摘要
无线电环境地图(REM)为认知无线网络动态频谱接入提供了精准、全面的信息支撑,由于现场样本数据的实测受到环境、设备和人为等因素的限制,其样本规模难以保证应用的需求。因此,利用空间相似性,将离散数据点扩展为面结构数据的空间插值方法研究有积极的意义和应用价值。在传统MSM算法基础上,提出了RMSM算法,从优化权值计算、灵活地使用局部特征和高效的近邻搜索法方面进行改进,通过搭建15 m×20 m的实验场景,进行样本数据采集和算法测试验证。结果表明RMSM算法与传统算法比较,其误差降低了1.96 dB,稳定性提高了55.37%,改进效果明显。
Radio Environment Map（REM） provides accurate or comprehensive information support to dynamic spectrum access of cognitive radio networks. Practically, the sample size always fails to reach the application requirements due to the limitation of environments, devices or human factors in the measurement of field data. Hence, research on the technique of Spatial Interpolation, which can expand the discrete data into surface data, is great of application value. Comparing to the traditional MSM algorithm, this paper presents the RMSM algorithm, which is improved by modifying the weights, flexibly using the local data features, and efficiently neighbor searching. Experiments are conducted in a 15 m × 20 m area, showing the obvious improvement of RMSM algorithm which reduces its error by 1. 96 dB and enhances the robustness by 55. 37 %.
出处
《电子技术应用》
2018年第3期103-107,共5页
Application of Electronic Technique
基金
云南省高校频谱传感与边疆无线电安全重点实验室开放课题(C6165903)
