Showing papers by "Mats Viberg published in 2012"
TL;DR: A Least-Squares (LS) based channel estimation algorithm is developed, that provides the destination with full knowledge of all channel responses involved in the transmission, and is asymptotically efficient.
Abstract: In this paper, we present a channel estimation scheme for Amplify-and-Forward (AF) relaying systems, using measurements at the destination only. A Least-Squares (LS) based channel estimation algorithm is developed, that provides the destination with full knowledge of all channel responses involved in the transmission. To investigate the algorithm performance, the Cramer-Rao lower bound (CRB) is analytically computed and compared with the asymptotic covariance of the proposed estimator. Since the existing estimator does not reach the CRB, we also propose and analyze an improved algorithm by taking into account the noise characteristics via weighted LS (WLS). The improved algorithm is asymptotically efficient, since it attains the CRB as the SNR tends to infinity.
45 citations
TL;DR: Simulation results show that the proposed coordinated user-specific tilting scheme outperforms the conventional schemes employing a fixed tilt angle at each BS.
Abstract: In this letter, we propose a novel framework to enhance the throughput in multiple-input single-output (MISO) mutually interfering channels via selecting the tilt angles at all base stations (BSs) in a coordinated fashion. In the proposed framework, multiple BSs adjust their tilt angles jointly to maximize the sum throughput of the scheduled users, denoted as coordinated user-specific tilting. Assuming the availability of location information of the scheduled users at all BSs, accurate analytical expression for user ergodic rate is provided, which enables a decentralized deployment of the proposed framework at each BS. Simulation results show that the proposed coordinated user-specific tilting scheme outperforms the conventional schemes employing a fixed tilt angle at each BS.
36 citations
TL;DR: This paper addresses the problem of regularization parameter selection in this method in a general case of complex-valued regressors and bases by introducing a numerically fast method of approximating the desired points by a recursive algorithm.
Abstract: The LASSO sparse regression method has recently received attention in a variety of applications from image compression techniques to parameter estimation problems. This paper addresses the problem of regularization parameter selection in this method in a general case of complex-valued regressors and bases. Generally, this parameter controls the degree of sparsity or equivalently, the estimated model order. However, with the same sparsity/model order, the smallest regularization parameter is desired. We relate such points to the nonsmooth points in the path of LASSO solutions and give an analytical expression for them. Then, we introduce a numerically fast method of approximating the desired points by a recursive algorithm. The procedure decreases the necessary number of solutions of the LASSO problem dramatically, which is an important issue due to the polynomial computational cost of the convex optimization techniques. We illustrate our method in the context of DOA estimation.
31 citations
TL;DR: This paper investigates the problem of channel estimation in amplify-and-forward multiple-input multiple-output relaying systems operating over random wireless channels using the Bayesian framework, and demonstrates that the incorporation of prior knowledge into the channel estimation algorithm offers significantly improved performance.
Abstract: In this paper, we investigate the problem of channel estimation in amplify-and-forward multiple-input multiple-output relaying systems operating over random wireless channels. Using the Bayesian framework, novel linear minimum mean square error and expectation-maximization based maximum a posteriori channel estimation algorithms are developed, that provide the destination with full knowledge of all channel parameters involved in the transmission. Moreover, new, explicit expressions for the Bayesian Cramer-Rao bound are deduced for predicting and evaluating the channel estimation accuracy. Our simulation results demonstrate that the incorporation of prior knowledge into the channel estimation algorithm offers significantly improved performance, especially in the low signal-to-noise ratio regime.
30 citations
06 May 2012
TL;DR: It is shown that the promised performance gains of network MIMO systems over conventional non-coordinated systems, crucially depend on the choice of the right tilt setting including the tilt type, i.e., mechanical or electrical, and the tilt angle.
Abstract: We study the downlink of a multicell MIMO system where clusters of multi-antenna base stations jointly serve multiple single-antenna users, commonly referred to as a network MIMO system. Most of the previous studies on network MIMO have only considered the azimuth pattern of the antenna, while ignoring the elevation pattern. In this paper, we consider both the azimuth and the elevation patterns and investigate the impact of the elevation angle tuning parameter, denoted as the antenna tilt, on the performance of such systems. Using system simulations, it is shown that the promised performance gains of network MIMO systems over conventional non-coordinated systems, crucially depend on the choice of the right tilt setting including the tilt type, i.e., mechanical or electrical, and the tilt angle. In particular, for tilt angles smaller than the optimum, network MIMO with intra-site coordination performs almost as well as the conventional system; while for tilt angles larger than the optimum, the performance of network MIMO with intra-site is similar to that of network MIMO with inter-site coordination.
22 citations
25 Mar 2012
TL;DR: This paper discusses resolution enhancement of a set of images with varying exposure durations, having a high combined dynamic range, and proposes a Super-Resolution method in the L*a*b* domain to bridge that gap and present some image reconstruction results.
Abstract: This paper discusses resolution enhancement of a set of images with varying exposure durations, having a high combined dynamic range. So far, little has been said in relation to the Human Visual System when it comes to Super-Resolution and High Dynamic Range fusion, unlike the case for traditional Super-Resolution where errors are measured with respect to human perception in the pixel domain. We propose a Super-Resolution method in the L*a*b* domain to bridge that gap and present some image reconstruction results.
21 citations
31 Dec 2012
TL;DR: This paper derives an expectation-maximization Kalman filter that utilizes the received signal at the destination to track the individual channel links in multiple-input multiple-output (MIMO) amplify-and-forward relaying systems operating over time varying channels.
Abstract: In this paper, we consider the problem of channel estimation in multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying systems operating over time varying channels. Only data at the receiving end are assumed available for the estimation. By employing a first-order autoregressive (AR) model for characterizing the time-varying nature of the channels to be estimated, we derive an expectation-maximization (EM) Kalman filter (KF) that utilizes the received signal at the destination to track the individual channel links. The extended KF algorithm is also derived and compared to the proposed EM-based KF. Our simulation results show that the proposed EM-based KF offers better estimation performance with less complexity when compared to the EKF algorithm.
5 citations
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TL;DR: The SPS-LASSO has recently been introduced as a solution to the problem of regularization parameter selection in the complex-valued LASSO problem as discussed by the authors, however, the dependence on the grid size and the polynomial time of performing convex optimization technique in each iteration, in addition to the deficiencies in the low noise regime, confines its performance for Direction of Arrival (DOA) estimation.
Abstract: The SPS-LASSO has recently been introduced as a solution to the problem of regularization parameter selection in the complex-valued LASSO problem. Still, the dependence on the grid size and the polynomial time of performing convex optimization technique in each iteration, in addition to the deficiencies in the low noise regime, confines its performance for Direction of Arrival (DOA) estimation. This work presents methods to apply LASSO without grid size limitation and with less complexity. As we show by simulations, the proposed methods loose a negligible performance compared to the Maximum Likelihood (ML) estimator, which needs a combinatorial search We also show by simulations that compared to practical implementations of ML, the proposed techniques are less sensitive to the source power difference.
4 citations
01 Nov 2012
TL;DR: Methods to apply LASSO without grid size limitation and with less complexity are presented, which show by simulations that compared to practical implementations of ML, the proposed techniques are less sensitive to the source power difference.
Abstract: The SPS-LASSO has recently been introduced as a solution to the problem of regularization parameter selection in the complex-valued LASSO problem Still, the dependence on the grid size and the polynomial time of performing convex optimization technique in each iteration, in addition to the deficiencies in the low noise regime, confines its performance for Direction of Arrival (DOA) estimation This work presents methods to apply LASSO without grid size limitation and with less complexity As we show by simulations, the proposed methods loose a negligible performance compared to the Maximum Likelihood (ML) estimator, which needs a combinatorial search We also show by simulations that compared to practical implementations of ML, the proposed techniques are less sensitive to the source power difference
2 citations
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TL;DR: This work provides theoretical expressions for the LASSO-based estimation error and false alarm rate in the asymptotic case of high SNR and dense grids and provides suggestions on the selection of the regularization parameter.
Abstract: The Least Absolute Shrinkage and Selection Operator (LASSO) has gained attention in a wide class of continuous parametric estimation problems with promising results. It has been a subject of research for more than a decade. Due to the nature of LASSO, the previous analyses have been non-parametric. This ignores useful information and makes it difficult to compare LASSO to traditional estimators. In particular, the role of the regularization parameter and super-resolution properties of LASSO have not been well-understood yet. The objective of this work is to provide a new insight into this context by introducing LASSO as a parametric technique of a varying order. This provides us theoretical expressions for the LASSO-based estimation error and false alarm rate in the asymptotic case of high SNR and dense grids. For this case, LASSO is compared to maximum likelihood and conventional beamforming. It is found that LASSO loses performance due to the regularization term, but the amount of loss is practically negligible with a proper choice of the regularization parameter. Thus, we provide suggestions on the selection of the regularization parameter. Without loss of generality, we present the comparative numerical results in the context of Direction of Arrival (DOA) estimation using a sensor array.