| 1. |
- Bengtsson, Mats, 1967-, et al.
(author)
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A generalization of weighted subspace fitting to full-rank models
- 2001
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In: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 49:5, s. 1002-1012
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Journal article (peer-reviewed)abstract
- The idea of subspace fitting provides a popular framework for different applications of parameter estimation and system identification. Previously, some algorithms have been suggested based on similar ideas, for a sensor array processing problem where the underlying data model is not low rank. We show that two of these algorithms (DSPE and DISPARE) fail to give consistent estimates and introduce a general class of subspace fitting-like algorithms for consistent estimation of parameters from a possibly full-rank data model. The asymptotic performance is analyzed, and an optimally weighted algorithm is derived. The result gives a lower bound on the estimation performance for any estimator based on a low-rank approximation of the linear space spanned by the sample data. We show that in general, for full-rank data models, no subspace-based method can reach the Cramer-Rao lower bound (CRB)
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| 2. |
- Klang, Göran, et al.
(author)
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Interference Rejection in Systems Employing Transmit Diversity
- 2002
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In: European Signal Processing Conference. - : EUSIPCO. - 2219-5491.
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Journal article (peer-reviewed)abstract
- Space diversity methods provide excellent means for increasing the robustness against interference and noise in communication systems. This is well known for systems using multiple receive antennas and it holdsforsystems applying transmit diversity as well. In fact, the use of transmit diversity provide two independent means for reducing the impact of interference. Via the diversity gain and via the code rate, both related to the underlying space-time code. In this paper, we are considering the use of space diversity methods in conjunction with interference rejection combining (IRC) to mitigate the impact of interference and noise for interference limited scenarios. Transmit diversity schemes based on space-time block codes (STBC) are assumed. We elucidate the importance of taking into account the spatio-temporal correlation induced by cochannel users employing transmit diversity. A novel space-time IRC (STIRC) scheme is proposed to handle the problem. The STIRC scheme combines the simplicity of conventional IRC while taking the structure of interfering users into account. Simulation examples demonstrate significant gains compared to the use of conventional IRC.
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| 3. |
- Klang, Göran, et al.
(author)
-
Structured Semi-Blind Interference Rejection in Dispersive Multichannel Systems
- 2002
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In: IEEE Transactions on Signal Processing. - : IEEE Signal Processing Society. - 1053-587X .- 1941-0476. ; 50:8, s. 2027-2036
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Journal article (peer-reviewed)abstract
- The impact of interference is one of the most capacity limiting factors in cellular networks today. In order to host more users in existing systems and maximize the capacity of future networks, the use of interference suppression techniques are instrumental. In this paper, we propose a structured semi-blind parameter estimation procedure to support rejection, of unknown cochannel interference (CCI) in systems facing multipath channels with non-negligible delay spread. Employing spatio-temporal processing, the proposed scheme exploits the temporal correlation present in the output signal from an antenna array and the training data of a desired user in order to find parameter estimates that fit the range space of a structured CCI model to the signal subspace of the interfering signals. Simulation results illustrating the behavior of a spatio-temporal receiver using the proposed scheme with respect to both signal-to-noise and signal-to-interference ratios (SNRs and SIRs) are presented. In addition, results highlighting the impact of the training sequence length as well as the impact of the channel model order of the CCI users are also included.
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| 4. |
- Kristensson, Martin, et al.
(author)
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Further Results and Insights on Subspace Based Sinusoidal Frequency Estimation
- 2001
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In: IEEE Transactions on Signal Processing. - : IEEE Signal Processing Society. - 1053-587X .- 1941-0476. ; 49:12, s. 2962-2974
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Journal article (peer-reviewed)abstract
- Subspace-based methods for parameter identification have received considerable attention in the literature. Starting with a scalar-valued process, it is well known that subspace-based identification of sinusoidal frequencies is possible if the scalar valued data is windowed to form a low-rank vector-valued process. MUSIC and ESPRIT-like estimators have, for some time, been applied to this vector model. In addition, a statistically attractive Markov-like procedure for this class of methods has been proposed. Herein, the Markov-like procedure is reinvestigated. Several results regarding rank, performance, and structure are given in a compact manner. The large sample equivalence with the approximate maximum likelihood method by Stoica et al. (1988) is also established
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