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The sub-sampling method for Orthogonal Frequency Division Multiplexing proposed recently, has been extended in this paper allowing the Analog-to-Digital Converter on the receiver side to operate in low power mode, up to 3/4 of the time. The predictability of the parity patterns generated by the Forward Error Correction encoder of the transmitter, when sparse data are exchanged, is exploited in order to define appropriate Inverse Fast Fourier Transform input symbol arrangements. These symbol arrangements allow the substitution of a number of samples by others that have already been received. Moreover, several operations of the Fast Fourier Transform can be omitted because their result is zero when identical values appear at its input. The advantages of the proposed method are: low power, higher speed and fewer memory resources. Despite other iterative sub-sampling approaches like Compressive Sampling, the proposed method is not iterative and thus it can be implemented with very low complexity hardware. The simulation results show that full input signal recovery or at least a very low Bit Error Rate is achieved in most of the cases that have been tested.
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Czasopismo
Rocznik
Tom
Strony
19--32
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Bibliogr. 18 poz., rys.
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autor
Bibliografia
- [1] Candes, E.J., Wakin, M. B. (2008). An introduction to compressive sampling. IEEE signal processing magazine, 25(2), 21–30
- [2] 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
- [3] Cooley, J.W., Tukey, J.W. (1965). An algorithm for the machine calculation of complex Fourier series. Mathematics of computation, 19(90), 297–301
- [4] Donoho, D.L. (2006). Compressed sensing. IEEE Transactions on information theory, 52(4), 1289–1306
- [5] 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
- [6] 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
- [7] 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
- [8] Petrellis, N. (2016). Low power OFDM receiver exploiting data sparseness and DFT symmetry. International Journal of Distributed Sensor Networks, 2016, 2
- [9] Petrellis, N. (2016). Extended Sub-sampling inOFDM Environment. Proceedings of the IEICEICTF
- [10] 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
- [11] Poshalla, P. (2013). Why oversampling when undersampling can do the job. Texas Instruments Application Report SLAA594A
- [12] 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
- [13] 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
- [14] 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
- [15] Tian, Q., Wu, J. (2013). A review on face recognition based on compressive sensing. IETE Technical Review, 30(5), 427–438
- [16] Vaswani, N. (2008, October). Kalman filtered compressed sensing. In 2008 15th IEEE International Conference on Image Processing (pp. 893–896). IEEE
- [17] Vaswani, N. (2010). LS-CS-residual (LS-CS): compressive sensing on least squares residual. IEEE Transactions on Signal Processing, 58(8), 4108–4120
- [18] 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
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
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Bibliografia
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bwmeta1.element.baztech-ca1e9be7-8703-4498-a84b-bc082a653298