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A Novel Method of Signal Fusion Based on Dimension Expansion

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Abstract

A novel method of signal fusion, namely multi-dimensional unified signal (MDUS) fusion algorithm, is proposed based on dimensionality expansion of the cognitive radio (CR). The paper focuses on the issue of under-utilized and overcrowded spectrum bands in CR, through multi-component signal fusion. The proposed MDUS fusion algorithm can meet the basic requirement of signal fusion, that is, transmitting multi-component signal in one signal simultaneously, while basic signal elements, such as amplitude and bit rate, are not constrained by the fusion. The one-dimensional imaginary domain of the signal can be expanded to two, three or higher-dimensional imaginary space by exploiting the property of logical recursion. The original data flows can be extended to three, four or higher dimensions according to functional demands. Thus, the complementary aims of mutually independent orthogonal space and multivariate signal transmission without interference, are achieved by fusing multiple uni-dimensional signals into a multi-dimensional signal. Hence, based on the electromagnetic environment, a more flexible solution can be provided for spectrum occupancy of CR users. Specifically, the number of Orthogonal Frequency Division Multiplexing (OFDM) sub-bands and MDUS dimension can be dynamically converted, based on the MDUS–OFDM structure. The proposed approach can be utilized to provide more accurate estimation and prediction of transmission quality for CR Networks. Simulation results show the MDUS offers both BER and dimensionality advantage compared to the OFDM. Additionally, single-dimensional spectrum patterns can be expanded to alterable multi-dimensional spectrum patterns owing to the flexibility of MDUS–OFDM structure.

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References

  1. A. Basit, I.M. Qureshi, W. Khan, A.N. Malik, Range—angle-dependent beamforming for cognitive antenna array radar with frequency diversity. Cognit. Comput. 8(2), 113 (2016)

    Article  Google Scholar 

  2. S. Bokharaiee, H.H. Nguyen, E. Shwedyk, A decoding-based fusion rule for cooperative spectrum sensing with nonorthogonal transmission of local decisions. Eurasip J. Wirel. Commun. Netw. 2013(1), 110 (2013)

    Article  Google Scholar 

  3. D. Davalle, R. Cassettari, S. Saponara, L. Fanucci, L. Cucchi, F. Bigongiari, W. Errico, Design, implementation and testing of a flexible fully-digital transponder for low-earth orbit satellite communications. J. Circuits Syst. Comput. 23(10), 13 (2014)

    Article  Google Scholar 

  4. G. Ding, J. Wang, Q. Wu, Y.D. Yao, F. Song, T.A. Tsiftsis, Cellular-base-station-assisted device-to-device communications in TV white space. IEEE J. Sel. Areas Commun. 34(1), 107–121 (2015)

    Article  Google Scholar 

  5. G. Ding, J. Wang, Q. Wu, L. Zhang, Robust spectrum sensing with crowd sensors. Commun. IEEE Trans. 62(9), 3129–3143 (2014)

    Article  Google Scholar 

  6. R. Etkin, A. Parekh, D. Tse, Spectrum sharing for unlicensed bands. IEEE J. Sel. Areas Commun. 25(3), 517528 (2007)

    Article  Google Scholar 

  7. X. He, D.N. Aloi, J. Li, Probabilistic multi-sensor fusion based indoor positioning system on a mobile device. Sensors 15(12), 31464–31481 (2015)

    Article  Google Scholar 

  8. Y. Hei, W. Li, M. Li, Z. Qiu, W. Fu, Optimization of multiuser MIMO cooperative spectrum sensing in cognitive radio networks. Cognit. Comput. 7(3), 359–368 (2015)

    Article  Google Scholar 

  9. R. Heideklang, P. Shokouhi, Multi-sensor image fusion at signal level for improved near-surface crack detection. Ndt E Int. 71, 16–22 (2015)

    Article  Google Scholar 

  10. N. Kausar, A. Majid, S.G. Javed, A novel ensemble approach using individual features for multi-focus image fusion. Comput. Electr. Eng. 54, 393–405 (2016)

    Article  Google Scholar 

  11. H. Li, X. Chen, G. Jing, Y. Wang, Y. Cao, F. Li, X. Zhang, X. Han, An indoor continuous positioning algorithm on the move by fusing sensors and Wi-Fi on smartphones. Sensors 15(12), 31244–31267 (2015)

    Article  Google Scholar 

  12. I. Maherin, Q. Liang, Multistep information fusion for target detection using UWB radar sensor network. IEEE Sens. J. 15(10), 5927–5937 (2015)

    Article  Google Scholar 

  13. I. Mitola Joseph, Gerald Q. Maguire, Cognitive radio: making software radios more personal. IEEE Pers. Commun. J. 6(4), 13–18 (1999)

    Article  Google Scholar 

  14. E.C.Y. Peh, Y.C. Liang, Y.L. Guan, Y. Zeng, Cooperative spectrum sensing in Cognitive Radio networks with weighted decision fusion schemes. IEEE Trans. Wirel. Commun. 9(12), 3838–3847 (2010)

    Article  Google Scholar 

  15. S. Poria, E. Cambria, A. Hussain, G.B. Huang, Towards an intelligent framework for multimodal affective data analysis. Neural Netw.Off. J. Int. Neural Netw. Soc. 63, 104–116 (2015)

    Article  Google Scholar 

  16. H. Sadeghi, P. Azmi, Performance analysis of linear cooperative cyclostationary spectrum sensing over nakagami-\(m\) fading channels. Veh. Technol. IEEE Trans. 63(9), 4748–4756 (2014)

    Article  Google Scholar 

  17. A.E. Shafie, T. Khattab, A.S. Salem, Relay-assisted primary and secondary transmissions in Cognitive Radio networks. IEEE Access 4, 6386–6400 (2016)

    Article  Google Scholar 

  18. Y. Sun, B.L. Mark, Y. Ephraim, Collaborative spectrum sensing via online estimation of Hidden Bivariate Markov models. IEEE Trans. Wirel. Commun. 15(8), 5430–5439 (2016)

    Article  Google Scholar 

  19. C.C. Took, D.P. Mandic, The quaternion LMS algorithm for adaptive filtering of hypercomplex processes. IEEE Trans. Signal Process. 57(4), 1316–1327 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  20. X. Yan, H. Qin, J. Li, H. Zhou, J.G. Zong, Infrared and visible image fusion with spectral graph wavelet transform. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 32(9), 1643–1652 (2015)

    Article  Google Scholar 

  21. H. Yin, Z. Liu, B. Fang, Y. Li, A novel image fusion approach based on compressive sensing. Opt. Commun. 354, 299–313 (2015)

    Article  Google Scholar 

  22. T. Yucek, H. Arslan, A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun. Surv. Tutor. 11(1), 116–130 (2009)

    Article  Google Scholar 

  23. Z. Zhang, Z. Zhang, S. Liu, Cross domain boosting for information fusion in heterogeneous sensor-cyber sources. Signal Process. 126, 180–186 (2015)

    Article  Google Scholar 

  24. S. Zhao, L. Zhang, Y. Zhou, N. Liu, Signal fusion-based algorithms to discriminate between radar targets and deception jamming in distributed multiple radar architectures. IEEE Sens. J. 15(11), 6697–6706 (2015)

    Article  Google Scholar 

  25. Y. Zhuang, H. Lan, Y. Li, N. ElSheimy, PDR/INS/WIFI integration based on handheld devices for indoor pedestrian navigation. Micromachines 6(6), 793–812 (2015)

    Article  Google Scholar 

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Acknowledgements

The authors are grateful to the anonymous reviewers for their insightful comments and suggestions, which helped to improve the quality of this paper.

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China

    Tianhong Zhang, Limei Xu, Enpin Yang, Xiao Yan, Kaiyu Qin & Qian Wang

  2. Cognitive Big Data Informatics (CogBiD) Lab, Division of Computing Science and Maths, University of Stirling, Stirling, FK9 4LA, Scotland, UK

    Amir Hussain

Authors
  1. Tianhong Zhang
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  2. Limei Xu
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  3. Enpin Yang
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  4. Xiao Yan
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  5. Kaiyu Qin
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  6. Qian Wang
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  7. Amir Hussain
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Corresponding author

Correspondence to Xiao Yan.

Additional information

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61301261 and 61601091, and the Fundamental Research Funds for the Central Universities of China under Grant No. ZYGX2015J121. Professor A. Hussain is supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant no. EP/M026981/1.

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Zhang, T., Xu, L., Yang, E. et al. A Novel Method of Signal Fusion Based on Dimension Expansion. Circuits Syst Signal Process 37, 4295–4318 (2018). https://doi.org/10.1007/s00034-018-0760-5

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  • DOI: https://doi.org/10.1007/s00034-018-0760-5

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