Turbo AI, Part V: Verifying AI-Enhanced Channel Estimation for RAN from System Level

Turbo-AI is an iterative Machine-Learning (ML) based channel estimator as an additional option to balance the performance-complexity trade-off and realize challenging scenarios towards future 6th Generation (6G) wireless communications in a complementary manner. In this paper, we move the focus from algorithmic aspects and pure level link investigations, addressed in previous Turbo-AI paper series, to system level exploitation and hardware-related implementation. We integrate Turbo-AI to a 5G compliant system level simulator, through which the performance gap between 5G legacy channel estimator and ML-based channel estimator can be described and quantized to the distributions of channel estimation Mean Squared Error (MSE). Finally, Turbo-AI, representing an important component of future AI/ML-enhanced Physical Layer (PHY), is realized in a hardware platform implementing the New Radio (NR) compliant gNB-functionality, which satisfies the L1 processing latency of the whole Physical Uplink Shared Channel (PUSCH) chain. The performance advantage of Turbo-AI over the 5G legacy channel estimator is not only reflected in the improved coded Block Error Rate (BLER) curves, but also exhibited by the real-time Radio Frequency (RF) experimentation towards an early adoption of AI/ML-enhanced RAN, seamless model management and performance-latency trade-off, depending on computational resources and system constraints.