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Technical Reports Authored or Co-Authored by Dr. Yimin Jiang (Ph.D.
Papers resulting from CSHCN-related research are periodically added to the Institute for Systems Research Technical Report Database where they can be browsed by year or searched by author or keywords.
On the True Cramer-Rao Lower Bound for the DA Joint Estimation of Carrier Phase and Timing Offsets (CSHCN TR 2000-14) by Y. Jiang, F.W. Sun, John S. Baras
The Cramer-Rao lower
bound (CRB) plays a pivotal role in
For DA parameter
synchronization, the CRB typically varies
In the literature, the widely cited close-form data-aided CRB for timing and phase recovering was derived under the assumption that the training sequence is independently identical distributed (i.i.d.) and the length of the training sequence is sufficiently long. We found that the CRB for a particular training sequence can be significantly lower than that with the long i.i.d. assumption. Therefore, the widely cited data-aided CRB actually does not give the fundamental limit for a particular training sequence.
In this manuscript, we derive a closed-form formula for data-aided CRB for timing and phase synchronization with respect to arbitrary training sequence. The bound illustrates the close relation between the training sequence and the fundamental limit on timing and phase synchronization. This bound provides additional insights on the sequence design.
2000 IEEE International
Conference on Communications
Maximum Likelihood Slow Frequency-Selective Fading Channel Estimation Using Frequency Domain Approach (CSHCN TR 2000-13) by Y. Jiang, John S. Baras
This paper addresses the channel estimation problem for slow frequency-selective fading channel using training sequence and maximum likelihood (ML) approach.
Traditional works assumed symbol period spaced delay-tapped line model and additive white Gaussian noise (AWGN). Because of pre-filtering in the receiver front end, if the sampling rate is larger than one sample per symbol or sampling epoch is unknown (i.e., timing information is not available), AWGN model is not valid anymore.
A more general ML channel estimation method using discrete Fourier transform (DFT) is derived with the assumption of colored Gaussian noise and over sampling. Similar idea can be adopted to derive the ML joint timing and phase estimation algorithm.
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