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In orthogonal frequency division multiplexing (OFDM) based cellular systems, co-channel interference (CCI) from adjacent interfering base stations (BSs) would greatly degrade the bit error rate (BER) performance of cell-border users. In the previous work, a blind single antenna interference cancellation (SAIC) algorithm named least mean square-blind joint maximum likelihood sequence estimation (LMS-BJMLSE)...
To mitigate the asynchronous ICI (inter-cell interference), the CFR (channel frequency response) and SCM (spatial covariance matrix) should be accurately estimated. Therefore, the enhanced asynchronous ICI mitigation scheme which can effectively reduce the estimation error of both CFR and SCM, and improve performance for MIMO (multiple-input multiple-output)-OFDMA (orthogonal frequency division multiple...
The classical channel estimation method in TDS-OFDM is carried out based on the PN sequence in the GI which suffers from the intersymbol interference of the previous OFDM data symbols. This paper presents a novel data-aided channel estimation method to refine the initial channel estimation obtained from PN sequence. The data symbols are rebuilt based on the log-likelihood ratio (LLR) from the output...
This paper presents a comparative study of different Recursive Least Squares algorithms to track a Rayleigh flat fading MIMO channel. We investigate the effect of the initialization and training of these algorithms on their performance. We propose a new training scheme which can deliver a lower mean squared estimation error without loss of bandwidth efficiency.
This paper addresses the issue of the optimization of the regularization constant in semi-blind channel estimation techniques, in which the training sequence-based criterion is combined linearly with the blind subspace criterion. In such semi-blind estimation techniques, the optimization of the regularizing constant with respect to the channel estimation error is mandatory, otherwise, the expected...
There are many algorithms that can be used in a channel estimator. In this investigation, performance of LS algorithm in a training-based as well as a semi-blind channel estimation evaluated and their results has been compared. Simulation results show that performance of a semi-blind estimator is better than a training-based estimator. It is interesting for us due to transmitting less required training...
In this work an iterative time domain least squares (LS) based channel estimation method using superimposed training (ST) for a multi input multi output(MIMO) orthogonal frequency division multiplexing (OFDM) system over frequency selective fading channels is proposed. The estimate of the channel is generalized to provide scope for exploiting the coherence time and the coherence bandwidth for an improved...
The topic of channel estimation for single-input single-output frequency-selective time-invariant channels has been recently addressed using superimposed training (ST) techniques. Improvement of these approaches using iterative algorithms has shown to achieve the best performance, although with the drawback of increasing computation complexity. In this work we propose a new iterative ST algorithm...
We use the particle filtering approach to develop a new Bayesian equalizer for turbo equalization under conditions of imperfect estimation of the channel impulse response (CIR) and the noise variance. This new equalizer is derived on the basis of fixed-lag smoothing, using the SIS (Sequential Importance Sampling) methodology to approximate the conditional probability distribution of a block of transmitted...
An iterative least squares (LS) based channel estimation method for superimposed training (ST) based OFDM systems is proposed. The performance of the method is analyzed in terms of the mean squared estimation error (MSEE) and its impact on the bit error rate (BER) of the OFDM system is studied. A training sequence selection criterion that jointly optimizes the MSEE and the BER of the OFDM system also...
In this paper, a new channel-estimation algorithm based on maximum-likelihood (ML) algorithm for estimation and tracking of the multiple-input-multiple-output (MIMO) channels is presented. The ML algorithm presents the optimum estimation when the exact channel model is known. The derived channel-estimation algorithm is very efficient, with a computational complexity comparable to the least mean square...
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