Phase Noise Estimation for Uncoded/Coded SISO and MIMO Systems (1210.6267v1)

Abstract: 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 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. 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 applied to an uncoded system. Next, an iterative receiver algorithm performing code-aided turbo synchronization is derived using the expectation maximization (EM) framework for a coded system. Two soft-decision directed estimators in the literature based on Kalman filtering 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 are compared through simulations. In Chapter 4 phase noise estimation in multi-input multi-output (MIMO) systems is investigated. First, a low complexity hard decision directed EKF is applied to an uncoded system. Next, a new receiver algorithm based on the EM framework for joint estimation and detection in coded MIMO systems 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. 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.