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 Joint Phase Noise and Doubly Selective Channel Estimation in Full-Duplex MIMO-OFDM Systems
Tác giả hoặc Nhóm tác giả: Vien Nguyen-Duy-Nhat, Tu Bui-Thi-Minh, Chien Tang-Tan, Vo Nguyen Quoc Bao, Hung Nguyen-Le
Nơi đăng: Proceedings of The 2016 International Conference on Advanced Technologies for Communications (ATC 2016); Số: October 12 - 14, 2016;Từ->đến trang: 413 - 418;Năm: 2016
Lĩnh vực: Khoa học công nghệ; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
This paper is concerned with the joint estimation of phase noise and time-varying channel responses in full-duplex multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The time-variation of phase noise and multipath MIMO channel responses gives rise to the identifiability problem of estimating a large number of unknowns which is much greater than the number of received signal samples. To overcome the resulting identifiability estimation problem, this paper employs basis expansion models (BEM) for reducing the number of time-varying channel impairment parameters to be estimated. Using the BEM-based approximation of the channel impairment responses, this paper formulates a pilot-aided algorithm for maximum-a-posteriori (MAP)-based estimation of the BEM coefficients. This paper provides empirical mean squared error (MSE) results of the joint channel impairment parameter estimation and related analytical values of Bayesian Cramer Rao lower bounds (BCRLB). The numerical results illustrate that the use of BEM facilitates reliable estimation of the time-varying channel impairment responses whereas the corresponding MSE and bit-error-rate (BER) values are comparable to related BCRLBs and ideal BER ones, respectively.
ABSTRACT
This paper is concerned with the joint estimation of phase noise and time-varying channel responses in full-duplex multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The time-variation of phase noise and multipath MIMO channel responses gives rise to the identifiability problem of estimating a large number of unknowns which is much greater than the number of received signal samples. To overcome the resulting identifiability estimation problem, this paper employs basis expansion models (BEM) for reducing the number of time-varying channel impairment parameters to be estimated. Using the BEM-based approximation of the channel impairment responses, this paper formulates a pilot-aided algorithm for maximum-a-posteriori (MAP)-based estimation of the BEM coefficients. This paper provides empirical mean squared error (MSE) results of the joint channel impairment parameter estimation and related analytical values of Bayesian Cramer Rao lower bounds (BCRLB). The numerical results illustrate that the use of BEM facilitates reliable estimation of the time-varying channel impairment responses whereas the corresponding MSE and bit-error-rate (BER) values are comparable to related BCRLBs and ideal BER ones, respectively.
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