Semi-Blind Interference Cancellation with Single Receive Antenna for Heterogeneous Networks

Huiyu YE  Kazuhiko FUKAWA  

Publication
IEICE TRANSACTIONS on Communications   Vol.E101-B   No.1   pp.232-241
Publication Date: 2018/01/01
Online ISSN: 1745-1345
Type of Manuscript: PAPER
Category: Wireless Communication Technologies
Keyword: 
heterogeneous networks,  interference cancellation,  quantized channel approach,  channel estimation,  multiuser detection,  

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Summary: 
In order to cope with severe interference in heterogeneous networks, this paper proposes a semi-blind interference cancellation scheme, which does not require multiple receive antennas or knowledge about training sequences of the interfering signals. The proposed scheme performs joint channel estimation and signal detection (JCESD) during the training period in order to blindly estimate channels of the interfering signals. On the other hand, maximum likelihood detection (MLD), which can be considered the optimum JCESD, must perform channel estimation for all transmitted signal candidates of the interfering signals and must search for the most likely signal candidate. Therefore, MLD incurs a prohibitive amount of computational complexity. To reduce such complexity drastically, the proposed scheme enhances the quantized channel approach, and applies the enhanced version to JCESD. In addition, a recalculation scheme is introduced to avoid inaccurate channel estimates due to local minima. Using the estimated channels, the proposed scheme performs multiuser detection (MUD) of the data sequences in order to cancel the interference. Computer simulations show that the proposed scheme outperforms a conventional scheme based on the Viterbi algorithm, and can achieve almost the same average bit error rate performance as the MUD with channels estimated from sufficiently long training sequences of both the desired signal and the interfering signals, while reducing the computational complexity significantly compared with full search involving all interfering signal candidates during the training period.