Aiming at making full use of analog to digital converter （ADC） digitalizing bit without oversaturation while keeping peak to average ratio （PAR） stable, this paper puts forward a new segmented full-digital （SFD）...Aiming at making full use of analog to digital converter （ADC） digitalizing bit without oversaturation while keeping peak to average ratio （PAR） stable, this paper puts forward a new segmented full-digital （SFD）-automatic gain control （AGC） algorithm for a new long term evolution （LTE） communication system. Segmented digital gain control strategy is adopted to adjust the gain by only one step based on detected power status. Whether the gain needs to be adjusted is determined by current signal state derived from the change ranges of adjacent root mean square （RMS） of input signal, but not the difference between the power level of current signal and target signal. Software simulation and hardware implementing had been conducted with LTE frequency division dual （FDD） uplink signal and the results indicated that the proposed AGC algorithm can judge power status accurately and hence adjust the gain precisely in one step with a short delay, further, it can make full use of ADC digitalizing bit without oversaturation as well as keeping stable PAR. In addition, the mean error vector magnitude （EVM） was confined less than 1.6% to meet the 3rd generation partnership project （3GPP） standard well.展开更多
Traditional river health assessment relies on limited water quality indices and representative organism activity,but does not comprehensively obtain biotic and abiotic information of the ecosystem.Here,we propose a ne...Traditional river health assessment relies on limited water quality indices and representative organism activity,but does not comprehensively obtain biotic and abiotic information of the ecosystem.Here,we propose a new approach to evaluate the ecological and health risks of river aquatic ecosystems.First,detailed physicochemical and biological characterization of a river ecosystem can be obtained through pollutant determination(especially emerging pollutants)and DNA/RNA sequencing.Second,supervised machine learning can be applied to perform classification analysis of characterization data and ascertain river ecosystem ecology and health.Our proposed methodology transforms river ecosystem health assessment and can be applied in river management.展开更多
基金supported by the Smart In-Building Wireless System Using Flexible Digital Transmission Technology (SWIFT)
文摘Aiming at making full use of analog to digital converter （ADC） digitalizing bit without oversaturation while keeping peak to average ratio （PAR） stable, this paper puts forward a new segmented full-digital （SFD）-automatic gain control （AGC） algorithm for a new long term evolution （LTE） communication system. Segmented digital gain control strategy is adopted to adjust the gain by only one step based on detected power status. Whether the gain needs to be adjusted is determined by current signal state derived from the change ranges of adjacent root mean square （RMS） of input signal, but not the difference between the power level of current signal and target signal. Software simulation and hardware implementing had been conducted with LTE frequency division dual （FDD） uplink signal and the results indicated that the proposed AGC algorithm can judge power status accurately and hence adjust the gain precisely in one step with a short delay, further, it can make full use of ADC digitalizing bit without oversaturation as well as keeping stable PAR. In addition, the mean error vector magnitude （EVM） was confined less than 1.6% to meet the 3rd generation partnership project （3GPP） standard well.
基金supported by the NationalNatural Science Foundation of China (No.52293442)the Special Fund from the State Key Joint Laboratory of Environment Simulation and Pollution Control (No.22Z01ESPCR)。
文摘Traditional river health assessment relies on limited water quality indices and representative organism activity,but does not comprehensively obtain biotic and abiotic information of the ecosystem.Here,we propose a new approach to evaluate the ecological and health risks of river aquatic ecosystems.First,detailed physicochemical and biological characterization of a river ecosystem can be obtained through pollutant determination(especially emerging pollutants)and DNA/RNA sequencing.Second,supervised machine learning can be applied to perform classification analysis of characterization data and ascertain river ecosystem ecology and health.Our proposed methodology transforms river ecosystem health assessment and can be applied in river management.
