Phase Noise Estimation for Uncoded/Coded SISO and MIMO Systems

New generation cellular networks have been forced to support high data rate communications. The demand for high bandwidth data services has rapidly increased with the advent of bandwidth hungry applications. To fulfill the bandwidth requirement, high throughput backhaul links are required. Microwave radio links operating at high frequency bands are used to fully exploit the available spectrum. Generating high carrier frequency becomes problematic due to the hardware limitations. Non-ideal oscillators both at the transmitter and the receiver introduces time varying phase noise which interacts with the transmitted data in a non-linear fashion. Phase noise becomes a detrimental problem in digital communication systems and needs to be estimated and compensated. In this thesis receiver algorithms are derived and evaluated to mitigate the effects of the phase noise in digital communication systems. The thesis is organized as follows: In Chapter 3 phase noise estimation in single-input single-output (SISO) systems is investigated. First, a hard decision directed extended Kalman filter (EKF) is derived and applied to track time varying phase noise for an uncoded system. Next, the problem of phase noise estimation for coded SISO system is investigated. An iterative receiver algorithm performing code-aided turbo synchronization is derived using the expectation maximization (EM) framework. Two soft-decision directed estimators in the literature based on Kalman filtering, the Kalman filter and smoother with maximum likelihood average (KS-MLA) and the extended Kalman filter and smoother (EKS), are evaluated. Low density parity check (LDPC) codes are proposed to calculate marginal a posteriori probabilities and to construct soft decision symbols. Error rate performance of both estimators, the KS-MLA and the EKS, are determined and compared through simulations. Simulations indicate that comparison on the performance of the existing estimators heavily depends on the system parameters such as block length and modulation order which are not taken into consideration in the literature. In Chapter 4 the thesis focuses on phase noise estimation in multi-input multi-output (MIMO) systems. MIMO technology is commonly used in microwave radio links to improve spectrum efficiency. First, an uncoded MIMO system is taken under consideration. A low complexity hard decision directed EKF is derived and evaluated. A new MIMO receiver algorithm that iterates between the estimator and the detector, based on the EM framework for joint estimation and detection in coded MIMO systems in the presence of time varying phase noise is proposed. A low complexity soft decision directed extended Kalman filter and smoother (EKFS) that tracks the phase noise parameters over a frame is proposed in order to carry out the maximization step. The proposed EKFS based approach is combined with an iterative detector that utilizes bit interleaved coded modulation and employs LDPC codes to calculate the marginal a posteriori probabilities of the transmitted symbols, i.e., soft decisions. Numerical investigations show that for a wide range of phase noise variances the estimation accuracy of the proposed algorithm improves at every iteration. Finally, simulation results confirm that the error rate performance of the proposed EM-based approach is close to the scenario of perfect knowledge of phase noise at low-to-medium signal-to-noise ratios.