In this paper, we focus on orthogonal time frequency space (OTFS) modulated transmissions over doubly-selective channels. Specifically, we investigate the state-of-the-art techniques for effective detection in high-mobility scenarios and introduce innovative strategies aimed at achieving an optimal trade-off between complexity and performance. The proposed solutions are based on the Ungerboeck observation model and exploit the structure of the resulting channel matrix in the Doppler-delay domain. In particular, the first proposed method, denoted as HBCJRG, belongs to the family of message-passing algorithms, whose strength lies in a scheduling scheme that prioritizes the strongest interferers during the detection process. In contrast, the second proposed approach, denoted as ICG, is based on interference cancellation. Differently from the HBCJRGapproach, whose required computational load grows quadratically with the constellation cardinality, the ICGalgorithm is characterized by extremely low complexity. Simulation results demonstrate that the latter strategy allows to achieve an optimal trade-off between complexity and performance when compared to existing state-of-the-art solutions.

In the context of signal detection in the presence of an unknown time-varying channel parameter, receivers based on the Expectation Propagation (EP) framework appear to be very promising. EP is a message-passing algorithm based on factor graphs with an inherent ability to combine prior knowledge of system variables with channel observations. This suggests that an effective estimation of random channel parameters can be achieved even with a very limited number of pilot symbols, thus increasing the payload efficiency. However, achieving satisfactory performance often requires ad-hoc adjustments in the way the probability distributions of latent variables - both data and channel parameters - are combined and projected. Here, we provide, for the first time, an analysis of EP-based algorithms for the classical problem of coded transmission on a strong Wiener phase noise channel, employing soft-input soft-output decoding. The analysis includes possible improvements over the native application of EP, in order to identify its limitations and propose new strategies which reach the performance benchmark while maintaining low complexity, with a primary focus on challenging scenarios where the state-of-the-art algorithms fail.

Expectation Propagation (EP) is a promising framework in message-passing algorithms based on factor-graphs. The inherent ability to combine prior (partial) knowledge of system variables with channel observations suggests that an effective estimation of random channel parameters can be achieved even with a very limited number of pilot symbols, thus increasing the payload efficiency. Yet, the way in which the probability distributions of latent variables (both data and parameters) are combined and projected often requires ad-hoc adjustments to reach satisfactory performance. Here, we apply EP to a classical problem of LDPC-coded transmission on a strong Wiener phase noise channel and discuss how and why, even in the simple case of binary modulation, EP can fail or succeed.