimation method is investigated and compared with other handover algorithms from the literature. The evaluation comparisons are performed in terms of Reference Signal Received Power (RSRP), Handover Probability (HOP), Handover Ping-Pong Probability (HPPP), and Radio Link Failure (RLF). The simulation results show that the proposed algorithm provides noticeable enhancements for various mobile speed scenarios as compared to the existing Handover Parameter Self-Optimization (HPSO) algorithms. more...
- Full Text
- View/download PDF
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