Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Antenna selection and adaptive power allocation for IA-based underlay CR

© The Institution of Engineering and Technology
Received 28/08/2016, Accepted 14/03/2017, Revised 01/02/2017, Published 17/03/2017

In underlay spectrum sharing cognitive radio (CR), the secondary users (SUs) share the spectrum with the primary users (PUs) in such a way that the interference caused by the SUs should be less than the interference temperature (IT) threshold. Interference Alignment (IA) is considered as a novel interference management technique for CR networks (CRN) which eliminates the interference at the PUs and also the interference among the SUs. However, most of the iterative solutions for IA aimed at removing the interference between the users, does not take into account the QoS (Quality of Service) of the PUs and SUs. Also the achievable rate of PUs by employing IA is less than that of the maximum achievable rate in a MIMO (Multiple input multiple output) system with no interference. In this paper we first present an Antenna Selection (AS) scheme to significantly improve the QoS of PUs and then we have proposed an adaptive power allocation (PA) scheme which aims at optimum PA in view of the proportional fairness among SUs. Numerical results are derived for comparing the proposed schemes in terms of outage performance of PUs and SUs. Simulation results show the effectiveness of the proposed methods in terms of SNR.

Inspec keywords: interference suppression; telecommunication power management; radio spectrum management; MIMO communication; antenna arrays; cognitive radio

Other keywords: interference alignment; interference management technique; transmission rate requirement; antenna selection scheme; underlay spectrum sharing cognitive radio; secondary users; primary users; proportional fairness; signal-to-noise ratio; adaptive power allocation scheme; multiple-input-multiple-output system; IA-based underlay CR

Subjects: Radio links and equipment; Electromagnetic compatibility and interference; Antenna arrays

References

    1. 1)
      • 17. Zhao, N., Richard Yu, F., Sun, H., et al: ‘Adaptive power allocation schemes for spectrum sharing in interference-alignment-based cognitive radio networks’, IEEE Trans. Veh. Technol., 2016, 65, (5), pp. 3700–3714.
    2. 2)
      • 5. Cadambe, V.R., Jafar, S.A.: ‘Interference alignment and degrees of freedom of the K-user interference channel’, IEEE Trans. Inf. Theory, 2008, 54, (8), pp. 3425–3441.
    3. 3)
      • 13. Koltz, J.G., Sezgin, A.: ‘Antenna selection criteria for interference alignment’. Proc. IEEE PIMRC, September 2010, pp. 527–531.
    4. 4)
      • 10. Rezai, F., Tadaion, A.: ‘Sum-rate improvement in cognitive radio through interference alignment’, IEEE Trans. Veh. Technol., 2016, 65, (1), pp. 145–154.
    5. 5)
      • 2. Scutari, G., Palomar, D., Barbarossa, S.: ‘Cognitive MIMO radio’, IEEE Signal Process. Mag., 2008, 25, (6), pp. 46–59.
    6. 6)
      • 3. Zhang, R., Liang, Y.C.: ‘Exploiting multi-antennas for opportunistic spectrum sharing in cognitive radio networks’, IEEE J. Sel. Top. Signal Process., 2008, 2, (1), pp. 88–102.
    7. 7)
      • 16. Yongjun, X., Xiaohui, Z.: ‘Optimal power allocation for multiuser underlay cognitive radio networks under QoS and interference temperature constraints’, China Commun., 2013, 10, (10), pp. 91–100.
    8. 8)
      • 12. Heath, R.W., Sandhu, S., Paulraj, A.: ‘Antenna selection for spatial multiplexing systems with linear receivers’, IEEE Commun. Lett., 2001, 5, (4), pp. 142–144.
    9. 9)
      • 4. Ian, F.A., Won-Yeol, L., Mehmet, C.V., et al: ‘NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey’, Comput. Netw. J., 2006, 50, (13), pp. 2127–2159.
    10. 10)
      • 11. Men, H., Zhao, N., Jin, M., et al: ‘Optimal transceiver design for interference alignment based cognitive radio networks’, IEEE Commun. Lett., 2015, 19, (8), pp. 1442–1445.
    11. 11)
      • 1. Mitola, J., Maguire, G.Q.: ‘Cognitive radio: making software radios more personal’, IEEE Pers. Commun., 1999, 6, pp. 13–18.
    12. 12)
      • 9. Xu, T., Ma, L., Sternberg, G.: ‘Practical interference alignment and cancellation for MIMO underlay cognitive radio networks with multiple secondary users’. Proc. IEEE Global Communications Conf. GLOBECOM, December 2013, pp. 1009–1014.
    13. 13)
      • 14. He, Y., Yin, H., Zhao, N.: ‘Multiuser-diversity-based interference alignment in cognitive radio networks’, Int. J. Electron. Commun., 2016, 70, (5), pp. 617–628.
    14. 14)
      • 15. Li, X., Zhao, N., Sun, Y., et al: ‘Interference alignment based on antenna selection with imperfect channel state information in cognitive radio networks’, IEEE Trans. Veh. Technol., 2016, 65, (7), pp. 5497–5511.
    15. 15)
      • 8. Du, H., Ratnarajah, T., Zhou, H., et al: ‘Interference alignment for peer-to-peer underlay MIMO cognitive radio network’. Proc. 45th Asilomar Conf. Signals, Systems and Computers, ASILOMAR, November 2011, pp. 349–353.
    16. 16)
      • 7. Gomadam, K., Cadambe, V.R., Jafar, S.A.: ‘A distributed numerical approach to interference alignment and applications to wireless interference networks’, IEEE Trans. Inf. Theory, 2011, 57, (6), pp. 3309–3322.
    17. 17)
      • 6. Yetis, C., Gou, T., Jafar, S.A., et al: ‘On feasibility of interference alignment in MIMO interference networks’, IEEE Trans. Signal Process., 2010, 58, (9), pp. 4771–4782.
    18. 18)
      • 18. Zhang, T., Zeng, Z., Qiu, Y.: ‘A subcarrier allocation algorithm for utility proportional fairness in OFDM systems’. Proc. 67th IEEE Vehicular Technology Conf. (VTC Spring 2008), Singapore, May 2008, pp. 1901–1905.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2016.0495
Loading

Related content

content/journals/10.1049/iet-spr.2016.0495
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address