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Computational Intelligence in Future Wireless and Mobile Communications by Employing Channel Prediction Technology

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Next Generation Data Technologies for Collective Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 352))

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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.

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Author information

Authors and Affiliations

  1. School of Computer and Communication Engineering, Universiti Malaysia Perlis, Perlis, Malaysia

    Abid Yahya, Farid Ghani & R. B. Ahmad

  2. Collaborative Microelectronic Design Excellence Center, Universiti Sains Malaysia, 14300, Nibong Tebal, Pulau Penang, Malaysia

    Othman Sidek

  3. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300, Nibong Tebal, Pulau Penang, Malaysia

    M. F. M. Salleh

  4. Department of Computer Systems Engineering, NWFP University of Engineering & Technology, Peshawar, Pakistan

    Khawaja M. Yahya

Authors
  1. Abid Yahya
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  2. Farid Ghani
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  3. Othman Sidek
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  4. R. B. Ahmad
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  5. M. F. M. Salleh
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  6. Khawaja M. Yahya
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Editor information

Editors and Affiliations

  1. School of Computing & Maths, University of Derby, DE22 1GB, Derby, United Kingdom (UK)

    Nik Bessis

  2. Dept de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Barcelona, Spain

    Fatos Xhafa

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© 2011 Springer-Verlag Berlin Heidelberg

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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: Bessis, N., Xhafa, F. (eds) Next Generation Data Technologies for Collective Computational Intelligence. Studies in Computational Intelligence, vol 352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20344-2_9

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  • DOI: https://doi.org/10.1007/978-3-642-20344-2_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20343-5

  • Online ISBN: 978-3-642-20344-2

  • eBook Packages: EngineeringEngineering (R0)

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