Abstract
Compared with the line-of-sight (LOS) condition, the multipath effect is more serious in the non-line-of-sight (NLOS) condition. Therefore, the LOS and NLOS identification is necessary for the multipath analysis of signal propagation. The commonly used method is the support vector machine (SVM) method with high computational complexity. To tackle this problem, this paper adopts the SVM classifier based on fewer selected features of the normalized power delay profile (PDP). Therein, the PDP can be obtained using the sliding correlation method. The results show that the SVM-based classifier can achieve high accuracy on LOS and NLOS identification. We then analyze the impact of the signal-to-noise ratio (SNR) and transmitting-receiving (Tx-to-Rx) distance on distinguishable multipaths under LOS and NLOS conditions. According to statistical measurement results, a function of distinguishable multipath numbers is established. Finally, we investigate the multipath power and delay parameters of average delay spread and root mean square (RMS) delay spread based on multipath results. The outcomes of this paper provide a useful support for analyzing signal propagation characteristics.
Highlights
For the ultrawideband (UWB) system, the support vector machines (SVMs) are developed in [14,15,16], with the results showing that the NLOS condition can be effectively identified based on UWB signal features
We investigate the CDF of the multipath power in the normalized power delay profile (PDP), which is shown in Figure 8. e overall observation from this figure, in comparison to the NLOS condition, shows that the multipath power of the LOS condition is higher
The SVM classifier was adopted to identify LOS and NLOS conditions. e results showed that the SVM with four conventional kernel functions can achieve a high identification accuracy
Summary
Communication systems can be developed in various environments such as tunnels instead of being limited to free space [1, 2]. erefore, it is necessary to explore signal propagation characteristics in narrow spaces [3, 4]. For the ultrawideband (UWB) system, the support vector machines (SVMs) are developed in [14,15,16], with the results showing that the NLOS condition can be effectively identified based on UWB signal features. We adopt the SVM-based method to identify LOS and NLOS conditions for the tunnel environment. (i) We only select four features to identify LOS and NLOS conditions with the SVM method for the tunnel environment, which significantly reduces the computational complexity. (ii) We analyze the change in distinguishable multipaths by comparing the normalized PDP of measurement data under LOS and NLOS conditions.
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