R1-2410589
discussion
Discussion an AI/ML based CSI Compression
From IIT Kanpur
Summary
IIT Kanpur presents evaluation results for AI/ML-based CSI compression using temporal-spatial-frequency domain approaches, focusing on Case 2 (reconstruction with temporal correlation) and Case 3 (joint prediction). The document contains 2 key observations about the performance differences between reconstructive and predictive tasks under ideal and non-ideal UCI conditions.
Position
IIT Kanpur advocates for enhanced temporal correlation representation in AI/ML models for CSI compression, particularly emphasizing that joint prediction tasks (Case 3) require more sophisticated temporal modeling compared to reconstruction tasks (Case 2). They push for improvements in temporal diversity in datasets and better data pre-processing for training data to handle non-ideal UCI conditions effectively.
Key proposals
- Observation 1 (Sec 8): The relatively lower gain for Case 3 indicates that joint prediction may need a more effective temporal correlation representation. This suggests that enhancing temporal diversity in the dataset could help improve the performance of joint prediction, particularly in handling non-ideal conditions.
- Observation 2 (Sec 8): The mitigation measures at the NW side work well for reconstructive tasks, as demonstrated in Case 2, where the performance gain is notable. However, these measures may fall short for joint prediction tasks like Case 3, which are inherently more complex. Joint prediction seems to be more sensitive to inconsistencies, especially under non-ideal feedback conditions, and may benefit from improvements in temporal modelling and data pre-processing of the training data.