Joint statistical framework for the estimation of channel and SFO in OFDM systems. Issue 7 (1st September 2017)

• Record Type: Journal Article
• Authors: Jose, Renu; Hari, K.V.S.
• Is Part Of: IET signal processing. Volume 11:Issue 7(2017)
• Page Start: 780
• Page End: 787
• Publication Date: 2017-09-01
• Subjects: OFDM modulation -- channel estimation -- sampling methods -- Bayes methods -- maximum likelihood estimation -- communication complexity -- numerical analysis; joint statistical framework -- channel estimation -- SFO estimation -- OFDM systems -- sampling frequency offset estimation -- orthogonal frequency division multiplexing systems -- Bayesian framework -- hybrid Cramer‐Rao lower bounds -- HCRLB -- joint maximum a posteriori algorithm -- JMAP estimator complexity reduction -- modified MAP algorithm -- channel statistics -- signal‐to‐noise ratio -- estimation accuracy -- numerical simulations; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2016.0580
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : Joint estimation of channel and sampling frequency offset (SFO) in orthogonal frequency division multiplexing (OFDM) systems, using Bayesian framework, is shown in this study. Hybrid Cramér–Rao lower bounds (HCRLBs) for the estimation of SFO together with channel are obtained. The significance of Bayesian approach in the formulation of joint estimator is shown by comparing HCRLB with the corresponding standard CRLB. The authors propose a joint maximum a posteriori (JMAP) algorithm for the estimation of channel and SFO in OFDM, utilising the prior statistical knowledge of channel. To reduce the complexity of JMAP estimator, a modified MAP algorithm, which has no grid searches, is also proposed. Also, they analyse the effect of inaccurate knowledge of channel statistics and signal‐to‐noise ratio on the estimation accuracy. The estimation methods are analysed by numerical simulations and resultant conclusions validate the better performance of the proposed algorithms when compared with previous algorithms.