Subspace-based Blind Channel Estimation Research Articles

Fast Iterative Semi-Blind Receiver for URLLC in Short-Frame Full-Duplex Systems With CFO

We propose an iterative semi-blind (ISB) receiver structure to enable ultra-reliable low-latency communications in short-frame full-duplex (FD) systems with carrier frequency offset (CFO). To the best of our knowledge, this is the first paper to propose an integral solution to channel estimation and CFO estimation for short-frame FD systems by utilizing a single pilot. By deriving an equivalent system model with the CFO included implicitly, a subspace-based blind channel estimation is proposed at the initial stage, followed by CFO estimation and channel ambiguities elimination. Then, the refinement of channel and the CFO estimates is conducted iteratively. The integer and fractional parts of CFO in the full range are estimated as a whole and in closed-form at each iteration. The proposed ISB receiver significantly outperforms the previous methods in terms of frame error rate, mean square errors of channel estimation and CFO estimation and output signal-to-interference-and-noise ratio, while at a halved spectral overhead. Cramer–Rao lower bounds are derived to verify the effectiveness of the proposed ISB receiver structure. It also demonstrates high-computational efficiency as well as the fast convergence speed.

On detecting primary user emulation attack using channel impulse response in the cognitive radio network

Cognitive radio is an effective technology to alleviate the spectrum resource scarcity problem by opportunistically allocating the spare spectrum to unauthorized users. However, a serious denial-of-service (DoS) attack, named the ‘primary user emulation attack (PUEA)’, exists in the network to deteriorate the system performance. In this paper, we propose a PUEA detection method that exploits the radio channel information to detect the PUEA in the cognitive radio network. In the proposed method, the uniqueness of the channel impulse response (CIR) between the secondary user (SU) and the signal source is used to determine whether the received signal is transmitted by the primary user (PU) or the primary user emulator (PUE). The closed-form expressions for the false-alarm probability and the detection probability of the proposed PUEA detection method are derived. In addition, a modified subspace-based blind channel estimation method is presented to estimate the CIR, in order for the proposed PUEA detection method to work in the scenario where the SU has no prior knowledge about the structure and content of the PU signal. Numerical results show that the proposed PUEA detection method performs well although the difference in channel characteristics between the PU and PUE is small.

Subspace-Based Blind Channel Estimation for OFDM by Exploiting Cyclic Prefix

A new subspace-based blind channel estimation (SBCE) approach for cyclically prefixed orthogonal frequency-division multiplexing (CP-OFDM) is proposed to provide enhanced estimation performance. Based on the redundant structure that CP reproduces the tail part of the useful signal vector, a predetermined null subspace corresponding to the transmitted CP-OFDM signal vector subspace is identified and enables the proposed estimator to extract the channel impluse response by projecting the received signal onto the predetermined null subspace. While requiring less implementation complexity, the proposed channel estimator is shown to outperform conventional SBCE approaches in mean-square error performance when the number of received blocks and the received signal-to-noise power ratio are not large.

Subspace-Based Blind Channel Estimation by Separating Real and Imaginary Symbols for Cyclic-Prefixed Single-Carrier Systems

Blind channel estimation based on a subspace-based algorithm for single-input single-output cyclic-prefixed single-carrier systems is proposed in this brief. The proposed method is different from conventional subspace-based approaches, which exploit the original complex structure of the received data symbols. In contrast, we separate the real part and the imaginary part of the received complex symbols and then exploit these two kinds of symbols to construct the signal model. The noise subspace can then be established to estimate the channel impulse response (CIR) using a subspace algorithm when real symbols, such as binary phase shift keying or pulse amplitude modulation symbols, are applied. The real part and the imaginary part of the CIR can be estimated individually and simultaneously with only a sign ambiguity. With the aid of repetition index, the proposed method is workable even if few data blocks are available. Simulation results demonstrate that the proposed approach outperforms conventional methods in normalized mean-squared error under static channel environments.

An Improved Subspace-Based Algorithm for Blind Channel Identification Using Few Received Blocks

In the last decade, several subspace-based algorithms for blind channel identification in zero-padded systems were introduced. These subspace-based methods are attractive because highly accurate estimates of the channel can be obtained by using only a few received blocks. However, they do not work when applied to the zero-padded orthogonal frequency division multiplexing systems (ZP-OFDM) with virtual carriers. In this paper, we propose two improvements on an earlier subspace-based blind channel estimation method for such systems. Firstly, we introduce a simple noise weighting approach. Unlike most of the earlier methods, the proposed weighting matrix is diagonal and it is independent of the channel noise variance and SNR. By doing so, the performance of previous work is significantly enhanced. Secondly, we extend the blind estimation method to ZP-OFDM systems with virtual carriers. Simulations are carried out to demonstrate the merits of the proposed method.

Blind channel estimation for cyclic prefix-free orthogonal frequency-division multiplexing systems with particular input symbols

Subspace algorithms are commonly used for blind channel estimation in orthogonal frequency-division multiplexing (OFDM) systems as they can establish the relation between the channel impulse response and singular vectors of the noise subspace. However, they are not workable under the conventional cyclic prefix-free OFDM signal model as the corresponding necessary condition of subspace algorithms is not satisfied. To solve this problem, this study proposes two subspace-based blind channel estimation approaches using the real and periodic symbols of time-domain transmitted OFDM blocks. From the simulation results, we can obtain the proposed approaches outperform conventional ones in terms of normalised mean-squared error and bit error rate under time-invariant channel environments.

Subspace-based blind channel estimation in nearly saturated down link multicarrier code division multiple access systems

A new subspace-based blind method for channel estimation in nearly saturated downlink multicarrier code division multiple access systems is proposed. In this method, the authors exploit the subspace structure of the received signal to estimate the channel directly in the frequency domain irrespective of the length of the finite impulse response (FIR) channel, thus allowing operation in nearly saturated systems (number of users can be one less than process gain). The method is shown to outperform some earlier blind channel estimation methods, whereas at the same time avoiding the limitations in the number of users that these methods suffer from.

Subspace-Based Blind Channel Estimation for MIMO-OFDM Systems With Reduced Time Averaging

Among the various approaches recently proposed for blind estimation of wideband multiple-input-multiple-output (MIMO) wireless channels, subspace-based algorithms are particularly attractive due to their good performance and simple structure. These algorithms primarily exploit the orthogonality of the noise and signal subspaces of the correlation matrix of the received signals to estimate the unknown channel coefficients. In practice, the correlation matrix is unknown and must be estimated through time averaging over multiple received samples. To this end, the unknown channel must remain time invariant through the averaging process, which may pose a serious problem in practical applications. In this paper, to relax this requirement, we propose a novel subspace-based blind channel-estimation algorithm with reduced time averaging, as obtained by exploiting the frequency correlation among adjacent subcarriers in MIMO orthogonal frequency-division multiplexing (OFDM) systems. Simulation results show that the proposed approach outperforms other previously proposed methods within a reasonable averaging time over a Third-Generation Partnership Project (3GPP) spatial channel model.

Widely Linear Versus Linear Blind Multiuser Detection With Subspace-Based Channel Estimation: Finite Sample-Size Effects

In a recent paper [A. S. Cacciapuoti <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">al.</i> , ldquoFinite-Sample Performance Analysis of Widely Linear Multiuser Receivers in DS-CDMA Systems, IEEE Transactions on Signal Processing, vol. 56, no. 4, pp. 1572-1588, Apr. 2008], we presented the finite-sample theoretical performance comparison between linear (L) and widely linear (WL) minimum output-energy (MOE) receivers for direct-sequence code-division multiple-access (DS-CDMA) systems, worked out under the assumption that the channel impulse response of the desired user is exactly known. The main scope of this paper is to extend such an analysis, taking into account not only autocorrelation matrix (ACM) estimation effects, but also the accuracy of subspace-based blind channel estimation (CE). 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xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L-MOE</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one,</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">is</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">finite-sample</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WL-MOE</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">receiver</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">with</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">blind</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CE</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">capable</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">of</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">achieving</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">performance</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gains</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">predicted</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">by</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">theory?</i> To this goal, simple and easily interpretable formulas are developed for the signal-to-interference-plus-noise ratio (SINR) at the output of the L- and WL-MOE receivers with blind CE, when they are implemented using either the sample ACM or its eigendecomposition. In addition, the derived formulas, which are validated by simulations, allow one to recognize and discuss interesting tradeoffs between the main parameters of the DS-CDMA system.

A Decorrelating-MRC Based Space–Time Multiuser Receiver for Time-Hopping UWB System

In this paper, we propose a two-stage linear multiuser detector (LMD) for ultra wideband (UWB) multiple single-input multi-output (M-SIMO) system and multipath fading environment. Time-hopping (TH) and antipodal pulse amplitude modulation (PAM) are employed for the multiple access system. The decorrelating detector is first employed at the front end of each receive antenna to eliminate the multi-user interference (MUI), then a set of maximum-ratio-combiners (MRC) are proceeded to maximize the signal-to-noise power ratio (SNR) for each user. Since the channel information is crucial for the Decorrelating-MRC (D-MRC) receiver, we develop a subspace-based blind M-SIMO channel estimation method. The effect of channel estimation error on system performance is extensively evaluated. It is also verified from the analytical and numerical results that by exploiting both spatial and temporal diversities, the D-MRC receiver dramatically improves system performance even without additional coding. Moreover, we demonstrate that both the D-MRC receiver and subspace-based blind channel estimator are computationally feasible and near-far resistant.

Blind Channel Estimation for OFDM Systems via a Generalized Precoding

In this paper, we consider the problem of blind channel estimation for single-input-single-output (SISO) orthogonal frequency-division multiplexing (OFDM) system via second-order statistics only. Based on the assumption that the transmitted symbols are independent and identically distributed, we develop a simple blind channel estimation technique for OFDM systems by utilizing a generalized linear nonredundant block precoding. Instead of using partial information from the signal covariance matrix, as done in previous works where a specific precoder is designed and only one column of the signal covariance matrix is exploited, our work jointly considers all the information contained in the signal covariance matrix. Compared to the popular subspace-based blind channel estimation methods, the proposed algorithm is much more computationally efficient. A design criterion of the precoders by which the performance can be improved is provided, and the closed-form stochastic Crameacuter-Rao bound is derived. The numerical results clearly show the effectiveness of our proposed algorithm, as well as its improvement over the existing techniques

Blind Channel Estimation for MIMO OFDM Systems via Nonredundant Linear Precoding

Based on the assumption that the transmitted symbols are independent and identically distributed (i.i.d.), we develop a simple subspace-based blind channel estimation technique for orthogonal frequency-division multiplexing (OFDM) systems by utilizing nonredundant linear block precoding. A novel contribution is that the proposed method can be applied for scenarios, where the number of receive antennas is less than the number of transmit antennas, e.g., multiple-input single-output (MISO) transmissions, in which case the traditional subspace-based methods could not be applied. Further consideration that can eliminate the multidimensional ambiguity in channel estimation under multiple transmitter scenarios is also proposed. The numerical results clearly show the effectiveness of our proposed algorithm

Subspace-based blind channel estimation: generalization and performance analysis

In this paper, we present a systematic study of the subspace-based blind channel estimation method. We first formulate a general signal model of multiple simultaneous signals transmitted through vector channels, which can be applied to a multitude of modern digital communication systems. Based on this model, we then propose a generalized subspace-based channel estimator by minimizing a novel cost function, which incorporates the set of kernel matrices of the signals sharing the target channel via a weighted sum of projection errors. We investigate the asymptotic performance of the proposed estimator, i.e., bias, covariance, mean square error (MSE), and Cramer-Rao bound, for large numbers of independent observations. We show that the performance of the estimator can be optimized by increasing the number of kernel matrices and by using a special set of weights in the cost function. Finally, we consider the application of the proposed estimator to a downlink code division multiple access (CDMA) system operating in a frequency-selective fading channel with negligible intersymbol interference (ISI). The results of the computer simulations fully support our analytical developments.

Subspace-based blind channel estimation method for MIMO-OFDM systems

A subspace-based blind channel estination algo rithm for MIMO-OFDM systems is proposed. This algorithm exploits the cyclostationarity introduced by cyclic prefix of OFDM to estimate the channel parameters. The proposed new algorithm is found to be outperforming the other algorithm with respect to convergence rate and achievable mean square error and robustness to channel order over determination.

A Proof of the Identifiability of a Subspace-Based Blind Channel Estimation for OFDM Systems

Muquet et al. (2002) have proposed a subspace-based blind method for estimating the channel responses of cyclic-prefixed OFDM systems. Their proof for the channel identifiability uses the roots of channel transfer function and therefore depends on the channel order. When only an upper bound is known for the channel order, the proof fails. In this letter, a new proof for the identifiability is given which does not require knowledge of the precise channel order and can handle various choices of length of the OFDM block.

Blind SOS subspace channel estimation and equalization techniques exploiting spatial diversity in OFDM systems

In this paper, we present second-order-statistics (SOS) subspace-based blind channel estimation techniques, which exploit the receive antenna diversity in each of the following situations: cyclic prefix-OFDM (CP-OFDM), zero padded-OFDM (ZP-OFDM), and bandwidth efficient-OFDM (BWE-OFDM) systems. We also propose a number of combinations of pre-FFT equalizer, post-FFT equalizer, zero-forcing (ZF) equalizer, and minimum-mean-square-error (MMSE) equalizer. For any number of receive antennas, the pre-FFT equalizers require only one FFT at the receiver. As a result, a considerable reduction in hardware complexity and power saving may be obtained especially for systems with higher number of sub-carriers. In contrast, post-FFT equalizers result in considerable reduction in processing complexity at the cost of one FFT for each antenna. Adaptive linear-complexity implementations of the proposed receivers are considered, along with some modifications in the presence of null side carriers. The effectiveness of the new techniques is demonstrated through simulations.

Subspace-based blind channel estimation for OFDM by exploiting virtual carriers

Reliable channel estimation is indispensable for orthogonal frequency-division multiplexing (OFDM) systems employing coherent detection and adaptive loading in order to achieve high data rate communications. Several options exist in practical OFDM systems-including training symbols, cyclic prefix, virtual carriers, pilot tones, and receiver diversity-to facilitate channel estimation. In this paper, a subspace blind channel estimation method based on exploiting the presence of virtual carriers is proposed for OFDM systems over a time-dispersive channel. The method can be applied to conventional OFDM systems with cyclic prefix as well as OFDM systems with no cyclic prefix. The reduction/elimination of cyclic prefix thereby provides the OFDM systems the potential to achieve higher channel utilization than most previously reported cyclic prefix based estimators. Sufficient channel identifiability condition is developed as well. Comparison with two other recently reported subspace methods is presented via computer simulations to support the effectiveness of the proposed method.

Subspace-based (semi-) blind channel estimation for block precoded space-time OFDM

Space time coding has by now been well documented as an attractive means of achieving high data rate transmissions with diversity and coding gains, provided that the underlying propagation channels can be accounted for. We rely on redundant linear precoding to develop a (semi-)blind channel estimation algorithm for space time (ST) orthogonal frequency division multiplexing (OFDM) transmissions with Alamouti's (see IEEE J. Select. Areas Commun., vol.16, p.1451-58, Oct. 1998) block code applied on each subcarrier. We establish that multichannel identifiability is guaranteed up to one or two scalar ambiguities, regardless of the channel zero locations and the underlying signal constellations, when distinct or identical precoders are employed for even and odd indexed symbol blocks. With known pilots inserted either before or after precoding, we resolve the residual scalar ambiguities and show that distinct precoders require half the number of pilots than identical precoders to achieve the same channel estimation accuracy. Simulation results confirm our theoretical analysis and illustrate that the proposed semi-blind algorithm is capable of tracking slow channel variations and improving the overall system performance relative to competing differential ST alternatives.

Blind adaptive channel estimation for dual-rate DS/CDMA signals

This letter proposes the subspace-based blind adaptive channel estimation algorithm for dual-rate quasi-synchronous DS/CDMA systems, which can operate at the low-rate (LR) or high-rate (HR) mode. Simulation results show that the proposed blind adaptive algorithm at the LR mode has a better performance than that at the HR mode, with the cost of an increasing computational complexity.