Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications

By Shu Sun, Theodore Ted S. Rappaport, Timothy A. Thomas, Amitava Ghosh, Huan Cong Nguyen, István Zsolt Kovács, Ignacio Rodriguez, Ozge Hizir Koymen, Andrzej Partyka. IEEE Transactions on Vehicular Technology, 2016

462 citations recorded by the source corpus.

This paper compares three candidate large-scale propagation path loss models for use over the entire microwave and millimeter-wave (mmWave) radio spectrum: the alpha-beta-gamma (ABG) model, the close-in (CI) free-space reference distance model, and the CI model with a frequency-weighted path loss exponent (CIF). Each of these models has been recently studied for use in standards bodies such as 3rd Generation Partnership Project (3GPP) and for use in the design of fifth-generation wireless systems in urban macrocell, urban microcell, and indoor office and shopping mall scenarios. Here, we compare the accuracy and sensitivity of these models using measured data from 30 propagation measurement data sets from 2 to 73 GHz over distances ranging from 4 to 1238 m. A series of sensitivity analyses of the three models shows that the four-parameter ABG model underpredicts path loss when relatively close to the transmitter, and overpredicts path loss far from the transmitter, and that the physically based two-parameter CI model and three-parameter CIF model offer computational simplicity, have very similar goodness of fit (i.e., the shadow fading standard deviation), exhibit more stable model parameter behavior across frequencies and distances, and yield smaller prediction error in sensitivity tests across distances and frequencies, when compared to the four-parameter ABG model. Results show the CI model with a 1-m reference distance is suitable for outdoor environments, while the CIF model is more appropriate for indoor modeling. The CI and CIF models are easily implemented in existing 3GPP models by making a very subtle modification - by replacing a floating non-physically based constant with a frequency-dependent constant that represents free-space path loss in the first meter of propagation. This paper shows this subtle change does not change the mathematical form of existing ITU/3GPP models and offers much easier analysis, intuitive appeal, better model parameter stability, and better accuracy in sensitivity tests over a vast range of microwave and mmWave frequencies, scenarios, and distances, while using a simpler model with fewer parameters.

Subfields: Electrical and Electronic Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/18bf63348b14489a34431ceb7a1811241b7d4239

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Citations in the corpus

Wireless Communications and Applications Above 100 GHz: Opportunities and Challenges for 6G and Beyond (2019, background): “such as provided in [133] has not yet been performed for indoor sub-THz channels) [4], [132], [134], [135].”
Overview of Millimeter Wave Communications for Fifth-Generation (5G) Wireless Networks—With a Focus on Propagation Models (2017, methodology): “TR 38.901 models omnidirectional path loss from 0.5–100 GHz, but lacks measurement validation in some cases. b) 5GCM: There are three UMa path loss models used in [12]: CI, CIF, and ABG [28], [84].”
Learning the MMSE Channel Estimator (2017, background): “Since a large array gain is essential in such setups, there is currently a lot of research going on concerning the modeling and verification of massive MIMO and/or millimeter wave channels [5], [6] and the question how these models can aid channel estimation [7].”
3-D Millimeter-Wave Statistical Channel Model for 5G Wireless System Design (2016, result): “Previous results yielded directional and omnidirectional path loss models in dense urban LOS and NLOS environments [12], [27], [32], [33], temporal and spatial channel parameters, such as cluster and angular spread statistics, and statistical distributions at 28 and 73 GHz based on measurements and…”
5G: A Tutorial Overview of Standards, Trials, Challenges, Deployment, and Practice (2017, background): “There often is no strong frequency dependence beyond the f 2 dependence of free-space pathloss [60], [61].”