Semi-Blind Interference Cancellation with Multiple Receive Antennas for MIMO Heterogeneous Networks

Huiyu YE  Kazuhiko FUKAWA  

Publication
IEICE TRANSACTIONS on Communications   Vol.E101-B   No.5   pp.1299-1310
Publication Date: 2018/05/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2017EBP3201
Type of Manuscript: PAPER
Category: Wireless Communication Technologies
Keyword: 
heterogeneous networks,  MIMO,  interference cancellation,  quantized channel approach,  channel estimation,  multiuser detection,  

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Summary: 
Our previous work proposed a semi-blind single antenna interference cancellation scheme to cope with severe inter-cell interference in heterogeneous networks. This paper extends the scheme to allow multiple-receive-antenna implementation. It does not require knowledge of the training sequences of interfering signals and can cancel multiple interfering signals irrespective of the number of receive antennas. The proposed scheme applies an enhanced version of the quantized channel approach to suboptimal joint channel estimation and signal detection (JCESD) during the training period in order to blindly estimate channels of the interfering signals, while reducing the computational complexity of optimum JCESD drastically. Different from the previous work, the proposed scheme applies the quantized channel generation and local search at each individual receive antenna so as to estimate transmitted symbol matrices during the training period. Then, joint estimation is newly introduced in order to estimate a channel matrix from the estimated symbol matrices, which operates in the same manner as the expectation maximization (EM) algorithm and considers signals received at all receive antennas. Using the estimated channels, the proposed scheme performs multiuser detection (MUD) during the data period under the maximum likelihood (ML) criterion in order to cancel the interference. Computer simulations with two receive antennas under two-interfering-stream conditions show that the proposed scheme outperforms interference rejection combining (IRC) with perfect channel state information (CSI) and MUD with channels estimated by a conventional scheme based on the generalized Viterbi algorithm, and can achieve almost the same average bit error rate (BER) performance as MUD with channels estimated from sufficiently long training sequences of both the desired stream(s) and the interfering streams, while reducing the computational complexity significantly compared with full search involving all interfering signal candidates during the training period.