Blind Channel Estimation in Full Duplex Systems: Identifiability Analysis, Bounds, and Estimators

Residual self-interference (SI) cancellation in the digital baseband is an important problem in full-duplex (FD) communication systems. In this paper, we propose a new technique for estimating the SI and communication channels in a FD communication system, which is inspired from superimposed signalling. In our proposed technique, we add a constant real number to each constellation point of a conventional modulation constellation to yield asymmetric shifted modulation constellations with respect to the origin. We show mathematically that such constellations can be used for bandwidth efficient channel estimation without ambiguity. We propose an expectation maximization (EM) estimator for use with the asymmetric shifted modulation constellations. We derive a closedform lower bound for the mean square error (MSE) of the channel estimation error, which allows us to find the minimum shift energy needed for accurate channel estimation in a given FD communication system. The simulation results show that the proposed technique outperforms the data-aided channel estimation method, under the condition that the pilots use the same extra energy as the shift, both in terms of MSE of channel estimation error and bit error rate. The proposed technique is also robust to an increasing power of the SI signal. Index Terms Full-duplex systems, channel estimation, self-interference cancellation, superimposed signaling. Abbas Koohian and Salman Durrani are with Research School of Engineering, Australian National University, Canberra, Australia (email: {abbas.koohian, salman.durrani}@anu.edu.au). Hani Mehrpouyan is with the Department of Electrical and Computer Engineering, Boise State University, Idaho, USA (email: [email protected]). Ali A. Nasir is with Department of Electrical Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia (email: [email protected]). Mohammad Azarbad is with the Department of Mathematics and Statistics, Shahid Chamran University, Ahvaz, Iran (email: [email protected]). Steven D. Blostein is with the Department of Electrical and Computer Engineering, Queen’s University, Kingston, Ontario, Canada (email:[email protected]). ar X iv :1 51 1. 04 79 4v 3 [ cs .I T ] 2 8 D ec 2 01 6