Wireless Communication Signal Strength Prediction Method Based on the K-nearest Neighbor Algorithm

• 作者: Chenjun Tang Decun Luo Hengkui Xiang Lingang Liu Ning Xiong Xiuqing Zou Yong Ding Yujue Wang Zhao Chen
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: INTERFERENCE protection K-nearest NEIGHBOR algorithm NON-PARAMETRIC KERNEL regression SIGNAL field STRENGTH
• 年，卷（期）: 2019,（1）
• 页码范围: 238-240
• 页数: 3页
• 分类号: C

摘要

Existing interference protection systems lack automatic evaluation methods to provide scientific, objective and accurate assessment results. To address this issue, this paper develops a layout scheme by geometrically modeling the actual scene, so that the hand-held full-band spectrum analyzer would be able to collect signal field strength values for indoor complex scenes. An improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression was proposed to predict the signal field strengths for the whole plane before and after being shield. Then the highest accuracy set of data could be picked out by comparison. The experimental results show that the improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression can scientifically and objectively predict the indoor complex scenes’ signal strength and evaluate the interference protection with high accuracy.