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Signal Processing, IEEE Transactions on

Issue 6  Part 1 • Date June 2007

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Displaying Results 1 - 25 of 44
  • Table of contents

    Page(s): C1 - C4
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  • IEEE Transactions on Signal Processing publication information

    Page(s): C2
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  • 2006 Best Paper Award Recipients: A Message From the Editor-in-Chief

    Page(s): 2373 - 2374
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    Freely Available from IEEE
  • Iterative Generalized-Likelihood Ratio Test for MIMO Radar

    Page(s): 2375 - 2385
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (914 KB) |  | HTML iconHTML  

    We consider a multiple-input multiple-output (MIMO) radar system where both the transmitter and receiver have multiple well-separated subarrays with each subarray containing closely spaced antennas. Because of this general antenna configuration, both the coherent processing gain and the spatial diversity gain can be simultaneously achieved. We compare several spatial spectral estimators, including Capon and APES, for target detection and parameter estimation. We introduce a generalized-likelihood ratio test (GLRT) and a conditional generalized-likelihood ratio test (cGLRT) for the general antenna configuration. Based on GLRT and cGLRT, we then propose an iterative GLRT (iGLRT) procedure for target detection and parameter estimation. Via several numerical examples, we show that iGLRT can provide excellent detection and estimation performance at a low computational cost View full abstract»

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  • GLRT-Based Direction Detectors in Homogeneous Noise and Subspace Interference

    Page(s): 2386 - 2394
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (634 KB) |  | HTML iconHTML  

    In this paper, we derive and assess decision schemes to discriminate, resorting to an array of sensors, between the H0 hypothesis that data under test contain disturbance only (i.e., noise plus interference) and the H1 hypothesis that they also contain signal components along a direction which is a priori unknown but constrained to belong to a given subspace of the observables. The disturbance is modeled in terms of complex normal random vectors plus deterministic interference assumed to belong to a known subspace. We assume that a set of noise-only (secondary) data is available, which possess the same statistical characterization of noise in the cells under test. At the design stage, we resort to either the plain generalized-likelihood ratio test (GLRT) or the two-step GLRT-based design procedure. The performance analysis, conducted resorting to simulated data, shows that the one-step GLRT performs better than the detector relying on the two-step design procedure when the number of secondary data is comparable to the number of sensors; moreover, it outperforms a one-step GLRT-based subspace detector when the dimension of the signal subspace is sufficiently high View full abstract»

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  • An Enhanced Deterministic Sequential Monte Carlo Method for Near-Optimal MIMO Demodulation With QAM Constellations

    Page(s): 2395 - 2406
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (634 KB) |  | HTML iconHTML  

    We propose a low-complexity, linear minimum mean squared error (MMSE)-based sequential Monte Carlo (SMC) technique as an alternative to the sphere decoder for near-optimal demodulation in multiple-input multiple-output (MIMO) systems. Prior to the SMC procedure, the received signal is passed through a linear MMSE-based preprocessing step, which also determines an optimal channel-dependent order of detection and produces a sequential structure. The algorithm then draws the symbol samples in a deterministic fashion, and the survivor paths are selected based on their importance weights. The proposed algorithm exploits the rectangular structure of the QAM signal constellation by separating the real and imaginary parts of the signal to reduce the complexity associated with the listing and weight update steps, resulting in a complexity (in terms of the constellation size M) of O(radicM) as compared to O(M) complexity of the existing SMC algorithms for an M-QAM constellation. We demonstrate through simulations that the new method achieves the sphere decoder performance for V-BLAST systems. Unlike the sphere decoder whose complexity is channel-dependent, our algorithm has a fixed complexity which is channel independent; thus it is well suited for use in practical MIMO systems. Some other interesting features of the algorithm are that it is able to handle MIMO systems with less receive antennas than transmit antennas; and can also deal with multiuser multirate MIMO systems, utilizing a novel ordering scheme. Finally we extend the proposed algorithm to solve the lattice decoding problem and demonstrate the effect of different preprocessing stages on the performance and complexity of the algorithm through extensive simulation results View full abstract»

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  • Generalized Spectral Coherences for Complex-Valued Harmonizable Processes

    Page(s): 2407 - 2413
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (326 KB) |  | HTML iconHTML  

    Complex-valued nonstationary random processes have nonvanishing complementary second-order moment functions. In this paper, we propose generalized dual-frequency and time-frequency coherence functions for harmonizable processes. The proposed generalized spectral coherences are based on widely linear estimators, and they result in coherence measures that combine Hermitian and complementary moment functions. We show that for analytic processes, and more surprisingly also for real-valued processes, additional second-order information becomes available through the generalized coherences. We offer illuminating geometrical interpretations of the proposed coherences through Hilbert space inner product formulations. Finally, we extend the theory to generalized cross-coherences between pairs of harmonizable processes View full abstract»

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  • Wavelet Deconvolution With Noisy Eigenvalues

    Page(s): 2414 - 2424
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (970 KB) |  | HTML iconHTML  

    Over the last decade, there has been a lot of interest in wavelet-vaguelette methods for the recovery of noisy signals or images in motion blur. Nonlinear wavelet estimators are known to have good adaptive properties and to outperform linear approximations over a wide range of signals and images, see e.g., the recent WaveD method of Johnstone, Kerkyacharian, Picard, and Raimondo (2004) or the ForWarD method of Neelamani, Choi, and Baraniuk (2004) and also Fan and Koo (2002) in the density setting. In the deblurring setting, wavelet-vaguelette methods rely on the complete knowledge of a convolution operator's eigenvalues. This is an unlikely situation in practice, however. A more realistic scenario, such as would arise when passing the Fourier basis as an input signal through a linear-time-invariant system, is to imagine that one also observes a set of noisy eigenvalues. In this paper, we define a version of the WaveD estimator which is near-optimal when used with noisy eigenvalues. A key feature of our method includes a data-driven method for choosing the fine resolution level in WaveD estimation. Asymptotic theory is illustrated with a wide range of finite sample examples View full abstract»

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  • Quantifying the Coding Performance of Zerotrees of Wavelet Coefficients: Degree-k Zerotree

    Page(s): 2425 - 2431
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (587 KB) |  | HTML iconHTML  

    Locating zerotrees in a wavelet transform allows encoding of sets of coefficients with a single symbol. It is an efficient means of coding if the overhead to identify the locations is small compared to the size of the zerotree sets on the average. It is advantageous in this regard to define classes of zerotrees according to the levels from the root until the remainder of the tree contains all zeroes. We call a tree with all zeroes except for the top k levels a degree-k zerotree. A degree-k zerotree coder is one that can encode degree-0 through degree-k zerotrees. We quantify the bit savings of a degree-k2 over a degree-k1, k2>k1, coder. Because SPIHT is a degree-2 zerotree coder and EZW a degree-0 zerotree coder, we are able to explain the superior efficiency of SPIHT. Finally, we gather statistics of degree-k zerotrees for different values of k in the bit planes of several image wavelet transforms to support our analysis of the coding performance of degree-k zerotree coders View full abstract»

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  • Efficient Blind System Identification of Non-Gaussian Autoregressive Models With HMM Modeling of the Excitation

    Page(s): 2432 - 2445
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (597 KB) |  | HTML iconHTML  

    We have previously proposed a blind system identification method that exploits the underlying dynamics of non-Gaussian signals in [Li and Andersen, "Blind identification of Non-Gaussian Autoregressive Models for Efficient Analysis of Speech Signals," Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2006, vol. 1, pp. I-1205-I-1208]. The signal model being identified is an autoregressive (AR) model driven by a discrete-state hidden Markov process. An exact expectation-maximization (EM) algorithm was derived for the joint estimation of the AR parameters and the hidden Markov model (HMM) parameters. In this paper, we extend the system model by introducing an additive measurement noise. The identification of the extended system model becomes much more complicated since the system output is now hidden. We propose an exact EM algorithm that incorporates a novel switching Kalman smoother, which obtains nonlinear minimum mean-square error (MMSE) estimates of the system output based on the state information given by the HMM filter. The exact EM algorithms for both models are obtainable only by appropriate constraints in the model design and have better convergence properties than algorithms employing generalized EM algorithm or empirical iterative schemes. The proposed methods also enjoy good data efficiency since only second-order statistics are involved in the computation. The signal models are general and suitable to numerous signals, such as speech and baseband communication signals. This paper describes the two system identification algorithms in an integrated form and provides supplementary results to the noise-free model and new results to the extended model with applications in speech analysis and channel equalization View full abstract»

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  • Fast Computation of Constrained Decision Feedback Equalizers

    Page(s): 2446 - 2457
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (846 KB) |  | HTML iconHTML  

    Constrained formulations of decision feedback equalizer (DFE) schemes arise whenever its intrinsic error propagation phenomenon must be reduced. This is the case when linear or a quadratic norm limits are enforced in the formulation of the usual minimum mean-square-error (mmse) DFE problem, so that superior performance can be achieved when compared to its unconstrained version. This paper solves the problem of designing fast algorithms for computing the DFE filters under the so-called magnitude and energy limiting norm criteria. The former is obtained by developing new updates in addition to the ones efficiently computed via a fast Kalman based method recently introduced. In a constrained energy formulation, however, it turns out that the desired shift structure of the channel convolution matrix that allows for a fast Kalman algorithm no longer exists. Still, we shall show how to properly correct for this discrepancy in structure in order to provide a fast recursion for computing the DFE coefficients in the constrained energy scenario. We verify the accuracy of the new algorithms under finite precision via computer simulations View full abstract»

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  • Characterization and Sampled-Data Design of Dual-Tree Filter Banks for Hilbert Transform Pairs of Wavelet Bases

    Page(s): 2458 - 2471
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (574 KB) |  | HTML iconHTML  

    Characterization and design of dual-tree filter banks for forming Hilbert transform pairs of wavelet bases are studied. The characterization extends the existing results for quadrature mirror filter banks to general prefect reconstruction filter banks that satisfy only a mild technical assumption regarding the ratio of determinants of the two filter banks. We establish equivalent relationships of Hilbert transform pairs on scaling filters, wavelet filters, or scaling functions. The design of scaling filters of a dual filter bank is formulated as a sampled-data Hinfin optimization problem. The wavelet filters are then determined using the relationship on the determinants of the filter banks. We convert the sampled-data problem into an equivalent discrete-time Hinfin control problem, which can be solved by standard Hinfin control theory. An analytical solution to the sampled-data design problem is obtained for a special case. The sampled-data design approach usually gives infinite impulse response filter. In the case where the primal filter bank is of finite impulse response (FIR), we may truncate the impulse responses to get FIR approximations. They also lead to approximate Hilbert transform pairs. Design examples are presented View full abstract»

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  • Robust Sequential Learning Algorithms for Linear Observation Models

    Page(s): 2472 - 2485
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (467 KB) |  | HTML iconHTML  

    This paper presents a study of sequential parameter estimation based on a linear non-Gaussian observation model. To develop robust algorithms, we consider a family of heavy-tailed distributions that can be expressed as the scale mixture of Gaussian and extend the development to include some robust penalty functions. We treat the problem as a Bayesian learning problem and develop an iterative algorithm by using the Laplace approximation for the posterior and the minorization-maximization (MM) algorithm as an optimization tool. We then study a one-step implementation of the iterative algorithm. This leads to a family of generalized robust RLS-type of algorithms which include several well-known algorithms as special cases. Using a further simplification that the covariance is fixed, leads to a family of generalized robust LMS-type of algorithms. Through mathematical analysis and simulations, we demonstrate the robustness of these algorithms View full abstract»

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  • Passive Source Localization Using an Airborne Sensor Array in the Presence of Manifold Perturbations

    Page(s): 2486 - 2496
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1096 KB) |  | HTML iconHTML  

    This paper studies localizing sources of electromagnetic energy using a passive sensor array whose manifold is only nominally known. The problem of source localization is studied in the context of an airborne array that is able to observe a ground-based source from multiple angles. External and self-calibration algorithms are developed as a means to obtain accurate source localization estimates when the sensor manifold is perturbed. External calibration establishes the expected difference between the actual and modeled array manifold using signals at known locations. Self-calibration assumes that this expected difference is known only approximately and relies on signals of opportunity in the environment to provide updates. Several novel calibration algorithms are proposed, and their performance is tested on experimental data. The results indicate that significant performance gains are achieved with the use of the proposed calibration algorithms View full abstract»

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  • Detection of the Number of Signals Using the Benjamini-Hochberg Procedure

    Page(s): 2497 - 2508
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (351 KB) |  | HTML iconHTML  

    This paper presents a novel approach to detect multiple signals embedded in noisy observations from a sensor array. We formulate the detection problem as a multiple hypothesis test. To control the global level of the multiple test, we apply the false discovery rate (FDR) criterion proposed by Benjamini and Hochberg. Compared to the classical familywise error rate (FWE) criterion, the FDR-controling procedure leads to a significant gain in power for large size problems. In addition, we apply the bootstrap technique to estimate the observed significance level required by the FDR-controling procedure. Simulations show that the FDR-controling procedure always provides higher probability of correct detection than the FWE-controling procedure. Furthermore, the reliability of the proposed test procedure is not affected by the gain in power of the test View full abstract»

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  • A Multistage Hybrid Constant Modulus Array With Constrained Adaptation for Correlated Sources

    Page(s): 2509 - 2519
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (572 KB) |  | HTML iconHTML  

    The multistage constant modulus (CM) array was previously proposed for capturing multiple received signals in a cochannel signal environment. It consists of a cascade of individual CM array stages combined with adaptive signal cancelers that remove the various signals captured across the stages. However, when the received signals are mutually correlated, the signals captured by the CM array stages are not completely canceled, and previous parallel extensions of the system do not guarantee that different signals will be captured across the stages. In this paper, we present a hybrid implementation of the multistage CM array for separating correlated signals where the canceler weights in the cascade structure provide estimates of the direction vectors of the captured signals. These estimates are then used in a parallel implementation of the linearly constrained CM (LCCM) array leading to the hybrid structure. Since the direction vectors are obtained directly from the canceler weights, the hybrid implementation does not require prior knowledge of the array response matrix and is independent of the type of antennas used in the receiver. The effect of a bias in the direction vector estimates for closely-spaced signals is analyzed, and the steady-state performance of the hybrid structure is compared to that of a conventional constrained implementation for correlated sources. Computer simulations for example cochannel scenarios are provided to illustrate various properties of the system. Mean-square-error (MSE) learning curves indicate that the proposed hybrid LCCM algorithm converges faster and has lower MSE than previous implementations View full abstract»

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  • Biochemical Transport Modeling and Bayesian Source Estimation in Realistic Environments

    Page(s): 2520 - 2532
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    Early detection and estimation of the spread of a biochemical contaminant are major issues in many applications, such as homeland security and pollution monitoring. We present an integrated approach combining the measurements given by an array of biochemical sensors with a physical model of the dispersion and statistical analysis to solve these problems and provide system performance measures. We approximate the dispersion model of a contaminant in a realistic environment through numerical simulations of reflected stochastic diffusions describing the microscopic transport phenomena due to wind and chemical diffusion and use the Feynmann-Kac formula. We consider arbitrary complex geometries and account for wind turbulence. Numerical examples are presented for two real-world scenarios: an urban area and an indoor ventilation duct. Localizing the dispersive sources is useful for decontamination purposes and estimation of the cloud evolution. To solve the associated inverse problem, we propose a Bayesian framework based on a random field that is particularly powerful for localizing multiple sources with small amounts of measurements View full abstract»

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  • Adaptive CFAR Radar Detection With Conic Rejection

    Page(s): 2533 - 2541
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (540 KB) |  | HTML iconHTML  

    In this paper, we deal with the problem of adaptive signal detection in colored Gaussian disturbance. Since the classical receivers may exhibit severe performance degradations in the presence of steering vector mismatches and sidelobe interfering signals, we try to account for the quoted drawbacks, very usual in realistic radar scenarios, at the design stage. To this end, we first characterize the set where the useful received signal may lie and its complement, i.e., the set which may contain the signals to be rejected. Then we resort to the generalized likelihood ratio (GLR) principle and devise detectors capable of operating in the presence of array response mismatches and sidelobe interfering signals. At the analysis stage, we assess the performance of the newly introduced receivers also in comparison with previously proposed detectors. The results show that the new processors are characterized by a wide range of performance compromises, selectable at the design stage through the regulation of a design parameter, between the detection of useful signals and the rejection of sidelobe interference View full abstract»

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  • Intrinsic Limits of Dimensionality and Richness in Random Multipath Fields

    Page(s): 2542 - 2556
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (690 KB) |  | HTML iconHTML  

    We study the dimensions or degrees of freedom of farfield multipath that is observed in a limited, source-free region of space. The multipath fields are studied as solutions to the wave equation in an infinite-dimensional vector space. We prove two universal upper bounds on the truncation error of fixed and random multipath fields. A direct consequence of the derived bounds is that both fixed and random multipath fields have an effective finite dimension. For circular and spherical spatial regions, we show that this finite dimension is proportional to the radius and area of the region, respectively. We use the Karhunen-Loegraveve (KL) expansion of random multipath fields to quantify the notion of multipath richness. The multipath richness is defined as the number of significant eigenvalues in the KL expansion that achieve 99% of the total multipath energy. We establish a lower bound on the largest eigenvalue. This lower bound quantifies, to some extent, the well-known reduction of multipath richness with reducing the angular power spread of multipath angular power spectrum View full abstract»

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  • Frame/Training Sequence Synchronization and DC-Offset Removal for (Data-Dependent) Superimposed Training Based Channel Estimation

    Page(s): 2557 - 2569
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (807 KB) |  | HTML iconHTML  

    Over the last few years there has been growing interest in performing channel estimation via superimposed training (ST), where a training sequence is added to the information-bearing data, as opposed to being time-division multiplexed with it. Recent enhancements of ST are data-dependent ST (DDST), where an additional data-dependent training sequence is also added to the information-bearing signal, and semiblind approaches based on ST. In this paper, along with the channel estimation, we consider new algorithms for training sequence synchronization for both ST and DDST and block (or frame) synchronization (BS) for DDST (BS is not needed for ST). The synchronization algorithms are based on the structural properties of the vector containing the cyclic means of the channel output. In addition, we also consider removal of the unknown dc offset that can occur due to using first-order statistics with a non-ideal radio-frequency receiver. The subsequent bit error rate (BER) simulations (after equalization) show a performance not far removed from the ideal case of exact synchronization. While this is the first synchronization algorithm for DDST, our new approach for ST gives identical results to an existing ST synchronization method but with a reduced computational burden. In addition, we also present analysis of BER simulations for time-varying channels, different modulation schemes, and traditional time-division multiplexed training. Finally, the advantage of DDST over (conventional, non semi-blind) ST will reduce as the constellation size increases, and we also show that even without a BS algorithm, DDST is still superior to conventional ST. However, iterative semiblind schemes based upon ST outperform DDST but at the expense of greater complexity View full abstract»

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  • A Novel Signaling Scheme for Blind Unique Identification of Alamouti Space-Time Block-Coded Channel

    Page(s): 2570 - 2582
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1065 KB) |  | HTML iconHTML  

    In this paper, we present a simple signaling scheme to blindly and uniquely identify the Alamouti space-time block-coded channel, first under a noise-free environment, and then, under a complex Gaussian noise environment in which pth- and qth-order statistics (p and q are coprime) of the received signals are available. In both cases, closed-form solutions to determine the channel coefficients are obtained by exploiting specific properties of the Alamouti space-time block code (STBC) and the linear Diophantine equation theory. Under Gaussian noise, when the length of the received data is finite, we propose to use the semidefinite relaxation (SDR) algorithm to approximate maximum-likelihood (ML) detection so that the joint estimation of the channel and symbols can be efficiently implemented. Simulation results show that while other existing blind methods fail, our signaling scheme works well View full abstract»

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  • Lossless Source Coding Using Nested Error Correcting Codes

    Page(s): 2583 - 2592
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (782 KB) |  | HTML iconHTML  

    We propose a tree-structured variable-length random binning scheme for lossless source coding. The existing source coding schemes based on turbo codes, low-density parity check codes, and repeat accumulate codes can be regarded as practical implementations of this random binning scheme. For sufficiently large data blocks, we show that the proposed scheme asymptotically achieves the entropy limit. We also derive the distribution of the compression rate achieved by the tree-structured random binning scheme. Comparing this distribution with the distribution obtained using a library of random binning schemes, we show that a nested code can achieve rates close to a library of codes but with much lower encoding/decoding complexity. With lossless turbo source coding being one of the most powerful source compression techniques, we investigate its performance relative to the proposed tree-structured random binning scheme. Our numerical results show that the compression rate achieved by lossless turbo source coding is far from the tree-structured random binning bound. In that, we suggest improvements to enable short-block-length turbo source codes to achieve compression rates close to the tree-structured random binning bound View full abstract»

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  • Linear Transceiver Design in Nonregenerative Relays With Channel State Information

    Page(s): 2593 - 2604
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1242 KB) |  | HTML iconHTML  

    This paper deals with the design of nonregenerative relaying transceivers in cooperative systems where channel state information (CSI) is available at the relay station. The conventional nonregenerative approach is the amplify and forward (A&F) approach, where the signal received at the relay is simply amplified and retransmitted. In this paper, we propose an alternative linear transceiver design for nonregenerative relaying (including pure relaying and the cooperative transmission cases), making proper use of CSI at the relay station. Specifically, we design the optimum linear filtering performed on the data to be forwarded at the relay. As optimization criteria, we have considered the maximization of mutual information (that provides an information rate for which reliable communication is possible) for a given available transmission power at the relay station. Three different levels of CSI can be considered at the relay station: only first hop channel information (between the source and relay); first hop channel and second hop channel (between relay and destination) information, or a third situation where the relay may have complete cooperative channel information including all the links: first and second hop channels and also the direct channel between source and destination. Despite the latter being a more unrealistic situation, since it requires the destination to inform the relay station about the direct channel, it is useful as an upper benchmark. In this paper, we consider the last two cases relating to CSI. We compare the performance so obtained with the performance for the conventional A&F approach, and also with the performance of regenerative relays and direct noncooperative transmission for two particular cases: narrowband multiple-input multiple-output transceivers and wideband single input single output orthogonal frequency division multiplex transmissions View full abstract»

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  • Bit Error Rate Minimizing Channel Shortening Equalizers for Cyclic Prefixed Systems

    Page(s): 2605 - 2616
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1862 KB) |  | HTML iconHTML  

    Cyclic prefixed communications, such as multicarrier communications, first became widely used in the context of digital subscriber lines (DSL). In DSL, bit loading is allowed at the transmitter, and the performance metric is the bit rate that can be provided without exceeding a given bit error rate (BER). Wireless cyclic prefixed systems are now becoming increasingly popular, and in such systems the appropriate performance metric is the BER for a given bit loading at the transmitter. Cyclic prefixed systems perform well in the presence of multipath, provided that the channel delay spread is shorter than the guard interval between transmitted blocks. If this condition is not met, a channel shortening equalizer can be used to shorten the channel to the desired length. Previous work on channel shortening has largely been in the context of DSL, thus it has focused on maximizing the bit rate. In this paper, we propose a channel shortener that attempts to directly minimize the BER for a multiple-input multiple-output channel model. We simulate the performance of the resulting channel shortener and compare it to existing designs and the matched filter bound View full abstract»

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  • Estimation of Carrier Frequency Offset for Multicarrier CDMA Uplink

    Page(s): 2617 - 2627
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (547 KB) |  | HTML iconHTML  

    This paper presents a training-based estimation method of carrier frequency offset (CFO) for multicarrier code-division multiple-access (MC-CDMA) uplink. The basic assumption is the same as in Morellis' work (OFDMA), i.e., active users in the network are classified into two categories: reference (synchronized) users (RUs) and new (asynchronized) users (NUs). But, we consider the system that RUs use CDMA to share the wireless medium. NUs employ time-division multiple-access (TDMA) for the training purpose. Our major idea is to perform the CFO estimation in the desired signal subspace that is orthogonal to the RU subspace. The proposed approach is investigated both in the subband and interleaved MC-CDMA systems. It is shown that implementation of the CFO estimation together with the training-pattern design could be relatively simple when users do not experience the considerable frequency-selective fade. In the highly frequency-selective fading scenario, an adaptive training-scheme is proposed to make the CFO estimator robust. We also consider NUs operating in the frequency-division multiple-access (FDMA) training mode. Due to the presence of inter-NU-interference, the estimation performance may be not as good as in the TDMA training mode. The performance degradation may be severe when RUs operate in the interleaved MC-CDMA. In this case, we propose an inter-NU-interference self-canceling training-pattern and use simulations to demonstrate its impact on the estimation performance View full abstract»

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Aims & Scope

IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals

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Meet Our Editors

Editor-in-Chief
Zhi-Quan (Tom) Luo
University of Minnesota