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Collaborative channel state perception with classification-based correction for heterogeneous networks 认领 引用
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作者 ZHAO Zhiyong PAN Yaozong +2 位作者 MAO Zhongyang WANG Mengjiao XU Jianwu 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期788-799,共12页
Accurately sensing the channel state of heterogeneous networks is key to matching users'diverse service communication demands with the channel state,and is an effective way to improve the utilization efficiency of... Accurately sensing the channel state of heterogeneous networks is key to matching users'diverse service communication demands with the channel state,and is an effective way to improve the utilization efficiency of network resource.However,existing channel state perception methods are not suitable for heterogeneous network,and their perception performance is easily affected by interference uncertainty.In order to achieve channel state perception of heterogeneous networks,this paper adopts a centralized collaborative perception model,where each node obtains local channel state perception results based on statistical pulse parameters at the physical layer.In order to reduce the impact of interference on perception performance,this paper uses the Jousselme distance to quantify the degree of difference among nodes caused by interference.Using the average credibility as a threshold,nodes in the sensing area are classified.On this basis,the local perception results of each node are performed classification-based correction to improve the accuracy and reliability of channel state perception.Simulation results indicate that the proposed method has good adaptability for channel state perception in complex electromagnetic environments.The perception results can accurately reflect the actual channel state,which is conducive to improving the network throughput. 展开更多
关键词 collaborative perception heterogeneous networks classification-based correction channel state
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Optical-Elevation Data Co-Registration and Classification-Based Height Normalization for Building Detection in Stereo VHR Images 认领 引用 被引量：1
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作者 Alaeldin Suliman Yun Zhang 《Advances in Remote Sensing》 2017年第2期103-119,共17页
Building detection in very high resolution (VHR) images is crucial for mapping and analysing urban environments. Since buildings are elevated objects, elevation data need to be integrated with images for reliable dete... Building detection in very high resolution (VHR) images is crucial for mapping and analysing urban environments. Since buildings are elevated objects, elevation data need to be integrated with images for reliable detection. This process requires two critical steps: optical-elevation data co-registration and aboveground elevation calculation. These two steps are still challenging to some extent. Therefore, this paper introduces optical-elevation data co-registration and normalization techniques for generating a dataset that facilitates elevation-based building detection. For achieving accurate co-registration, a dense set of stereo-based elevations is generated and co-registered to their relevant image based on their corresponding image locations. To normalize these co-registered elevations, the bare-earth elevations are detected based on classification information of some terrain-level features after achieving the image co-registration. The developed method was executed and validated. After implementation, 80% overall-quality of detection result was achieved with 94% correct detection. Together, the developed techniques successfully facilitate the incorporation of stereo-based elevations for detecting buildings in VHR remote sensing images. 展开更多
关键词 Building Detection Very High Resolution Images Optical-Elevation Data Co-Registration Classification-Based Height Normalization
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