Computational intelligence in future wireless and mobile communications by employing channel prediction technology

Abid Yahya, Farid Ghani, Othman Sidek, R. B. Ahmad, M. F.M. Salleh, Khawaja M. Yahya

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This work presents a new scheme for channel prediction in multicarrier frequency hopping spread spectrum (MCFH-SS) system. The technique adaptively estimates the channel conditions and eliminates the need for the system to transmit a request message prior to transmit the packet data. The new adaptive MCFH-SS system employs the Quasi-Cyclic low density parity check (QC-LDPC) codes instead of the regular conventional LDPC codes. In this work performance of the proposed MCFH-SS system with adaptive channel prediction scheme is compared with the fast frequency hopping spread spectrum (FFH-SS) system. The proposed system has full control of that spectrum; it plans for the system to keep off unacceptable adjacent channel interference. When an interferer suddenly changes its carrier, the set of appropriate channels has a large return and resultantly the adjacent channel interference between the systems is reduced. It has been shown from results that the signal power in FFH system exceeds the average by at least 6.54 dB while in the proposed MCFH-SS system signal power exceeds the average only 0.84 dB for 1% (correct use) of the time. The proposed MCFH-SS system is more robust to narrow band interference and multipath fading than the FFH-SS system, because such system requires more perfect autocorrelation function.

Original languageEnglish
Title of host publicationNext Generation Data Technologies for Collective Computational Intelligence
EditorsNik Bessis, Fatos Xhafa
Pages225-250
Number of pages26
DOIs
Publication statusPublished - Aug 4 2011

Publication series

NameStudies in Computational Intelligence
Volume352
ISSN (Print)1860-949X

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Frequency hopping
Artificial intelligence
Communication
Signal systems
Multipath fading
Autocorrelation

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Yahya, A., Ghani, F., Sidek, O., Ahmad, R. B., Salleh, M. F. M., & Yahya, K. M. (2011). Computational intelligence in future wireless and mobile communications by employing channel prediction technology. In N. Bessis, & F. Xhafa (Eds.), Next Generation Data Technologies for Collective Computational Intelligence (pp. 225-250). (Studies in Computational Intelligence; Vol. 352). https://doi.org/10.1007/978-3-642-20344-2_9
Yahya, Abid ; Ghani, Farid ; Sidek, Othman ; Ahmad, R. B. ; Salleh, M. F.M. ; Yahya, Khawaja M. / Computational intelligence in future wireless and mobile communications by employing channel prediction technology. Next Generation Data Technologies for Collective Computational Intelligence. editor / Nik Bessis ; Fatos Xhafa. 2011. pp. 225-250 (Studies in Computational Intelligence).
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Yahya, A, Ghani, F, Sidek, O, Ahmad, RB, Salleh, MFM & Yahya, KM 2011, Computational intelligence in future wireless and mobile communications by employing channel prediction technology. in N Bessis & F Xhafa (eds), Next Generation Data Technologies for Collective Computational Intelligence. Studies in Computational Intelligence, vol. 352, pp. 225-250. https://doi.org/10.1007/978-3-642-20344-2_9

Computational intelligence in future wireless and mobile communications by employing channel prediction technology. / Yahya, Abid; Ghani, Farid; Sidek, Othman; Ahmad, R. B.; Salleh, M. F.M.; Yahya, Khawaja M.

Next Generation Data Technologies for Collective Computational Intelligence. ed. / Nik Bessis; Fatos Xhafa. 2011. p. 225-250 (Studies in Computational Intelligence; Vol. 352).

Research output: Chapter in Book/Report/Conference proceedingChapter

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Yahya A, Ghani F, Sidek O, Ahmad RB, Salleh MFM, Yahya KM. Computational intelligence in future wireless and mobile communications by employing channel prediction technology. In Bessis N, Xhafa F, editors, Next Generation Data Technologies for Collective Computational Intelligence. 2011. p. 225-250. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-642-20344-2_9