Abstract
This chapter provides an overview of the background knowledge of sparse representation with particular focus on compressive sensing, including basic principles of CS, reweighted CS, and distributed CS. Moreover, this chapter also introduces the basic framework of compressive spectrum sensing, which applies compressive sensing to wideband spectrum sensing to achieve sub-Nyquist sampling.
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References
Baron, D., Duarte, M. F., Sarvotham, S., Wakin, M. B., & Baraniuk, R. G. (2009). Distributed compressive sensing. arXiv:0901.3403, https://arxiv.org/abs/0901.3403.
Bhargavi, D., & Murthy, C. R. (2010). Performance comparison of energy, matched-filter and cyclostationarity-based spectrum sensing. In International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (pp. 1–5).
Candes, E. (2006). Compressive sampling. In International Congress of Mathematicians, Madrid, Spain (vol. 3, pp. 1433–1452).
Candes, E., & Romberg, J. (2007). Sparsity and incoherence in compressive sampling. Inverse Problems, 23, 969.
Cands, E. J. (2008). The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique, 346, 589–592.
Candes, E. J., Romberg, J., & Tao, T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52, 489–509.
Chang, H. S., Weiss, Y., & Freeman, W. T. (2009a). Informative sensing. CoRR abs/0901.4275.
Chang, H. S., Weiss, Y., & Freeman, W. T. (2009b). Informative sensing of natural images. In IEEE International Conference on Image Processing (ICIP), Cairo, Egypt (pp. 3025–3028).
Chartrand, R. (2007). Exact reconstruction of sparse signals via nonconvex minimization. IEEE Signal Processing Letters, 14, 707–710.
Chartrand, R., & Staneva, V. (2008). Restricted isometry properties and nonconvex compressive sensing. Inverse Problems, 24, 035020.
Chartrand, R., & Yin, W. (2008). Iteratively reweighted algorithms for compressive sensing. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, NV (pp. 3869–3872).
Choi, J. W., Shim, B., Ding, Y., Rao, B., & Kim, D. I. (2017). Compressed sensing for wireless communications: Useful tips and tricks. IEEE Communications Surveys & Tutorials, 19, 1527–1550.
Kolodzy, P., & Avoidance, I. (2002). Spectrum policy task force. Federal Communications Commission, Washington, DC, Rep. ET Docket.
Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6, 13–18.
Nekovee, M. (2008). Impact of cognitive radio on future management of spectrum. In International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom), Singapore (pp. 1–6)
Qin, Z., Fan, J., Liu, Y., Gao, Y., and Li, G. Y. (2018). Sparse representation for wireless communications: A compressive sensing approach. IEEE Signal Processing Magazine, 35, 40–58.
Rao, B. D., & Kreutz-Delgado, K. (1999). An affine scaling methodology for best basis selection. IEEE Transactions on Signal Processing, 47, 187–200.
Tropp, J., Laska, J., Duarte, M., Romberg, J., & Baraniuk, R. (2010). Beyond Nyquist: Efficient sampling of sparse bandlimited signals. IEEE Transactions on Information Theory, 56, 520–544.
Tropp, J. A., & Gilbert, A. C. (2007). Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 53, 4655–4666.
UK Office of Communications (Ofcom). (2009). Statement on cognitive access to interleaved spectrum.
Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T. S., & Yan, S. (2010). Sparse representation for computer vision and pattern recognition. Proceedings of IEEE, 98, 1031–1044.
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Gao, Y., Qin, Z. (2019). Sparse Representation in Wireless Networks. In: Data-Driven Wireless Networks. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-00290-9_2
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DOI: https://doi.org/10.1007/978-3-030-00290-9_2
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