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Semi-blind joint channel estimation and data detection on sphere manifold for MIMO with high-order QAM signaling

Hong, X., Gao, J. and Chen, S. (2020) Semi-blind joint channel estimation and data detection on sphere manifold for MIMO with high-order QAM signaling. Journal of the Franklin Institute, 357 (9). pp. 5680-5690. ISSN 0016-0032

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To link to this item DOI: 10.1016/j.jfranklin.2020.04.009

Abstract/Summary

A low-complexity semi-blind scheme is proposed for joint channel estimation and data detection on sphere manifold for multiple-input multiple-output (MIMO) systems with high-order quadrature amplitude modulation signaling. Specifically, the optimal channel estimator is expressed in the least squares form in terms of the received signals and unknown transmitted data, and by splitting the channel and transmitted data into their real parts and imaginary parts, the data detection becomes a problem defined on a scaled sphere manifold in the real domain. Our semi-blind algorithm consists of three stages: (i) a few training symbols are employed to provide a rough initial MIMO channel estimate which in turn yields the initial zero-forcing (ZF) estimate of data samples; (ii) the Riemannian conjugate gradient algorithm is used to estimate the data samples in real domain, and the detected data samples are used to estimate the final MIMO channel matrix; and (iii) the final ZF data detection is carried out based on the final MIMO channel estimate. In particular, we present the first order Riemannian geometry of the sphere manifold which is utilized in the Riemannian conjugate gradient algorithm for solving (ii). Simulation results are employed to demonstrate the effectiveness of the proposed approach.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:93978
Publisher:Elsevier

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