A Summative Comparison of Blind Channel Estimation Techniques for Orthogonal Frequency Division Multiplexing Systems

The OFDM techniquei.e. Orthogonal frequency division multiplexing has become prominent in wireless communication since its instruction in 1950’s due to its feature of combating the multipath fading and other losses. In an OFDM system, a large number of orthogonal, overlapping, narrow band subchannels or subcarriers, transmitted in parallel, divide the available transmission bandwidth. The separation of the subcarriers is theoretically optimal such that there is a very compact spectral utilization. This paper reviewed the possible approaches for blind channel estimation in the light of the improved performance in terms of speed of convergence and complexity. There were various researches which adopted the ways for channel estimation for Blind, Semi Blind and trained channel estimators and detectors. Various ways of channel estimation such as Subspace, iteration based, LMSE or MSE based (using statistical methods), SDR, Maximum likelihood approach, cyclostationarity, Redundancy and Cyclic prefix based. The paper reviewed all the above approaches in order to summarize the outcomes of approaches aimed at optimum performance for channel estimation in OFDM system

Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems

Blind channel identification/equalization with applications in wireless communications

Ph.DDOCTOR OF PHILOSOPH

Blind estimation of FIR channels using spatial separation

Master'sMASTER OF ENGINEERIN

Subspace-based channel estimation for DS/CDMA systems exploiting pulse- shaping information.

Thesis (M.Sc.Eng.)-University of Natal, Durban, 2003.Third generation wireless systems have adopted Direct-Sequence/Code-Division Multiple-Access (DS/CDMA) as the multiple access scheme of communication. This system would typically operate in a multipath fading channel. This dissertation only deals with the task of channel estimation at the base station where the multipath delays and attenuations for each user are estimated. This information is used to aid the recovery of data that was transmitted by each user. Subspace-based algorithms are popularly used to perform the task of channel estimation because they have the desirable property of perfectly estimating the channel in a noise-free environment. In this dissertation a new subspace-based channel estimation algorithm for DS/CDMA systems is presented. The proposed algorithm is based on the Parametric Subspace algorithm by Perros-Meilhac et al. for single-user systems. The main focus of this dissertation is to convert the Parametric Subspace algorithm from a single-user system to a multi-user DS/CDMA system. It has been shown in the literature that by using information of the pulse-shaping filter in the Channel Subspace algorithm, the variance of the channel estimates is decreased. However, this has only been applied to a single-user system. There are several subspace algorithms that have been proposed for DS/CDMA systems. Most of these algorithms sample the received signal at the chip rate, making it impossible to exploit knowledge of the pulse-shaping filter in the channel estimation algorithm. In this dissertation a new subspace-based channel estimation algorithm is derived for a DS/CDMA system with multiple receive antennas, where the output is oversampled with respect to the chip rate. By oversampling the received signal, knowledge of the pulse-shaping filter is used in the channel estimation algorithm. It is shown that the variance of the channel estimate for the proposed subspace algorithm is less than the Torlak/Xu subspace algorithm that does not exploit information of the pulse-shaping filter. A mathematical expression of the mean square error of estimation for the new algorithm is also derived. It was shown that the analytic expression provides a good approximation of the actual MSE for high SNR. The Parametric Subspace Delay Estimation (PSDE) algorithm was developed by Perros-Meilhac et al. to estimate the multipath delays introduced by the communications channel. The limitation of the PSDE algorithm is that the performance of the algorithm deteriorates as the power of the multipath signals decrease with increasing delay time. This dissertation proposes a modified version of the PSDE algorithm, called the Modified Parametric Subspace Delay Estimation (MPSDE) algorithm, which performs better than the PSDE algorithm in an environment where the power of the multipath signals varies. The final part of this dissertation discusses the Torlak/Xu channel estimation algorithm and the Bensley/Aazbang delay estimation algorithm. In order to compare the performance of these two subspace algorithms, the Torlak/Xu algorithm is converted to a delay estimation algorithm that is called the Parametric TX algorithm. The performance of the Bensley/Aazbang delay estimation algorithm and the proposed Parametric TX algorithm are compared and it is shown that the Parametric TX algorithm offers the better performance

System Identification with Applications in Speech Enhancement

As the increasing popularity of integrating hands-free telephony on mobile portable devices and the rapid development of voice over internet protocol, identification of acoustic systems has become desirable for compensating distortions introduced to speech signals during transmission, and hence enhancing the speech quality. The objective of this research is to develop system identification algorithms for speech enhancement applications including network echo cancellation and speech dereverberation. A supervised adaptive algorithm for sparse system identification is developed for network echo cancellation. Based on the framework of selective-tap updating scheme on the normalized least mean squares algorithm, the MMax and sparse partial update tap-selection strategies are exploited in the frequency domain to achieve fast convergence performance with low computational complexity. Through demonstrating how the sparseness of the network impulse response varies in the transformed domain, the multidelay filtering structure is incorporated to reduce the algorithmic delay. Blind identification of SIMO acoustic systems for speech dereverberation in the presence of common zeros is then investigated. First, the problem of common zeros is defined and extended to include the presence of near-common zeros. Two clustering algorithms are developed to quantify the number of these zeros so as to facilitate the study of their effect on blind system identification and speech dereverberation. To mitigate such effect, two algorithms are developed where the two-stage algorithm based on channel decomposition identifies common and non-common zeros sequentially; and the forced spectral diversity approach combines spectral shaping filters and channel undermodelling for deriving a modified system that leads to an improved dereverberation performance. Additionally, a solution to the scale factor ambiguity problem in subband-based blind system identification is developed, which motivates further research on subbandbased dereverberation techniques. Comprehensive simulations and discussions demonstrate the effectiveness of the aforementioned algorithms. A discussion on possible directions of prospective research on system identification techniques concludes this thesis