Subspace-based blind channel identification for noncircular multicarrier transmissions (original) (raw)
Related papers
A subspace based blind and semi-blind channel identification method for OFDM systems
1999 2nd IEEE Workshop on Signal Processing Advances in Wireless Communications (Cat. No.99EX304), 1999
A new subspace method performing the blind and semi-blind identification of the transmission channel suited to multicarrier systems with cyclic prefix (OFDM) is proposed in this paper. This technique has the advantage to preserve the classical OFDM emitter structure based on a cyclic prefix insertion. Therefore it applies to all existing standardized multicarrier systems (DAB, ADSL, etc.) and does not prevent the use of the classical very simple equalization scheme. Moreover the method detailed provides an unbiased channel estimation and is robust to channel order overdetermination provided that the channel frequency response has no zeroes located on a subcarrier.
Subspace-Based Blind Channel Identification for Cyclic Prefix Systems Using Few Received Blocks
IEEE Transactions on Signal Processing, 2000
In this paper, a novel generalization of subspace-based blind channel identification methods in cyclic prefix (CP) systems is proposed. For the generalization, a new system parameter called repetition index is introduced whose value is unity for previously reported special cases. By choosing a repetition index larger than unity, the number of received blocks needed for blind identification is significantly reduced compared to all previously reported methods. This feature makes the method more realistic especially in wireless environments where the channel state is usually fast-varying. Given the number of received blocks available, the minimum value of repetition index is derived. Theoretical limit allows the proposed method to perform blind identification using only three received blocks in absence of noise. In practice, the number of received blocks needed to yield a satisfactory bit-error-rate (BER) performance is usually on the order of half the block size. Simulation results not only demonstrate the capability of the algorithm to perform blind identification using fewer received blocks, but also show that in some cases system performance can be improved by choosing a repetition index larger than needed. Simulation of the proposed method over time-varying channels clearly demonstrates the improvement over previously reported methods.
A cumulant matrix subspace algorithm for blind single FIR channel identification
IEEE Transactions on Signal Processing, 2001
Blind identification of discrete-time single-user FIR channels with nonminimum phase is studied here. Exploiting higher order cumulants of output signals of unknown channels, a new closed-form solution to the FIR channel impulse response is derived. The algorithm is simple and fast. It relies only on nullspace decomposition of some cumulant matrices. This method neither involves the difficult task of iterative global minimization of nonunimodal cost functions, nor does it require overparametrization, which poses consistency difficulties. It can be used either as the final channel estimate or as a good initial point in nonlinear cumulant matching techniques. The application of this identification method is broad and not limited to the use of any fixed-order cumulants. Its application in identifying data communication systems shows great potential and promise. Zhi Ding (M'87-SM'95) was born in Harbin, China. He received the B.Eng. degree from the Department of Wireless Engineering, Nanjing Institute of Technology, Nanjing, China, in July 1982, the M.A.Sc. degree from the
Subspace-based blind and semi-blind channel estimation for OFDM systems
IEEE Transactions on Signal Processing, 2002
This paper proposes a new blind channel estimation method for orthogonal frequency division multiplexing (OFDM) systems. The algorithm makes use of the redundancy introduced by the cyclic prefix to identify the channel based on a subspace approach. Thus, the proposed method does not require any modification of the transmitter and applies to most existing OFDM systems. Semi-blind procedures taking advantage of training data are also proposed. These can be training symbols or pilot tones, the latter being used for solving the intrinsic indetermination of blind channel estimation. Identifiability results are provided, showing that in the (theoretical) situation where channel zeros are located on subcarriers, the algorithm does not ensure uniqueness of the channel estimation, unless the full noise subspace is considered. Simulations comparing the proposed method with a decision-directed channel estimator finally illustrates the performance of the proposed algorithm.
IEEE Transactions on Signal Processing, 2005
This work addresses the problem of designing efficient detection techniques for multicarrier transmission systems operating in the presence of narrowband interference (NBI). In this case, conventional linear receivers, such as the zero-forcing (ZF) or the minimum-mean square error (MMSE) ones, usually perform poorly since they are not capable of suppressing satisfactorily the NBI. To synthesize interference-resistant detection algorithms, we resort to widely linear (WL) filtering, which allows one to exploit the noncircularity property of the desired signal constellation by jointly processing the received signal and its complex-conjugate version. In particular, we synthesize new WL-ZF receivers for multicarrier systems, which mitigate, in the minimum output-energy (MOE) sense, the NBI contribution at the receiver output, without requiring knowledge of the NBI statistics. By exploiting the noncircularity property, we also propose a new subspace-based blind channel identification algorithm and derive the channel identifiability condition. Blind identification can be performed satisfactorily also in the presence of NBI, requiring only an approximate rank determination of the NBI autocorrelation matrix. The performance analysis shows that the proposed MOE WL-ZF receiver, even when implemented blindly, assures a substantial improvement over the conventional linear ZF and MMSE ones, particularly when the NBI bandwidth is very small in comparison with the intercarrier spacing and the NBI is not exactly located on a subcarrier.
Blind subspace identification of a BPSK communication channel
1996
This paper considers the problem of blind estimation of multiple FIR channels. When a subspace algorithm is applied to the blind identification problem, incorporating information about the symbol constellation is in general not possible. However, by exploiting special properties of one dimensional symbol constellations (BPSK), it is shown that it is possible to improve or simplify a class of algorithms for blind channel identification. It is also shown that in the case of one dimensional symbol constellations there is a third way, apart from multiple antennas and oversampling, of arriving at a multi channel representation of the communication system.
Blind Channel Identification and Equalisation in OFDM using Subspace-Based Methods
Journal of Communications, 2009
A subspace-based method is proposed for estimating the channel responses of single-input-multiple-output (SIMO) Orthogonal Frequency Division Multiplexing (OFDM) system. Our technique relies on minimum noise subspace (MNS) decomposition to obtain noise subspace in a parallel structure from a set of pairs (combinations) of system outputs that form a properly connected sequence (PCS). The developed MNS-OFDM estimator is more efficient in computation than subspace (SS)-OFDM estimator, although the former is less robust to noise than the later. To maximise the MNS-OFDM estimator performance, a symmetric version of MNS is implemented. We present simulation results demonstrating the channel identification performance of the corresponding OFDM-based SIMO systems employ cyclic prefixing approach.
9Th European Signal Processing Conference, 1998
The least-squares and the subspace methods are two well-known approaches for blind channel identification/ equalization. When the order of the channel is known, the algorithms are able to identify the channel, under the so-called length and zero conditions. Furthermore, in the noiseless case, the channel can be perfectly equalized. Less is known about the performance of these algorithms in the practically inevitable cases in which the channel possesses long tails of "small" impulse response terms. We study the performance of the m m mth-order least-squares and subspace methods using a perturbation analysis approach. We partition the true impulse response into the m m mth-order significant part and the tails. We show that the m m mth-order least-squares or subspace methods estimate an impulse response that is "close" to the m m mth-order significant part. The closeness depends on the diversity of the m m mth-order significant part and the size of the tails. Furthermore, we show that if we try to model not only the "large" terms but also some "small" ones, then the quality of our estimate may degrade dramatically; thus, we should avoid modeling "small" terms. Finally, we present simulations using measured microwave radio channels, highlighting potential advantages and shortcomings of the least-squares and subspace methods.
2006
The problem of blind channel identification in quasisynchronous (QS) multicarrier code-division multiple-access (MC-CDMA) systems is considered. When improper modulation schemes are adopted, improved subspace-based algorithms, which process both the received signal and its complex-conjugate version, must be employed in order to exploit also the channel information contained in the conjugate correlation function of the channel output. An improved subspace-based algorithm for QS-MC-CDMA systems is devised herein which, compared with a recently proposed subspace-based identification method [1], allows one to achieve improved performances. The identifiability issues concerning the proposed method are addressed in detail, and translated into explicit conditions regarding the maximum number of users, their corresponding channels, and their spreading codes. Finally, numerical simulations are provided to assess the performances of the considered algorithm, in comparison with those of [1].
Linear prediction and subspace fitting blind channel identification based on cyclic statistics
Proceedings of 13th International Conference on Digital Signal Processing, 1997
Blind channel identification and equalization based on second-order statistics by subspace fitting and linear prediction have received a lot of attention lately. On the other hand, the use of cyclic statistics in fractionally sampled channels has also raised considerable interest. We propose to use these statistics in subspace fitting and linear prediction for (possibly multiuser and multiple antennas) channel identification. We base our identification schemes on the cyclic statistics, using the stationary multivariate representation introduced by [2] and [4] . This leads to the use of all cyclic statistics. The methods proposed appear to have good performance. 3 7 7 7 5