What are the most common error metrics used for transmission loss prediction?
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The most common error metrics used for transmission loss prediction include the optical signal-to-noise power ratio (OSNR), Q-factor, error vector magnitude (EVM), and bit error ratio (BER) . Additionally, a new quality metric called Cumulative Distortion using Structural Similarity (CDSSIM) has been proposed to estimate the impact of packet loss on video quality over error-prone networks . Power losses in distribution grids are also crucial, with efforts made to obtain unbiased estimators and confidence intervals considering measurement inaccuracies . Furthermore, for transmission line loss rate prediction, methods like SSA signal decomposition, sliding window feature enhancement, and CNN-LSTM prediction have been employed, showing improved accuracy in forecasting line loss rates .
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| Papers (5) | Insight |
|---|---|
01 Dec 2022 | The most common error metrics used for transmission line loss rate prediction include prediction error indicators, with CNN-LSTM showing the best performance in accuracy improvement. |
01 Dec 2022 | Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are common error metrics used for transmission line loss rate prediction in the study. |
07 Nov 2019 | The paper does not address the most common error metrics used for transmission loss prediction. |
| Common error metrics for transmission quality prediction include optical signal-to-noise power ratio (OSNR), Q-factor, error vector magnitude (EVM), and bit error ratio (BER) in optical communications. | |
03 Aug 2016 2 Citations | The paper introduces a novel Cumulative Distortion using Structural Similarity (CDSSIM) metric for predicting packet prioritization in video transmission, surpassing traditional error metrics. |
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