In this paper, we present drive test results for mobile WiMAX system for desert and cosmopolitan terrains where there are few studies reported in the literature. The extensive measurement is performed in the framework...In this paper, we present drive test results for mobile WiMAX system for desert and cosmopolitan terrains where there are few studies reported in the literature. The extensive measurement is performed in the framework of the physical performance of the WiMAX technology which is often considered as a 4G system. Path loss model is fitted for the collected data. The work is unique in the sense that most empirical channel models are produced in regions where the environments (weather, buildings, vegetation, among others) are quite different from the desert terrains that are considered in this study. We also show that shadowing is truly lognormal in dB and the standard deviation values are calculated for the desert terrain from the measurement data. The measurements are collected using WiMAX BS station, with greenpacket dongle, and NEMO versatile outdoor drive test equipment to evaluate and characterize the performance of the system. The received signal strength indicators measured, are analyzed to complement network design and network optimization for regions where the popular models may not be accurate.展开更多
针对传统信号传播路径损耗模型接收的信号强度指示(received signal strength indication,RSSI)测距误差较大,提出了基于反向传播(back propagation,BP)神经网络模型的RSSI测距方法.首先,研究分析传统信号传播路径损耗模型及测距误差;其...针对传统信号传播路径损耗模型接收的信号强度指示(received signal strength indication,RSSI)测距误差较大,提出了基于反向传播(back propagation,BP)神经网络模型的RSSI测距方法.首先,研究分析传统信号传播路径损耗模型及测距误差;其次,利用BP神经网络构建新的路径损耗模型,并将该模型应用到RSSI测距中,对基于BP神经网络模型的RSSI测距方法进行研究;最后,通过实验和MATLAB仿真对测距方法进行验证.仿真结果表明:BP神经网络模型的RSSI测距误差比传统信号传播路径损耗模型的RSSI测距误差要小.展开更多
文摘In this paper, we present drive test results for mobile WiMAX system for desert and cosmopolitan terrains where there are few studies reported in the literature. The extensive measurement is performed in the framework of the physical performance of the WiMAX technology which is often considered as a 4G system. Path loss model is fitted for the collected data. The work is unique in the sense that most empirical channel models are produced in regions where the environments (weather, buildings, vegetation, among others) are quite different from the desert terrains that are considered in this study. We also show that shadowing is truly lognormal in dB and the standard deviation values are calculated for the desert terrain from the measurement data. The measurements are collected using WiMAX BS station, with greenpacket dongle, and NEMO versatile outdoor drive test equipment to evaluate and characterize the performance of the system. The received signal strength indicators measured, are analyzed to complement network design and network optimization for regions where the popular models may not be accurate.
文摘针对传统信号传播路径损耗模型接收的信号强度指示(received signal strength indication,RSSI)测距误差较大,提出了基于反向传播(back propagation,BP)神经网络模型的RSSI测距方法.首先,研究分析传统信号传播路径损耗模型及测距误差;其次,利用BP神经网络构建新的路径损耗模型,并将该模型应用到RSSI测距中,对基于BP神经网络模型的RSSI测距方法进行研究;最后,通过实验和MATLAB仿真对测距方法进行验证.仿真结果表明:BP神经网络模型的RSSI测距误差比传统信号传播路径损耗模型的RSSI测距误差要小.
