Channel Estimation and Self-Interference Cancellation in Full-Duplex Communication Systems

Full-duplex (FD) wireless communications, along with millimeter wave (mmWave), and massive multiple-input multiple-output (MIMO) are key technologies for future communication networks, known as 5G networks. The main challenge in exploiting the full potential of FD communication systems lies in cancellation of strong self-interference (SI) signal. In particular, since SI cancellation requires accurate knowledge of both SI and communication channels, bandwidth efficient channel estimation techniques are of practical interest. Furthermore, SI cancellation encounters new challenges, once FD technology is combined with mmWave or massive MIMO technologies. This is because FD communication at mmWave frequencies needs to be able to deal with fast phase noise (PN) variation, and FD massive MIMO base station (BS) requires simultaneous cancellation of SI and multi-user interference (MUI). The first half of this thesis investigates channel estimation techniques to simultaneously estimate both SI and communication channels for FD communication at microwave and mmWave frequencies. We first consider FD communication at microwave frequencies and inspired by superimposed signalling, we propose a novel bandwidth efficient channel estimation technique for estimating the SI and communication channels. To evaluate the performance of the proposed estimator, we derive the lower bound for the estimation error, and show that the proposed estimator reaches the performance of the bound. In contrast to microwave frequencies, at mmWave frequencies the challenge lies in jointly estimating the channels and tracking the fast varying PN process. We address this problem by proposing an Extended Kalman filter to jointly estimate the channels and track the PN process. We derive a lower bound for the estimation error of PN at mmWave, and numerically show that the mean square error performance of the proposed estimator approaches the lower bound. The second half of this thesis focuses on the SI cancellation and data detection problems. The ultimate goal of SI cancellation in FD communication is to allow reliable data detection. However, achieving perfect SI cancellation is not always feasible. This is because accurate channel estimates might not be available. In this regard, we investigate blind data detection problem, when only statistical properties of SI and communication channels are available. We propose a maximum aposterior probability (MAP) based blind detector, which allows for data detection without channel estimation and SI cancellation stages. This blind detection is achieved by using the statistical properties of the SI and communication channels instead of accurate channel estimation and SI cancellation. Finally, we rigorously study precoder design for a FD enabled massive MIMO BS. The main design challenge in here is to design precoders that can simultaneously cancel SI and MUI. We prove that in order to suppress both SI and MUI, the number of transmit antennas must be greater than or equal to the sum of the number of receive antennas and the number of uplink users. In addition, we rigorously show that the problem of simultaneous suppression of SI and MUI has a solution with probability 1. These results validate previous heuristic assumptions made in the literature