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CATECAS: a content-aware transmission efficiency based channel allocation scheme for cognitive radio users with improved QoE

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Abstract

Cognitive radio (CR) is a promising technology for the upcoming 5G communication which addresses opportunistic channel usage for enhanced spectrum utilization. However, Quality of Service (QoS) provisioning is a major challenge for CR Network due to the service interruption and packet error caused by random primary activities. In addition to this, periodic spectrum sensing for primary user protection reduces the effective throughput of the secondary users (SUs). However, to ensure QoS of SUs especially for video application, throughput enhancement is necessary which can be achieved by efficient spectrum sensing and channel allocation policy. As the QoS requirements are different for different secondary applications, we propose a novel content aware channel allocation scheme that enhances the Quality of Experience (QoE) of SUs. At first, the proposed scheme analyzes the QoS requirements of different SUs and prioritizes them. Consequently, the optimum sensing duration is determined to maximize the transmission efficiency and throughput of SUs. Finally, a novel content aware transmission efficiency-based channel assignment scheme (CATECAS) is proposed for SUs, considering the estimated channel quality and QoS requirements concurrently. Extensive performance analysis of CATESCAS on real-time video and file download applications confirms significant QoE improvement for SUs especially for rapid movement type of video application, which is considered as the most critical among different secondary applications.

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References

  1. 1.

    Federal Communications Commission et al. (2002). Spectrum policy task force report, FCC 02-155.

  2. 2.

    5G Infrastructure PPP Association et al. (2015). 5G vision-the 5G infrastructure public private partnership: The next generation of communication networks and services. White Paper, February.

  3. 3.

    Langtry, C. (2016). ITU-R activities on 5G. In IEEE World Forum on the Internet of Things, (pp. 14–16).

  4. 4.

    Jiang, W., & Cao, H. (2015). SIG on cognitive radio for 5G. Retrieved December 2015, from http://cn.committees.comsoc.org/.

  5. 5.

    Zhang, N., Zhou, H., Zheng, K., Cheng, N., Mark, J. W., & Shen, X. (2015). Cooperative heterogeneous framework for spectrum harvesting in cognitive cellular network. IEEE Communications Magazine, 53(5), 60–67.

  6. 6.

    VNI CISCO. (2014). Cisco visual networking index: Forecast and methodology, 2013–2018: Visual networking index (VNI).

  7. 7.

    Nguyen, V. T., Villain, F., & Guillou, Y. L. (2012). Cognitive radio RF: Overview and challenges. VLSI Design, 2012, 1.

  8. 8.

    Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.

  9. 9.

    Amraoui, A., Benmammar, B., Krief, F., & Bendimerad, F. T. (2012). Intelligent wireless communication system using cognitive radio. International Journal of Distributed and Parallel Systems, 3(2), 91.

  10. 10.

    Chair, Z., & Varshney, P. K. (1986). Optimal data fusion in multiple sensor detection systems. IEEE Transactions on Aerospace and Electronic Systems, 1, 98–101.

  11. 11.

    Gardner, W. A. (1991). Exploitation of spectral redundancy in cyclostationary signals. IEEE Signal Processing Magazine, 8(2), 14–36.

  12. 12.

    Hillenbrand, J., Weiss, T. A., & Jondral, F. K. (2005). Calculation of detection and false alarm probabilities in spectrum pooling systems. IEEE Communications Letters, 9(4), 349–351.

  13. 13.

    Ganesan, G., & Li, Y. (2005). Cooperative spectrum sensing in cognitive radio networks. In First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005, (pp. 137–143). IEEE.

  14. 14.

    Ghasemi, A., & Sousa, E. S. (2005). Collaborative spectrum sensing for opportunistic access in fading environments. In First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005., (pp. 131–136). IEEE.

  15. 15.

    Lindeberg, M., Kristiansen, S., Plagemann, T., & Goebel, V. (2011). Challenges and techniques for video streaming over mobile ad hoc networks. Multimedia Systems, 17(1), 51–82.

  16. 16.

    Hassan, M., & Krunz, M. (2007). Video streaming over wireless packet networks: An occupancy-based rate adaptation perspective. IEEE Transactions on Circuits and Systems for Video Technology, 17(8), 1017–1027.

  17. 17.

    Hassan, M., & Krunz, M. (2005). A playback-adaptive approach for video streaming over wireless networks. In GLOBECOM’05. IEEE Global Telecommunications Conference, 2005., (Vol. 6, p. 5). IEEE.

  18. 18.

    Kalman, M., Steinbach, E., & Girod, B. (2004). Adaptive media playout for low-delay video streaming over error-prone channels. IEEE Transactions on Circuits and Systems for Video Technology, 14(6), 841–851.

  19. 19.

    Wang, Y., & Zhu, Q.-F. (1998). Error control and concealment for video communication: A review. Proceedings of the IEEE, 86(5), 974–997.

  20. 20.

    Hassan, M., El-Taruni, M., & Zrae, R. (2015). A joint adaptive modulation and channel coding scheme for multimedia communications over wireless channels. International Journal of Wireless and Mobile Computing, 9(1), 99–114.

  21. 21.

    Kushwaha, H., Xing, Y., Chandramouli, R., & Heffes, H. (2008). Reliable multimedia transmission over cognitive radio networks using fountain codes. Proceedings of the IEEE, 96(1), 155–165.

  22. 22.

    Yi, X., Donglin, H., & Mao, S. (2014). Relay-assisted multiuser video streaming in cognitive radio networks. IEEE Transactions on Circuits and Systems for Video Technology, 24(10), 1758–1770.

  23. 23.

    Bourdena, A., Pallis, E., Kormentzas, G., & Mastorakis, G. (2014). Efficient radio resource management algorithms in opportunistic cognitive radio networks. Transactions on Emerging Telecommunications Technologies, 25(8), 785–797.

  24. 24.

    Bayrakdar, M. E., Atmaca, S., & Karahan, A. (2016). A slotted aloha-based cognitive radio network under capture effect in Rayleigh fading channels. Turkish Journal of Electrical Engineering and Computer Sciences, 24(3), 1955–1966.

  25. 25.

    He, Z., Mao, S., & Kompella, S. (2016). Quality of experience driven multi-user video streaming in cellular cognitive radio networks with single channel access. IEEE Transactions on Multimedia, 18(7), 1401–1413.

  26. 26.

    Zhang, N., Zhang, S., Zheng, J., Fang, X., Mark, J. W., & Shen, X. (2017). QOE driven decentralized spectrum sharing in 5G networks: Potential game approach. IEEE Transactions on Vehicular Technology, 66(9), 7797–7808.

  27. 27.

    Piran, M. J., Tran, N. H., Suh, D. Y., Song, J. B., Hong, C. S., & Han, Z. (2016). QOE-driven channel allocation and handoff management for seamless multimedia in cognitive 5G cellular networks. IEEE Transactions on Vehicular Technology, 66(7), 6569–6585.

  28. 28.

    Lin, S., Kong, L., Gao, Q., Khan, M. K., Zhong, Z., Jin, X., et al. (2017). Advanced dynamic channel access strategy in spectrum sharing 5G systems. IEEE Wireless Communications, 24(5), 74–80.

  29. 29.

    Fan, S., & Zhao, H. (2018). Delay-based cross-layer QOS scheme for video streaming in wireless ad hoc networks. China Communications, 15(9), 215–234.

  30. 30.

    Donglin, H., & Mao, S. (2010). Streaming scalable videos over multi-hop cognitive radio networks. IEEE Transactions on Wireless Communications, 9(11), 3501–3511.

  31. 31.

    Hu, D., & Mao, S. (2012). On cooperative relay networks with video applications. arXiv preprint arXiv:1209.2086.

  32. 32.

    Li, S., Luan, T. H., & Shen, X. (2010). Channel allocation for smooth video delivery over cognitive radio networks. In 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, (pp. 1–5). IEEE.

  33. 33.

    Bhattacharya, A., Ghosh, R., Sinha, K., & Sinha, B. P. (2011). Multimedia communication in cognitive radio networks based on sample division multiplexing. In 2011 Third International Conference on Communication Systems and Networks (COMSNETS 2011), (pp. 1–8). IEEE.

  34. 34.

    Hassan, M. S., Abusara, A., Din, M. S. E., & Ismail, M. H. (2016). On efficient channel modeling for video transmission over cognitive radio networks. Wireless Personal Communications, 91(2), 919–932.

  35. 35.

    Khan, A., Sun, L., & Ifeachor, E. (2009). Content clustering based video quality prediction model for MPEG4 video streaming over wireless networks. In 2009 IEEE International Conference on Communications, (pp. 1–5). IEEE.

  36. 36.

    Zhou, X., Ma, J., Li, G. Y., Kwon, Y. H., & Soong, A. C. K. (2009). Probability-based optimization of inter-sensing duration and power control in cognitive radio. IEEE Transactions on Wireless Communications, 8(10), 4922–4927.

  37. 37.

    Yang, L., Cao, L., & Zheng, H. (2008). Proactive channel access in dynamic spectrum networks. Physical Communication, 1(2), 103–111.

  38. 38.

    Liang, Y., Zeng, Y., Peh, E. C. Y., & Hoang, A. T. (2008). Sensing-throughput tradeoff for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(4), 1326–1337.

  39. 39.

    Ghasemi, A., & Sousa, E. S. (2007). Optimization of spectrum sensing for opportunistic spectrum access in cognitive radio networks. In 2007 4th IEEE Consumer Communications and Networking Conference, (pp. 1022–1026). IEEE.

  40. 40.

    Khan, S., Duhovnikov, S., Steinbach, E., & Kellerer, W. (2007). MOS-based multiuser multiapplication cross-layer optimization for mobile multimedia communication. Advances in Multimedia, 2007(1), 6–6.

  41. 41.

    Lincke, T. R. (2002) Exploring the computational limits of large exhaustive search problems. PhD thesis, ETH Zurich.

  42. 42.

    Cormen, T. H., Leiserson, C. E., Rivest, R. L., Stein, C., et al. (2001). Introduction to algorithms, chapter 11. Cambridge: MIT press.

  43. 43.

    Oto, M. C., & Akan, O. B. (2012). Energy-efficient packet size optimization for cognitive radio sensor networks. IEEE Transactions on Wireless Communications, 11(4), 1544–1553.

  44. 44.

    Pitrey, Y., Barkowsky, M., Le Callet, P., & Pépion, R. (2010). Subjective quality assessment of MPEG-4 scalable video coding in a mobile scenario. In 2010 2nd European Workshop on Visual Information Processing (EUVIP), (pp. 86–91). IEEE.

  45. 45.

    Lee, J.-S., De Simone, F., Ebrahimi, T., Ramzan, N., & Izquierdo, E. (2012). Quality assessment of multidimensional video scalability. IEEE Communications Magazine, 50(4), 38–46.

  46. 46.

    Wang, D., Speranza, F., Vincent, A., Martin, T., & Blanchfield, P. (2003). Toward optimal rate control: A study of the impact of spatial resolution, frame rate, and quantization on subjective video quality and bit rate. In Visual Communications and Image Processing 2003, (Vol. 5150, pp. 198–210). International Society for Optics and Photonics.

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Acknowledgements

The authors deeply acknowledge the support from Visvesvaraya Ph.D. Scheme, (DeitY), Govt. of India.

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Correspondence to Sudipta Dey.

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Dey, S., Misra, I.S. CATECAS: a content-aware transmission efficiency based channel allocation scheme for cognitive radio users with improved QoE. Telecommun Syst (2020) doi:10.1007/s11235-019-00648-7

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Keywords

  • Cognitive radio
  • Quality of experience
  • MOS
  • Content-aware channel allocation
  • Transmission efficiency