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Orthogonal superimposed training design for doubly selective channel estimation using basis expansion models
Author(s) -
Dou Gaoqi,
He Xianwen,
Deng Ren,
Li Lihua,
Gao Jun
Publication year - 2017
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.3157
Subject(s) - precoding , computer science , orthogonality , channel (broadcasting) , algorithm , fading , affine transformation , transmission (telecommunications) , polyphase system , sequence (biology) , mathematics , electronic engineering , telecommunications , mimo , engineering , geometry , biology , pure mathematics , genetics
In this paper, we consider channel estimation for single‐carrier block transmission over doubly selective fading channel, where basis expansion models are used to describe the time‐varying channel. The affine precoding model is used as the transmission strategy, wherein the training sequence is added on the top of the precoded data prior to transmission. By enforcing a special form of orthogonality between training sequence and precoded matrix, we propose orthogonal superimposed training design for doubly selective channel that makes channel estimation decouple from symbol detection, and then the affine precoding model based on orthogonal polyphase sequence set is designed under the orthogonal conditions. To further improve the estimation performance, we exploited a joint iterative approach is used, where the detected symbols, to enhance the estimation performance. Simulations results show that the proposed scheme can yield better detection performance than the data‐dependent superimposed training and can be competitive with time‐multiplexed training scheme with higher bandwidth efficiency.