Wolfgang Utschick

Signal Processing Inc., Роквилл, Maryland, United States

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Publications (298)247.36 Total impact

  • Christoph Hellings, Wolfgang Utschick
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    ABSTRACT: Two main lines of approach can be identified in the recent literature on improper signals and widely linear operations. The augmented complex formulation based on the signal and its complex conjugate is considered as more insightful since it leads to convenient mathematical formulations for many considered problems. Moreover, it allows an easy distinction between proper and improper signals as well as between linear and widely linear operations. On the other hand, the composite real representation using the real and imaginary parts of the signal is closer to the actual implementation, and it allows to readily reuse results that have originally been derived for real-valued signals or proper complex signals. In this work, we aim at getting the best of both worlds by introducing mathematical tools that make the composite real representation more powerful and elegant. The proposed approach relies on a decomposition of real matrices into a block-skew-circulant and a block-Hankel-skew-circulant component. By means of various application examples from the field of signal processing for communications, we demonstrate the usefulness of the proposed framework.
    IEEE Transactions on Signal Processing 04/2015; 63(8):2093-2107. DOI:10.1109/TSP.2015.2395992 · 3.20 Impact Factor
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    David Neumann, Michael Joham, Wolfgang Utschick
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    ABSTRACT: We introduce novel blind and semi-blind channel estimation methods for cellular time-division duplexing systems with a large number of antennas at each base station. The methods are based on the maximum a-posteriori principle given a prior for the distribution of the channel vectors and the received signals from the uplink training and data phases. Contrary to the state-of-the-art massive MIMO channel estimators which either perform linear estimation based on the pilot symbols or rely on a blind principle, the proposed semi-blind method efficiently suppresses most of the interference caused by pilot-contamination. The simulative analysis illustrates that the semi-blind estimator outperforms state- of-the-art linear and non-linear approaches to the massive MIMO channel estimation problem.
  • Quan Kuang, Wolfgang Utschick, Andreas Dotzler
    19th International ITG Workshop on Smart Antennas, WSA 2015; 03/2015
  • Samer Bazzi, Guido Dietl, Wolfgang Utschick
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    ABSTRACT: In this paper, analytical rate expressions of interference alignment (IA) algorithms applied to the multiple-input multiple-output interference channel are derived in the large system limit. A large system analysis is performed by fixing the number of users and letting the number of transmit and receive antennas go to infinity at a finite fixed ratio. The asymptotic eigenvalue distribution of the direct channel gains resulting from IA algorithms is first obtained. Based on that, large system rate expressions under both water-filling power allocation and equal power allocation are derived in closed form. The obtained expressions are functions of the transmit power and noise power at the receivers and are independent of any other system parameters. Simulation results show that the achievable rates of different IA algorithms converge to the large system rates as the number of transmit and receive antennas increases, thereby showing that the large system expressions are valid for different IA variants. Simulation results also show that large system expressions provide accurate estimates of the average achievable rates for small and finite system parameters.
    IEEE Transactions on Signal Processing 03/2015; 63(6):1490-1499. DOI:10.1109/TSP.2015.2398842 · 3.20 Impact Factor
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    ABSTRACT: This paper considers the partial decode-and-forward (PDF) strategy for the Gaussian multiple-input multiple-output (MIMO) relay channel. Unlike for the decode-and-forward (DF) strategy or point-to-point (P2P) transmission, for which Gaussian channel inputs are known to be optimal, the input distribution that maximizes the achievable PDF rate for the Gaussian MIMO relay channel has remained unknown so far. For some special cases, e.g., for relay channels where the optimal PDF strategy reduces to DF or P2P transmission, it could be deduced that Gaussian inputs maximize the PDF rate. For the general case, however, the problem has remained open until now. In this work, we solve this problem by proving that the maximum achievable PDF rate for the Gaussian MIMO relay channel is always attained by Gaussian channel inputs. Our proof relies on the channel enhancement technique, which was originally introduced by Weingarten et al. to derive the (private message) capacity region of the Gaussian MIMO broadcast channel. By combining this technique with a primal decomposition approach, we first establish that jointly Gaussian source and relay inputs maximize the achievable PDF rate for the aligned Gaussian MIMO relay channel. Subsequently, we use a limiting argument to extend this result from the aligned to the general Gaussian MIMO relay channel.
  • Quan Kuang, Wolfgang Utschick, Andreas Dotzler
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    ABSTRACT: This paper studies the joint user association and resource allocation in heterogeneous networks (HetNets) from a novel perspective, motivated by and generating the idea of fractional frequency reuse. By treating the multi-cell multi-user resource allocation as resource partitioning among multiple reuse patterns, we propose a unified framework to analyze and compare a wide range of user association and resource allocation strategies for HetNets, and provide a benchmark of ultimate limit on network performance. The enabling mechanisms are a novel formulation to consider all possible patterns or any pre-defined subset of patterns and an efficient sparsity-pursuit algorithm. More importantly, in view of the fact that multi-cell resource allocation is very computational demanding, our framework provides a systematic way to trade off performance for the reduction of computational complexity by restricting the candidate patterns to a small number of feature patterns. Relying on the sparsity-pursuit capability of the proposed algorithm, we develop practical guideline to identify the feature patterns in the given HetNet. Our treatment is very general in that it covers the case where users are allowed to associate with multiple base stations and the more restrictive situation where the number of serving base stations for each user is limited to one or a given number. Numerical results show that the identified feature patterns can significantly improve the existing strategies, and jointly optimizing the user association and resource allocation indeed brings considerable gain.
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    ABSTRACT: For Gaussian multiple-input multiple-output (MIMO) relay channels with partial decode-and-forward, the optimal type of input distribution is still an open question in general. Recent research has revealed that in some other scenarios with unknown optimal input distributions (e.g., interference channels), improper (i.e., noncircular) Gaussian distributions can outperform proper (circular) Gaussian distributions. In this paper, we show that this is not the case for partial decode-and-forward in the Gaussian MIMO relay channel with Gaussian transmit signals, i.e., we show that a proper Gaussian input distribution is the optimal one among all Gaussian distributions. In order to prove this property, an innovation covariance matrix is introduced, and a decomposition is performed by considering the optimization over this matrix as an outer problem. A key point for showing optimality of proper signals then is a reformulation that reveals that one of the subproblems is equivalent to a sum rate maximization in a two-user MIMO broadcast channel under a sum covariance constraint, for which the optimality of proper signals can be shown.
    IEEE Transactions on Signal Processing 06/2014; 62(12):3153-3164. DOI:10.1109/TSP.2014.2321737 · 3.20 Impact Factor
  • Samer Bazzi, Guido Dietl, Wolfgang Utschick
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    ABSTRACT: A non-iterative two-stage precoding scheme for the multiple-input-multiple-output interference channel (IC) with a large number of transmit antennas is presented. Second stage precoders are used to compress the channel state information (CSI) of interfering links, while keeping the multiplexing gain of the IC constant. First stage precoders zero-force the interference based on the compressed CSI of interfering links. In addition, a proper design of the second stage precoders forces the first stage precoders to span a reduced subspace containing the maximal direct links' power, resulting in a better zero-forcing solution in terms of sum-rate. For a fixed number of receive antennas and transmitter/receiver pairs, the required feedback amount of interfering links is constant, regardless of the number of transmit antennas. As cooperative algorithms are very sensitive to CSI errors, the proposed scheme offers an attractive solution in case CSI feedback constitutes the system bottleneck.
    2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM); 06/2014
  • Source
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    ABSTRACT: Coordinated training significantly reduces the impact of pilot contamination in massive MIMO systems. Moreover, coordinated systems can use additional training resources in an effective manner, making it worthwhile to spend more resources on training than the necessary minimum. For a fixed channel coherence time, we analyze the trade-off between spending resources on training or data symbols for the uncoordinated and the coordinated case.
    2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM); 06/2014
  • C. Lameiro, I. Santamaria, W. Utschick
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    ABSTRACT: In this paper, a cognitive radio (CR) scenario comprised of a secondary interference channel (IC) and a primary point-to-point link (PPL) is studied, when the former interferes the latter. In order to satisfy a given rate requirement at the PPL, typical approaches impose an interference temperature constraint (IT). When the PPL transmits multiple streams, however, the spatial structure of the interference comes into play. In such cases, we show that spatial interference shaping constraints can provide higher sum-rate performance to the IC while ensuring the required rate at the PPL. Then, we extend the interference leakage minimization algorithm (MinIL) to incorporate such constraints. An additional power control step is included in the optimization procedure to improve the sum-rate when the interference alignment (IA) problem becomes infeasible due to the additional constraint. Numerical examples are provided to illustrate the effectiveness of the spatial shaping constraint in comparison to IT when the PPL transmits multiple data streams.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
  • Christoph Hellings, Lorenz Weiland, Wolfgang Utschick
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    ABSTRACT: Proper (i.e., circularly symmetric) Gaussian signals are known to be capacity-achieving in Gaussian multiple-input multiple-output (MIMO) broadcast channels with proper noise in the sense that the sum rate capacity under a sum power constraint is achievable with proper Gaussian signaling. In this paper, we generalize this statement by proving that the optimality of proper Gaussian signals also holds under a shaping constraint, i.e., a sum covariance constraint instead of a power constraint. Moreover, we show that not only the sum rate optimal point, but the whole capacity region can be achieved with proper Gaussian signals. Finally, we prove that the worst-case noise in a MIMO broadcast channel with shaping constraints is proper.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
  • Alexander Krebs, Michael Joham, Wolfgang Utschick
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    ABSTRACT: We present a low complexity soft detector for multiple-input multiple-output (MIMO) channels. Our proposed minimum mean square error successive interference cancellation (MMSE-SIC) detector is based on a regularization mechanism which reduces error propagation in the channel iterative decoder. Although our proposed detector is easy to implement and has a complexity order that is cubic in the number of transmit antennas, it can reach the performance of the soft max-log maximum-likelihood detector (MLD) under realistic system assumptions, as demonstrated in our simulations.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
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    ABSTRACT: For the Gaussian MIMO relay channel, we consider rates that can be achieved with the relay using the partial decode-and-forward (PDF) scheme, which is a generalization of the decode-and-forward (DF) scheme. Since for the general case the optimal channel input distribution for the PDF strategy is unknown, the maximum PDF rate for the Gaussian relay channel can only be determined if the best PDF strategy is equivalent to the DF strategy, point-to-point (P2P) transmission from source to destination, or if PDF achieves the cut-set bound (CSB), where Gaussian channel inputs are known to be optimal. In this paper, we identify and discuss two new such special cases, the stochastically degraded and the reversely stochastically degraded Gaussian relay channel.
    2014 48th Annual Conference on Information Sciences and Systems (CISS); 03/2014
  • Maximilian Riemensberger, Wolfgang Utschick
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    ABSTRACT: We propose a new model for the wireless broadcast advantage based on a polymatroid structure. This model is a generalization of the predominating hypergraph model. The polymatroid structure yields a general max-flow min-cut characterization of multicast rate regions, which applies to a large variety of channel, physical layer, and medium access models. It includes the state-of-the-art hypergraph flow regions with lossless and lossy hyperarcs, i.e., Shannon rate models and packet erasure networks. Additionally, it generalizes to various other rate regions, e.g., the cut-set outer bounds for networks of a large variety of independent broadcast channels, including networks of independent Gaussian multiple-input multiple-output channels, and the capacity regions for networks of independent deterministic broadcast channels, which can in general not be modeled by the hypergraph flow model. We propose a dual decomposition approach for network utility optimization problems on the polymatroid broadcast flow region, which subsumes existing dual decomposition approaches based on lossless and lossy hypergraph flow regions. Our approach significantly simplifies the decomposition, especially for lossy hypergraph models in packet erasure networks, by fully exploiting the inherent polymatroid structure of the wireless broadcast. Additionally, it can be directly used to fully characterize and evaluate the cut-set bounds for networks of independent broadcast channels with polymatroid structure without previous knowledge about the relevant cuts.
    IEEE Transactions on Information Theory 01/2014; 60(1):443-460. DOI:10.1109/TIT.2013.2287498 · 2.65 Impact Factor
  • Christoph Hellings, Michael Joham, Wolfgang Utschick
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    ABSTRACT: The use of proper, i.e., circularly symmetric, complex Gaussian signals for all users is known to be optimal in broadcast channels with proper complex Gaussian noise from an information theoretic point of view, i.e., they are employed in the capacity-achieving strategy. However, such proper per-user transmit signals are not necessarily optimal for problems with quality-of-service (QoS) constraints if the transmit strategy is restricted to widely linear transceivers without time-sharing. This is shown by deriving the QoS feasibility region of the multiple-input multiple-output broadcast channel with improper Gaussian per-user transmit signals and widely linear transceivers.
    IEEE Signal Processing Letters 11/2013; 20(11):1134-1137. DOI:10.1109/LSP.2013.2282186 · 1.64 Impact Factor
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    ABSTRACT: This paper presents a comparative study of algorithms for jointly optimizing beamformers and receive filters in an interference network, where each node may have multiple antennas, each user transmits at most one data stream, and interference is treated as noise. We focus on techniques that seek good suboptimal solutions by means of iterative and distributed updates. Those include forward-backward iterative algorithms (max-signal-to-interference-plus-noise ratio (SINR) and interference leakage), weighted sum mean-squared error (MSE) algorithms, and interference pricing with incremental signal-to-noise ratio (SNR) adjustments. We compare their properties in terms of convergence and information exchange requirements, and then numerically evaluate their sum rate performance averaged over random (stationary) channel realizations. The numerical results show that the max-SINR algorithm achieves the maximum degrees of freedom (i.e., supports the maximum number of users with near-zero interference) and exhibits better convergence behavior at high SNRs than the weighted sum MSE algorithms. However, it assumes fixed power per user and achieves only a single point in the rate region whereas the weighted sum MSE criterion gives different points. In contrast, the incremental SNR algorithm adjusts the beam powers and deactivates users when interference alignment is infeasible. Furthermore, that algorithm can provide a slight increase in sum rate, relative to max-SINR, at the cost of additional iterations.
    IEEE Transactions on Signal Processing 07/2013; 61(13):3476-3489. DOI:10.1109/TSP.2013.2257761 · 3.20 Impact Factor
  • Christoph Hellings, Stephan Herrmann, Wolfgang Utschick
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    ABSTRACT: Even though parallel multiple-input multiple-output (MIMO) broadcast channels are known to be separable from an information theoretic point of view, performing separate encoding and decoding on each of the parallel channels has been shown to be potentially suboptimal in broadcast channels with linear transceivers. In this paper, we show that suboptimality of such a carrier-noncooperative transmission also occurs in broadcast channels with zero-forcing and quality of service constraints if time-sharing is not allowed. The proof is given by constructing a minimal example and identifying a rate tuple that is achievable using carrier-cooperative zero-forcing with a certain sum power but requires a higher sum power with carrier-noncooperative zero-forcing. This observation is of practical relevance since zero-forcing without time-sharing is a popular assumption in the design of low-complexity optimization algorithms.
    IEEE Transactions on Signal Processing 06/2013; 61(12):3021-3027. DOI:10.1109/TSP.2013.2254482 · 3.20 Impact Factor
  • Christian Guthy, Wolfgang Utschick, M.L. Honig
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    ABSTRACT: We analyze the achievable sum rate of the Gaussian MIMO broadcast channel. We first consider Multiple-Input Single-Output (MISO) channels and derive the large system limit of the sum capacity as the number of users and transmit antennas go to infinity with a fixed ratio. We then consider Multiple-Input Multiple-Output (MIMO) broadcast channels and fix the number of users and let the number of transmit and receive antennas tend to infinity with fixed ratio. As in this case an asymptotic expression for sum capacity is hard to obtain, we evaluate the large system sum rate corresponding to successive zero-forcing beamforming with Dirty-Paper Coding. The analysis gives a lower bound on the large system sum capacity, which is numerically observed to be quite close. In addition, large system analysis is applied to estimate the relatively small performance losses with respect to sum capacity of successive zero-forcing beamforming with and without Dirty-Paper Coding in finite MISO systems.
    IEEE Journal on Selected Areas in Communications 02/2013; 31(2):149-159. DOI:10.1109/JSAC.2013.130204 · 4.14 Impact Factor
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    ABSTRACT: We consider wireless multihop networks with intrasession network coding and advanced physical layer techniques. In particular, we study the influence of multiuser decoding at receivers on the network utility maximization problem. To this end, we combine the polymatroid broadcast function model with the Gaussian compound multiple access channel (MAC) to model the information flow when multiple receivers decode messages from multiple transmitters. For this model, we formulate a network utility maximization problem and use a dual decomposition approach to separate the polymatroid flow subproblem, the scheduling and decoding assignment subproblem, and the compound MAC physical layer subproblem. The polymatroid flow and the compound MAC subproblems can be significantly simplified by exploiting their inherent polymatroid structure. For the scheduling and decoding assignment problem, we discuss the optimal solution, which results in an exhaustive search, and a simple greedy heuristic. In numerical simulations, we show a significant gain in network utility due to the advanced receiver capabilities compared to simple receivers.
    Systems, Communication and Coding (SCC), Proceedings of 2013 9th International ITG Conference on; 01/2013
  • L. Gerdes, L. Weiland, W. Utschick
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    ABSTRACT: In this paper, we consider achievable rates for the Gaussian multiple-input multiple-output (MIMO) relay channel that can be obtained with the relay using the partial decode-and-forward scheme. The partial decode-and-forward strategy allows to optimize the amount of information the relay has to decode and can hence be seen as a generalization of the decode-and-forward strategy, where the relay must decode the entire source message. Since we cannot determine the maximal achievable partial decode-and-forward rate, we propose a suboptimal approach that is based on zero-forcing the interference the relay would suffer from the part of the source signal that it is not required to decode. For this purpose, a zero-forcing receive filter is introduced at the relay. We then show that, if the receive filter is fixed, standard convex optimization techniques can be used to evaluate the best rate our suboptimal partial decode-and-forward scheme can achieve. Simulation results demonstrate that the coding scheme we propose significantly outperforms the decode-and-forward scheme and/or approximates the cut-set bound for different network scenarios.
    Communications (ICC), 2013 IEEE International Conference on; 01/2013

Publication Stats

3k Citations
247.36 Total Impact Points

Institutions

  • 2007–2014
    • Signal Processing Inc.
      Роквилл, Maryland, United States
    • DOCOMO Euro-Labs
      München, Bavaria, Germany
  • 1999–2014
    • Technische Universität München
      • Department of Circuit Theory and Signal Processing
      München, Bavaria, Germany
  • 2005
    • Purdue University
      • School of Electrical and Computer Engineering
      ウェストラファイエット, Indiana, United States
  • 2004
    • The Hong Kong University of Science and Technology
      Chiu-lung, Kowloon City, Hong Kong