Abstract
Conventional spectrum sensing (SS) schemes in a multiantenna cognitive radio utilize matrix-inverse based prewhitening to decorrelate the spatially correlated received signals. However, the improved SS schemes can be proposed by efficiently exploiting the spatial correlation information. In this paper, two detectors—weighted cross-correlation absolute value detector (WCCAVD) and weighted energy detector (WED), are proposed by exploiting the spatial correlation of noise. It is shown that in the presence of high spatial correlation and low signal-to-noise ratio, the proposed WCCAVD and WED outperform the conventional cross-correlation absolute value detector and energy detector (ED), respectively, by more than 1 dB. For the spatial correlation below 0.5, both the proposed detectors are shown to have comparable performance with respect to the corresponding conventional detectors. The analytical expressions for the decision threshold, the false-alarm and the detection probabilities of the proposed detectors are derived. The analytical results are validated by Monte-Carlo simulations.
Similar content being viewed by others
Notes
Channel can also assumed to be spatially correlated in a multiantenna CR system. Other than this, the noiseless received signals and noise signals can also be assumed to be correlated in the time domain. In this paper, we are interested in obtaining the improved SS schemes by assuming only the noise to be spatially correlated.
References
Ali, A., & Hamouda, W. (2017). Advances on spectrum sensing for cognitive radio networks: Theory and applications. IEEE Communications Surveys and Tutorials, 19(2), 1277–1304.
Claudino, L., & Abrão, T. (2017). Spectrum sensing methods for cognitive radio networks: A review. Wireless Personal Communications, 95(4), 5003–5037.
Gavrilovska, L., & Atanasovski, V. (2011). Spectrum sensing framework for cognitive radio networks. Wireless Personal Communications, 59(3), 447–469.
Axell, E., Leus, G., Larsson, E., & Poor, H. (2012). Spectrum sensing for cognitive radio: State-of-the-art and recent advances. IEEE Signal Processing Magazine, 29(3), 101–116.
Taherpour, A., Nasiri-Kenari, M., & Gazor, S. (2010). Multiple antenna spectrum sensing in cognitive radios. IEEE Transactions on Wireless Communications, 9(2), 814–823.
Fouda, H. S., Hussein, A. H., & Attia, M. A. (2018). Efficient GLRT/DOA spectrum sensing algorithm for single primary user detection in cognitive radio systems. AEÜ International Journal of Electronics and Communications, 88, 98–109.
Ganesan, G., & Ye, Li. (2007). Cooperative spectrum sensing in cognitive radio, Part I: Two user networks. IEEE Transactions on Wireless Communications, 6(6), 2204–2213.
Sudhamani, C., & Satya Sai Ram, M. (2019). Energy efficiency in cognitive radio network using cooperative spectrum sensing. Wireless Personal Communications, 104(3), 907–919.
Zeng, Y., & Liang, Y. C. (2009). Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communications, 57(6), 1784–1793.
Han, W., Huang, C., Li, J., Li, Z., & Cui, S. (2015). Correlation-based spectrum sensing with oversampling in cognitive radio. IEEE Journal on Selected Areas in Communications, 33(5), 788–802.
Vazquez-Vilar, G., Lopez-Valcarce, R., & Sala, J. (2011). Multiantenna spectrum sensing exploiting spectral a priori information. IEEE Transactions on Wireless Communications, 10(12), 4345–4355.
Huang, Y., & Huang, X. (2013). Detection of temporally correlated signals over multipath fading channels. IEEE Transactions on Wireless Communications, 12(3), 1290–1299.
Chen, A., Shi, Z., & He, Z. (2018). A robust blind detection algorithm for cognitive radio networks with correlated multiple antennas. IEEE Communications Letters, 22(3), 570–573.
Sharma, S. K., Chatzinotas, S., & Ottersten, B. (2013). Eigenvalue-based sensing and SNR estimation for cognitive radio in presence of noise correlation. IEEE Transactions on Vehicular Technology, 62(8), 3671–3684.
Sharma, S. K., Bogale, T. E., Chatzinotas, S., Ottersten, B., Le, L. B., & Wang, X. (2015). Cognitive radio techniques under practical imperfections: A survey. IEEE Communications Surveys and Tutorials, 17(4), 1858–1884.
Shayegh, F., & Labeau, F. (2014). On signal detection in the presence of weakly correlated noise over fading channels. IEEE Transactions on Communications, 62(3), 797–809.
Cardoso, L. S., Debbah, M., Bianchi, P., & Najim, J. (2008). Cooperative spectrum sensing using random matrix theory. In 3rd International Symposium on Wireless Pervasive Computing (pp. 334–338). Santorini.
Kang, M. S., Jung, B. C., Sung, D. K., & Choi, W. (2008). A pre-whitening scheme in a MIMO-based spectrum-sharing environment. IEEE Communications Letters, 12(11), 831–833.
Kim, J., Choi, W., Nam, S., & Han, Y. (2014). An efficient prewhitening scheme for MIMO cognitive radio systems. IEEE Transactions on Vehicular Technology, 63(4), 1934–1939.
Hager, W. (1989). Updating the inverse of a matrix. SIAM Review, 31(2), 221–239.
Hayya, J., Armstrong, D., & Gressis, N. (1975). A note on the ratio of two normally distributed variables. Management Science, 21(11), 1338–1341.
Janssen, P. H. M., & Stoica, P. (1988). On the expectation of the product of four matrix-valued Gaussian random variables. IEEE Transactions on Automatic Control, 33(9), 867–870.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
1.1 Calculation of Mean and Variance of \(g_{ij}\)
For large values of N, \(g_{ij}\) can be approximated to Gaussian distributed with mean and variance determined as
The first term in (32) can be calculated as
We use an identity [22] \(\text{ E }[x_1 x_2 x_3 x_4 ] = \text{ E }[x_1 x_2 ]\text{ E }[x_3 x_4 ] + \text{ E }[x_1 x_3 ]\text{ E }[x_2 x_4 ] + \text{ E }[x_1 x_4 ]\text{ E }[x_2 x_3 ] - 2\text{ E }[x_1 ]\text{ E }[x_2 ]\text{ E }[x_3 ]\text{ E }[x_4 ]\) to calculate \(\mathrm{{E}}[\left| {z_i^* (n_1 )z_j (n_1 )e^{\mathbf{{j}}\phi _{ij} } } \right| ^2 ] = (\left| {h_i } \right| \left| {h_j } \right| \varepsilon _s + \sigma _w^2 \lambda _{i( = j)} \delta _{ij} )^2 + (\left| {h_i } \right| ^2 \varepsilon _s + \sigma _w^2 \lambda _i )(\left| {h_j } \right| ^2 \varepsilon _s + \sigma _w^2 \lambda _j )\). Also, it can be easily obtained \(\left| {\mathrm{E}[z_i^* (n_1 )z_j (n_1 )e^{\mathbf{j }\phi _{ij} } ]} \right| ^2 = (\left| {h_i } \right| \left| {h_j } \right| \varepsilon _s + \sigma _w^2 \lambda _{i( = j)} \delta _{ij} )^2.\) Hence, the first term of (32) is
The second term of (32) is calculated as
Finally, (32) can be determined as
Rights and permissions
About this article
Cite this article
Kumar, S., Eswaran, S.P. Improved Spectrum Sensing Schemes Using Prewhitening and Weights Under Spatially Correlated Noise. Wireless Pers Commun 115, 153–171 (2020). https://doi.org/10.1007/s11277-020-07565-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-020-07565-y