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Full-Text Articles in Electrical and Computer Engineering
Channel Estimation And Carrier Recovery In The Presence Of Phase Noise In Ofdm Relay Systems, Rui Wang, Hani Mehrpouyan, Meixia Tao, Yingbo Hua
Channel Estimation And Carrier Recovery In The Presence Of Phase Noise In Ofdm Relay Systems, Rui Wang, Hani Mehrpouyan, Meixia Tao, Yingbo Hua
Hani Mehrpouyan
In this paper, we analyze joint channel, carrier frequency offset (CFO), and phase noise estimation in orthogonal frequency division multiplexing (OFDM) relaying networks. To achieve this goal, a detailed transmission framework involving both training and data symbols is first presented. Next, a novel algorithm that applies the training symbols to jointly estimate the channel responses, CFO, and phase noise parameters based on the maximum a posteriori criterion is proposed. Additionally, to evaluate the performance of the proposed channel estimation and carrier recovery algorithms, we analyze the ambiguities among the estimated parameters. Based on this analysis, a new Hybrid Cramér-Rao Lower …
Phase Noise And Carrier Frequency Offset In Ofdm Systems: Joint Estimation And Hybrid Cramér-Rao Lower Bound, Omar Hazim Salim, Ali A. Nasir, Hani Mehrpouyan, Wei Xiang
Phase Noise And Carrier Frequency Offset In Ofdm Systems: Joint Estimation And Hybrid Cramér-Rao Lower Bound, Omar Hazim Salim, Ali A. Nasir, Hani Mehrpouyan, Wei Xiang
Hani Mehrpouyan
In this paper, a new iterative pilot-aided algorithm based on expectation conditional maximization (ECM) for joint estimation of Wiener phase noise (PHN) and carrier frequency offset (CFO) in orthogonal frequency division multiplexing (OFDM) systems is proposed. Next, a new expression for the hybrid Cramér-Rao lower bound (HCRB) for joint estimation of PHN and CFO in OFDM systems is derived. Numerical results show that the proposed estimator outperforms existing algorithms in terms of mean square error while performing close to the derived HCRB at moderate PHN variances. Moreover, the proposed estimator is found to be computationally more efficient than existing algorithms …