Turbo-AI, Part VI: Achieving Robust Downlink AI-Based Channel Prediction

In previous paper series, we proposed a Machine- Learning (ML) based channel estimator and investigated it from different aspects. For the nature of its iterative architecture, it was named as Turbo-AI and could fully exploit the high dimensional granularity of wireless channel to approach the channel estimation bound with low complexity. Turbo-AI established a beneficial performance-complexity trade-off for enabling challenging use cases toward future 6
^th
Generation (6G) wireless communications. In this paper, we target to the challenging channel prediction problem for a Time Division Duplex (TDD) system. We propose a concept named Turbo-Predictor, which concatenates two Neural Networks (NN), in which the first one provides an initialized channel prediction, and the second stage passes the approximation as virtual observation to Turbo-AI, in which both the channel estimation part and the prediction part will be jointly and iteratively processed. Furthermore, we exhibit potential enhancement in channel prediction, after selecting independent prediction paths through the multi-dimensional Sounding Reference Signal (SRS) structures. 5G New Radio (NR) compliant link level simulations demonstrate that Turbo- Predictor can support different SRS configurations and reach high prediction quality for the terminals with medium to high mobility up to 120km/h, which can be fundamentally regarded as a solid prerequisite for the robust downlink performance in a TDD system.