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A Predictive Resource Allocation for Wireless Communications Systems.

Márcio J Teixeira1, Varese S Timóteo1

  • 1Grupo de Óptica e Modelagem Numérica - GOMNI, Faculdade de Tecnologia - FT, Universidade Estadual de Campinas - UNICAMP, Limeira, SP Brazil.

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Summary
This summary is machine-generated.

This study introduces a predictive resource allocation scheme using the Kalman Filter for wireless communications. The Kalman Filter improved throughput and spectral efficiency in simulations across various speeds and device numbers.

Keywords:
Event-driven simulationMobile networksPredictive schedulerResource allocation

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Area of Science:

  • Wireless Communications
  • Signal Processing
  • Computer Simulation

Background:

  • Broadband wireless systems require efficient resource allocation for optimal performance.
  • Predictive schemes can enhance network efficiency by anticipating future needs.
  • Kalman Filters are established tools for state estimation in dynamic systems.

Purpose of the Study:

  • To evaluate a predictive resource allocation scheme for broadband wireless communications using the Kalman Filter.
  • To assess the performance of the Kalman Filter integrated into different scheduling algorithms.
  • To analyze the effectiveness of the scheme under diverse mobility and traffic conditions.

Main Methods:

  • Event-driven computer simulations were employed to test the predictive resource allocation scheme.
  • The Kalman Filter was implemented within six distinct scheduling algorithms.
  • The best-performing algorithms (EXP Rule and Frame Level Scheduler) were selected for detailed analysis.
  • Simulations covered velocities from 0 to 350 km/h and device counts from 10 to 250.

Main Results:

  • The Kalman Filter-based predictive scheme demonstrated improved throughput and spectral efficiency.
  • Packet loss rates were significantly decreased with the EXP Rule and Frame Level Scheduler.
  • The scheme showed robust performance across various mobility scenarios (pedestrian, vehicular, high-speed train).

Conclusions:

  • The predictive resource allocation scheme based on the Kalman Filter enhances wireless system performance.
  • The EXP Rule and Frame Level Scheduler are effective when combined with this predictive approach.
  • The proposed predictor is adaptable for centralized resource allocation in diverse wireless networks.