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Parameter estimation in cognitive communications can be formulated as amulti-user estimation problem, which is solvable under maximum likelihoodsolution but involves high computational complexity. This paper presents atime-sharing and interference mitigation based EKF (Extended Kalman Filter)design for joint CFO (carrier frequency offset) and channel estimation atmultiple cognitive users. The key objective is to realize low implementationcomplexity by decomposing highdimensional parameters into multiple separatelow-dimensional estimation problems, which can be solved in a time-sharedmanner via pipelining operation. We first present a basic EKF design thatestimates the parameters from one TX user to one RX antenna. Then such basicdesign is time-shared and reused to estimate parameters from multiple TX usersto multiple RX antennas. Meanwhile, we use interference mitigation module tocancel the co-channel interference at each RX sample. In addition, we furtherpropose adaptive noise variance tracking module to improve the estimationperformance. The proposed design enjoys low delay and low buffer size (becauseof its online real-time processing), as well as low implementation complexity(because of time-sharing and pipeling design). Its estimation performance isverified to be close to Cramer-Rao bound.

A Low-Delay Low-Complexity EKF Design for Joint Channel and CFO Estimation in Multi-User Cognitive Communications