A Soft Computing Approach to Dynamic Load Balancing in 3GPP LTE

Atayero, Aderemi A.; Luka, Matthew K.

doi:10.5120/6213-8895

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…The use of a soft computing technique that is based on the hybrid of Neural Network (NN) and Fuzzy Logic (FL) for load balancing in mobile communication was proposed in [22]. An artificial intelligence (AI) based on three calculated parameters; the virtual load of the serving BS, the number of unsatisfied users in the serving BS and the overall load state of the target BS, were used to determine the handover hysteresis margin adaptively.…”

Section: Review Of Related Workmentioning

confidence: 99%

Dynamic Load Balancing for Congestion Avoidance using Adaptive Neuro-Fuzzy Inference System in Mobile Communication Network

Collins

Chukwuchekwa

Sunday

2022

Int. j. electr. comput. eng. syst. (Online)

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Network congestion is one of the key challenges to mobile communication services. This is because of rapid and constant growth in the mobile subscriber base that has led to an increase in network traffic. The more the network traffic the more economic opportunity from the business perspective for the operators and the challenges it poses on the network. If not followed by network capacity expansion it will defiantly pose a serious network issue like network congestion that leads to call drop and poor Quality of Service (QoS). Many research works adopted handover mechanism as a means of reducing congestion in a network. However, the decision on when to initiate a handover, which cell to receive the Mobile Station (MS) and how to ensure that the QoS requirements are maintained are the paramount research questions that must be resolved. Therefore, a handover process using soft computing in the Adaptive Neuro-Fuzzy Inference System (ANFIS) to ensure balanced traffic load distribution in-network and reduce the probability of congestion is proposed. The study allows simultaneous evaluation of three major network parameters Received Signal Strength (RSS), Received Signal Quality (RxQual) and network traffic using ANFIS to improve system performance. The results show that when the Hysteresis value approaches 6 the handover processes are triggered. The hysteresis value of the concerned MS with neighbouring cells is considered to determine the most suitable cell to handover. This work will be able to achieve dynamic load balancing, congestion avoidance and avoided the ‘ping pong’ effect that is often an issue with handover with less computation. At the end customer satisfaction will be achieve Quality of experience (QoE).

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Section: Review Of Related Workmentioning

confidence: 99%

Dynamic Load Balancing for Congestion Avoidance using Adaptive Neuro-Fuzzy Inference System in Mobile Communication Network

Collins

Chukwuchekwa

Sunday

2022

Int. j. electr. comput. eng. syst. (Online)

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“…To properly understand the significance and potential of soft computing research, it is essential to have a comprehensive understanding of its evolution and current dynamics. Soft computing can be implemented across a wide variety of industries, including mobile and wireless communication (Atayero and Luka, 2012), agriculture (Huang et al ., 2010), oil industry (Aminzadeh, 2005), transportation (Masegosa et al ., 2018), textile (Sette and Van Langenhove, 2003) and few others. While the application of soft computing methodologies constitutes a viable solution in many industries, this technology has certain limitations (Huang et al ., 2010; Khan and Alotaibi, 2021).…”

Section: Introductionmentioning

confidence: 99%

Soft computing in business: exploring current research and outlining future research directions

Singh

Koohang

et al. 2023

IMDS

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PurposeThe primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.Design/methodology/approachThis research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.FindingsThis study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.Practical implicationsThis analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.Originality/valueThis study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

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Neural-encoded Fuzzy Models for Load Balancing in 3GPP LTE

Atayero¹,

Luka²,

Alatishe³

2012

IJAIS

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Post third generation (3G) broadband mobile networks such as HSPA+, LTE and LTE-Advanced offer improved spectral efficiency and higher data rates using innovative technologies such as relay nodes and femto cells. In addition, these networks are normally deployed for parallel operation with existing heterogeneous networks. This increases the complexity of network management and operations, which reflects in higher operational and capital cost. In order to address these challenges, self-organizing network operations were envisioned for these next generation networks. For LTE in particular, Self-organizing networks operations were built into the specifications for the radio access network. Load balancing is a key self-organizing operation aimed at ensuring an equitable distribution of users in the network. Several iterative techniques have been adopted for load balancing. However, these iterative techniques require precision, rigor and certainty, which carry a computational cost. Retrospect, these techniques use load indicators to achieve load balancing. This paper proposes two neural encoded fuzzy models, developed from network simulation for load balancing. The two models use both load indicators and key performance indicators for a more informed and intuitive load balancing. The result of the model checking and testing satisfactorily validates the model. General TermsAccess Network, Broadband, Models, Soft computing Wireless communication. KeywordsLoad balancing, neural network, fuzzy logic, LDI Model, USU Model.

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A Soft Computing Approach to Dynamic Load Balancing in 3GPP LTE

Cited by 3 publications

References 13 publications

Dynamic Load Balancing for Congestion Avoidance using Adaptive Neuro-Fuzzy Inference System in Mobile Communication Network

Dynamic Load Balancing for Congestion Avoidance using Adaptive Neuro-Fuzzy Inference System in Mobile Communication Network

Soft computing in business: exploring current research and outlining future research directions

Neural-encoded Fuzzy Models for Load Balancing in 3GPP LTE

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