Change search
Refine search result
1234 151 - 174 of 174
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 151.
    Wang, Jiaheng
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Payaro, Miquel
    On the Robustness of Transmit Beamforming2010In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 58, no 11, p. 5933-5938Article in journal (Refereed)
    Abstract [en]

    Beamforming is a simple transmit strategy that uses only one eigen-direction in multiple-input multiple-output channels. This simplicity makes beamforming a competitive strategy in practice, but at the same time poses a doubt on the sensitivity of beamforming to the imperfectness of the channel state information at the transmitter (CSIT). This paper studies beamforming from the perspective of worst-case robustness. We show that beamforming can achieve the maximum received signal-to-noise ratio (SNR) or guarantees a given received SNR with the minimum transmit power, in the worst channel within an elliptical uncertainty region defined by the weighted spectral norm. This result further implies that beamforming has the ability to combat against the imperfectness of CSIT, especially for small channel dimensions or small channel uncertainty.

  • 152.
    Weeraddana, Pradeep Chathuranga
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Codreanu, M.
    Latva-Aho, M.
    Ephremides, A.
    Multicell MISO downlink weighted sum-rate maximization: A distributed approach2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 3, p. 556-570Article in journal (Refereed)
    Abstract [en]

    We develop an easy to implement distributed method for weighted sum-rate maximization (WSRMax) problem in a multicell multiple antenna downlink system. Unlike the recently proposed minimum weighted mean-squared error based algorithms, where at each iteration all mobile terminals needs to estimate the covariance matrices of their received signals, compute and feedback over the air certain parameters to the base stations (BS), our algorithm operates without any user terminal assistance. It requires only BS to BS signalling via reliable backhaul links (e.g., fiber, microwave links) and all required computation is performed at the BSs. The algorithm is based on primal decomposition and subgradient methods, where the original nonconvex problem is split into a master problem and a number of subproblems (one for each BS). A novel sequential convex approximation strategy is proposed to address the nonconvex master problem. In the case of subproblems, we adopt an existing iterative approach based on second-order cone programming and geometric programming. The subproblems are coordinated to find a (possibly suboptimal) solution to the master problem. Subproblems can be solved by BSs in a fully asynchronous manner, though the coordination between subproblems should be synchronous. Numerical results are provided to see the behavior of the algorithm under different degrees of BS coordination. They show that the proposed algorithm yields a good tradeoff between the implementation-level simplicity and the performance.

  • 153.
    Weeraddana, Pradeep Chathuranga
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Codreanu, Marian
    University of Oulu, Finland.
    Latva-aho, Matti
    University of Oulu, Finland.
    Ephremides, Anthony
    University of Maryland.
    Multicell Downlink Weighted Sum-Rate Maximization: A Distributed Approach2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 3, p. 556-570Article in journal (Refereed)
    Abstract [en]

    We develop an easy to implement distributed method for weighted sum-rate maximization (WSRMax) problem in a multicell multiple antenna downlink system. Unlike the recently proposed minimum weighted mean-squared error based algorithms, where at each iteration all mobile terminals needs to estimate the covariance matrices of their received signals, compute and feedback over the air certain parameters to the base stations (BS), our algorithm operates without any user terminal assistance. It requires only BS to BS signalling via reliable backhaul links (e.g. fiber, microwave links) and all required computation is performed at the BSs. The algorithm is based on primal decomposition and subgradient methods, where the original nonconvex problem is split into a master problem and a number of subproblems (one for each BS). A novel sequential convex approximation strategy is proposed to address the nonconvex master problem. In the case of subproblems, we adopt an existing iterative approach based on second-order cone programming and geometric programming. The subproblems are coordinated to find a (possibly suboptimal) solution to the master problem. Subproblems can be solved by BSs in a fully asynchronous manner, though the coordination between subproblems should be synchronous. Numerical results are provided to see the behavior of the algorithm under different degrees of BS coordination. They show that the proposed algorithm yields a good tradeoff between the implementation-level simplicity and the performance.

  • 154.
    Werner, Karl
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    DOA estimation and detection in colored noise using additional noise-only data2007In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 55, no 11, p. 5309-5322Article in journal (Refereed)
    Abstract [en]

    In a typical array processing scenario, noise acting on the array can not be assumed spatially white. It is in many cases necessary to use quiet periods, when only noise is received, to estimate the noise covariance. If estimation of the signal parameters and noise covariance is performed jointly, performance can be improved. This is especially true when stationarity considerations limit the amount of available valid noise-only data. An asymptotically valid approximative maximum likelihood method (AML) for the estimation problem is derived in this work. The resulting criterion is, when concentrated with respect to the signal parameters, relatively simple. In numerical experiments, AML shows promising small-sample performance compared to earlier methods. The criterion function is also well suited for numerical optimization. The new criterion function allows for the development of a novel, MODE-like, non-iterative estimation procedure if the array belongs to the important class of uniform linear arrays. The resulting procedure retains the asymptotic properties of maximum likelihood, and numerical simulations indicate superior threshold performance when compared to an optimally weighted subspace fitting (WSF) formulation of MODE. For the detection problem, no method has been presented that takes the unknown noise covariance into account. Here, a well known detection scheme for WSF is extended to work in this scenario as well. The derivations of this scheme further stress the importance of using the correct weighting in WSF when the noise covariance is unknown. It is also shown that the minimum value of the criterion function associated with AML can be used for the detection purpose. Numerical experiments indicate very promising performance for the AML-detection scheme.

  • 155.
    Werner, Karl
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Optimal utilization of signal-free samples for array processing in unknown colored noise fields2006In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 54, no 10, p. 3861-3872Article in journal (Refereed)
    Abstract [en]

    Many algorithms for direction-of-arrival (DOA) estimation require the noise covariance matrix to be known or to possess a known structure. In many cases, the noise covariance is, in fact, estimated from separate measurements. This paper addresses the combined effects of finite sample sizes, both in the estimated noise covariance matrix and in the data with signals present. It is assumed that a batch of signal-free samples is available in addition to the signal-containing samples. No assumption is made on the structure of the noise covariance. In this paper, the asymptotic covariance of the weighted subspace fitting (WSF) algorithm is derived for the case in which the data are whitened using an estimated noise covariance. The expression obtained suggests an optimal weighting that improves performance compared to the standard choice. In addition, a new method based on covariance matching is proposed. Both methods are asymptotically statistically efficient. The Cramer-Rao lower bound (CRB) on the covariance of the estimate for the data model is also derived. Monte Carlo simulations show promising small sample performance for the two new methods and confirm,the asymptotic results.

  • 156.
    Werner, Karl
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Reduced rank linear regression and weighted low rank approximations2006In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 54, no 6, p. 2063-2075Article in journal (Refereed)
    Abstract [en]

    This paper addresses parameter estimation in reduced rank linear regressions. This estimation problem has applications in several subject areas including system identification, sensor array processing, econometrics and statistics. A new estimation procedure, based on instrumental variable principles, is derived and analyzed. The proposed method is designed to handle noise that is both spatially and temporally autocorrelated. An asymptotical analysis shows that the proposed method outperforms previous methods when the noise is temporally correlated and that it is asymptotically efficient otherwise. A numerical study indicates that the performance is significantly improved also for finite sample set sizes. In addition, the Cramer-Rao lower bound (CRB) on unbiased estimator covariance for the data model is derived. A statistical test for rank determination is also developed. An important step in the new algorithm is the weighted low rank approximation (WLRA). As the WLRA lacks a closed form solution in its general form, two new, noniterative and approximate solutions are derived, both of them asymptotically optimal when part of the estimation procedure proposed here. These methods are also interesting in their own right since the WLRA has several applications.

  • 157.
    Werner, Karl
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Signal Processing.
    Stoica, Petre
    Systems and Control Division, Information Technology, Department of Information Technology, Uppsala University, SE-751 05 Uppsala, Sweden.
    On estimation of covariance matrices with Kronecker product structure2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 2, p. 478-491Article in journal (Refereed)
    Abstract [en]

    The estimation of signal covariance matrices is a crucial part of many signal processing algorithms. In some applications, the structure of the problem suggests that the underlying, true covariance matrix is the Kronecker product of two valid covariance matrices. Examples of such problems are channel modeling for multiple-input multiple-output (MIMO) communications and signal modeling of EEG data. In applications, it may also be that the Kronecker factors in turn can be assumed to possess additional, linear structure. The maximum-likelihood (ML) method for the associated estimation problem has been proposed previously. It is asymptotically efficient but has the drawback of requiring an iterative search for the maximum of the likelihood function. Two methods that are fast and noniterative are proposed in this paper. Both methods are shown to be asymptotically efficient. The first method is a noniterative variant of a well-known alternating maximization technique for the likelihood function. It performs on par with ML in simulations but has the drawback of not allowing for extra structure in addition to the Kronecker structure. The second method is based on covariance matching principles and does not suffer from this drawback. However, while the large sample performance is the same, it performs somewhat worse than the first estimator in small samples. In addition, the Cramer-Rao lower bound for the problem is derived in a compact form. The problem of estimating the Kronecker factors and the problem of detecting if the Kronecker structure is a good model for the covariance matrix of a set of samples are related. Therefore, the problem of detecting the dimensions of the Kronecker factors based on the minimum values of the criterion functions corresponding to the two proposed estimation methods is also treated in this work.

  • 158.
    Wirfält, Petter
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    On Kronecker and Linearly Structured Covariance Matrix Estimation2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 6, p. 1536-1547Article in journal (Refereed)
    Abstract [en]

    The estimation of covariance matrices is an integral part of numerous signal processing applications. In many scenarios, there exists prior knowledge on the structure of the true covariance matrix; e. g., it might be known that the matrix is Toeplitz in addition to Hermitian. Given the available data and such prior structural knowledge, estimates using the known structure can be expected to be more accurate since more data per unknown parameter is available. In this work, we study the case when a covariance matrix is known to be the Kronecker product of two factor matrices, and in addition the factor matrices are Toeplitz. We devise a two-step estimator to accurately solve this problem: the first step is a maximum likelihood (ML) based closed form estimator, which has previously been shown to give asymptotically (in the number of samples) efficient estimates when the relevant factor matrices are Hermitian or persymmetric. The second step is a re-weighting of the estimates found in the first steps, such that the final estimate satisfies the desired Toeplitz structure. We derive the asymptotic distribution of the proposed two-step estimator and conclude that the estimator is asymptotically statistically efficient, and hence asymptotically ML. Through Monte Carlo simulations, we further show that the estimator converges to the relevant Cramer-Rao lower bound for fewer samples than existing methods.

  • 159.
    Wu, Junfeng
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Meng, Ziyang
    Yang, Tao
    Shi, Guodong
    Johansson, Karl H.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Sampled-Data Consensus Over Random Networks2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 17, p. 4479-4492Article in journal (Refereed)
    Abstract [en]

    This paper considers the consensus problem for a network of nodes with random interactions and sampled-data control actions. We first show that consensus in expectation, in mean square, and almost surely are equivalent for a general random network model when the inter-sampling interval and maximum node degree satisfy a simple relation. The three types of consensus are shown to be simultaneously achieved over an independent or a Markovian random network defined on an underlying graph with a directed spanning tree. For both independent and Markovian random network models, necessary and sufficient conditions for mean-square consensus are derived in terms of the spectral radius of the corresponding state transition matrix. These conditions are then interpreted as the existence of critical value on the intersampling interval, below which a global mean-square consensus is achieved and above which the system diverges in a mean-square sense for some initial states. Finally, we establish an upper bound on the intersampling interval below which almost sure consensus is reached, and a lower bound on the intersampling interval above which almost sure divergence is reached. Some numerical simulations are given to validate the theoretical results and some discussions on the critical value of the inter-sampling intervals for the mean-square consensus are provided.

  • 160.
    Xiao, Hanshen
    et al.
    MIT, CSAIL, Cambridge, MA 02139 USA.;MIT, Dept EECS, Cambridge, MA 02139 USA..
    Huang, Yufeng
    Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China..
    Ye, Yu
    KTH, School of Electrical Engineering (EES).
    Xiao, Guoqiang
    Southwest Univ, Coll Comp & Informat Sci, Chongqing 400715, Peoples R China..
    Robustness in Chinese Remainder Theorem for Multiple Numbers and Remainder Coding2018In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 16, p. 4347-4361Article in journal (Refereed)
    Abstract [en]

    Chinese remainder theorem (CRT) has been widely studied with its applications in frequency estimation, phase unwrapping, coding theory, and distributed data storage. Since traditional CRT is greatly sensitive to the errors in residues due to noises, the problem of robustly reconstructing integers via the erroneous residues has been intensively studied in the literature. In order to robustly reconstruct integers, there are basically two approaches: one is to introduce common divisors in the moduli and the other is to directly decrease the dynamic range. In this paper, we take further insight into the geometry property of the linear space associated with CRT. Echoing both ways to introduce redundancy, we propose a pseudometric as a uniform framework to analyze the tradeoff between the error bound and the dynamic range for robust CRT. Furthermore, we present the first robust CRT for multiple numbers to solve the problem raised by CRT-based undersampling frequency estimation in general. Based on symmetric polynomials proposed, we proved that in most cases, the problem can he solved efficiently in the polynomial time.

  • 161. Yang, Chao
    et al.
    Wu, Junfeng
    KTH, School of Electrical Engineering (EES).
    Ren, Xiaoqiang
    Yang, Wen
    Shi, Hongbo
    Shi, Ling
    Deterministic Sensor Selection for Centralized State Estimation Under Limited Communication Resource2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 9, p. 2336-2348Article in journal (Refereed)
    Abstract [en]

    This paper studies a sensor selection problem. A group of sensors measure the state of a process and send their measurements to a remote estimator. Due to communication constraints, only limited sensors are allowed to communicate with the estimator. The paper intends to answer which sensors should be chosen such that the estimation performance of the estimator is optimized. Both reliable and packet-dropping channels are considered. It is required to minimize the steady-state estimation error covariance for reliable channels and to minimize the upper bound of the expected estimation error covariance for packet-dropping channels. For both scenarios, the original optimization problems are transformed to problems which can be solved by convex optimization techniques.

  • 162. Yang, Y.
    et al.
    Pesavento, M.
    Chatzinotas, S.
    Ottersten, Björn
    Interdisciplinary Centre for Security, Reliability and Trust University of Luxembourg, Luxembourg City, L-1855, Luxembourg.
    Energy Efficiency Optimization in MIMO Interference Channels: A Successive Pseudoconvex Approximation Approach2019In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 15, p. 4107-4121Article in journal (Refereed)
    Abstract [en]

    In this paper, we consider the (global and sum) energy efficiency optimization problem in downlink multi-input multi-output multi-cell systems, where all users suffer from multi-user interference. This is a challenging problem due to several reasons: First, it is a nonconvex fractional programming problem; second, the transmission rate functions are characterized by (complex-valued) transmit covariance matrices; and third the processing-related power consumption may depend on the transmission rate. We tackle this problem by the successive pseudoconvex approximation approach, and we argue that pseudoconvex optimization plays a fundamental role in designing novel iterative algorithms, not only because every locally optimal point of a pseudoconvex optimization problem is also globally optimal but also because a descent direction is easily obtained from every optimal point of a pseudoconvex optimization problem. The proposed algorithms have the following advantages: First, fast convergence as the structure of the original optimization problem is preserved as much as possible in the approximate problem solved in each iteration; second, easy implementation as each approximate problem is suitable for parallel computation and its solution has a closed-form expression; and third, guaranteed convergence to a stationary point or a Karush-Kuhn-Tucker point. The advantages of the proposed algorithm are also illustrated numerically.

  • 163.
    Zachariah, Dave
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Dynamic Iterative Pursuit2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 9, p. 4967-4972Article in journal (Refereed)
    Abstract [en]

    For compressive sensing of dynamic sparse signals, we develop an iterative pursuit algorithm. A dynamic sparse signal process is characterized by varying sparsity patterns over time/space. For such signals, the developed algorithm is able to incorporate sequential predictions, thereby providing better compressive sensing recovery performance, but not at the cost of high complexity. Through experimental evaluations, we observe that the new algorithm exhibits a graceful degradation at deteriorating signal conditions while capable of yielding substantial performance gains as conditions improve.

  • 164.
    Zachariah, Dave
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Shariati, Nafiseh
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Estimation for the Linear Model With Uncertain Covariance Matrices2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 6, p. 1525-1535Article in journal (Refereed)
    Abstract [en]

    We derive a maximum a posteriori estimator for the linear observation model, where the signal and noise covariance matrices are both uncertain. The uncertainties are treated probabilistically by modeling the covariance matrices with prior inverse-Wishart distributions. The nonconvex problem of jointly estimating the signal of interest and the covariance matrices is tackled by a computationally efficient fixed-point iteration as well as an approximate variational Bayes solution. The statistical performance of estimators is compared numerically to state-of-the-art estimators from the literature and shown to perform favorably.

  • 165. Zachariah, Dave
    et al.
    Stoica, Petre
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Information Science and Engineering.
    Comments on "Enhanced PUMA for Direction-of-Arrival Estimation and Its Performance Analysis"2017In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 65, no 22, p. 6113-6114Article in journal (Other academic)
    Abstract [en]

    We show that the recently proposed (enhanced) principal-singular-vector utilization for modal analysis (PUMA) estimator for array processing [C. Qian, L. Huang, N. Sidiropoulos, and H. C. So, "Enhanced PUMA for direction-of-arrival estimation and its performance analysis," IEEE Trans. Signal Process., vol. 64, no. 16, pp. 4127-4137, Aug. 2016], minimizes the same criterion function as the well-established method of direction estimation (MODE) estimator.

  • 166. Zhang, G.
    et al.
    Klejsa, Janusz
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Kleijn, W. Bastiaan
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Optimal index assignment for multiple description scalar quantization with translated lattice codebooks2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 8, p. 4444-4451Article in journal (Refereed)
    Abstract [en]

    We design a K -description scalar quantizer, whose construction is based on a structure of translated scalar lattices and a lattice in K-1 dimensional space. The use of translated lattices provides a performance advantage by exploiting a so-called staggering gain. The use of the K-1 dimensional lattice facilitates analytic insight into the performance and significantly speeds up the computation of the index assignment compared to state-of-the-art methods. Using a common decoding method, the proposed index assignment is proven to be optimal for the K-description case. It is shown that the optimal index assignment is not unique. This is illustrated for the two-description case, where a periodic index assignment is selected from possible optimal assignments and described in detail. The performance of the proposed quantizer accurately matches theoretic analysis over the full range of operational redundancies. Moreover, the quantizer outperforms the state-of-the-art MD scheme as the redundancy among the description increases.

  • 167. Zhang, Jianjun
    et al.
    Huang, Yongming
    Wang, Jiaheng
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing. University of Luxembourg.
    Yang, Luxi
    Per-Antenna Constant Envelope Precoding and Antenna Subset Selection: A Geometric Approach2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 23, p. 6089-6104Article in journal (Refereed)
    Abstract [en]

    Constant envelope (CE) precoding can efficiently control the peak-to-average power ratio (PAPR) and improve the power efficiency of power amplifiers in large-scale antenna array systems. Antenna subset selection (ASS), combined with CE precoding, can further improve power efficiency by using a part of antennas to combine the desired signal. However, due to the inherent nonlinearity, the joint optimization of CE precoding and ASS is very challenging and satisfactory solutions are yet not available. In this paper, we present new methods for CE precoding and ASS optimization from a geometric perspective. First, we show the equivalence between the CE precoder design and a polygon construction problem in the complex plane, thus transforming the algebraic problem into a geometric problem. Aiming to minimize the computational complexity, we further transform the CE precoder design into a triangle construction problem, and propose a novel algorithm to achieve the optimal CE precoder with only linear complexity in the number of used antennas. Then, we investigate the joint optimization of ASS and CE precoding to minimize the total transmit power while satisfying the QoS requirement. Based on the geometric interpretation, we develop an efficient ASS algorithm, which, using only addition and comparison operations, is guaranteed to find the globally optimal solution and provides robustness to channel uncertainty. The complexity of the proposed ASS algorithm is at most quadratic in the number of antennas in the worst case. The optimality and superiority of the proposed geometric methods are demonstrated via numerical results.

  • 168.
    Zhang, Xi
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Palomar, Daniel P.
    Electrical Engineering, Princeton University, Princeton.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Statistically robust design of linear MIMO transceivers2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 8, p. 3678-3689Article in journal (Refereed)
    Abstract [en]

    The treatment of channel state information (CSI) is critical in the design of MIMO systems. Accurate CSI at the transmitter is often not possible or may require high feedback rates. Herein, we consider the robust design of linear MIMO transceivers with perfect CSI either at the transmitter or at both sides of the link. The framework considers the design problem where the imperfect CSI consists of the channel mean and covariance matrix or, equivalently, the channel estimate and the estimation error covariance matrix. The robust transceiver design is based on a general cost function of the average MSEs as well as a design with individual MSE based constraints. In particular, a lower bound of the average MSE matrix is explored for the design when only the CSI at the transmitter is imperfect. Under different CSI conditions, the proposed robust transceivers exhibit a similar structure to the transceiver designs for perfect CSI, but with a different equivalent channel and/or noise covariance matrix.

  • 169. Zheng, G.
    et al.
    Ho, Z.
    Jorswieck, E. A.
    Ottersten, Björn
    Univ. of Luxembourg.
    Information and energy cooperation in cognitive radio networks2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 9, p. 2290-2303Article in journal (Refereed)
    Abstract [en]

    Cooperation between the primary and secondary systems can improve the spectrum efficiency in cognitive radio networks. The key idea is that the secondary system helps to boost the primary system's performance by relaying, and, in return, the primary system provides more opportunities for the secondary system to access the spectrum. In contrast to most of existing works that only consider information cooperation, this paper studies joint information and energy cooperation between the two systems, i.e., the primary transmitter sends information for relaying and feeds the secondary system with energy as well. This is particularly useful when the secondary transmitter has good channel quality to the primary receiver but is energy constrained. We propose and study three schemes that enable this cooperation. First, we assume there exists an ideal backhaul between the two systems for information and energy transfer. We then consider two wireless information and energy transfer schemes from the primary transmitter to the secondary transmitter using power splitting and time splitting energy harvesting techniques, respectively. For each scheme, the optimal and zero-forcing solutions are derived. Simulation results demonstrate promising performance gain for both systems due to the additional energy cooperation. It is also revealed that the power splitting scheme can achieve larger rate region than the time splitting scheme when the efficiency of the energy transfer is sufficiently large

  • 170. Zheng, Gan
    et al.
    Krikidis, I.
    Li, Jiangyuan
    Petropulu, A. P.
    Ottersten, Björn
    University of Luxembourg.
    Improving Physical Layer Secrecy Using Full-Duplex Jamming Receivers2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 20, p. 4962-4974Article in journal (Refereed)
    Abstract [en]

    This paper studies secrecy rate optimization in a wireless network with a single-antenna source, a multi-antenna destination and a multi-antenna eavesdropper. This is an unfavorable scenario for secrecy performance as the system is interference-limited. In the literature, assuming that the receiver operates in half duplex (HD) mode, the aforementioned problem has been addressed via use of cooperating nodes who act as jammers to confound the eavesdropper. This paper investigates an alternative solution, which assumes the availability of a full duplex (FD) receiver. In particular, while receiving data, the receiver transmits jamming noise to degrade the eavesdropper channel. The proposed self-protection scheme eliminates the need for external helpers and provides system robustness. For the case in which global channel state information is available, we aim to design the optimal jamming covariance matrix that maximizes the secrecy rate and mitigates loop interference associated with the FD operation. We consider both fixed and optimal linear receiver design at the destination, and show that the optimal jamming covariance matrix is rank-1, and can be found via an efficient 1-D search. For the case in which only statistical information on the eavesdropper channel is available, the optimal power allocation is studied in terms of ergodic and outage secrecy rates. Simulation results verify the analysis and demonstrate substantial performance gain over conventional HD operation at the destination.

  • 171. Zheng, Gan
    et al.
    Song, Shenghui
    Wong, Kai-Kit
    Ottersten, Björn
    University of Luxembourg.
    Cooperative Cognitive Networks: Optimal, Distributed and Low-Complexity Algorithms2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 11, p. 2778-2790Article in journal (Refereed)
    Abstract [en]

    This paper considers the cooperation between a cognitive system and a primary system where multiple cognitive base stations (CBSs) relay the primary user's (PU) signals in exchange for more opportunity to transmit their own signals. The CBSs use amplify-and-forward (AF) relaying and coordinated beamforming to relay the primary signals and transmit their own signals. The objective is to minimize the overall transmit power of the CBSs given the rate requirements of the PU and the cognitive users (CUs). We show that the relaying matrices have unity rank and perform two functions: Matched filter receive beamforming and transmit beamforming. We then develop two efficient algorithms to find the optimal solution. The first one has a linear convergence rate and is suitable for distributed implementation, while the second one enjoys superlinear convergence but requires centralized processing. Further, we derive the beamforming vectors for the linear conventional zero-forcing (CZF) and prior zero-forcing (PZF) schemes, which provide much simpler solutions. Simulation results demonstrate the improvement in terms of outage performance due to the cooperation between the primary and cognitive systems.

  • 172.
    Zheng, Gan
    et al.
    Department of Electrical and Electronic Engineering, University College London.
    Wong, Kai-Kit
    Department of Electrical and Electronic Engineering, University College London.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Robust Cognitive Beamforming With Bounded Channel Uncertainties2009In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 57, no 12, p. 4871-4881Article in journal (Refereed)
    Abstract [en]

    This paper studies the robust beamforming design for a multi-antenna cognitive radio (CR) network, which transmits to multiple secondary users (SUs) and coexists with a primary network of multiple users. We aim to maximize the minimum of the received signal-to-interference-plus-noise ratios (SINRs) of the SUs, subject to the constraints of the total SU transmit power and the received interference power at the primary users (PUs) by optimizing the beamforming vectors at the SU transmitter based on imperfect channel state information (CSI). To model the uncertainty in CSI, we consider a bounded region for both cases of channel matrices and channel covariance matrices. As such, the optimization is done while satisfying the interference constraints for all possible CSI error realizations. We shall first derive equivalent conditions for the interference constraints and then convert the problems into the form of semi-definite programming (SDP) with the aid of rank relaxation, which leads to iterative algorithms for obtaining the robust optimal beamforming solution. Results demonstrate the achieved robustness and the performance gain over conventional approaches and that the proposed algorithms can obtain the exact robust optimal solution with high probability.

  • 173.
    Zheng, Gan
    et al.
    Department of Electrical and Electronic Engineering, University College London.
    Wong, Kai-Kit
    Department of Electrical and Electronic Engineering, University College London.
    Paulraj, Arogyasvsami
    Information Systems Laboratory, Stanford University, US.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Robust Collaborative-Relay Beamforming2009In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 57, no 8, p. 3130-3143Article in journal (Refereed)
    Abstract [en]

    Relay communications is a promising technique to extend the range of wireless communications by forwarding the message from the sender to the intended destination. While fixed or variable-power relays have been previously investigated, this paper addresses the collaborative use of variable-phase variable-power amplify-and-forward (AF) relays for robust beamforming, with the aid of imperfect channel state information (CSI) at the sender. In particular, the maximization of the worst-case signal-to-noise ratio (SNR) at the destination terminal is studied under a bounded spherical region for the norm of the CSI error vector from the relays to the destination. Our main contribution is that we prove, under a condition on the quality of the estimated CSI, the robust-optimal collaborative-relay beamforming (CRBF) can be obtained by S-Procedure and rank relaxation techniques. In addition, a distributed algorithm is developed by examining the structure of the optimal CRBF solution. Results demonstrate a significant gain of CRBF over non-robust approaches.

  • 174.
    Zhu, Shanying
    et al.
    Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China.;Minist Educ China, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China..
    Chen, Cailian
    Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China.;Minist Educ China, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China..
    Xu, Jinming
    Arizona State Univ, Ira A Fulton Sch Engn, Tempe, AZ 85281 USA..
    Guan, Xinping
    Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China.;Minist Educ China, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China..
    Xie, Lihua
    Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore..
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Mitigating Quantization Effects on Distributed Sensor Fusion: A Least Squares Approach2018In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 13, p. 3459-3474Article in journal (Refereed)
    Abstract [en]

    In this paper, we consider the problem of sensor fusion over networks with asymmetric links, where the common goal is linear parameter estimation. For the scenario of bandwidth-constrained networks, existing literature shows that nonvanishing errors always occur, which depend on the quantization scheme. To tackle this challenging issue, we introduce the notion of virtual measurements and propose a distributed solution LS-DSFS, which is a combination of a quantized consensus algorithm and the least squares approach. We provide detailed analysis of the LS-DSFS on its performance in terms of unbiasedness and mean square property. Analytical results show that the LS-DSFS is effective in smearing out the quantization errors, and achieving the minimum mean square error (MSE) among the existing centralized and distributed algorithms. Moreover, we characterize its rate of convergence in the mean square sense and that of the mean sequence. More importantly, we find that the LS-DSFS outperforms the centralized approaches within a moderate number of iterations in terms of MSE, and will always consume less energy and achieve more balanced energy expenditure as the number of nodes in the network grows. Simulation results are presented to validate theoretical findings and highlight the improvements over existing algorithms.

1234 151 - 174 of 174
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf