Mats Viberg

Bio: Mats Viberg is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Sensor array & Estimation theory. The author has an hindex of 41, co-authored 231 publications receiving 11749 citations. Previous affiliations of Mats Viberg include Linköping University & Blekinge Institute of Technology.

A study of adaptive arrays for mobile communication systems

Abstract: The application of adaptive antenna techniques to increase the channel capacity in mobile radio communication is discussed. Directional sensitivity is obtained by using an antenna array at the base station, possibly both in receiving and transmitting mode. A scheme for separating several signals at the same frequency is proposed. The method is based on high-resolution direction finding following by optimal combination of the antenna outputs. Comparisons to a method based on reference signals are made. Computer simulations are carried out to test the applicability of the technique to scattering scenarios that typically arise in urban areas. The proposed scheme is found to have great potential in rejecting cochannel interference, albeit at the expense of high computational requirements. >

Improved DOA estimators using partial relaxation approach

Abstract: In this paper, the partial relaxation approach is introduced and applied to DOA estimation using spectral search. Unlike existing methods like Capon or MUSIC which can be considered as single source approximations of multi-source estimation criteria, the proposed approach accounts for the existence of multiple sources. At each direction, the manifold structure of interfering signals impinging on the sensor array is relaxed, which results in closed form estimates for the interference parameters. The conventional multidimensional optimization problem reduces, thanks to this relaxation, to a simple spectral search. Following this principle, proposed estimators based on the Deterministic Maximum Likelihood, Weighted Subspace Fitting and Covariance Fitting method are derived. Simulation results show that the performance of the proposed estimators is superior to conventional methods especially in the case of low SNR and low number of snapshots, irrespectively of the special structure of the sensor array.

Multimode transmission in network MIMO downlink with incomplete CSI

Abstract: We consider a cooperative multicell MIMO (a.k.a networkMIMO) downlink system with multiantenna base stations (BSs), which are connected to a central unit and communicate with multiantenna users. In such a network, obtaining perfect channel state information (CSI) of all users at the central unit to exploit opportunistic scheduling requires a substantial amount of feedback and backhaul signaling. We propose a scheduling algorithm based only on the knowledge of the average SNR at each user from all the cooperating BSs, denoted as incomplete CSI. Multimode transmission is applied that is able to adaptively adjust the number of data streams transmitted to each user. Utilizing the results of random matrix theory, an analytical framework is proposed to approximate the ergodic rate of each user with different number of data streams. Using these ergodic rates, a joint user and mode selection algorithm is proposed, where only the scheduled users need to feed back instantaneous CSI. Simulation results demonstrate that the developed analytical framework provides a good approximation for a practical number of antennas. While substantially reducing the feedback overhead, it is shown that the proposed scheduling algorithm performs closely to the opportunistic scheduling algorithm that requires instantaneous CSI feedback from all users.

Array Processing in the Face of Nonidealities

Abstract: Real-world sensor arrays are typically composed of elements with individual directional beampatterns and are subject to mutual coupling, cross-polarization effects as well as mounting platform reflections. Errors in the array elements’ positions are also common in sensor arrays built in practice. Such nonidealities need to be taken into account for optimal array signal processing and in finding related performance bounds. Moreover, problems related to beam-steering and cancellation of the signal-of-interest in beamforming applications may be prevented. Otherwise, an array processor may experience a significant performance degradation. In this chapter we provide techniques that allow the practitioner to acquire the steering vector model of real-world sensor arrays so that various nonidealities are taken into account. Consequently, array processing algorithms may avoid performance losses caused by array modeling errors. These techniques include model-based calibration and auto-calibration methods, array interpolation, as well as the wavefield modeling principle or manifold separation technique. Robust methods are also briefly considered since they are useful when the array nonidealities are not described by the employed steering vector model. Extensive array processing examples related to direction-finding and beamforming are included demonstrating that optimal or close-to optimal performance may be achieved despite the array nonidealities.

Array interpolation based on Local Polynomial Approximation with application to DOA estimation using weighted MUSIC

Abstract: The problem of Direction-of-Arrival (DOA) estimation using an array of sensors has received much attention for more than 3 decades. This is due to a rich interest from application areas such as radar, sonar and wireless communication channel characterization. However, high resolution DOA estimation requires an accurate model of the array response. This is usually achieved by measuring the response using sources at known positions (calibration). This paper considers interpolation of the calibration measurements using knowledge of a nominal response model. Standard linear interpolation is compared to an approach based on Local Polynomial Approximation (LPA). We also derive a weighted MUSIC estimator, which is applied using error estimates from the interpolation. Both LPA interpolation and weighted MUSIC are found to improve the performance, but not uniformly in all scenarios.

On Limits of Wireless Communications in a Fading Environment when UsingMultiple Antennas

Abstract: This paper is motivated by the need for fundamental understanding of ultimate limits of bandwidth efficient delivery of higher bit-rates in digital wireless communications and to also begin to look into how these limits might be approached. We examine exploitation of multi-element array (MEA) technology, that is processing the spatial dimension (not just the time dimension) to improve wireless capacities in certain applications. Specifically, we present some basic information theory results that promise great advantages of using MEAs in wireless LANs and building to building wireless communication links. We explore the important case when the channel characteristic is not available at the transmitter but the receiver knows (tracks) the characteristic which is subject to Rayleigh fading. Fixing the overall transmitted power, we express the capacity offered by MEA technology and we see how the capacity scales with increasing SNR for a large but practical number, n, of antenna elements at both transmitter and receiver. We investigate the case of independent Rayleigh faded paths between antenna elements and find that with high probability extraordinary capacity is available. Compared to the baseline n = 1 case, which by Shannon‘s classical formula scales as one more bit/cycle for every 3 dB of signal-to-noise ratio (SNR) increase, remarkably with MEAs, the scaling is almost like n more bits/cycle for each 3 dB increase in SNR. To illustrate how great this capacity is, even for small n, take the cases n = 2, 4 and 16 at an average received SNR of 21 dB. For over 99% of the channels the capacity is about 7, 19 and 88 bits/cycle respectively, while if n = 1 there is only about 1.2 bit/cycle at the 99% level. For say a symbol rate equal to the channel bandwith, since it is the bits/symbol/dimension that is relevant for signal constellations, these higher capacities are not unreasonable. The 19 bits/cycle for n = 4 amounts to 4.75 bits/symbol/dimension while 88 bits/cycle for n = 16 amounts to 5.5 bits/symbol/dimension. Standard approaches such as selection and optimum combining are seen to be deficient when compared to what will ultimately be possible. New codecs need to be invented to realize a hefty portion of the great capacity promised.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Two decades of array signal processing research: the parametric approach

Abstract: The quintessential goal of sensor array signal processing is the estimation of parameters by fusing temporal and spatial information, captured via sampling a wavefield with a set of judiciously placed antenna sensors. The wavefield is assumed to be generated by a finite number of emitters, and contains information about signal parameters characterizing the emitters. A review of the area of array processing is given. The focus is on parameter estimation methods, and many relevant problems are only briefly mentioned. We emphasize the relatively more recent subspace-based methods in relation to beamforming. The article consists of background material and of the basic problem formulation. Then we introduce spectral-based algorithmic solutions to the signal parameter estimation problem. We contrast these suboptimal solutions to parametric methods. Techniques derived from maximum likelihood principles as well as geometric arguments are covered. Later, a number of more specialized research topics are briefly reviewed. Then, we look at a number of real-world problems for which sensor array processing methods have been applied. We also include an example with real experimental data involving closely spaced emitters and highly correlated signals, as well as a manufacturing application example.

Survey of Wireless Indoor Positioning Techniques and Systems

Abstract: Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

Convergence of probability measures

Abstract: The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an