A new method to compute optimal periodic sampling patterns

Owrang, Arash; Viberg, Mats; Nosratinia, Mohsen; Rashidi, Moslem

doi:10.1109/dsp-spe.2011.5739222

Cited by 4 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the proposed reconstructor, the real-time reconfiguration is simple and fast as it suffices to determine the multiplier values β km ℓ and θ km ℓ using (11). Thus, during reconfiguration, only the coefficients of the 2K 2 multipliers corresponding to the scaled cos(·) and sin(·) terms in (19) need to be updated.…”

Section: Reconfiguration Complexitymentioning

confidence: 99%

“…Further, the subfilters F ℓ (z) and G ℓ (z) in the analysis FB are designed and fixed based on the sampling instants. In applications where all the L possible combinations (L modes) of the K active subbands use the same set of sampling instants, during reconfiguration, it suffices to redetermine the complex constants v k in (11). Following a least-squares approach similar to the one outlined in Section VI-B, the coefficients of the subfilters F ℓ (z) and G ℓ (z) for the reconfigurable reconstructor are then determined using…”

Section: Design Of Reconfigurable Reconstructorsmentioning

confidence: 99%

“…. , K, for a given set of K granularity bands, provided the sampling instants m ℓ are selected properly [11].…”

Section: Sub-nyquist Cyclic Nonuniform Sampling Of Sparse Multi-mentioning

confidence: 99%

“…However, these sampling instants may not guarantee that the matrix D is well conditioned. In order to ensure that D is well conditioned, optimal sampling instants can be selected depending on the active subband locations as outlined in [9], [11].…”

Section: Theoremmentioning

confidence: 99%

“…For the CNUS approach, it is known that the reconstruction can be done, in principle, via a set of ideal multi-level synthesis filters, given the sampling pattern [7]- [9]. The related problem of selecting the optimal sampling patterns has also been addressed [9]- [11]. However, the straightforward CNUS recovery scheme has very high design and implementation complexities 1 .…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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.

View full text Add to dashboard Cite

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.

show abstract

Section: Reconfiguration Complexitymentioning

confidence: 99%

Section: Design Of Reconfigurable Reconstructorsmentioning

confidence: 99%

“…. , K, for a given set of K granularity bands, provided the sampling instants m ℓ are selected properly [11].…”

Section: Sub-nyquist Cyclic Nonuniform Sampling Of Sparse Multi-mentioning

confidence: 99%

Section: Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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.

View full text Add to dashboard Cite

show abstract

Efficient reconfigurable scheme for the recovery of sub-Nyquist sampled sparse multi-band signals

Pillai

Johansson

2013

2013 IEEE Global Conference on Signal and Information Processing

View full text Add to dashboard Cite

Sub-Nyquist sampling makes use of sparsities in analog signals to sample them at a rate lower than the Nyquist rate. The reduction in sampling rate, however, comes at the cost of additional digital signal processing (DSP) which is required to reconstruct the uniformly sampled sequence at the output of the sub-Nyquist sampling analog-to-digital converter. At present, this additional processing is computationally intensive and time consuming and offsets the gains obtained from the reduced sampling rate. This paper focuses on sparse multi-band signals where the user band locations can change from time to time and the reconstructor requires real-time redesign. We propose a technique that can reduce the computational complexity of the reconstructor. At the same time, the proposed scheme simplifies the online reconfigurability of the reconstructor

show abstract

A novel synchronization method for DS-CDMA systems

Chen

2012

2012 8th International Wireless Communications and Mobile Computing Conference (IWCMC)

View full text Add to dashboard Cite

A new method to compute optimal periodic sampling patterns

Cited by 4 publications

References 9 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

Efficient reconfigurable scheme for the recovery of sub-Nyquist sampled sparse multi-band signals

A novel synchronization method for DS-CDMA systems

Contact Info

Product

Resources

About