Browsing by Author "Gau, Chi-Shiang"

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A Low-Complexity Joint Angle-of-Arrival and Channel Estimation for Single-Input-Multiple-Output OFDM Systems
(2015)
Chen, Chih-Yu
;
Wu, Wen-Rong
;
Gau, Chi-Shiang
;
資訊工程學系
;
電機學院
In future wireless communication system, beam-forming plays an important role in improving link quality. As a result, the angle information is required. In this paper, a low complexity joint angle-of-arrival (AoA) and channel estimation for single-input-multiple-output (SIMO) OFDM system is considered. By exploiting the sparsity of wireless channel, we first propose a compressive-sensing (CS) based channel estimation for pilot-assisted OFDM systems with multiple receive antennas. Then, a low-complexity method is proposed to conduct AoA estimation. Finally, simulations are provided to show the effectiveness of the proposed scheme.
Joint AoA and Channel Estimation for SIMO- OFDM Systems: A Compressive-Sensing Approach
(2016-05)
Chen, Chih-Yu
;
Wu, Wen-Rong
;
Gau, Chi-Shiang
;
資訊工程學系
;
電機學院
Millimeter-wave (mmWave) transmission has been considered in future fifth generation (5G) communication systems. Since the pathloss in mmWave is severe, beamforming with antenna arrays, an enabling technology in the 5G era, will become a must. To conduct receive beamforming, however, we need to know the information about the angle-of-arrival (AoA). In this paper, we consider joint AoA and channel estimation for single-input-multiple-output (SIMO) OFDM systems. As known, wireless channels are sparse, and this is particularly true for mmWave environments. Conventional compressive-sensing (CS) based channel estimation methods only consider single-input-single-output systems. We propose new matching-pursuit-based CS methods for channel estimation in SIMO-OFDM systems, using frequency-domain pilots. With the estimated channels, AoA\'s are then estimated by the maximum-likelihood method. Since a hidden parameter is involved in the problem, an expectation-maximization (EM) algorithm is then employed. The Cramer-Rao lower bound (CRLB) is also derived for the AoA estimation. Simulation results show that the proposed channel estimation can significantly outperform existing methods while the proposed AoA estimation attains the CRLB.