Skip to main content
SpringerLink
Log in
Book cover

Cognitive Radio Networks pp 9–25Cite as

Dynamic Resource Allocation

  • Chapter
  • First Online:

  • 575 Accesses

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

Dynamic Resource Allocation is an essential technique to exploit the time-space-frequency variation in wireless channels by adaptively distributing precious radio resources, such as spectrum and power, to either maximize or minimize the concerned network performance metrics. In traditional static resource allocation strategies, subchannels are distributed in a predetermined manner; that is, each user is assigned fixed frequency bands regardless of the channel status. In this case, the resource allocation problem reduces to power allocation or bits loading on each subchannel, which fails to fully exploit the potential of multiuser diversity in wireless environment.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. T. S. Rappaport, Wireless Communications. Prentice Hall PTR, 2002.

    Google Scholar 

  2. A. Duel-Hallen, S. Hu, and H. Hallen, “Long-range prediction of fading signals,” IEEE Signal Process. Mag., vol. 17, pp. 62–75, May 2000.

    Google Scholar 

  3. A. Forenza and R. W. Heath, “Link adaptation and channel prediction in wireless OFDM systems,” in Proc. 45th IEEEMWSCAS, vol. 3, pp. 211–214, Aug. 2002.

    Google Scholar 

  4. M. Sternad and D. Aronsson, “Channel estimation and prediction for adaptive OFDM downlinks [vehicular applications],” in Proc. IEEEVTC, vol. 2, pp. 1283–1287, Oct. 2003.

    Google Scholar 

  5. I. C. Wong, A. Forenza, R. W. Heath, and B. L. Evans, “Long range channel prediction for adaptive OFDM systems,” in Proc. IEEE ACSSC, vol. 1, pp. 732–736, Nov. 2004.

    Google Scholar 

  6. I. C. Wong and B. L. Evans, “Joint channel estimation and prediction for OFDM systems,” in Proc. IEEE Globecom’05, vol. 4, pp. 2255–2259, Dec. 2005.

    Google Scholar 

  7. D. Schafhuber and G. Matz, “MMSE and adaptive prediction of time varying channels for OFDM systems,” IEEE Trans. Wireless Commun., vol. 4, pp. 593–602, Mar. 2005.

    Google Scholar 

  8. I. C. Wong and B. L. Evans, “Low-complexity adaptive high-resolution channel prediction for OFDM systems,” in Proc. IEEE Globecom’06, Nov. 2006.

    Google Scholar 

  9. S. Sadr, A. Anpalagan, and K. Raahemifar, “Radio resource allocation algorithms for the downlink of multiuser OFDM communication systems,” IEEE Commun. Surv. & Tutor., vol. 11, no. 3, pp. 92–106, Sep. 2009.

    Google Scholar 

  10. J. Jang and K. B. Lee, “Transmit power adaptation for multiuser OFDM systems,” IEEE J. Select. Areas Commun., vol. 21, pp. 171–178, Feb. 2003.

    Google Scholar 

  11. Y. Chen, S. Zhang, S. Xu, and G. Li, “Fundamental trade-offs on green wireless networks,” IEEE Commun. Mag., vol. 49, no. 6, pp. 30–37, June 2011.

    Google Scholar 

  12. D. Feng, C. Jiang, G. Lim, L. Cimini, Jr., G. Feng, and G. Li, “A survey of energy-efficient wireless communications,” IEEE Commun. Surv. & Tutor., vol. PP, no. 99, pp. 1–12, 2012.

    Google Scholar 

  13. G. Miao, N. Himayat, G. Li, and S. Talwar, “Low-complexity energy efficient scheduling for uplink OFDMA,” IEEE Trans. Commun., vol. 60, no. 1, pp. 112–120, Jan. 2012.

    Google Scholar 

  14. C. Xiong, G. Li, S. Zhang, Y. Chen, and S. Xu, “Energy- and spectral- efficiency trade off in downlink OFDMA networks,” IEEE Trans. Wireless Commun., vol. 10, no. 11, pp. 3874–3886, Nov. 2011.

    Google Scholar 

  15. G. Miao, N. Himayat, G. Li, and S. Talwar, “Distributed interference aware energy-efficient power optimization,” IEEE Trans. Wireless Commun., vol. 10, no. 4, pp. 1323–1333, Apr. 2011.

    Google Scholar 

  16. D. Ng, E. Lo, and R. Schober, “Energy-efficient resource allocation in OFDMA systems with large numbers of base station antennas,” IEEE Trans. Wireless Commun., vol. 11, no. 9, pp. 3292–3304, Sep. 2012.

    Google Scholar 

  17. D. W. K. Ng, E. S. Lo, and R. Schober, “Energy-efficient resource allocation in multi-cell OFDMA systems with limited backhaul capacity,” IEEE Trans. Wireless Commun., vol. 11, no. 10, pp. 3618–3631, Oct. 2012.

    Google Scholar 

  18. Y. Pei, Y.-C. Liang, K. C. Teh, and K. H. Li, “Energy-efficient design of sequential channel sensing in cognitive radio networks: Optimal sensing strategy, power allocation, and sensing order,” IEEE J. Sel. Areas Commun., vol. 29, no. 8, pp. 1648–1659, Sep. 2011.

    Google Scholar 

  19. Y. Otani, S. Ohno, K. Ann Donny Teo, and T. Hinamoto, “Subcarrier allocation for multi-user OFDM system,” in Proc. Asia-Pasific Conf. on Commun., pp. 1073–1077, 2005.

    Google Scholar 

  20. W. Rhee and J. M. Cioffi, “Increase in capacity of multiuser OFDM system using dynamic subchannel allocation,” in Proc. IEEE VTC’00, vol. 2, pp. 1085–1089, May 2000.

    Google Scholar 

  21. Z. Shen, J. G. Andrews, and B. L. Evans, “Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints,” IEEE Trans. Wireless Commun., vol. 4, pp. 2726–2737, Nov. 2005.

    Google Scholar 

  22. C. Y. Wong, R. S. Cheng, K. B. Letaief, and R. D. Murch, “Multiuser OFDM with adaptive subcarrier, bit and power allocation,” IEEE J. Select. Areas Commun., vol. 17, pp. 1747–1758, Oct. 1999.

    Google Scholar 

  23. G. Zhang, “Subcarrier and bit allocation for real-time services in multiuser OFDM systems,” in Proc. IEEE ICC’04, vol. 5, pp. 2985–2989, June 2004.

    Google Scholar 

  24. L. Xiaowen and Z. Jinkang, “An adaptive subcarrier allocation algorithm for multiuser OFDM system,” in Proc. IEEE VTC’03, vol. 3, pp. 1502–1506, Oct. 2003.

    Google Scholar 

  25. G. Song and Y. G. Li, “Cross-layer optimization for OFDM wireless networks-Part I: theoretical framework,” IEEE Trans. Wireless Commun., vol. 4, pp. 614–624, Mar. 2005.

    Google Scholar 

  26. G. Song and Y. G. Li, “Cross-layer optimization for OFDM wireless networks-Part II: Algorithm development,” IEEE Trans. Wireless Commun., vol. 4, pp. 625–634, Mar. 2005.

    Google Scholar 

  27. Z. Shen, J. G. Andrews, and B. L. Evans, “Optimal power allocation in multiuser OFDM systems,” in Proc. IEEE Globecom’03, vol. 1, pp. 337–341, Dec. 2003.

    Google Scholar 

  28. I. C. Wong, Z. Shen, B. L. Evans, and J. G. Andrews, “A low complexity algorithm for proportional resource allocation in OFDMA systems,” in Proc. IEEE Workshop on Signal Processing Systems, Oct. 2004.

    Google Scholar 

  29. H. Yin and H. Liu, “An efficient multiuser loading algorithm for OFDM based broad band wireless systems,” in Proc. IEEE Globecom’00, vol. 1, pp. 103–107, Nov. 2000.

    Google Scholar 

  30. G. Song and Y. G. Li, “Utility-based joint physical-MAC layer optimization in OFDM,” in Proc. IEEE Globecom’02, vol. 1, pp. 671–675, Nov. 2002.

    Google Scholar 

  31. G. Song and Y. G. Li, “Adaptive subcarrier and power allocation in OFDM based on maximizing utility,” in Proc. IEEE VTC, vol. 2, pp. 905–909, Apr. 2003.

    Google Scholar 

  32. M. Tao, Y.-C. Liang, and F. Zhang, “Resource allocation for delay differentiated traffic in multiuser OFDM systems,” IEEE Trans. Wireless Commun., vol. 7, no. 6, pp. 2190–2201, June 2008.

    Google Scholar 

  33. C. Xiong, G. Y. Li, S. Zhang, Y. Chen, S. Xu, “Energy-Efficient Resource Allocation in OFDMA Networks,” in Proc. IEEEGlobecom’11, Dec. 2011.

    Google Scholar 

  34. C. Xiong, G. Y. Li, S. Zhang, Y. Chen, S. Xu, “Energy-Efficient Resource Allocation in OFDMA Networks,” IEEE Trans. Commun., vol. 60, no. 12, pp. 3767–3778, Dec. 2012.

    Google Scholar 

  35. A. Zappone, G. Alfano, S. Buzzi, M, Meo, “Energy-efficient non-cooperative resource allocation in multi-cell OFDMA systems with multiple base station antennas,” in Proc. IEEE GreenCom’11, pp. 82–87, Sep. 2011.

    Google Scholar 

  36. G. J. Foschini and J. Salz, “Digital communications over fading radio channels,” Bell Syst. Tech. J., pp. 429–456, Feb. 1983.

    Google Scholar 

  37. A. J. Goldsmith and Soon-Ghee Chua, “Variable-rate variable-power MQAM for fading channels,” IEEE Trans. Commun., vol. 45, pp. 1218–1230, Oct. 1997.

    Google Scholar 

  38. Q. Zhao and B. M. Sadler, “A survey of dynamic spectrum access,” IEEE Signal Processings. Mag., vol. 24, no. 3, pp. 79–89, May 2007.

    Google Scholar 

  39. A. Goldsmith, S. A. Jafar, I. Mari’c, and S. Srinivasay, “Breaking spectrum gridlock with cognitive radios: An information theoretic perspective,” Proc. IEEE, vol. 97, no. 5, pp. 894–914, May 2009.

    Google Scholar 

  40. Z. Quan, S. Cui, and A. Sayed, “Optimal linear cooperation for spectrum sensing in cognitive radio networks,” IEEE J. Select. Topics Signal Process., vol. 2, no. 1, pp. 28–40, Feb. 2008.

    Google Scholar 

  41. Y.-C. Liang, Y. Zeng, E. C. Y. Peh, and A. T. Hoang, “Sensing-throughput tradeoff for cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 7, no. 4, pp. 1326–1337, Apr. 2008.

    Google Scholar 

  42. B. H. Juang, Y. Li, and J. Ma, “Signal processing in cognitive radio,” Proc. IEEE, vol. 97, no. 5, pp. 805–823, May 2009.

    Google Scholar 

  43. Y. H. Zeng, Y.-C. Liang, A. T. Hoang, and R. Zhang, “A review on spectrum sensing for cognitive radio: challenges and solutions,” EURASIP J. Advances Signal Process., 2010.

    Google Scholar 

  44. P. Setoodeh and S. Haykin, “Robust transmit power control for cognitive radio,” Proc. of the IEEE, vol. 97, no. 5, pp. 915–939, May 2009.

    Google Scholar 

  45. S. Wang, “Efficient resource allocation algorithm for cognitive OFDM systems,” IEEE Commun. Lett., vol. 14, no. 8, pp. 725–27, Aug. 2010.

    Google Scholar 

  46. M. Ge and S. Wang, “Fast optimal resource allocation is possible for multiuser OFDM-based cognitive radio networks with heterogeneous services,” IEEE Trans. Wireless Commun., vol. 11, no. 4, pp. 1500–1509, Apr. 2012.

    Google Scholar 

  47. S. Wang, Z.-H. Zhou, M. Ge and C. Wang, “Resource allocation for heterogeneous cognitive radio networks with imperfect spectrum sensing,” IEEE J. Sel. Areas Commun., vol. 31, no. 3, pp. 464–475, 2013.

    Google Scholar 

  48. S. Wang, M. Ge and W. Zhao, “Energy-Efficient Resource Allocation for OFDM-based Cognitive Radio Networks,” IEEE Trans. Commun., vol. 61, no. 8, pp. 3181–3191, Aug. 2013.

    Google Scholar 

  49. S. Wang, M. Ge, C. Wang, “Efficient Resource Allocation for Cognitive Radio Networks with Cooperative Relays,” IEEE J. Sel. Areas Commun., vol. 31, no. 11, pp. 2432–2441, Nov. 2013.

    Google Scholar 

  50. S. Wang, Z.-H. Zhou, M. Ge and C. Wang, “Resource Allocation for Heterogeneous Multiuser OFDM-based Cognitive Radio Networks with Imperfect Spectrum Sensing,” In Proc. IEEE INFOCOM’12, pp. 2264–2272, Mar. 2012.

    Google Scholar 

  51. S. Wang, F. Huang and Z.-H. Zhou, “Fast Power Allocation Algorithm for Cognitive Radio Networks,” IEEE Commun. Lett., vol. 15, no. 8, pp. 845–847, Aug. 2011.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Nanjing University, Nanjing, Jiangsu, China

    Shaowei Wang

Authors
  1. Shaowei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaowei Wang .

Rights and permissions

Reprints and permissions

Copyright information

© 2014 The Author(s)

About this chapter

Cite this chapter

Wang, S. (2014). Dynamic Resource Allocation. In: Cognitive Radio Networks. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-08936-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-08936-2_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08935-5

  • Online ISBN: 978-3-319-08936-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation