Paper Info
Title
Joint Channel Estimation and Decision Directed Decoding for OFDM-IDMA Systems over Sparse Channels.
Abstract
Combining Orthogonal Frequency Division Multiplexing (OFDM) and Interleave Division Multiple Access IDMA (OFDM-IDMA) in the mobile radio environment has been proven to be a promising multiple access scheme for wireless communications thanks to its low decoding complexity and potential for achieving a high spectral efficiency over severe frequency selective channels. Channel estimation accuracy is crucial for the coherent demodulation in OFDM systems. In this paper, we consider this issue where our aim is to exploit some structural properties of the channel response in the aim of higher estimation accuracy. This work is concerned with sparse channels in the temporal domain. Our aim is mainly to show that by associating a simple denoising processing, accounting for the channel sparsity, within the joint channel estimation decision directed decoding scheme, a better tradeoff between performance and complexity is achieved. More precisely, an MST (Most Strong Taps) algorithm is incorporated within a Maximum Likelihood solution using the Space Alternating Generalized Expectation Maximization (SAGE-ML) algorithm [1]. Simulation results have shown the superiority of SAGE-ML with MST in terms of Mean Squared Error (MSE) and Bit Error Rate (BER) compared to the SAGEML not exploiting the channel sparsity.
Year
Venue
Field
2013
EW
Demodulation,Wireless,Pattern recognition,Expectation–maximization algorithm,Computer science,Algorithm,Mean squared error,Communication channel,Artificial intelligence,Decoding methods,Orthogonal frequency-division multiplexing,Bit error rate
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
4
3
Name
Order
Citations
PageRank
Khalil Elkhalil101.01
Leïla Najjar Atallah24414.04
Hichem Besbes38022.41