Parameter selection in periodic nonuniform sampling of multiband signals

Rashidi, Moslem; Mansouri, Sara

doi:10.1109/iseee.2010.5628538

Cited by 6 publications

(9 citation statements)

References 7 publications

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“…This is in line with the complexity comparison in Section V-B. Here, we consider an example where the information containing frequency bands are { , which were determined using the method in [29]. For the synthesis FB, a power-symmetric lowpass prototype filter of order 1162 is required to keep the aliasing terms below −40 dB at the output of the proposed reconstructor.…”

Section: Design Examplesmentioning

confidence: 64%

“…, N P /2, are the impulse response coefficients of the synthesis filters C k (e jω ). Using (34), we can rewrite (29) and (30) as…”

Section: B Least-squares Design Of F ℓ (Z) and G ℓ (Z)mentioning

confidence: 99%

“…For the above two possible combination of band locations (two modes), we used the subNyquist sampling points, m = 0, 3,5,14,16,19,21,30, which ensures that D in (11) is an invertible matrix. The sampling instants were determined using the method in [29].…”

Section: Design Complexitymentioning

confidence: 99%

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Efficient Recovery of Sub-Nyquist Sampled Sparse Multi-Band Signals Using Reconfigurable Multi-Channel Analysis and Modulated Synthesis Filter Banks

Pillai

Johansson

2015

IEEE Trans. Signal Process.

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Abstract-Sub-Nyquist cyclic nonuniform sampling (CNUS) of a sparse multi-band signal generates a nonuniformly sampled signal. Assuming that the corresponding uniformly sampled signal satisfies the Nyquist sampling criterion, the sequence obtained via CNUS can be passed through a reconstructor to recover the missing uniform-grid samples. At present, these reconstructors have very high design and implementation complexity that offsets the gains obtained due to sub-Nyquist sampling. In this paper, we propose a scheme that reduces the design and implementation complexity of the reconstructor. In contrast to the existing reconstructors which use only a multi-channel synthesis filter bank (FB), the proposed reconstructor utilizes both analysis and synthesis FBs which makes it feasible to achieve an orderof-magnitude reduction of the complexity. The analysis filters are implemented using polyphase networks whose branches are allpass filters with distinct fractional delays and phase shifts. In order to reduce both the design and the implementation complexity of the synthesis FB, the synthesis filters are implemented using a cosine-modulated FB. In addition to the reduced complexity of the reconstructor, the proposed multi-channel recovery scheme also supports online reconfigurability which is required in flexible (multi-mode) systems where the user subband locations vary with time.Index Terms-Sub-Nyquist sampling, sparse multi-band signals, reconstruction, nonuniform sampling, time-interleaved analog-to-digital converters, filter banks.

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Section: Design Examplesmentioning

confidence: 64%

“…, N P /2, are the impulse response coefficients of the synthesis filters C k (e jω ). Using (34), we can rewrite (29) and (30) as…”

Section: B Least-squares Design Of F ℓ (Z) and G ℓ (Z)mentioning

confidence: 99%

See 1 more Smart Citation

Efficient Recovery of Sub-Nyquist Sampled Sparse Multi-Band Signals Using Reconfigurable Multi-Channel Analysis and Modulated Synthesis Filter Banks

Pillai

Johansson

2015

IEEE Trans. Signal Process.

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“…With sub-Nyquist sampling scheme, aliased noise and signal sideband may cause considerable error for signal recovery. On the reconstruction stage, the periodically non-uniform sampled signal can be sensitive to the adding errors as shown in previous researches [2]- [5]. Yuan-Pei Lin and P.P.Vaidyanathan [2] has developed periodically non-uniform sampling of Lth order and extended PNS to multiband signal sampling.…”

Section: Introductionmentioning

confidence: 99%

“…Dennis M. Akos [4] has studied the sub-Nyquist sampling on multiband signal. Recently Moslem Rashidi [5] has presented a feasible algorithm of getting a suitable sampling pattern by analyzing the condition number of measure matrix. However the research mentioned above does not combine the imperfectly band-limited condition with the signal reconstruction formula and lacks an explicit optimization method of sampling parameter based on the scheme of reconstruction from PNS.…”

Section: Introductionmentioning

confidence: 99%

Parameter optimization in reconstruction of multiband signal from periodically non-uniform sampling

Zhang

et al. 2014

2014 IEEE/ACIS 13th International Conference on Computer and Information Science (ICIS)

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Periodically non-uniform sampling (PNS) is considered as an effective approach for multi-band signal sampling. Reconstruction of signal from PNS can be achieved without distortion under the assumption that the signal is perfectly band-limited. However, most signals in practice could not meet such requirement, thus the sampling parameters play an important role on the quality of reconstructed signal. In this paper, an optimization method of sampling parameter is proposed in the presence of non-ideality condition. The optimization method deals with time shifts of each sampling channel at fixed sampling rate and is based on minimizing the 2-norm of coefficients function of the reconstruction filters. Simulation results are provided to demonstrate the effectiveness of the proposed method.

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Cyclostationary-based jammer detection for wideband radios using compressed sensing and artificial neural network

Nawaz

Regazzoni

2017

International Journal of Distributed Sensor Networks

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Cognitive radio is a promising technology for frequency allocation to improve the spectrum utilization efficiency of licensed bands. However, in recent years, the attention of the researchers is focused on security issues that have to be faced by cognitive radio technology. One of the most important issues consists of radio frequency jamming attacks, where adversaries can use on-the-fly reconfigurability and learning capabilities of cognitive radios in order to devise and deploy advanced jamming tactics. Jamming attacks can noticeably affect the performance of wireless communication systems and can lead to significant overhead in terms of data re-transmission and increased power consumption. In this article, a novel compressed sensing-based jammer detection algorithm is proposed using cyclic spectral analysis and artificial neural networks for wideband cognitive radios. A wideband spectrum is considered that is composed of multiple narrowband signals. Narrowband signals can be legitimate or jamming signals. Compressed sensing is used to reduce the overhead of the analog-to-digital conversion and it allows one to estimate a wideband spectrum with sub-Nyquist rate sampling. After the signal has been estimated, the second-order statistics, namely, spectral correlation function, is computed to extract cyclic features of the wideband signal. Finally, a pre-trained artificial neural network is proposed to classify each narrowband signal as a legitimate or jamming signal. Performances of proposed algorithm are shown with Monte-Carlo simulations under different empirical setups.

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Parameter selection in periodic nonuniform sampling of multiband signals

Cited by 6 publications

References 7 publications

Efficient Recovery of Sub-Nyquist Sampled Sparse Multi-Band Signals Using Reconfigurable Multi-Channel Analysis and Modulated Synthesis Filter Banks

Efficient Recovery of Sub-Nyquist Sampled Sparse Multi-Band Signals Using Reconfigurable Multi-Channel Analysis and Modulated Synthesis Filter Banks

Parameter optimization in reconstruction of multiband signal from periodically non-uniform sampling

Cyclostationary-based jammer detection for wideband radios using compressed sensing and artificial neural network

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