Abstract
The growth of wireless multimedia applications has increased demand for efficient utilization of scarce spectrum resources which is being realized through technologies such as Dynamic Spectrum Access, source and channel coding, distributed streaming and multicast. Using a mix of DSA and channel coding, we propose an efficient power and channel allocation framework for cognitive radio network to place multimedia data of opportunistic Secondary Users over the unused parts of radio spectrum without interfering with licensed Primary Users. We model our method as an optimization problem which determines achievable physical transmission parameters and distributes available spectrum resources among competing secondary devices. We also consider noise contributions and channel capacity as design factors. We use Luby Transform codes for encoding multimedia traffic in order to reduce dependencies involved in distributing data over multiple channels, mitigate Primary User interference and compensate channel noise and distortion caused by sudden arrival of Primary devices. Tradeoffs between number of competing users, coding overhead, available spectrum resources and fairness in channel allocation have also been studied. We also analyze the effect of number of available channels and coding overhead on quality of media content. Simulation results of the proposed framework show improved gain in-terms of PSNR of multimedia content; hence better media quality achieved strengthens the efficacy of proposed model.
This research was supported in part by Higher Education Commission Pakistan grants National Research Program for Universities:1667 and 1668, King Abdul Aziz City for Science and Technology (KACST) grants: NPST-11-INF1688-10 & NPST-10-ELE1238-10 and National ICTRDF Pakistan grant SAHSE-11.
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Ali, S., Munir, A., Qaisar, S.B., Qadir, J. (2012). A Genetic Algorithm Assisted Resource Management Scheme for Reliable Multimedia Delivery over Cognitive Networks. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31137-6_27
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DOI: https://doi.org/10.1007/978-3-642-31137-6_27
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