Turbo AI, Part IV: Estimating Uplink Channels for Ultra High Mobility with Sparse Pilots

Turbo-AI is an iterative Machine Learning (ML) based channel estimator, processed through frequency, time and spatial domains consecutively with low complexity. Within the context of 5th Generation (5G) and Beyond 5G (B5G) wireless communications, we embed Turbo-AI into Demodulation Reference Signal (DMRS) processing in this paper. The extension is not technically obvious, comparing to our previous Turbo-AI paper series, because the main challenges come from not only estimating the channel response exactly for the DMRS pilot resource elements, but also precisely interpolating the channel response for the data resource elements, especially when the user mobility or equivalently the sparsity of the pilot structure significantly increases. Throughout this paper, we propose a universal interpolation approach, named Firecracker Algorithm, which is jointly introduced to DMRS-Turbo-AI. Numerical results show that DMRS-Turbo-AI can deliver high quality channel estimation for the users with high mobility, and demonstrate that Firecracker Algorithm is even capable of tracking the channel of a user, who moves at a supersonic speed.