Performance Comparison of High Order Moments Blind Channel Identification

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Interference cancellation and replica filtre

Ce mémoire est scindé en deux parties. La première partie traite de l'estimation de l'optimum combiner. Traditionnellement, l'optimum combiner est estimé à travers l'estimation du canal du signal désiré et la matrice de covariance de l'interférence plus bruit. Dans cette première partie, on propose d'estimer l'optimum combiner à travers l'estimation de deux filtres séparés, à savoir un filtre qui suppose que la transmission est sans bruit (ce filtre prend en compte l'interférence mais pas le bruit) et un filtre qui suppose que la transmission est sans interférences (ce filtre prend en compte le bruit mais pas l'interférence). Néanmoins ce type d'estimation reste optimal seulement dans le cas où le récepteur est composé de deux antennes et/ou la transmission n'est perturbée que par une seule source d'interférence, indépendamment du nombre d'antennes. Le cas d'une transmission avec un récepteur à deux antennes et une source d'interférence est simulé puis implémenté sur une cible type DSP en virgule fixe au format 16 bits.La deuxième partie est dédiée à l'estimation aveugle du canal où deux méthodes sont proposées. Ces deux méthodes sont basées sur les moments d'ordre supérieurs. La première méthode est une extension de l'algorithme de Viterbi & Viterbi avec résolution de l'ambiguïté inter sous-porteuses, pour le cas d'une transmission basée sur une forme d'onde OFDM. La deuxième méthode est construite autour du principe de l'auto déconvolution. On propose aussi dans cette partie une extension aux systèmes MIMO par l'introduction d'un précodage et d'un postcodage spatio-temporel adapté à la méthode d'estimation du canal et au type de la transmission. Enfin une étude de cas d'utilisation de cette extension MIMO est donnée pour un système de transmission basé sur le standard IEEE 802.11.This thesis is split into two parts. The first part deals with the estimation of the optimum combiner. Traditionally, the optimum combiner is estimated through the estimation of both the desired signal channel and the covariance matrix of interference plus noise.In this first part, we propose to estimate the optimum combiner by estimating two separate filters, namely a filter which assumes that the transmission is noiseless (taking into account the interference but not the noise) and a filter which assumes that the transmission is no interfered (taking into account the noise but not the interference). However this method of estimation is optimal only in the case where the receiver has two antennas and/or transmission undergone a single source of interference regardless of the number of antennas. The case of a transmission with a receiver equipped with two antennas and interfered by a single source of interference, is simulated and implemented on a fixed-point DSP target in 16 bit format.The second part is dedicated to the blind channel estimation where two methods are proposed. Both methods are based on the higher order moments. The first method can be viewed as an extension of the Viterbi & Viterbi algorithm, with inter subcarrier ambiguity solving, for the case of an OFDM waveform. The second method is built around the principle of self deconvolution. In this section, an extension to MIMO systems based on a space-time pre-coding and postcoding is introduced. Finally a case of application of this extension, for a MIMO transmission system based on the IEEE 802.11 standard, is analysed and simulated

Blind Channel Estimation and Interference Management in MIMO/OFDM Uplink Communication

This paper addresses two majors problems in today?s wireless network using MIMO-OFDM. In fact, the performances of such a network depend on the interference management and on the equalization. These two problems are closely linked to channel estimation algorithms. In this paper, we propose a resource sharing scenario combined with a blind channel estimation algorithm based on fourth order statistics of the received signal. The proposed scheme is compared to the existing T/OFDM scenario and simulation results show its outperformance

Performance Comparison of High Order Moments Blind Channel Identification

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Performance Analysis of Modified Gram-Schmidt Cholesky Implementation on 16 bits-DSP-chip

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Self-Adjustment Channel Rank Based on Reordered Cholesky Factorization

http://www.wocn2012.org/This paper presents a self-adjustment of the length of the channelimpulse response using a classical Cholesky Factorization. This isof special importance in the context of algorithms containing amatrix inversion and the rank of this matrix is strongly related tothe length of the channel impulse, i.e. interferer cancellationalgorithm.When the length of the channel impulse response is overestimated,the matrix inversion is carried out with a singularmatrix. A self-adjustment is then required to avoid an increase inthe noise level

Smoothed Viterbi Viterbi Blind Channel Estimation with Ambiguity Cancellation for Multicarrier Transmission

This paper presents a general blind channel estimation algorithm for multicarrier transmission. The first stage is based on the classical Viterbi Viterbi approach with the estimation of the mean value of the 4 received estimated independently, phase ambiguities between frequencies have then to be fixed. This correction is done considering only an upper bound for the delay spread of the propagation channel. A projection algorithm is then introduced leading to the estimation of the channel impulse response. The proposed algorithm can be applied to all kinds of multicarrier waveforms. It leads to a communication capacity increase and it can also efficiently in a cognitive radio network context

Reduced complexity null subspace channel impulse response based blind identification

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DSP Implementation of Interference Cancellation Algorithm for a SIMO System

This paper presents an implementation on the fixed-point DSPchip of an interference cancellation algorithm containing a linearsystem. Difficulty in matching the algorithms performanceobtained in full precision (IEEE float-point) implementation tothose given by a finite precision tends to increase when thealgorithms contains one or several linear system to solve.Gauss and Cholesky methods are implemented. Theirperformance results are showed and compared. The globalimplementation margins are discussed

4th Order Statistics Based Blind Channel Estimation for Multicarrier Transmission

This paper presents a blind channel estimation algorithm for multicarrier systems. The proposed scheme is estimation and tracking. Blind algorithms were analyzed for a based on fourth order statistics estimation of received data long time and it was often concluded [1] that these algorithms followed by a Gauss linearization of a non-linear system. The asked, to work properly, a signal to noise ratio higher than did channel estimation is performed over a very short number of a standard transmission based on pilot symbols. In other words symbols in order to stay compliant with the channel coherence the efficiency gains provided by the blind algorithms were lost time. The proposed approach is well suited to CP-OFDM system by the increase in the signal to noise ratio that they were transmitting circular M-QAM communication symbols but, being not based on the cyclic prefix properties, it could be asking. This first conclusion has to be analyzed with more applied, with some adjustments, to filterbank multicarrier details and it will be shown that the algorithm proposed in this waveforms transmitting M-OQAM communication symbols. The paper does not require a significant increase in the signal to blind algorithm presented provides finally an increase of the noise ratio and allows proposing an extremely adaptable useful throughput and it gives a high flexibility for the waveform waveform, without any pilot's constraints, that can then be usage, avoiding the difficult time and frequency pilot location perfectly suited to the future needs of radio systems. optimization problem