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Throughput and load balancing optimization in heterogeneous networks using Red-Tailed Hawk Algorithm.

I Chandra1, K Ramkumar2, Balaji Maram3

  • 1Department of Electronics and Communication Engineering, Rajalakshmi Institute of Technology, Chennai, Tamilnadu, India. chandraiece@outlook.com.

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Summary
This summary is machine-generated.

This study introduces a Red-Tailed Hawk algorithm with Cell Range Extension to improve wireless network performance. The method enhances Quality of Service by balancing user demands and reducing call drops.

Keywords:
Cell range extensionHeterogeneous networksLoad balancing optimizationRed-Tailed Hawk AlgorithmThroughput optimization

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Area of Science:

  • Wireless Communication Networks
  • Network Optimization
  • Telecommunications Engineering

Background:

  • Increasing data demands and Quality of Service (QoS) needs strain wireless networks.
  • Heterogeneous Networks (HetNets) face challenges due to traffic surges and imbalanced cell association.
  • Traditional methods struggle to meet diverse user traffic needs in complex network environments.

Purpose of the Study:

  • To maximize the number of users with fulfilled downlink demands in wireless networks.
  • To address network imbalances and improve overall Quality of Service (QoS) in Heterogeneous Networks (HetNets).
  • To develop an optimized Cell Range Extension (CRE) strategy for Small Base Stations (SBSs).

Main Methods:

  • Integration of a Red-Tailed Hawk (RTH) algorithm with a Cell Range Extension (CRE) approach.
  • Formulation of a fitness function considering base station workload and user Signal to Interference-plus-Noise Ratio (SINR).
  • Determination of appropriate CRE bias values for individual Small Base Stations (SBSs).

Main Results:

  • Significant improvements in load balancing (56.67%) and user throughput (49.23%).
  • Drastic reduction in call drop rates (91.49%) and network delay (92.68%).
  • Enhanced execution time (77.55%) and convergence rate (20.11%) compared to conventional methods.

Conclusions:

  • The proposed RTH algorithm with CRE effectively meets user throughput requirements while minimizing network imbalances.
  • The methodology significantly reduces call drop rates and improves overall network efficiency.
  • Experimental results validate the superiority of the proposed model in optimizing wireless network performance.