The Optimal MMSE Transceiver Design for IoT-oriented Cognitive Radio Systems

Subject Areas : Sensor Networks

Nguyen-Duy-Nhat Vien ¹ , Tri Ngo Minh ² , Thanh Vu Van ³

1 - The University of Danang, University of Science and Technology, Vietnam
2 - Department of Electronics and Telecommunications Engineering, University of Science and Technology - The University of Danang , Vietnam
3 - Department of Electronics and Telecommunications Engineering, University of Science and Technology - The University of Danang , Vietnam

Received: 2019-04-17 Accepted : 2019-07-03 Published : 2019-08-01

Keywords: MMSE, Cognitive Radio, Interference Alignment, Precoding, Internet of Things,

Abstract :

This paper studies interference alignment scheme and minimum mean square error (MMSE) improvement in Internet of Things (IoT)-oriented cognitive systems, where IoT devices share the licensed spectrum by cognitive radio in spectrum underlay. Target to manage the inter-tier interference caused by cognitive spectrum sharing as well as ensure an MMSE at receivers, the interference alignment algorithms is proposed. In particular, we focus on the problem of designing the optimal linear pre-coding to minimize the total mean square error while satisfying transmit power constraints. Firstly, we introduce a system model of the downlink IoT-oriented cognitive multi-input multi-output (MIMO) system. Secondly, we propose an interference nulling based cognitive interference alignment scheme, and then, the pre-coding and post-coding matrix designs for the primary transceivers to minimum mean square error (MSE), as well as to eliminate the co-channel interference to the primary receivers. We also apply these interference alignment scheme and matrix designs for the secondary links. Finally, the numerical results are used to evaluate performance of the proposed algorithm.

References:

[1] D. Giusto, A. Iera, G. Morabito, L. Atzori (Eds.), 2010. The Internet of Things, Springer
[2] Fadi Al-Turjman, Enver Ever, Hadi Zahmatkesh, 2018, Small Cells in the Forthcoming 5G/IoT: Traffic Modelling and Deployment Overview , IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 28 – 65.
[3] A. A. Khan, M. H. Rehmani, and A. Rachedi, 2016. When Cognitive Radio Meets the Internet of Things?, IEEE 12th Int’l. Wireless Commun. & Mobile Computing Conf., Paphos, Cyprus, pp.5–9.
[4] A. A. Khan, M.H. Rehmani, Rachedi, 2017. Cognitive-radio-based internet of things: Applications, architectures, spectrum related functionalities, and future research directions. IEEE Wirel. Commun. vol. 24, pp.17–25
[5] Priyanka Rawat, Kamal Deep Singh, Jean Marie Bonnin, 2016, Cognitive radio for m2m and internet of things: A survey. Comput. Commun., vol. 94, pp. 1–29.
[6] Hasani, B.M.; Abouei, J.; Zeinalpour, Y.Z. 2016. Interference alignment in overlay cognitive radio femtocell networks. IET Commun. vol.10, pp.1401–1410
[7] Tian, R., Wang, Z., & Tan, X. 2018. A New Leakage-Based Precoding Scheme in IoT Oriented Cognitive MIMO-OFDM Systems. IEEE Access, vol. 6, pp.41023-41033,
[8] Tian, R., Ma, L., Wang, Z., & Tan, X. 2018. Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks. Sensors, vol. 18.8: 2548, pp.1-28.
[9] Perlaza SM, Fawaz N, Lasaulce S, Debbah M. 2010, From spectrum pooling to space pooling to space pooling: opportunistic interference alignment in MIMO cognitive networks. IEEE Trans Signal Proccess vol. 58, pp. 3728–41.
[10] Abdelhamid B, Elsabrouty M, Elramly S. 2012, Novel interference alignment in multi-secondary users cognitive radio system. IEEE ISCC, pp. 785-789.
[11] Rezaei F, Tadaion A. 2014, Interference alignment in cognitive radio networks. IET Communications, vol. 8, pp. 1769–77.
[12] Guler, B.; Yener, A. 2014, Selective interference alignment for MIMO cognitive femtocell networks. IEEE J. Sel. Areas Commun. vol. 32, pp. 439–450.
[13] Nguyen Duy Nhat Vien, 2018, MMSE Beamforming Design for IoT MIMO SWIPT system, Journal of Science and Technology: Issue on Information and Communications Technology, vol. 4, no.1, pp. 28-32
[14] Huang, Y., Li, Z., Zhou, F., & Zhu, R. 2017. Robust AN-aided beamforming design for secure MISO cognitive radio based on a practical nonlinear EH model. IEEE Access, vol. 5, pp. 14011-14019.
[15] Spencer, Q. H., Swindlehurst, A. L., & Haardt, M. (2004). Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels. IEEE transactions on signal processing, 52(2), 461-471.
[16] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press, 2004.