name: CIR
full_form: Channel Impulse Response
context: simulation assumption (e.g., AI/ML complexity). For direct AI/ML positioning, the evaluation of positioning accuracy at model inference is affected by the type of model input and AI/ML complexity. For a given AI/ML model design, there is a tradeoff between model input, AI/ML complexity (model complexity and computational complexity), and positioning accuracy. Evaluation results show that if changing model input type while holding other parameters (e.g., Nt, N't, Nport, N'TRP) the same, - When comparing PDP and CIR as model input, - 9 sources showed evaluation results where the positioning error of PDP as model input is 1.06 ~ 1.62 times the positioning error of CIR as model input. - 5 sources showed evaluation results where the positioning error of PDP as model input is 0.61 ~ 0.96 times the positioning error of CIR as model input. - When comparing DP and CIR as model input, - 4 sources showed evaluation results where the positioning error of DP as model input is 1.18 ~ 1.96 times the
description: Channel Impulse Response (CIR) is a parameter used in telecommunications to describe the response of a communication channel to an impulse signal. It is a critical attribute for evaluating positioning accuracy in AI/ML-based direct positioning methods.
explanation: The context describes CIR as a model input that influences positioning accuracy, aligning it with the definition of a parameter within the 3GPP specification.
section_title: 6.4.2.4Model-input Size Reduction
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