Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Microwave Mobile Communications
Microwave Mobile Communications
A forward-backward Kalman filter-based STBC MIMO OFDM receiver
EURASIP Journal on Advances in Signal Processing
Intercarrier interference in MIMO OFDM
IEEE Transactions on Signal Processing
An EM-Based Forward-Backward Kalman Filter for the Estimation of Time-Variant Channels in OFDM
IEEE Transactions on Signal Processing - Part II
ICI mitigation for pilot-aided OFDM mobile systems
IEEE Transactions on Wireless Communications
Coherent and Differential ICI Cancellation for Mobile OFDM with Application to DVB-H
IEEE Transactions on Wireless Communications - Part 1
IEEE Transactions on Consumer Electronics
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Orthogonal frequency-division multiplexing (OFDM) combines the advantages of high performance and relatively low implementation complexity. However, for reliable coherent detection of the input signal, the OFDM receiver needs accurate channel information. When the channel exhibits fast time variation as it is the case with several recent OFDM-based mobile broadband wireless standards (e.g., WiMAX, LTE, DVB-H), channel estimation at the receiver becomes quite challenging for two main reasons: 1) the receiver needs to perform this estimation more frequently and 2) channel time-variations introduce intercarrier interference among the OFDM subcarriers which can degrade the performance of conventional channel estimation algorithms significantly. In this paper, we propose a new pilot-aided algorithm for the estimation of fast time-varying channels in OFDM transmission. Unlike many existing OFDM channel estimation algorithms in the literature, we propose to perform channel estimation in the frequency domain, to exploit the structure of the channel response (such as frequency and time correlations and bandedness), optimize the pilot group size and perform most of the computations offline resulting in high performance at substantial complexity reductions.