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Venkitaraman, A., Chatterjee, S. & Händel, P. (2019). On Hilbert transform, analytic signal, and modulation analysis for signals over graphs. Signal Processing, 156, 106-115
Open this publication in new window or tab >>On Hilbert transform, analytic signal, and modulation analysis for signals over graphs
2019 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 156, p. 106-115Article in journal (Refereed) Published
Abstract [en]

We propose Hilbert transform and analytic signal construction for signals over graphs. This is motivated by the popularity of Hilbert transform, analytic signal, and modulation analysis in conventional signal processing, and the observation that complementary insight is often obtained by viewing conventional signals in the graph setting. Our definitions of Hilbert transform and analytic signal use a conjugate symmetry-like property exhibited by the graph Fourier transform (GFT), resulting in a 'one-sided' spectrum for the graph analytic signal. The resulting graph Hilbert transform is shown to possess many interesting mathematical properties and also exhibit the ability to highlight anomalies/discontinuities in the graph signal and the nodes across which signal discontinuities occur. Using the graph analytic signal, we further define amplitude, phase, and frequency modulations for a graph signal. We illustrate the proposed concepts by showing applications to synthesized and real-world signals. For example, we show that the graph Hilbert transform can indicate presence of anomalies and that graph analytic signal, and associated amplitude and frequency modulations reveal complementary information in speech signals.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Graph signal processing, Analytic signal, Hilbert transform, Demodulation, Anomaly detection
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-240687 (URN)10.1016/j.sigpro.2018.10.016 (DOI)000453494200011 ()2-s2.0-85056192636 (Scopus ID)
Note

QC 20190109

Available from: 2019-01-09 Created: 2019-01-09 Last updated: 2019-01-09Bibliographically approved
Venkitaraman, A., Chatterjee, S. & Händel, P. (2019). On Hilbert transform, analytic signal, and modulation analysis for signals over graphs. Signal Processing, 156, 106-115
Open this publication in new window or tab >>On Hilbert transform, analytic signal, and modulation analysis for signals over graphs
2019 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 156, p. 106-115Article in journal (Refereed) Published
Abstract [en]

We propose Hilbert transform and analytic signal construction for signals over graphs. This is motivated by the popularity of Hilbert transform, analytic signal, and modulation analysis in conventional signal processing, and the observation that complementary insight is often obtained by viewing conventional signals in the graph setting. Our definitions of Hilbert transform and analytic signal use a conjugate symmetry-like property exhibited by the graph Fourier transform (GFT), resulting in a 'one-sided' spectrum for the graph analytic signal. The resulting graph Hilbert transform is shown to possess many interesting mathematical properties and also exhibit the ability to highlight anomalies/discontinuities in the graph signal and the nodes across which signal discontinuities occur. Using the graph analytic signal, we further define amplitude, phase, and frequency modulations for a graph signal. We illustrate the proposed concepts by showing applications to synthesized and real-world signals. For example, we show that the graph Hilbert transform can indicate presence of anomalies and that graph analytic signal, and associated amplitude and frequency modulations reveal complementary information in speech signals.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Graph signal processing, Analytic signal, Hilbert transform, Demodulation, Anomaly detection
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-240988 (URN)10.1016/j.sigpro.2018.10.016 (DOI)000453494200011 ()2-s2.0-85056192636 (Scopus ID)
Note

QC 20190110

Available from: 2019-01-10 Created: 2019-01-10 Last updated: 2019-01-10Bibliographically approved
Wahlström, J., Jaldén, J., Skog, I. & Händel, P. (2018). Alternative em Algorithms for Nonlinear State-Space Models. In: 2018 21st International Conference on Information Fusion, FUSION 2018: . Paper presented at 21st International Conference on Information Fusion, FUSION 2018, 10 July 2018 through 13 July 2018 (pp. 1260-1267). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Alternative em Algorithms for Nonlinear State-Space Models
2018 (English)In: 2018 21st International Conference on Information Fusion, FUSION 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 1260-1267Conference paper, Published paper (Refereed)
Abstract [en]

The expectation-maximization algorithm is a commonly employed tool for system identification. However, for a large set of state-space models, the maximization step cannot be solved analytically. In these situations, a natural remedy is to make use of the expectation-maximization gradient algorithm, i.e., to replace the maximization step by a single iteration of Newton's method. We propose alternative expectation-maximization algorithms that replace the maximization step with a single iteration of some other well-known optimization method. These algorithms parallel the expectation-maximization gradient algorithm while relaxing the assumption of a concave objective function. The benefit of the proposed expectation-maximization algorithms is demonstrated with examples based on standard observation models in tracking and localization. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
Keywords
Expectation-maximization, Levenberg-Marquardt, system identification, the Gauss-Newton method, trust region, Identification (control systems), Image segmentation, Information fusion, Newton-Raphson method, Positron emission tomography, Religious buildings, Signal receivers, State space methods, Concave objective functions, Expectation - maximizations, Expectation-maximization algorithms, Gauss-Newton methods, Nonlinear state space models, State - space models, Maximum principle
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-236699 (URN)10.23919/ICIF.2018.8455234 (DOI)2-s2.0-85054096276 (Scopus ID)9780996452762 (ISBN)
Conference
21st International Conference on Information Fusion, FUSION 2018, 10 July 2018 through 13 July 2018
Funder
Swedish Foundation for Strategic Research
Note

Conference code: 139346; Export Date: 22 October 2018; Conference Paper; Funding details: SSF, Stiftelsen för Strategisk Forskning; Funding details: SSF, Sjögren’s Syndrome Foundation; Funding text: This research is financially supported by the Swedish Foundation for Strategic Research (SSF) via the project ASSEMBLE. QC 20181112

Available from: 2018-11-12 Created: 2018-11-12 Last updated: 2018-11-12Bibliographically approved
Alizadeh, M., Händel, P. & Rönnow, D. (2018). Basis Function Decomposition Approach in Piece-Wise Modeling for RF Power Amplifiers. In: 6th Telecommunications forum TELFOR 2018: . Paper presented at 26th Telecommunications forum TELFOR 2018, Serbia, Belgrade, November 20-21, 2018.. Belgrade, Serbia
Open this publication in new window or tab >>Basis Function Decomposition Approach in Piece-Wise Modeling for RF Power Amplifiers
2018 (English)In: 6th Telecommunications forum TELFOR 2018, Belgrade, Serbia, 2018Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, a new approach is proposed to decompose the basis functions in a piece-wise modeling technique for nonlinear radio frequency (RF) power amplifiers. The proposed technique treats the discontinuity problem of the model output at the joint points between different operating points, whereas preserves the linear and nonlinear properties of the original model within each region. Experimental results show that the proposed technique outperforms the conventional piece-wise model in terms of model errors.

Place, publisher, year, edition, pages
Belgrade, Serbia: , 2018
Keywords
Nonlinear dynamic RF power amplifier, behavioral modeling, piece-wise models, digital predistortion.
National Category
Signal Processing
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-241117 (URN)978-1-5386-7171-9 (ISBN)
Conference
26th Telecommunications forum TELFOR 2018, Serbia, Belgrade, November 20-21, 2018.
Note

QC 20190114

Available from: 2019-01-11 Created: 2019-01-11 Last updated: 2019-01-14Bibliographically approved
Alizadeh, M., Händel, P. & Rönnow, D. (2018). Basis Function Decomposition Approach in Piece-Wise Modeling for RF Power Amplifiers. In: 2018 26TH TELECOMMUNICATIONS FORUM (TELFOR): . Paper presented at 2018 26th Telecommunications Forum (TELFOR) (pp. 140-143). IEEE
Open this publication in new window or tab >>Basis Function Decomposition Approach in Piece-Wise Modeling for RF Power Amplifiers
2018 (English)In: 2018 26TH TELECOMMUNICATIONS FORUM (TELFOR), IEEE , 2018, p. 140-143Conference paper, Published paper (Refereed)
Abstract [en]

In this paper a new approach is proposed to decompose the basis functions in a piece-wise modeling technique for nonlinear radio frequency (RF) power amplifiers. The proposed technique treats the discontinuity problem of the model output at the joint points between different operating points, whereas preserves the linear and nonlinear properties of the original model within each region. Experimental results show that the proposed technique outperforms the conventional piece-wise model in terms of model errors.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Nonlinear dynamic RF power amplifier, behavioral modeling, piece-wise models, digital predistortion
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-246305 (URN)10.1109/TELFOR.2018.8611865 (DOI)000459714200035 ()2-s2.0-85062060510 (Scopus ID)
Conference
2018 26th Telecommunications Forum (TELFOR)
Note

QC 20190319

Available from: 2019-03-19 Created: 2019-03-19 Last updated: 2019-03-19
Alizadeh, M., Händel, P. & Rönnow, D. (2018). Behavioural modelling and digital pre-distortion techniques for RF PAs in a 3x3 MIMO system. International journal of microwave and wireless technologies
Open this publication in new window or tab >>Behavioural modelling and digital pre-distortion techniques for RF PAs in a 3x3 MIMO system
2018 (English)In: International journal of microwave and wireless technologies, ISSN 1759-0795, E-ISSN 1759-0787Article in journal (Refereed) Submitted
Abstract [en]

Modern telecommunications are moving towards (massive) multi-input multi-output systems in 5th generation (5G) technology, increasing the dimensionality of the system dramatically. In this paper, the impairments of radio frequency (RF)power amplifiers (PAs) in a 3x3 MIMO system are compensated in both time and frequency domains. A three-dimensional(3D) time-domain memory polynomial-type model is proposed as an extension of conventional 2D models. Furthermore, a 3D frequency-domain technique is formulated based on the proposed time-domain model to reduce the dimensionality of the model, while preserving the performance in terms of model errors. In the 3D frequency-domain technique, the bandwidth of a system is split into several narrow sub-bands, and the parameters of the system are estimated for each subband. This approach requires less computational complexity, and also the procedure of the parameters estimation for each sub-band can be implemented independently. The device-under-test (DUT) consists of three RF PAs including input and output cross-talk channels. The proposed techniques are evaluated in both behavioural modelling and digital pre-distortion(DPD) perspectives. The results show that the proposed DPD technique can compensate the errors of non-linearity and memory effects by about 23.5 dB and 7 dB in terms of the normalized mean square error and adjacent channel leakage ratio, respectively.

Keywords
Radio frequency power amplifier, non-linearity, memory effects, multiple-input multiple-output (MIMO), digital pre-distortion.
National Category
Signal Processing
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-241118 (URN)
Note

QC 20190114

Available from: 2019-01-11 Created: 2019-01-11 Last updated: 2019-01-14Bibliographically approved
Händel, P. & Ronnow, D. (2018). Dirty MIMO Transmitters: Does It Matter?. IEEE Transactions on Wireless Communications, 17(8), 5425-5436
Open this publication in new window or tab >>Dirty MIMO Transmitters: Does It Matter?
2018 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 17, no 8, p. 5425-5436Article in journal (Refereed) Published
Abstract [en]

The radio frequency transmitter is a key component in contemporary multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing systems. A detailed study of a 2 x 2 MIMO transmitter subjected to correlated input data streams, nonlinear distortion, thermal noise, and crosstalk is provided by stochastic modeling. The effects of correlated input streams, crosstalk, and nonlinearities are studied in detail and exemplified both by approximate expressions and numerical simulations. Key results include exact and approximate expressions for the normalized mean-squared error (NMSE) for systems with or without digital predistortion; the relationship between NMSE and the signal-to-noise-and-distortion ratio, the properties of the distortion noise, and a novel design for power amplifier back-off for MIMO transmitters subject to crosstalk. The theoretical derivations are illustrated by numerical examples and simulation results, and their relationships to the state-of-the-art research are discussed.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Orthogonal frequency division multiplexing (OFDM), input back-off, power amplifier, optimization, Bussgang theory
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-234629 (URN)10.1109/TWC.2018.2843764 (DOI)000441933900034 ()2-s2.0-85048562714 (Scopus ID)
Note

QC 20190913

Available from: 2018-09-13 Created: 2018-09-13 Last updated: 2018-09-13Bibliographically approved
Khan, Z. A., Zenteno, E., Händel, P. & Isaksson, M. (2018). Extraction of the Third-Order 3x3 MIMO VolterraKernel Outputs Using Multitone Signals. IEEE transactions on microwave theory and techniques, 66(11), 4985-4999
Open this publication in new window or tab >>Extraction of the Third-Order 3x3 MIMO VolterraKernel Outputs Using Multitone Signals
2018 (English)In: IEEE transactions on microwave theory and techniques, ISSN 0018-9480, E-ISSN 1557-9670, Vol. 66, no 11, p. 4985-4999Article in journal (Refereed) Published
Abstract [en]

This paper uses multitone signals to simplify theanalysis of 3×3 multiple-input multiple-output (MIMO) Volterrasystems by isolating the third-order kernel outputs from eachother. Multitone signals fed to an MIMO Volterra system yielda spectrum that is a permutation of the sums of the inputsignal tones. This a priori knowledge is used to design multitonesignals such that the third-order kernel outputs are isolated inthe frequency domain. The signals are designed by deriving theconditions for the offset and spacing of the input frequency grids.The proposed technique is then validated for the six possibleconfigurations of a 3x3 RF MIMO transmitter impaired bycrosstalk effects. The proposed multitone signal design is usedto extract the third-order kernel outputs, and their relativecontributions are analyzed to determine the dominant crosstalkeffects for each configuration.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-233974 (URN)10.1109/TMTT.2018.2854186 (DOI)000449354500028 ()
Note

QC 20180904

Available from: 2018-09-01 Created: 2018-09-01 Last updated: 2018-11-26Bibliographically approved
Venkitaraman, A., Chatterjee, S. & Händel, P. (2018). Extreme learning machine for graph signal processing. In: 2018 26th European Signal Processing Conference (EUSIPCO): . Paper presented at 26th European Signal Processing Conference, EUSIPCO 2018, Rome, Italy, 3 September 2018 through 7 September 2018 (pp. 136-140). European Signal Processing Conference, EUSIPCO, Article ID 8553088.
Open this publication in new window or tab >>Extreme learning machine for graph signal processing
2018 (English)In: 2018 26th European Signal Processing Conference (EUSIPCO), European Signal Processing Conference, EUSIPCO , 2018, p. 136-140, article id 8553088Conference paper, Published paper (Refereed)
Abstract [en]

In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization. We assume that the target signal for prediction or regression is a graph signal. With this assumption, we use the regularization to enforce that the output of an extreme learning machine is smooth over a given graph. Simulation results with real data confirm that such regularization helps significantly when the available training data is limited in size and corrupted by noise.

Place, publisher, year, edition, pages
European Signal Processing Conference, EUSIPCO, 2018
Series
European Signal Processing Conference, ISSN 2219-5491
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-241525 (URN)10.23919/EUSIPCO.2018.8553088 (DOI)000455614900028 ()2-s2.0-85059801757 (Scopus ID)9789082797015 (ISBN)
Conference
26th European Signal Processing Conference, EUSIPCO 2018, Rome, Italy, 3 September 2018 through 7 September 2018
Note

QC 20180123

Available from: 2019-01-23 Created: 2019-01-23 Last updated: 2019-02-01Bibliographically approved
Wahlström, J., Skog, I., Nordström, R. L. & Händel, P. (2018). Fusion of OBD and GNSS Measurements of Speed. IEEE Transactions on Instrumentation and Measurement, 67(7), 1659-1667
Open this publication in new window or tab >>Fusion of OBD and GNSS Measurements of Speed
2018 (English)In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 67, no 7, p. 1659-1667Article in journal (Refereed) Published
Abstract [en]

There are two primary sources of sensor measurements for driver behavior profiling within insurance telematics and fleet management. The first is the on-board diagnostics system, typically found within most modern cars. The second is the global navigation satellite system, whose associated receivers commonly are embedded into smartphones or off-the-shelf telematics devices. In this paper, we present maximum likelihood and maximum a posteriori estimators for the problem of fusing speed measurements from these two sources to jointly estimate a vehicle's speed and the scale factor of the wheel speed sensors. In addition, we analyze the performance of the estimators by use of the Cramer-Rao bound, and discuss the estimation of model parameters describing measurement errors and vehicle dynamics. Last, simulations and real-world data are used to show that the proposed estimators yield a substantial performance gain compared to when employing only one of the two measurement sources.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2018
Keywords
Driver behavior profiling, fleet management, global navigation satellite system (GNSS), insurance telematics, on-board diagnostics (OBD)
National Category
Control Engineering Vehicle Engineering
Identifiers
urn:nbn:se:kth:diva-231179 (URN)10.1109/TIM.2018.2803998 (DOI)000434457600016 ()2-s2.0-85042872129 (Scopus ID)
Note

QC 20180720

Available from: 2018-07-20 Created: 2018-07-20 Last updated: 2018-07-20Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-2718-0262

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