Robust adaptive power control for cognitive radio networks

Yongjun Xu; Xiaohui Zhao

Robust adaptive power control for cognitive radio networks

View Fulltext

Author(s): Yongjun Xu ¹ and Xiaohui Zhao ¹
- Affiliations: 1: College of Communication Engineering, Jilin University, Changchun 130012, People's Republic of China
Source: Volume 10, Issue 1, February 2016, p. 19 – 27
DOI: 10.1049/iet-spr.2015.0022 , Print ISSN 1751-9675, Online ISSN 1751-9683

Received 09/07/2014, Accepted 08/07/2015, Revised 29/06/2015, Published 01/02/2016

In this study, the problem of robust adaptive power control (PC) in an underlay cognitive radio network with multiple secondary users (SUs) and primary users (PUs) is considered. Due to the effects of uncertainties (i.e. estimation errors, delays), the optimal PC (resource allocation) cannot guarantee the quality of service of SUs and PUs under imperfect channel state information and interference power of PUs. A robust resource allocation problem is formulated to maximise sum throughput of SUs under individual power constraints and signal-to-interference-and-noise ratio constraints of SUs as well as interference temperature constraints of PUs, whereas channel uncertainties and interference uncertainties induced into the secondary system are modelled by multiplicative uncertainties. Under the worst-case approach, the problem is transformed into a geometric programming problem solved by Lagrange dual methods. The performance of the different algorithms and the impact of uncertainties are discussed according to several simulation results.

References

1. 1)
  - 28. Dimitris, B., Pachamanova, D., Sim, M.: ‘Robust linear optimization under general norms’, Oper. Res. Lett., 2014, 32, (6), pp. 510–516.
2. 2)
  - 25. Bertsekas, D.P.: ‘Nonlinear processing’ (Athena Scientific, 1999).
3. 3)
  - 15. Parsaeefard, S., Sharafat, A.R.: ‘Robust worst-case interference control in underlay cognitive radio networks’, IEEE Trans. Veh. Technol., 2012, 61, (8), pp. 3731–3745 (doi: 10.1109/TVT.2012.2205719).
4. 4)
  - 3. Xu, Y.J., Zhao, X.H.: ‘Optimal power allocation for multiuser underlay cognitive radio networks under QoS and interference temperature constraints’, Chin. Commun., 2013, 10, (10), pp. 91–100 (doi: 10.1109/CC.2013.6650322).
5. 5)
  - 27. Luo, Z.Q., Yu, W.: ‘An introduction to convex optimization for communications and signal processing’, IEEE J. Select. Areas Commun., 2006, 24, (8), pp. 1426–1438 (doi: 10.1109/JSAC.2006.879347).
6. 6)
  - 2. Haykin, S.: ‘Cognitive radio: brain-empowered wireless communications’, IEEE J. Sel. Areas Commun., 2005, 32, (2), pp. 201–220 (doi: 10.1109/JSAC.2004.839380).
7. 7)
  - 26. Peng, J., Huang, Z., Zhu, Z.F., et al: ‘A worst-case robust distributed power allocation in OFDM-based cognitive radio networks’. The 32nd Chinese Conrtol Conf., Xi'an, China, July 2013, pp. 6422–6427.
8. 8)
  - 18. Cumanan, K., Krishna, R., Xiong, Z., et al: ‘Multiuser spatial multiplexing techniques with constraints on interference temperature for cognitive radio networks’, IET Signal Process., 2010, 4, (6), pp. 666–672 (doi: 10.1049/iet-spr.2009.0204).
9. 9)
  - 12. Dall'Anese, E., Kim, S.J., Giannakis, G.B., et al: ‘Power control for cognitive radio networks under channel uncertainty’, IEEE Trans. Wirel. Commun., 2011, 10, (10), pp. 3541–3551 (doi: 10.1109/TWC.2011.081711.110323).
10. 10)
  - 5. Xu, Y.J., Zhao, X.H.: ‘Distributed power control for multiuser cognitive radio networks with quality of service and interference temperature constraints’, Wilrel. Commun. Mob. Comput., 2015, 15, (14), pp. 1773–1783 (doi: 10.1002/wcm.2466).
11. 11)
  - 2. Xu, Y.J., Zhao, X.H., Liang, Y.C.: ‘Robust power control and beamforming in cognitive radio networks: a survey’, IEEE Commun. Surv. Tutor., 2015, PP, (99), doi: 10.1109/COMST.2015.2425040.
12. 12)
  - 8. Adian, M.G., Aghaeinia, H.: ‘Optimal and sub-optimal resource allocation in multiple-input multiple-output-orthogonal frequency division multiplexing-based multi-relay cooperative cognitive radio networks’, IET Commun., 2014, 8, (5), pp. 646–657 (doi: 10.1049/iet-com.2013.0676).
13. 13)
  - 4. Zou, J.N., Xiong, H.K., Wang, D.W., et al: ‘Optimal power allocation for hybrid overlay/underlay spectrum sharing in multiband cognitive radio networks’, IEEE Trans. Veh. Technol., 2013, 62, (4), pp. 1827–1837 (doi: 10.1109/TVT.2012.2235152).
14. 14)
  - 6. Wang, Z.Q., Jiang, L.G., He, C.: ‘Optimal price-based power control algorithm in cognitive radio networks’, IEEE Trans. Wirel. Commun., 2014, 13, (11), pp. 5909–5920 (doi: 10.1109/TWC.2014.2318708).
15. 15)
  - 19. Sun, S.Q., Ni, W.M., Zhu, Y.: ‘Robust power control in cognitive radio networks: a distributed way’. IEEE Int. Conf. Communications, Kyoto, Japan, June 2011, pp. 1–6.
16. 16)
  - 22. Tal, A.B., Ghaoui, L.E., Nemirovski, A.: ‘Robust optimization’ (Princeton University, 2009).
17. 17)
  - 15. Setoodeh, P., Haykin, S.: ‘Robust transmit power control for cognitive radio’, Proc. IEEE, 2009, 97, (5), pp. 915–939 (doi: 10.1109/JPROC.2009.2015718).
18. 18)
  - 23. Singh, S., Teal, P.D., Dmochowski, P.A., et al: ‘Power allocation in underlay cognitive radio systems with feasibility detection’. Australian Communications Theory Workshop, Wellington, New Zealand, February 2012, pp. 135–139.
19. 19)
  - 10. Soltani, N.Y., Kim, S.J., Giannakis, G.B.: ‘Chance-constrained optimization of OFDMA cognitive radio uplinks’, IEEE Trans. Wirel. Commun., 2013, 12, (3), pp. 1098–1107 (doi: 10.1109/TWC.2013.011713.120155).
20. 20)
  - 11. Zheng, G., Ma, S.D., Wong, K.K., et al: ‘Robust beamforming in cognitive radio’, IEEE Trans. Wirel. Commun., 2010, 9, (2), pp. 570–576 (doi: 10.1109/TWC.2010.5403537).
21. 21)
  - 21. Xing, Y.P., Mathur, C.N., Haleem, M.A., et al: ‘Dynamic spectrum access with QoS and interference temperature constraints’, IEEE Trans. Mob. Comput., 2007, 6, (4), pp. 423–433 (doi: 10.1109/TMC.2007.50).
22. 22)
  - 14. Kim, S.J., Soltani, N.Y., Giannakis, G.B.: ‘Resource allocation for OFDMA cognitive radios under channel uncertainty’, IEEE Trans. Wirel. Commun., 2013, 12, (7), pp. 3578–3587 (doi: 10.1109/TWC.2013.062413.121892).
23. 23)
  - 37. Zheng, G., Wong, K.-K., Ottersten, B.: ‘Robust cognitive beamforming with bounded channel uncertainties’, IEEE Trans. Signal Process., 2009, 57, (12), pp. 4871–4881 (doi: 10.1109/TSP.2009.2027462).
24. 24)
  - 24. Gatsis, N., Marques, A.G., Giannakis, G.B.: ‘Power control for cooperative dynamic spectrum access networks with diverse QoS constraints’, IEEE Trans. Commun., 2010, 58, (3), pp. 933–944 (doi: 10.1109/TCOMM.2010.03.080491).
25. 25)
  - 27. Dimitris, B., Sim, M.: ‘The price of robustness’, Oper. Res., 2004, 52, (1), pp. 35–53 (doi: 10.1287/opre.1030.0065).
26. 26)
  - 13. Xu, Y.J., Zhao, X.H.: ‘Robust power control for underlay cognitive radio networks under probabilistic quality of service and interference constraints’, IET Commun., 2014, 8, (18), pp. 3333–3340 (doi: 10.1049/iet-com.2014.0300).
27. 27)
  - 20. Parsaeefard, S., Sharafat, A.R.: ‘Robust distributed power control in cognitive radio networks’, IEEE Trans. Mob. Comput., 2013, 12, (4), pp. 609–620 (doi: 10.1109/TMC.2012.28).
28. 28)
  - 9. Bertsimas, D., Brown, D.B., Caramanis, C.: ‘Theory and applications of robust optimization’, SIAM Rev., 2011, 53, (3), pp. 464–501 (doi: 10.1137/080734510).

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Robust adaptive power control for cognitive radio networks

References

Related content