Complexity/Performance Trade-Off of Kalman Smoothing for Iterative Equalization Based on EP

Kalman smoothers have been successfully used in many digital communication receiver algorithms, including channel equalization. In this paper, we propose low complexity Kalman equalizers based on expectation propagation (EP), and we compare them with state-of-the-art EP-based Kalman equalizers to identify the structure with the most attractive complexity/performance trade-off. In addition, we provide a practical approach for further reducing the computational complexity of Kalman equalizers in time-varying channels. Our analysis show that the Kalman equalizer we derived based on the backward information forward marginals (BIFM) structure is the most efficient in terms of complexity/performance trade-off and numerical results attest its superiority in high-speed vehicle cellular communication scenarios above 50 km/h.