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Generalized Bandpass Sampling Receivers for Software Defined Radio
KTH, School of Information and Communication Technology (ICT), Microelectronics and Information Technology, IMIT.
2006 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

Based on different sampling theorem, for example classic Shannon’s sampling theorem and Papoulis’ generalized sampling theorem, signals are processed by the sampling devices without loss of information. As an interface between radio receiver front-ends and digital signal processing blocks, sampling devices play a dominant role in digital radio communications.

Under the concept of Software Defined Radio (SDR), radio systems are going through the second evolution that mixes analog, digital and software technologies in modern radio designs. One design goal of SDR is to put the A/D converter as close as possible to the antenna. BandPass Sampling (BPS) enables one to have an interface between the RF or the higher IF signal and the A/D converter, and it might be a solution to SDR. However, three sources of performance degradation present in BPS systems, harmful signal spectral overlapping, noise aliasing and sampling timing jitter, hinder the conventional BPS theory from practical circuit implementations.

In this thesis work, Generalized Quadrature BandPass Sampling (GQBPS) is first invented and comprehensively studied with focus on the noise aliasing problem. GQBPS consists of both BPS and FIR filtering that can use either real or complex coefficients. By well-designed FIR filtering, GQBPS can also perform frequency down-conversion in addition to noise aliasing reduction. GQBPS is a nonuniform sampling method in most cases. With respect to real circuit implementations, uniform sampling is easier to be realized compared to nonuniform sampling. GQBPS has been also extended to Generalized Uniform BandPass Sampling (GUBPS). GUBPS shares the same property of noise aliasing suppression as GQBPS besides that the samples are uniformly spaced. Due to the moving average operation of FIR filtering, the effect of sampling jitter is also reduced to a certain degree in GQBPS and GUBPS. By choosing a suitable sampling rate, harmful signal spectral overlapping can be avoided. Due to the property of quadrature sampling, the “self image” problem caused by I/Q mismatches is eliminated. Comprehensive theoretical analyses and program simulations on GQBPS and GUBPS have been done based on a general mathematic model. Circuit architecture to implementing GUBPS in Switched-Capacitor circuit technique has been proposed and analyzed. To improve the selectivity at the sampling output, FIR filtering is extended by adding a 1st order complex IIR filter in the implementation.

GQBPS and GUBPS operate in voltage-mode. Besides voltage sampling, BPS can also be realized by charge sampling in current-mode. Most other research groups in this area are focusing on bandpass charge sampling. However, the theoretical analysis shows that our GQBPS and GUBPS in voltage mode are more efficient to suppress noise aliasing as compared to bandpass charge sampling with embedded filtering. The aliasing bands of sampled-data spectrum are always weighted by continuous-frequency factors for bandpass charge sampling with embedded filtering while discrete-frequency factors for GQBPS and GUBPS. The transmission zeros of intrinsic filtering will eliminate the corresponding whole aliasing bands of both signal and noise in GQBPS and GUBPS, while it will only cause notches at a limited set of frequencies in bandpass charge sampling. In addition, charge sampling performs an intrinsic continuous-time sinc function that always includes lowpass filtering. This is a drawback for a bandpass input signal.

Place, publisher, year, edition, pages
Stockholm: KTH , 2006. , xvi, 104 p.
Series
Trita-ICT-ECS AVH, ISSN 1653-6363 ; 06:01
Keyword [en]
Radio receivers, Software Defined Radio (SDR), uniform/nonuniform sampling, reconstruction, bandpass sampling, noise aliasing, sampling jitter, generalized bandpass sampling, complex signal processing, complex FIR and IIR filter design, Switched-Capacitor (SC) circuit
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-4009ISBN: 91-7178-350-4 (print)OAI: oai:DiVA.org:kth-4009DiVA: diva2:10373
Public defence
2006-06-09, Sal E, KTH-Forum, Isafjordsgatan 39, Kista, 14:00
Opponent
Supervisors
Note
QC 20100921Available from: 2006-05-30 Created: 2006-05-30 Last updated: 2010-09-21Bibliographically approved
List of papers
1. Theory of Generalized Bandpass Sampling in Subsampling Receivers
Open this publication in new window or tab >>Theory of Generalized Bandpass Sampling in Subsampling Receivers
2005 (English)In: IEEE Trans. of CAS-IArticle in journal (Other academic) Submitted
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-5856 (URN)
Note
QS 20120316Available from: 2006-05-30 Created: 2006-05-30 Last updated: 2012-03-16Bibliographically approved
2. Filtering Transformation in GeneralizedQuadrature Bandpass Sampling
Open this publication in new window or tab >>Filtering Transformation in GeneralizedQuadrature Bandpass Sampling
2005 (English)In: Electronics, Circuits and Systems, 2005. ICECS 2005. 12th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Tunis, Tunisia, December2005., 2005, 1-4 p.Conference paper, Published paper (Refereed)
Abstract [en]

According to recent research, Generalized Quadrature BandPass Sampling (GQBPS) with FIR filtering is promising to deal with the noise aliasing problem in subsampling systems. However, the interesting band of folded spectra by GQBPS with real FIR filtering is still located at the input carrier frequency. In this paper, inherent FIR filtering in GQBPS with real coefficients is transformed such that the interesting band is shifted to a lower frequency. The main advantage of such transformation is to achieve a frequency downconversion besides sampling and dealing with noise aliasing in GQBPS. As a special case of GQBPS, Uniform Quadrature BandPass Sampling (UQBPS) is also discussed including the filter transformation. Both theoretical analyses and simulations are included. It is verified that the expected noise aliasing improvement of GQBPS or UQBPS does not change with the filter transformation. The whole subsampling system with GQBPS or UQBPS might be simplified by using complex filtering.

Keyword
FIR filters, band-pass filters, filtering theory, signal sampling
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-5855 (URN)10.1109/ICECS.2005.4633565 (DOI)2-s2.0-56749174181 (Scopus ID)
Note
QC 20100921Available from: 2006-05-30 Created: 2006-05-30 Last updated: 2010-09-21Bibliographically approved
3. Analysis and Implementation of Uniform Quadrature Bandpass Sampling
Open this publication in new window or tab >>Analysis and Implementation of Uniform Quadrature Bandpass Sampling
2005 (English)In: 2005 IEEE WORKSHOP ON SIGNAL PROCESSING SYSTEMS: DESIGN AND IMPLEMENTATION (SIPS), 2005, 137-142 p.Conference paper, Published paper (Refereed)
Abstract [en]

Sampling noise folding causes a large SNR degradation at the output of BandPass Sampling (BPS) system. A sampling architecture based on Generalized Quadrature BandPass Sampling (GQBPS) was proposed in [1]. Theoretical analysis showed that such architecture is promising to reduce the SNR degradation due to noise aliasing. In this paper, Uniform Quadrature BandPass Sampling (UQBPS) as a special case of GQBPS is analyzed for both ideal sampling and a sample-and-hold. One available implementation method to UQBPS is shown and discussed at the circuit level.

Series
IEEE Workshop on Signal Processing Systems, ISSN 1520-6130
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-5854 (URN)10.1109/SIPS.2005.1579853 (DOI)000236758900025 ()2-s2.0-33846986762 (Scopus ID)0-7803-9333-3 (ISBN)
Conference
IEEE Workshop on Signal Processing Systems Design and Implementations (SiPS 05). Athens, GREECE. NOV 02-04, 2005
Note
QC 20100921 QC 20111014Available from: 2006-05-30 Created: 2006-05-30 Last updated: 2011-10-14Bibliographically approved
4. Generalized Quadrature Bandpass Sampling with FIR Filtering
Open this publication in new window or tab >>Generalized Quadrature Bandpass Sampling with FIR Filtering
2005 (English)In: 2005 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), VOLS 1-6, CONFERENCE PROCEEDINGS, 2005, 4429-4432 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, generalized quadrature BandPass Sampling (BPS) in voltage-mode with inherent FIR filtering is presented. By using the theory of sampling equivalence, this sampling strategy is comparable to another strategy, charge sampling with intrinsic IIR filtering. The theoretical analysis and simulation results show that this inherent FIR filtering not only has the advantage to reject or attenuate images and interferences, but is also helpful to suppress noise aliasing. A realizable implementation of the proposed sampling strategy by uniform quadrature BPS (UQBPS) promises to suppress noise aliasing in BPS systems to a large extent.

Series
IEEE INTERNATIONAL SYMP ON CIRCUITS AND SYSTEMS, ISSN 0277-674X
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-5853 (URN)10.1109/ISCAS.2005.1465614 (DOI)000232002404064 ()2-s2.0-33846982989 (Scopus ID)
Conference
IEEE International Symposium on Circuits and Systems (ISCAS) Kobe, JAPAN, MAY 23-26, 2005
Note
QC 20100921Available from: 2006-05-30 Created: 2006-05-30 Last updated: 2011-10-14Bibliographically approved
5. A Generalized Quadrature Bandpass Sampling in Radio Receivers
Open this publication in new window or tab >>A Generalized Quadrature Bandpass Sampling in Radio Receivers
2005 (English)In: 10th Asia and South Pacific Design Automation Conference Shanghai, PEOPLES R CHINA, JAN 18-21, 2005, 2005, 1288-1291 p.Conference paper, Published paper (Refereed)
Abstract [en]

Bandpass sampling (BPS) realizes frequency down-conversion by undersampling. Noise aliasing as the direct consequence of the lower sampling rate causes a performance degradation. In this paper, a generalized quadrature BPS (GQBPS) combined with a filter which performs both reconstruction and bandpass filtering is studied in the frequency domain with respect to both signal reconstruction and noise aliasing reduction. The theoretical analyses show that GQBPS might be a potential way to reduce noise aliasing at the cost of a more complicated reconstruction algorithm.

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-5852 (URN)10.1109/ASPDAC.2005.1466579 (DOI)000245021700273 ()2-s2.0-33847005476 (Scopus ID)
Note
QC 20100921Available from: 2006-05-30 Created: 2006-05-30 Last updated: 2010-09-21Bibliographically approved
6. A Novel Quadrature BandpassSampling in SDR Front-Ends
Open this publication in new window or tab >>A Novel Quadrature BandpassSampling in SDR Front-Ends
2004 (English)In: Proceeding of Biennial Analog Signal ProcessingConference, (ASP 2004), Oxford: Oxford Brookes University , 2004Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, a Generalized Quadrature BandPass Sampling (GQBPS) and inherent FIR filteringare briefly reviewed. Both theoretical analysis and simulation results show that this inherentFIR filtering not only has the advantage of image and interference rejection, but also is helpfulto suppress noise aliasing. A realizable implementation by uniform quadrature BPS (UQBPS)shows promise to sufficiently suppress the noise aliasing in BPS systems.

Place, publisher, year, edition, pages
Oxford: Oxford Brookes University, 2004
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-24664 (URN)
Note
QC 20100921Available from: 2010-09-21 Created: 2010-09-21 Last updated: 2010-09-21Bibliographically approved
7. A Generalized Quadrature Bandpass Samplingwith Noise Aliasing Suppression
Open this publication in new window or tab >>A Generalized Quadrature Bandpass Samplingwith Noise Aliasing Suppression
2004 (English)In: Workshop on Wireless Circuits andSystems (WoWCAS),Vancouver B.C., Canada, May 2004, 2004, 41-42 p.Conference paper, Published paper (Refereed)
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-24667 (URN)
Note
QC 20100921Available from: 2010-09-21 Created: 2010-09-21 Last updated: 2010-09-21Bibliographically approved
8. Effects of Noise and Jitter on Algorithms for Bandpass Sampling in Radio Receiver
Open this publication in new window or tab >>Effects of Noise and Jitter on Algorithms for Bandpass Sampling in Radio Receiver
2004 (English)In: 2004 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOL 1, PROCEEDINGS, 2004, Vol. 1, 1761-1764 p.Conference paper, Published paper (Refereed)
Abstract [en]

BandPass Sampling (BPS) is an undersampling technique by intentional aliasing. Conventional uniform discrete sampling within an f(s) band normally results in a bad signal-to-noise ratio (SNR) due to signal spectrum aliasing. The noise combined in each of the f(s) bands below the highest frequency of the signal (the so called noise spectrum aliasing) and timing jitter are two causes of performance degradation in BPS system. Nonuniform BPS has the potential to suppress signal spectrum aliasing due to the aperiodic property of NonUniform Sampling (NUS). In this paper, the frequency spectra of Uniform Sampling (US) and NUS are analyzed, signal spectrum aliasing, noise spectrum aliasing and jitter effects in BPS are studied. Finally, the performance of reconstruction algorithms (RAs) for nonuniform BPS in the presence of sources of performance degradation are discussed based on simulations.

Keyword
Algorithms, Computer simulation, Functions, Jitter, Lagrange multipliers, Natural frequencies, Poisson distribution, Radio receivers, Signal interference, Signal reconstruction, Signal to noise ratio, Spectrum analysis
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-5850 (URN)000223122300191 ()0-7803-8251-X (ISBN)
Conference
IEEE International Symposium on Circuits and Systems Location: Vancouver, CANADA Date: MAY 23-26, 2004
Note
QC 20100921Available from: 2006-05-30 Created: 2006-05-30 Last updated: 2011-11-02Bibliographically approved
9. Effects of Noise and Jitter in Bandpass Sampling
Open this publication in new window or tab >>Effects of Noise and Jitter in Bandpass Sampling
2005 (English)In: ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING, 2005, Vol. 42, no 1, 85-97 p.Conference paper, Published paper (Refereed)
Abstract [en]

BandPass Sampling (BPS) is an undersampling technique by intentional aliasing. BPS enables one to have an interface between the IF stage and the ADC in a radio receiver. Conventional uniform BPS at Nyquist rate normally results in a low Signal-to-Noise Ratio (SNR) due to noise spectrum aliasing. The noise (e.g. kT/C noise introduced in a voltage-mode sampler) is combined in each of the Nyquist bands within the bandwidth of the sampling device. Also timing jitter causes a performance degradation in BPS. In this paper, signal spectrum aliasing, noise aliasing and jitter effects in BPS is analyzed. It is verified by simulation that NonUniform Sampling (NUS) has the potential to suppress signal spectrum aliasing and relax the requirement on the anti-aliasing (AA) filter. Jitter effects in BPS are compared to LowPass Sampling (LPS) case. However, a signal cannot be reconstructed from its nonuniform samples by using only ideal lowpass filtering (classic Shannon's reconstruction). Finally, signal reconstruction in the presence of noise and jitter are investigated for three Reconstruction Algorithms (RAs) aimed at NUS.

Series
Analog integrated circuits and signal processing, ISSN 0925-1030
Keyword
bandpass sampling, jitter, noise aliasing, nonuniform sampling, reconstruction algorithms
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-5851 (URN)10.1023/B:ALOG.0000042331.21974.85 (DOI)000223983300010 ()2-s2.0-4544262338 (Scopus ID)
Conference
21st NORCHIP Conference Riga, LATVIA, 2003
Note
QC 20100910Available from: 2006-05-30 Created: 2006-05-30 Last updated: 2011-10-11Bibliographically approved
10. Jitter Performance of Reconstruction Algorithmsfor Nonuniform Bandpass Sampling
Open this publication in new window or tab >>Jitter Performance of Reconstruction Algorithmsfor Nonuniform Bandpass Sampling
2003 (English)In: Proceedings of EuropeanConference of Circuit Theory and Design (ECCTD), Krakow,Poland, September 2003., 2003, 353-356 p.Conference paper, Published paper (Refereed)
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-24665 (URN)
Note
QC 20100921Available from: 2010-09-21 Created: 2010-09-21 Last updated: 2010-09-21Bibliographically approved
11. Algorithms for Nonuniform Bandpass Sampling in Radio Receiver
Open this publication in new window or tab >>Algorithms for Nonuniform Bandpass Sampling in Radio Receiver
2003 (English)In: PROCEEDINGS OF THE 2003 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOL I - ANALOG CIRCUITS AND SIGNAL PROCESSING, 2003, 1-4 p.Conference paper, Published paper (Refereed)
Abstract [en]

Bandpass Sampling (BPS) technique enables one to have an interface between IF stage and ADC in a radio receiver. Nonuniform BPS has the potential to suppress aliasing without care of the information band position of a modulated signal. However, a signal cannot be reconstructed from its nonuniform samples by using only an ideal lowpass filter. In this paper, a filter is, generalized to a reconstruction algorithm (RA). Six different algorithms for reconstructing a signal from its nonuniform samples are summarized. A general reconstruction formula in terms of a basis-kernel (BK) function is used to describe the algorithms. Finally, with regard to. the application of radio communications, accuracy of reconstruction, computational complexity and hardware implementation are shown and compared for these algorithms.

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-5849 (URN)
Conference
IEEE International Symposium on Circuits and Systems BANGKOK, THAILAND, MAY 25-28, 2003
Note
QC 20100921Available from: 2006-05-30 Created: 2006-05-30 Last updated: 2010-09-21Bibliographically approved

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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