Publication detail

WiMAX Parameters Adaptation Through A Baseband Processor Using Discrete Particle Swarm Method

AL-SHERBAZ, A. KUSELER, T. ADAMS, C. MARŠÁLEK, R. POVALAČ, K.

Original Title

WiMAX Parameters Adaptation Through A Baseband Processor Using Discrete Particle Swarm Method

English Title

WiMAX Parameters Adaptation Through A Baseband Processor Using Discrete Particle Swarm Method

Type

journal article - other

Language

en

Original Abstract

The measurements of physical level parameters can become the area where decisions about cognitive radio will have the most striking effect. FPGA enables real time analyses of physical layer data to satisfy constraints like dynamic spectrum allocations, data throughput and the coding rate. Cognitive radio will be based on simple network management techniques, using remote procedure calls. Intelligent Knowledge-Base System (IKBS) techniques will be used to search the parameter space in selecting changes to the system. WiMAX PHY-layer functions will be managed cognitively by a FPGA based controller to optimise the performance of the system. Instead of simple bit loading methods, the global multi-criteria optimisation promise possibility to adapt more parameters with respect to several objectives. In this paper the application of particle swarm optimisation to fixed WiMAX-OFDM parameter adaptation is presented and compared with the greedy bit loading algorithm.

English abstract

The measurements of physical level parameters can become the area where decisions about cognitive radio will have the most striking effect. FPGA enables real time analyses of physical layer data to satisfy constraints like dynamic spectrum allocations, data throughput and the coding rate. Cognitive radio will be based on simple network management techniques, using remote procedure calls. Intelligent Knowledge-Base System (IKBS) techniques will be used to search the parameter space in selecting changes to the system. WiMAX PHY-layer functions will be managed cognitively by a FPGA based controller to optimise the performance of the system. Instead of simple bit loading methods, the global multi-criteria optimisation promise possibility to adapt more parameters with respect to several objectives. In this paper the application of particle swarm optimisation to fixed WiMAX-OFDM parameter adaptation is presented and compared with the greedy bit loading algorithm.

Keywords

WiMAX, Cognitive Radio, Particle Swarm Optimisation, PSO

RIV year

2010

Released

27.04.2010

ISBN

1759-0787

Periodical

International Journal of Microwave and Wireless Technologies

Year of study

2010 (2)

Number

2

State

GB

Pages from

165

Pages to

171

Pages count

7

Documents

BibTex


@article{BUT47686,
  author="Ali {Al-Sherbaz} and Torben {Kuseler} and Chris {Adams} and Roman {Maršálek} and Karel {Povalač}",
  title="WiMAX Parameters Adaptation Through A Baseband Processor Using Discrete Particle Swarm Method",
  annote="The measurements of physical level parameters can become the area where decisions about cognitive radio will have the most striking effect. FPGA enables real time analyses of physical layer data to satisfy constraints like dynamic spectrum allocations, data throughput and the coding rate. Cognitive radio will be based on simple network management techniques, using remote procedure calls. Intelligent Knowledge-Base System (IKBS) techniques will be used to search the parameter space in selecting changes to the system. WiMAX PHY-layer functions will be managed cognitively by a FPGA based controller to optimise the performance of the system. Instead of simple bit loading methods, the global multi-criteria optimisation promise possibility to adapt more parameters with respect to several objectives. In this paper the application of particle swarm optimisation to fixed WiMAX-OFDM parameter adaptation is presented and compared with the greedy bit loading algorithm.",
  chapter="47686",
  journal="International Journal of Microwave and Wireless Technologies",
  number="2",
  volume="2010 (2)",
  year="2010",
  month="april",
  pages="165--171",
  type="journal article - other"
}