Path Loss Characterization of Outdoor and Indoor Microwave Links

Abstract

실내 및 실외에서 무선 링크의 품질은 사용주파수가 높아질수록 많은 영향을 받는다. 실외의 경우 많은 대기 요인 중 강우강도는 밀리미터파 대역에서 전파경로 손실에 많은 영향을 주고 있으며, 링크버짓 (link budget)을 위해 충분히 고려해야 한다. 실내의 경우 장애물로 인한 산란, 반사, 회절, 분산 및 침투 등이 전파 경로손실의 주요 요인이다. 결과적으로, 전파 경로손실을 예측함으로써 송신 측에서 전력 레벨을 설정하기 위하여 실내 및 실외 전파 경로손실에 대해 아는 것이 중요하다. 따라서 본 논문에서는 실내 및 실외 전파환경에서 경로손실에 대해 연구하였다. 실외 밀리미터파 대역에서의 강우감쇠를 추정하기 위해 강우강도와 수신신호 전력은 4개의 지상 링크를 대상으로 3년간 측정한 데이터를 사용하였으며, 기존에 강우감쇠 예측을 위해 널리 활용되고 있는 ITU-R 530.17, Lin 모델, Revised Silva Mello 모델의 예측값과 비교하였다. 실측값과 예측값의 비교 결과, 차이가 남에 따라 기존 모델보다 더 나은 예측값을 제공하기 위하여 지도 학습 기반 강우감쇠 예측 방법을 제안하였고, 제안된 모델의 예측값과 강우감쇠 실측값을 비교 및 분석 함으로써 제안 모델의 적정성을 확인하였다. 또한, 다른 주파수 대역과 편파에 대한 스케일링 (scaling) 기법을 연구하기 위하여 2013년부터 2015년까지 무궁화위성 6호의 12.25 GHz와 20.73 GHz 대역의 데이터를 사용하였으며, ANN (Artificial Neural Network) 기반의 새로운 스케일링 기법을 제안하였다. 비교 및 분석 결과, 제안한 스케일링 예측 모델이 만족함을 확인하였다. 일반적으로 건물 내 복도 링크에 대한 기존 경로손실 예측 모델은 전파전파 (wave propagation)의 개별적 경로손실 감쇠 요소를 활용하지만 계산하기가 복잡하다. 그리고 열차 터널 내부 경로손실 모델링의 경우, FDTD (Finite Difference Time Domain), CN (Crank Nicolson), VPE (Vector Parabolic Equation), SPE (Scalar Parabolic Equation), UTD (Uniform Theory of Diffraction), RT (Ray-tracing Technique), TDL (Tapped Delay Line)를 활용하고 있으며, 이러한 모든 방법은 계산 절차를 고려할 때 너무 복잡하다. 반면에 large-scale 경로손실 예측 기법은 건물 내 복도와 열차 터널 환경하에서 개별적 경로손실 감쇠 요소 효과를 계산하지 않고도 총체적인 의미에서 감쇠를 예측할 수 있기 때문에 효율적으로 계산할 수 있다. 본 논문에서는 건물 내 복도와 1.7 km 길이의 열차 터널 환경하에서의 전파전파 특성을 분석하기 위해 5G 주파수 대역인 3.7 GHz와 28 GHz에 대해 혼 안테나와 TAS (Tracking Antenna System)를 사용하여 측정하였으며, 실측 데이터는 FI, CI, CIF 및 ABG 모델과 같은 전파전파 예측 모델의 예측값과 비교 및 분석하였다. 터널 환경하에서의 비교 및 분석 결과, 비교적 낮은 전파경로 손실 지수 (n < 2)를 나타내었다. 특히, FI 모델의 예측값이 실측값과 유사함을 확인하였다.|Several disturbances limit the quality of radio links at higher frequency bands in outdoor and indoor usage. In outdoor use, among other atmospheric factors, rainfall is the significant propagation impairment at millimetric wavebands, which needs to be considered during the link budget planning. Moreover, for indoor usage, the blockage material exists along with the transmitted wave that results in scattering, reflection, diffraction, dispersion, and penetration problems that create path loss of the propagated wave. Consequently, it is essential to know the path loss both for the outdoor and indoor microwave links. Because knowing the expected attenuation, it is possible to set the power level at the transmitter side (within the permissible level). This investigation discusses the path loss characteristics in both outdoor and indoor links and proposes new methods of path loss prediction in outdoor and indoor links. Three years of rainfall and received signal power were measured over four terrestrial links to estimate rain attenuation. And using the measured received signal power — three existing attenuation models, ITU-R 530.17, Lin, and revised Silva Mello models, were compared with the measured rain attenuation. It was found that these models did not correspond with the measured results. Therefore, a supervised learning-based attenuation prediction method was proposed, which provides better performance than existing models. Furthermore, the proposed model with measured received signal level and rainfall data at the above-mentioned operating frequencies was validated. Another experiment measured the received power of earth-space links data, which were recorded from 2013 to 2015 through Koreasat 6 satellite at 12.25 and 20.73 GHz. In that experiment, the previously mentioned terrestrial link data and the earth-space links data were validated with the existing ITU-R long-term frequency, and it was found that the current ITU-R long-term frequency scaling technique did not perform well in South Korea. Consequently, a new scaling technique was proposed using artificial neural networks. The experimental results confirm that the proposed artificial neural network (ANN)-based scaling model showed superior performance in predicting attenuation for frequency scaling for outdoor terrestrial and slant links. The existing path loss model for indoor corridor links usually uses the attenuation resulting from individual elements path loss of the propagated wave — which is challenging to compute. And in the case of path loss modeling inside the tunnel, modal expansion, numeric methods Finite Difference Time Domain (FDTD), Crank Nicolson (CN), Vector Parabolic Equation (VPE), Scalar Parabolic Equation (SPE), Uniform Theory of Diffraction (UTD), Ray-Tracing Technique (RT), Tapped Delay Line (TDL) is used. All of these methods are too complex considering the computing procedure. On the other hand, a large-scale path loss technique can predict attenuation in a gross sense without computing individual element effects in the indoor corridors and tunnels — which is computationally efficient. Therefore, large-scale path losses of LOS links in the extended indoor corridor and 1.7 km long train tunnel environments were simulated using real observed path loss datasets at 3.7 and 28 GHz. The minimum mean square error (MMSE) approach was used to evaluate the distance and frequency-dependent optimized coefficients of the large-scale models: close-in (CI) model with a frequency-weighted path loss exponent (CIF), floating-intercept (FI), and alpha-beta–gamma (ABG) models. The outcome of the indoor corridor showed that the large-scale FI and CI models fitted the measured results at 3.7 and 28 GHz. The tunnel data outcome showed a comparatively lower (n<2) path loss exponent inside the tunnel compared to the outdoor environment. In the tunnel, the FI model outperformed all examined models as it yielded a path loss closer to the measured datasets and a minimum standard deviation of the shadow factor.