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“…The change of the AOA and AOD can be quantified by RMS angle spread (RMS‐AS). It is given by (Czink & Xuefeng, 2005; Zhou, Wang, et al, 2020) $${\sigma}_{\theta}=\sqrt{\overline{{\theta}^{2}}-{\left(\overline{\theta}\right)}^{2}}$$ where $\overline{{\theta}^{2}}$ and $\overline{\theta}$ can be written as $$\overline{{\theta}^{2}}=\frac{\sum _{k}P\left({\theta}_{k}\right){\theta}_{k}^{2}}{\sum _{k}P\left({\theta}_{k}\right)}$$ $$\overline{\theta}=\frac{\sum _{k}P\left({\theta}_{k}\right){\theta}_{k}}{\sum _{k}P\left({\theta}_{k}\right)}$$ where P ( θ k ) and θ k are the power and angle of the k th multipath component. Figure 8 shows that at 1.4725 GHz, when the Tx and Rx are both located in the rectangular tunnel, the RMS‐AS of AOA and AOD is ~13° to 16°; when the Tx and Rx are located in differently shaped sections of the tunnel (and link distance is larger), the RMS‐AS of AOA is 6° to 15° and the RMS‐AS of AOD is 4° to 15°.…”

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“…The change of the AOA and AOD can be quantified by RMS angle spread (RMS‐AS). It is given by (Czink & Xuefeng, 2005; Zhou, Wang, et al, 2020) $${\sigma}_{\theta}=\sqrt{\overline{{\theta}^{2}}-{\left(\overline{\theta}\right)}^{2}}$$ where $\overline{{\theta}^{2}}$ and $\overline{\theta}$ can be written as $$\overline{{\theta}^{2}}=\frac{\sum _{k}P\left({\theta}_{k}\right){\theta}_{k}^{2}}{\sum _{k}P\left({\theta}_{k}\right)}$$ $$\overline{\theta}=\frac{\sum _{k}P\left({\theta}_{k}\right){\theta}_{k}}{\sum _{k}P\left({\theta}_{k}\right)}$$ where P ( θ k ) and θ k are the power and angle of the k th multipath component. Figure 8 shows that at 1.4725 GHz, when the Tx and Rx are both located in the rectangular tunnel, the RMS‐AS of AOA and AOD is ~13° to 16°; when the Tx and Rx are located in differently shaped sections of the tunnel (and link distance is larger), the RMS‐AS of AOA is 6° to 15° and the RMS‐AS of AOD is 4° to 15°.…”

“…• Zhou et al [25] propose a deep neural network (DNN) and a score fusion scheme for classification purposes. Four scenarios related to high-speed railway channels (Rural, Station, Suburban and Multi-link) are classified by using four channel features (K Factor, RMS delay spread, RMS Doppler power spectrum and RMS angular spread).…”

“…• Zhou et al [24] propose a deep neural network (DNN) and a score fusion scheme for classification purposes. Four scenarios related to high-speed railway channels (Rural, Station, Suburban and Multi-link) are classified by using four channel features (K Factor, RMS delay spread, RMS Doppler power spectrum and RMS angular spread).…”