### Sub-sampling in OFDM with Constant Time Signal Recovery

#### Abstract

#### References

Candes, E.J., Wakin, M. B. (2008). An introduction to compressive sampling. IEEE signal processing magazine, 25(2), 21–30

Carmi, A., Gurfil, P., Kanevsky, D. (2010). Methods for sparse signal recovery using Kalman filtering with embedded pseudo-measurement norms and quasi-norms. IEEE Transactions on Signal Processing, 58(4), 2405–2409

Cooley, J.W., Tukey, J.W. (1965). An algorithm for the machine calculation of complex Fourier series. Mathematics of computation, 19(90), 297–301

Donoho, D.L. (2006). Compressed sensing. IEEE Transactions on information theory, 52(4), 1289–1306

Fazel, F., Fazel, M., Stojanovic, M. (2011). Random access compressed sensing for energy-efficient underwater sensor networks. IEEE Journal on Selected Areas in Communications, 29(8), 1660–1670

Hormati, A., Vetterli, M. (2011). Compressive sampling of multiple sparse signals having common support using finite rate of innovation principles. IEEE Signal Processing Letters, 18(5), 331–334

Mahalanobis, A., Muise, R. (2009). Object specific image reconstruction using a compressive sensing architecture for application in surveillance systems. IEEE transactions on aerospace and electronic systems, 45(3), 1167–1180

Petrellis, N. (2016). Low power OFDM receiver exploiting data sparseness and DFT symmetry. International Journal of Distributed Sensor Networks, 2016, 2

Petrellis, N. (2016). Extended Sub-sampling inOFDM Environment. Proceedings of the IEICEICTF

Pimentel, C., Souza, R.D., Uchôa-Filho, B.F., Benchimol, I. (2011, July). Minimal trellis for systematic recursive convolutional encoders. In Information Theory Proceedings (ISIT), 2011 IEEE International Symposium on (pp. 2477–2481). IEEE

Poshalla, P. (2013). Why oversampling when undersampling can do the job. Texas Instruments Application Report SLAA594A

Qi, C., Wu, L. (2011, May). A hybrid compressed sensing algorithm for sparse channel estimation in MIMO OFDM systems. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3488–3491). IEEE

Stanislaus, J.L., Mohsenin, T. (2013, January). Low-complexity FPGA implementation of compressive sensing reconstruction. In Computing, Networking and Communications (ICNC), 2013 International Conference on (pp. 671–675). IEEE

Tsai, Y.M., Huang, K.Y., Kung, H.T., Vlah, D., Gwon, Y. L., Chen, L.G. (2012, October). A chip architecture for compressive sensing based detection of IC trojans. In 2012 IEEE Workshop on Signal Processing Systems (pp. 61–66). IEEE

Tian, Q., Wu, J. (2013). A review on face recognition based on compressive sensing. IETE Technical Review, 30(5), 427–438

Vaswani, N. (2008, October). Kalman filtered compressed sensing. In 2008 15th IEEE International Conference on Image Processing (pp. 893–896). IEEE

Vaswani, N. (2010). LS-CS-residual (LS-CS): compressive sensing on least squares residual. IEEE Transactions on Signal Processing, 58(8), 4108–4120

Xu, L., Liang, Q. (2012, June). Compressive sensing in radar sensor networks using pulse compression waveforms. In 2012 IEEE International Conference on Communications (ICC) (pp. 794–798). IEEE

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