Power Allocation in OFDM Based Cognitive Radio System

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 202)

Abstract

In this chapter, we investigate one suboptimal power allocation scheme for an OFDM based cognitive radio system. Optical power allocation for secondary user in OFDM subcarrier is complex since it has a power as well as an interference constraint. Mathematical analysis shows that interference between primary and secondary in OFDM based system depends on theirs spectral distance. In our suboptimal method we consider the spectral distance between the secondary and primary user and modify the traditional water-filling algorithm. By applying our suboptimal power allocation method, it is possible that we can get good performance comparable to the optimal scheme. We compare our method with traditional power allocations schemes like equal power allocation and water filling power allocation. Result shows that distance dependent modified water filling (DDMWF) scheme can achieve the highest data rate for the cognitive radio based Secondary user.

Keywords

Cognitive radio Water filling OFDM Power allocation 

Notes

Acknowledgments

This work was supported in part by National Science Foundation (NSF) under grant CNS-0964713, CNS-1050618 and office of Naval research (ONR) under grant N00014-11-1-0071 and N00014-11-1-0865.

References

  1. 1.
    Federal Communications Commission (2002) Spectrum Policy Task Force Trans Rep. ET Docket no. 02–135, Nov 2002Google Scholar
  2. 2.
    Kang X, Liang Y, Nallanathan A, Garg HK, Zhang R (2009) Optimal power allocation for fading channels in cognitive radio networks: ergodic capacity and outage capacity. IEEE Trans Wireless Commun 8(2):940–950CrossRefGoogle Scholar
  3. 3.
    Chai CC, Che YH (2010) Power control for cognitive radios in Nakagami fading channels with outage probability requirement. In: IEEE Global telecommunication conference (GLOBECOM 2010), Miami, Florida, 6–10 Dec 2010, pp 1–5Google Scholar
  4. 4.
    Shahrokh H, Mohamed-pour K (2010) Sub-optimal power allocation in MIMO-OFDM based cognitive radio networks. In: 6th International conference wireless communication networking and mobile computing (WiCOM), Chengdu, China, 23–25 Sept 2010, pp 1–5Google Scholar
  5. 5.
    Hu Y, Kuo G (2007) Space-time-frequency domain water-filling in MIMO-OFDM fading system. In: IEEE conference on vehicular technology, Baltimore, MD, 22–25 Apr 2007, pp. 2475–2480Google Scholar
  6. 6.
    Tiwary PK, Maskey N, Khakurel S, Sachdeva G (2010) Effects of co-channel interference in WLAN and cognitive radio based approach to minimize it. In: International conference on advances in recent technologies in communication and computing (ARTCom), Kottayam, India, 2010, 16–17 Oct 2010, pp 158–160Google Scholar
  7. 7.
    Almalfouh SM, Stüber GL (2011) Interference-aware radio resource allocation in OFDMA-based cognitive radio networks. IEEE Trans Veh Technol 60(4):1699–1713CrossRefGoogle Scholar
  8. 8.
    Babaei A, Jabbari B (2010) Throughput optimization in cognitive random wireless ad hoc networks. In: IEEE conference on global telecommunication (GLOBECOM 2010), Miami, Florida, 6–10 Dec 2010, pp 1–5Google Scholar
  9. 9.
    Bansal G, Hossain MJ, Bhargava VK (2008) Optimal and suboptimal power allocation schemes for OFDM-based cognitive radio systems. IEEE Trans Wireless Commun 7(11):4710–4718CrossRefGoogle Scholar
  10. 10.
    Jian W, Longxiang Y, Xu L (2011) Subcarrier and power allocation in OFDM based cognitive radio systems. In: International conference on intelligent computer technology and automation (ICICTA), Shenzhen, China, vol 2, 28–29 Mar 2011, pp 728–731Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Department of Electrical EngineeringUniversity of Texas at ArlingtonArlingtonUSA

Personalised recommendations