Decision Directed Channel Estimation Aided OFDM Employing Sample-Spaced and Fractionally-Spaced CIR Estimators

Akhtman, J.; Hanzo, Lajos

doi:10.1109/twc.2007.348308

Cited by 36 publications

(26 citation statements)

References 18 publications

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“…3 The absolute values of the elements are shown on a dB-scale referenced to the largest element of the respective matrices. While some structure does appear to exist in the matrices, the structure is dramatically different for the two different environmental conditions.…”

Section: A Motivating Example: Adaptive Equalization In Underwater Acmentioning

confidence: 99%

“…To see where such a model could be useful, consider the problem of fractionally-spaced channel identification, a fundamental problem in communication systems [3], [51], [142]. ...…”

Section: Fractionally-spaced Channel Identificationmentioning

confidence: 99%

“…3 Thus, the classical result on almost sure convergence of the largest eigenvalue for Wishart matrices [69] can be applied, leading to λ max a.s. 19) as N → ∞.…”

Section: Case 1: N ≥ Mmentioning

confidence: 99%

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Graphical model driven methods in adaptive system identification

Yellepeddi¹

2016

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Identifying and tracking an unknown linear system from observations of its inputs and outputs is a problem at the heart of many different applications. Due to the complexity and rapid variability of modern systems, there is extensive interest in solving the problem with as little data and computation as possible.This thesis introduces the novel approach of reducing problem dimension by exploiting statistical structure on the input. By modeling the input to the system of interest as a graph-structured random process, it is shown that a large parameter identification problem can be reduced into several smaller pieces, making the overall problem considerably simpler.Algorithms that can leverage this property in order to either improve the performance or reduce the computational complexity of the estimation problem are developed. The first of these, termed the graphical expectation-maximization least squares (GEM-LS) algorithm, can utilize the reduced dimensional problems induced by the structure to improve the accuracy of the system identification problem in the low sample regime over conventional methods for linear learning with limited data, including regularized least squares methods.Next, a relaxation of the GEM-LS algorithm termed the relaxed approximate graph structured least squares (RAGS-LS) algorithm is obtained that exploits structure to perform highly efficient estimation. The RAGS-LS algorithm is then recast into a recursive framework termed the relaxed approximate graph structured recursive least squares (RAGS-RLS) algorithm, which can be used to track time-varying linear systems with low complexity while achieving tracking performance comparable to much more computationally intensive methods.The performance of the algorithms developed in the thesis in applications such as channel identification, echo cancellation and adaptive equalization demonstrate that the gains admitted by the graph framework are realizable in practice. The methods have wide applicability, and in particular show promise as the estimation and adaptation algorithms for a new breed of fast, accurate underwater acoustic modems.The contributions of the thesis illustrate the power of graphical model structure in simplifying difficult learning problems, even when the target system is not directly structured.Thesis Supervisor: James C. Preisig Title: Scientist Emeritus, Woods Hole Oceanographic Institution AcknowledgmentsPiglet noticed that even though he had a Very Small Heart, it could hold a rather large amount of Gratitude. Winnie the PoohA.A.MilneMy years at MIT have been about so much more than a degree. In the past six years you have introduced to more worlds than I ever knew existed. As this long and wonderful journey ends, I find that my heart can, too, hold so much gratitude, for everything that you have given.Of course, I haven't said who "you" is, so here goes.It is impossible to write any list of acknowledgements without having at the very top my thesis advisor Dr. James Preisig, who has truly been a wide-angle lens f...

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Section: A Motivating Example: Adaptive Equalization In Underwater Acmentioning

confidence: 99%

“…To see where such a model could be useful, consider the problem of fractionally-spaced channel identification, a fundamental problem in communication systems [3], [51], [142]. ...…”

Section: Fractionally-spaced Channel Identificationmentioning

confidence: 99%

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Graphical model driven methods in adaptive system identification

Yellepeddi¹

2016

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“…Recently, orthogonal frequency division multiplexing (OFDM) communication systems such as IEEE 802.11n WLAN, UTRAN-LTE, and IEEE 802.16e mobile WiMAX have received significant attention due to much higher spectral efficiency and transfer reliability [1]. Especially, WiMAX systems that stably provide packet services, such as TV, music, and internet at ground speeds up to about 120 km/h, are attached.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, OFDM requires accurate channel estimation to perform demodulation due to its sensitivity to noise. In order to mitigate the requirement, various channel estimation algorithms are proposed [1,2]. In this paper, we present a FFT-based channel estimation algorithm using DWT.…”

Section: Introductionmentioning

confidence: 99%

Implementation of improved channel estimation using transform domain in mobile OFDM systems

Lee

Shin

Wang

et al. 2010

IEICE Electron. Express

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Abstract:In this paper, we propose a low-complexity and lowlatency method of FFT based channel estimation for Mobile OFDM systems. Mobile WiMAX system and LTE are next generation communication systems which adopt OFDM systems to achieve high mobility and a high data rate. Especially for high mobility communication systems, efficient channel estimation, technology should be developed. The proposed channel estimation calculates threshold value of channel impulse response (CIR) using discrete wavelet transform (DWT) in a frequency domain. In the experimental results, the proposed method increased performance with low-complexity and low-latency. Implementaion and simulation results show that the proposed method reduced the latency about 43% compared with conventional DFT based channel estimators.

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