WiMAX parameters adaptation through a baseband processor using discrete particle swarm method

Abstract

The measurements of physical level parameters can become the area where decisions about cognitive radio will have the most striking effect. Field-programmable gate array (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. Worldwide Interoperability for Microwave Access (WiMAX) PHY-layer functions will be managed cognitively by a FPGA-based controller to optimize the performance of the system. Instead of simple bit loading methods, the global multi-criteria optimization promise possibility to adapt more parameters with respect to several objectives. In this paper, the application of particle swarm optimization to fixed WiMAX-OFDM (Orthogonal Frequency Division Multiplexing) parameter adaptation is presented and compared with the greedy bit loading algorithm.

A new approach for solving set covering problem using jumping particle swarm optimization method

“…The velocity V t i , called velocity trail, is inspired by the frequency-based memory which records the number of times that a job visits a particular position. Al- Sherbaz et al (2010) used the DPSO method described in Kennedy and Eberhart (1997) for Wimax parameter adaptation through baseband processor, in this DPSO the solution space is the number of bits to represent a particle x t i , This number of bits is determined by the range and the necessary precision of the optimized parameters. Shi et al (2007) presented a novel DPSO based algorithm for Traveling Salesman Problem.…”