name: SQ
full_form: Scalar Quantization
context: that VQ under Case 2-1 has -1%~-4.5% degradation over SQ under Case 2-1, - observed by 1 source that VQ under Case 2-1 has 1.1% gain over SQ under Case 2-1, and - observed by 3 sources that VQ under Case 2-2 has 0.7%~3.8% gain over SQ under Case 2-2. - Note: VQ under Case 2-1 has 8% gains over SQ under Case 2-1 as observed from 1 source due to SQ parameter chosen without matching latent distribution. - For SQ and VQ across training cases, it is - observed by 6 sources that VQ under Case 2-2 has 0.46%~4% gain over SQ under Case 2-1, and - observed by 1 source that VQ under Case 2-2 has -1.3% degradation over SQ under Case 2-1. - observed by 1 source that VQ under Case 2-1 has -2.9%~-6.4% degradation over SQ under Case 2-2. - Note: in general, more companies observing gain of VQ over SQ than companies observing loss. - Note: it is observed by 1 source that combined SQ and VQ under Case 2-2 has minor gain of 0.2% over VQ only under Case 2-2. The above results are based on the following
description: Scalar Quantization (SQ) is a technique used in telecommunications for signal processing and compression, particularly relevant to CSI (Channel State Information) compression in 5G networks.
explanation: The context describes SQ as a method compared against Vector Quantization (VQ) for performance metrics in 5G network training cases. This comparison within a technical specification indicates that SQ is a specific technology or technique being evaluated.
section_title: 6.2.2.11-on-1 joint training for CSI compression
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