Power allocation for orthogonal frequency division multiplexing-based cognitive radio systems with and without integral bit rate consideration

Author(s): Y. Ma ; Y. Xu ; D. Zhang
DOI: 10.1049/iet-com.2010.0193

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Author(s): Y. Ma ¹ ; Y. Xu ^{1, 2} ; D. Zhang ¹
- Affiliations: 1: Institute of Communications Engineering, PLA University of Science and Technology, Nanjing, People's Republic of China
  2: Department of Electronic Engineering, Institute of Wireless Communication Technology, Shanghai Jiao Tong University, Shanghai, People's Republic of China
Source: Volume 5, Issue 5, 25 March 2011, p. 575 – 586
DOI: 10.1049/iet-com.2010.0193 , Print ISSN 1751-8628, Online ISSN 1751-8636

Cognitive radio (CR) has been recognised as one of the promising technologies to provide efficient utilisation of the limited wireless spectrum. The authors investigate the power allocation problem for orthogonal frequency division multiplexing (OFDM)-based CR systems that coexist with primary networks. Specifically, in two scenarios with and without integral bit rate constraint, the authors study the secondary users (SUs) power allocation under both individual transmit power constraints of their own and interference power constraints of all the primary users. When the bit rate of SUs can be continuous on each OFDM subchannel, an efficient power allocation algorithm that contains two level Lagrangian dual variable iterations is proposed to maximise the CR system sum-rate. Besides, when integral bit requirement exists, a heuristic power allocation and bit loading methodology which based on the iterative power rescaling operation is developed. Simulation results show that the algorithm proposed for the continuous rate case can achieve the same rate performance as the standard interior-point algorithm, but converges much faster. The proposed allocation scheme for the discrete rate case has the same bit rate performance as the greedy-based Max-Min scheme.

References

1. 1)
  - Z. Yonghong , L. Cyril . Resource allocation in an OFDM-based cognitive radio system. IEEE Trans. Commun. Lett. , 7 , 1928 - 1931
2. 2)
  - T. Luo , T. Jiang , W. Xiang , H.H. Chen . A subcarriers allocation scheme for cognitive radio systems based on multi-carrier modulation. IEEE Trans. Wirel. Commun. , 9 , 3335 - 3340
3. 3)
  - P. Cheng , Z. Zhang , H.H. Chen , P. Qiu . Optimal distributed joint frequency, rate, and power allocation in cognitive OFDMA systems. IET Commun. , 815 - 826
4. 4)
  - S. Haykin . Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. , 2 , 201 - 220
5. 5)
  - R.G. Bland , D. Goldfarb , M.J. Todd . The ellipsoid method: a survey. Oper. Res. , 6 , 1039 - 1091
6. 6)
  - C.Y. Wong , R.S. Cheng , K.B. Letaief , R.D. Murch . Multiuser OFDM with adaptive subcarrier, bit, and power allocation. IEEE J. Sel. Areas Commun. , 10 , 1747 - 1758
7. 7)
  - L. Musavian , S. Aissa . Capacity and power allocation for spectrum- sharing communications in fading channels. IEEE Trans. Wirel. Commun. , 1 , 148 - 156
8. 8)
  - J. Jang , K.B. Lee . Transmit power adaptation for multiuser OFDM systems. IEEE J. Sel. Areas Commun. , 2 , 171 - 178
9. 9)
  - S. Boyd , L. Vandenberghe . (2004) Convex optimization.
10. 10)
  - Federal Communications Commission: http://hraunfoss.fcc.gov/edocs-public/attachmatch/FCC-03-289A1.pdf, 2003.
11. 11)
  - Qin, T., Leung, C.: `Fair adaptive resource allocation for multiuser OFDM cognitive radio systems', Proc. ChinaCom 2007, 2007, p. 115–119.
12. 12)
  - A. Feiten , R. Mathar , M. Reyer . Rate and power allocation for multiuser OFDM: an effective heuristic verified by branch-and-bound. IEEE Trans. Wirel. Commun. , 3 , 60 - 64
13. 13)
  - M. Gastpar . On capacity under receive and spatial spectrum-sharing constraints. IEEE Trans. Inf. Theory , 2 , 471 - 487
14. 14)
  - A. Ghasemi , E.S. Sousa . Fundamental limits of spectrum-sharing in fading environments. IEEE Trans. Wirel. Commun. , 2 , 649 - 658
15. 15)
  - B. Hamdaoui . Adaptive spectrum assessment for opportunistic access in cognitive radio networks. IEEE Trans. Wirel. Commun. , 2 , 922 - 930
16. 16)
  - J. Mitola , G.Q. Maguire . Cognitive radio: making software radios more personal. IEEE Pers. Commun. , 9 , 13 - 18
17. 17)
  - Haiyan, L., Guanding, Y., Zhaoyang, Z.: `Optimal bit and power allocation in broadband cognitive radio system', Proc. IEEE VTC, 2008, p. 1569–1573.
18. 18)
  - Wang, P., Zhao, M., Xiao, L., Zhou, S., Wnag, J.: `Power allocation in OFDM-based cognitive radio systems', Proc. IEEE Global Communication Conf. 2007 (Globalcom'07), 2007, p. 4061–4065.
19. 19)
  - A.P. Iserte , M.A. Lagunas , A.I. Perez-Neira . Robust power allocation for minimum BER in a SISO-OFDM system.
20. 20)
  - , : `Spectrum policy task force', Report ET Docket, November 2002.
21. 21)
  - D.P. Palomar , M. Chiang . A tutorial on decomposition methods for network utility maximization. IEEE J. Sel. Areas Commun. , 8 , 1439 - 1451
22. 22)
  - A.J. Goldsmith , S.G. Chua . Variable-rate variable-power MQAM for fading channels. IEEE Trans. Commun. , 1218 - 1230
23. 23)
  - Y. Yao , G.B. Giannakis . Rate-maximizing power allocation in OFDM based on partial channel knowledge. IEEE Trans. Wirel. Commun. , 3 , 1073 - 1083
24. 24)
  - Bansal, G., Hossain, J., Vijay, K.: `Adaptive power loading for OFDM-based cognitive radio systems', Proc. IEEE ICC'07, 2007, p. 5137–5142.
25. 25)
  - T. Weiss , F.K. Jondral . Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency. IEEE Commun. Mag. , 3 , S8 - S14
26. 26)
  - Kang, X., Zhang, R., Liang, Y.C., Garg, H.K.: `Optimal power allocation for cognitive radio under primary user's outage loss constraint', Proc. IEEE ICC'09, 2009, p. 1–5.
27. 27)
  - V.K. Bhargava , E. Hossain . (2007) Cognitive wireless communication networks.
28. 28)
  - Jovicic, A., Viswanath, P.: `Cognitive radio: an information-theoretic perspective', Proc. IEEE ISIT'06, 2006, p. 2413–2417.
29. 29)
  - N. Devroye , P. Mitran , V. Tarokh . Achievable rates in cognitive radio channels. IEEE Trans. Inf. Theory , 5 , 1813 - 1827
30. 30)
  - Weiss, T., Hillenbrand, J., Jondral, F.K.: `Mutual interference in OFDM-based spectrum pooling systems', Proc. IEEE Vehicular Technol. Conf. (VTC'04), 2004, p. 1873–1877.
31. 31)
  - J. Jang , K.B. Lee , Y.H. Lee . Transmit power and bit allocations for OFDM systems in a fading channel.
32. 32)
  - Hasan, Z., Hossain, E., Despins, C., Bhargava, V.K.: `Power allocation for cognitive radios based on primary user activity in an OFDM system', Proc. IEEE Global Communication Conf. 2008 (Globalcom'08), 2008.

Power allocation for orthogonal frequency division multiplexing-based cognitive radio systems with and without integral bit rate consideration

Power allocation for orthogonal frequency division multiplexing-based cognitive radio systems with and without integral bit rate consideration

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