Abstract
In this paper, the challenging problem of joint channel estimation and data detection for multiple-input multiple-output orthogonal frequency division multiplexing systems operating in time-frequency dispersive channels under unknown background noise is investigated. Based on two different but equivalent signal models, two expectation-maximization algorithm-based iterative schemes for joint data detection and channel and noise variance estimation are proposed. The first scheme jointly detects data and estimates the channel and noise variance, but the computational complexity is high, owing to the simultaneous detection and estimation for all antennas. To reduce the computational complexity, a complexity-reduced scheme that is detecting data and estimating channel for only one antenna during each iteration and holding the unknown quantities of other antennas to their last values is proposed, whose performance only slightly degrades compared to the first scheme. Moreover, both schemes are derived as closed-form expressions, and therefore, our proposed schemes are free of exhaustive search. Simulation results demonstrate quick convergence of the proposed algorithm, and after convergence, the performance of the proposed algorithm is close to that of the optimal channel estimation and data detection case, which assumes full training and perfect channel state information.
Highlights
Multiple-input multiple-output (MIMO) communication [1] can significantly increase the throughput without increasing the transmit power and additional bandwidth
Joint data detection and channel and noise variance estimation for MIMO-Orthogonal frequency division multiplexing (OFDM) systems operating in TF dispersive channels under unknown background noise are investigated
To reduce the computational complexity of the first scheme, another scheme that performs data detection and channel estimation for only one antenna during each iteration and holding the unknown quantities of other antennas to their last values is proposed, whose performance only slightly degrades compared to the first scheme
Summary
Multiple-input multiple-output (MIMO) communication [1] can significantly increase the throughput without increasing the transmit power and additional bandwidth. Joint data detection and channel and noise variance estimation for MIMO-OFDM systems operating in TF dispersive channels under unknown background noise are investigated. In this paper, based on two different but equivalent signal models, two EM algorithm-based iterative schemes which integrate data detection and channel and noise variance estimation are proposed in a consistent way so as to iteratively improve the system performance. The estimates of data, channel, and noise variance are all obtained as closed-form results, and the proposed schemes are free of exhaustive search. A cyclic prefix (CP) with length Ncp larger than that of the longest channel response is inserted at the beginning of each OFDM symbol to prevent intersymbol interference
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