name: NT
full_form: Non Terrestrial
context: Observations: Direct AI/ML positioning For the evaluation of direct AI/ML positioning, with Nt consecutive time domain samples used as model input, evaluation results show that when CIR, PDP, or DP is used as model input, using different Nt while holding other parameters the same, - Reducing Nt from 256 to 128 does not appreciably degrade the positioning accuracy, while the measurement size and signalling overhead shrink to (approximately) 1/2 that of Nt=256. - Positioning error of Nt=128 is 0.81 ~ 1.19 times the positioning error of Nt=256; - Reducing Nt from 256 to 64~32 may degrade the positioning accuracy, while the measurement size and signalling overhead shrink to (approximately) 1/4 ~1/8 that of Nt=256, respectively. - Positioning error of Nt=64 is 0.88 ~ 3.00 times the positioning error of Nt=256; - Positioning error of Nt=32 is 1.05 ~ 4.29 times the positioning error of Nt=256; - Note: the variation in the positioning accuracy depends on each company's simulation assumption
description: NT (Non Terrestrial) refers to a parameter used in direct AI/ML positioning. It represents the number of consecutive time domain samples used as model input. This affects both the measurement size and signalling overhead, with varying values impacting the positioning accuracy.
explanation: The context describes NT as a specific parameter within the framework of direct AI/ML positioning. Its role in influencing measurement size, signalling overhead, and ultimately positioning accuracy aligns with the definition of a parameter.
section_title: 6.4.2.4Model-input Size Reduction
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