Nonlinear distortion is one of key limiting factors in radio over fiber (RoF) transmission systems. To suppress the nonlinear distortion, digital pre-distortion (DPD) has been investigated considerably. However, for m...Nonlinear distortion is one of key limiting factors in radio over fiber (RoF) transmission systems. To suppress the nonlinear distortion, digital pre-distortion (DPD) has been investigated considerably. However, for multi-band signals, DPD becomes very complex, which limits the applications. To reduce the complexity, many simplified DPDs have been proposed. In this work, a new multidimensional DPD is proposed, in which in-band and out-of-band distortion are separated and the out-of-band distortion is evaluated by sum and differences of all input signals instead of all individual input signals, thus complexity is reduced. An up to 6-band 64-QAM orthogonal frequency division multiplexing (OFDM) signal with each bandwidth of 200 MHz in simulations and a 5-band 20 MHz 64-QAM OFDM signal in experiments are used to validate the pro-posed DPD. The validation is illustrated in the means of power spectrum, AM/AM and AM/PM distortion, and error vector magnitude (EVM) of the received signal constellations. The average EVM improvement by simulation for 3-band, 4-band, 5-band and 6-band signals is 19.97 dB, 18.65 dB, 16.64 dB and 15.44 dB, respectively. The average EVM improvement by experiments for 5-band signals is 8.1 dB. Considering the ten times of bandwidth difference, experiments and simulation agree well.展开更多
Environmental assessments are critical for ensuring the sustainable development of human civilization.The integration of artificial intelligence(AI)in these assessments has shown great promise,yet the"black box&q...Environmental assessments are critical for ensuring the sustainable development of human civilization.The integration of artificial intelligence(AI)in these assessments has shown great promise,yet the"black box"nature of AI models often undermines trust due to the lack of transparency in their decision-making processes,even when these models demonstrate high accuracy.To address this challenge,we evaluated the performance of a transformer model against other AI approaches,utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators.We further explored the application of saliency maps as a novel explainability tool in multi-source AI-driven environmental assessments,enabling the identification of individual indicators'contributions to the model's predictions.We find that the transformer model outperforms others,achieving an accuracy of about 98%and an area under the receiver operating characteristic curve(AUC)of 0.891.Regionally,the environmental assessment values are predominantly classified as level II or III in the central and southwestern study areas,level IV in the northern region,and level V in the western region.Through explainability analysis,we identify that water hardness,total dissolved solids,and arsenic concentrations are the most influential indicators in the model.Our AI-driven environmental assessment model is accurate and explainable,offering actionable insights for targeted environmental management.Furthermore,this study advances the application of AI in environmental science by presenting a robust,explainable model that bridges the gap between machine learning and environmental governance,enhancing both understanding and trust in AI-assisted environmental assessments.展开更多
文摘Nonlinear distortion is one of key limiting factors in radio over fiber (RoF) transmission systems. To suppress the nonlinear distortion, digital pre-distortion (DPD) has been investigated considerably. However, for multi-band signals, DPD becomes very complex, which limits the applications. To reduce the complexity, many simplified DPDs have been proposed. In this work, a new multidimensional DPD is proposed, in which in-band and out-of-band distortion are separated and the out-of-band distortion is evaluated by sum and differences of all input signals instead of all individual input signals, thus complexity is reduced. An up to 6-band 64-QAM orthogonal frequency division multiplexing (OFDM) signal with each bandwidth of 200 MHz in simulations and a 5-band 20 MHz 64-QAM OFDM signal in experiments are used to validate the pro-posed DPD. The validation is illustrated in the means of power spectrum, AM/AM and AM/PM distortion, and error vector magnitude (EVM) of the received signal constellations. The average EVM improvement by simulation for 3-band, 4-band, 5-band and 6-band signals is 19.97 dB, 18.65 dB, 16.64 dB and 15.44 dB, respectively. The average EVM improvement by experiments for 5-band signals is 8.1 dB. Considering the ten times of bandwidth difference, experiments and simulation agree well.
基金Dreams Foundation of Jianghuai Advance Technology Center(No.2023-ZM01D006)National Natural Science Foundation of China(No.62305389)Scientific Research Project of National University of Defense Technology under Grant(22-ZZCX-07)。
文摘Environmental assessments are critical for ensuring the sustainable development of human civilization.The integration of artificial intelligence(AI)in these assessments has shown great promise,yet the"black box"nature of AI models often undermines trust due to the lack of transparency in their decision-making processes,even when these models demonstrate high accuracy.To address this challenge,we evaluated the performance of a transformer model against other AI approaches,utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators.We further explored the application of saliency maps as a novel explainability tool in multi-source AI-driven environmental assessments,enabling the identification of individual indicators'contributions to the model's predictions.We find that the transformer model outperforms others,achieving an accuracy of about 98%and an area under the receiver operating characteristic curve(AUC)of 0.891.Regionally,the environmental assessment values are predominantly classified as level II or III in the central and southwestern study areas,level IV in the northern region,and level V in the western region.Through explainability analysis,we identify that water hardness,total dissolved solids,and arsenic concentrations are the most influential indicators in the model.Our AI-driven environmental assessment model is accurate and explainable,offering actionable insights for targeted environmental management.Furthermore,this study advances the application of AI in environmental science by presenting a robust,explainable model that bridges the gap between machine learning and environmental governance,enhancing both understanding and trust in AI-assisted environmental assessments.
