Residual Self-Interference Cancellation and Data Detection in Full-Duplex Communication Systems

Residual self-interference cancellation is an important practical requirement for realizing the full potential of full-duplex (FD) communication. Traditionally, the residual selfinterference is cancelled via digital processing at the baseband, which requires accurate knowledge of channel estimates of the desired and self-interference channels. In this work, we consider point-to-point FD communication and propose a superimposed signaling technique to cancel the residual self-interference and detect the data without estimating the unknown channels. We show that when the channel estimates are not available, data detection in FD communication results in ambiguity if the modulation constellation is symmetric around the origin. We demonstrate that this ambiguity can be resolved by superimposed signalling, i.e., by shifting the modulation constellation away from the origin, to create an asymmetric modulation constellation. We compare the performance of the proposed detection method to that of the conventional channel estimation-based detection method, where the unknown channels are first estimated and then the data signal is detected. Simulations show that for the same average energy over a transmission block, the bit error rate performance of the proposed detection method is better than that of the conventional method. The proposed method does not require any channel estimates and is bandwidth efficient

Superimposed Signaling Inspired Channel Estimation in Full-Duplex Systems

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 signaling. 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 closed-form 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

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

Joint channel and phase noise estimation for mmWave full-duplex communication systems

Abstract Full-duplex (FD) communication at millimeter-wave (mmWave) frequencies suffers from a strong self-interference (SI) signal, which can only be partially canceled using conventional RF cancelation techniques. This is because current digital SI cancellation techniques, designed for microwave frequencies, ignore the rapid phase noise (PN) variation at mmWave frequencies, which can lead to large estimation errors. In this work, we consider a multiple-input multiple-output mmWave FD communication system. We propose an extended Kalman filter-based estimation algorithm to track the rapid variation of PN at mmWave frequencies. 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. We also simulate the bit error rate performance of the proposed system and show the effectiveness of a digital canceler, which uses the proposed estimator to estimate the SI channel. The results show that for a 2×2 FD system with 64−QAM modulation and PN variance of 10−4, the residual SI power can be reduced to − 25 dB and − 40 dB, respectively, for signal-to-interference ratio of 0 and 15 dB