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Bayesian and Hybrid Cramér–Rao Bounds for the Carrier Recovery Under Dynamic Phase Uncertain Channels

Abstract : —In this paper, we study Bayesian and hybrid Cramér–Rao bounds (BCRB and HCRB) for the code-aided (CA), the data-aided (DA), and the non-data-aided (NDA) dynamical phase estimation of QAM modulated signals. We address the bounds derivation for both the offline scenario, for which the whole observation frame is used, and the online which only takes into account the current and the previous observations. For the CA scenario we show that the computation of the Bayesian information matrix (BIM) and of the hybrid information matrix (HIM) is NP hard. We then resort to the belief-propagation (BP) algorithm or to the Bahl–Cocke–Jelinek–Raviv (BCJR) algorithm to obtain some approximate values. Moreover, in order to avoid the calculus of the inverse of the BIM and of the HIM, we present some closed form expressions for the various CRBs, which greatly reduces the computation complexity. Finally, some simulations allow us to compare the possible improvements enabled by the offline and the CA scenarios. Index Terms—Bayesian Cramér–Rao bound (BCRB), code-aided (CA) bound, data-aided (DA) bound, dynam-ical phase estimation, hybrid Cramér–Rao bound (HCRB), non-data-aided (NDA), offline, online.
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Jianxiao Yang, Benoit Geller, Stéphanie Bay. Bayesian and Hybrid Cramér–Rao Bounds for the Carrier Recovery Under Dynamic Phase Uncertain Channels. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2011, pp.667-680. ⟨10.1109/TSP.2010.2081981⟩. ⟨hal-01180818⟩



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