name: SGCS
full_form: Squared Generalized Cosine Similarity
context: (FLOPs), AI/ML memory storage in terms of AI/ML model size and number of AI/ML parameters reported by companies who may select either or both - Reported separately for the CSI generation part and the CSI reconstruction part (for CSI compression sub-use case) - When reporting the computational complexity including the pre-processing and post-processing, the complexity metric of FLOPs may be reported separately for the AI/ML model and the pre/post processing. While reporting the FLOPs of pre-processing and post-processing the following boundaries are considered: - Estimated raw channel matrix per each frequency unit as an input for pre-processing of the CSI generation part. - Precoding vectors per each frequency unit as an output of post-processing of the CSI reconstruction part. - CSI compression: Intermediate KPIs: SGCS and/or NMSE to evaluate the accuracy of the AI/ML output CSI - For rank>1 cases, SGCS calculation/extension methods are to be reported: - SGCS separately calculated
description: Squared Generalized Cosine Similarity (SGCS) is a metric used to evaluate the accuracy of AI/ML output Channel State Information (CSI). It measures the similarity between the original and reconstructed CSI, providing an indication of the quality of the compressed CSI.
explanation: SGCS is classified as a Measurement because it quantifies the performance of AI/ML models in compressing CSI by comparing the original and reconstructed CSI values.
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biotag_Description: B-Measurement,I-Measurement,I-Measurement,I-Measurement,O,O,O,O,O,O,O,O,O,O,B-Specification,O,B-Parameter,I-Parameter,I-Parameter,O,O,O,O,O,O,O,O,O,O,O,O,O,O,O,O,O,O,O,O,O