name: SNIR
full_form: Signal to Noise Interference Ratio
context: can be smoothened by the fast Space-Time scrambling scheme described earlier. This smoothing of the SNIRs of different symbols within a TTI does not reduce the resulting variance of SNIR within the TTI, but it can help to improve the decoding performance in some high code rate cases. However, this seems to be a minor effect. In order to provide a useful CQI feedback, the UE needs to perform a calculation of an effective SNIR at its equalizer output. Such a calculation needs to take into account the different SNIR levels seen for the different transmit symbols within one code block. Since such variations do have an impact on the decoding performance of a turbo decoder, it is rather important to do this mapping in a correct way. Various different mapping methods for deriving an effective SNIR have been discussed in 3GPP before [4][5][6]. A useful mapping method is the convex metric, which is based on the assumption that the sum of capacities for the individual SNIRs would be equal to
description: The Signal to Noise Interference Ratio (SNIR) is a measurement used in telecommunications to quantify the ratio of signal power to noise and interference power at the receiver. In the context of physical layer aspects for MCS Selection, SNIR is crucial for calculating an effective value that takes into account variations across different transmit symbols within one code block, impacting decoding performance.
explanation: SNIR is classified as a Measurement because it refers to a specific metric used in telecommunications to assess signal quality and strength relative to noise and interference. The context highlights its importance in calculating effective SNIR for CQI feedback and its impact on decoding performance.
section_title: 5.2.11.2.3	Physical layer aspects for MCS Selection
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