Findings from Nanjing University of Science and Technology Provides New Data abo ut Machine Learning (Spatial Sensitivity Synthesis Based On Alternate Projection for the Machine-learningbased Coding Digital Receiving Array)
Findings from Nanjing University of Science and Technology Provides New Data abo ut Machine Learning (Spatial Sensitivity Synthesis Based On Alternate Projection for the Machine-learningbased Coding Digital Receiving Array)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting from Nanjing, People’s Republic of China, by NewsRx journalists, research stated, “Recently, a novel low-cost coding digi tal receiving array based on machine learning (ML-CDRA) has been proposed to red uce the required radio frequency channels in modern wireless systems. The spatia l sensitivity of ML-CDRA is studied which describes the spatial accumulation gai n in different directions.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from the Nanjing Univ ersity of Science and Technology, “It is demonstrated that the spatial sensitivi ty is determined by the encoding network, decoding network, and beamforming crit erion. To obtain the desired spatial sensitivity, a spatial sensitivity synthesi s method is proposed based on the alternate projection by optimising the encodin g network with the constraint of amplitude-phase quantisation. Simulation result s show that the proposed method can significantly improve the spatial sensitivit y of ML-CDRA. Furthermore, in the directions of interest, the spatial accumulati on gain of ML-CDRA can exceed the full-channel digital receiving array. The spat ial sensitivity of a machine-learning-based coding digital receiving array (ML-C DRA) is studied which describes the spatial accumulation gain in different direc tions. It is demonstrated that spatial sensitivity is determined by the encoding network, decoding network, and beamforming criterion.”
Key words
Nanjing/People’s Republic of China/Asi a/Cyborgs/Emerging Technologies/Machine Learning/Nanjing University of Scien ce and Technology
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