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NYU WIRELESS The World's First Academic Research Center Combining Wireless, Computing, and Medical Applications Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications Shu Sun, George R. MacCartney, Jr., Theodore S. Rappaport {ss7152,gmac,tsr}@nyu.edu And co-authors Please see IEEE Trans. Vehicular Technology paper of same title  T. S. Rappaport 2016 NYU WIRELESS

Multi-Frequency Path Loss Model Performance Using Results from Industry White Paper UMa LOS Scenario: CI: PL 𝑔 , 𝑒 = 32.4 + 20 𝑚𝑚𝑚 10 𝑒 + 20 𝑚𝑚𝑚 10 𝑔 , 𝜏 = 4.1 dB UMa NLOS Scenario: ABG: PL 𝑔 , 𝑒 = 19.2 + 34 𝑚𝑚𝑚 10 𝑒 + 23 𝑚𝑚𝑚 10 𝑔 , 𝜏 = 6.5 dB Note: f is in GHz and d is in meters. CI: PL 𝑔 , 𝑒 = 32.4 + 30 𝑚𝑚𝑚 10 𝑒 + 20 𝑚𝑚𝑚 10 𝑔 , 𝜏 = 6.8 dB These forms are in 3GPP/ITU style. UMi SC LOS Scenario: CI: PL 𝑔 , 𝑒 = 32.4 + 21 𝑚𝑚𝑚 10 𝑒 + 20 𝑚𝑚𝑚 10 𝑔 , 𝜏 = 3.8 dB K. Haneda et al ., “5G 3GPP-like channel models for outdoor UMi SC NLOS Scenario: urban microcellular and macrocellular environments,” 2016 ABG: PL 𝑔 , 𝑒 = 22.4 + 35 𝑚𝑚𝑚 10 𝑒 + 21 𝑚𝑚𝑚 10 𝑔 , 𝜏 = 7.8 dB IEEE 83rd Vehicular Technology Conference (VTC Spring) , May 2016. [Online]. Available: CI: PL 𝑔 , 𝑒 = 32.4 + 32 𝑚𝑚𝑚 10 𝑒 + 20 𝑚𝑚𝑚 10 𝑔 , 𝜏 = 8.1 dB http://arxiv.org/abs/1602.07533. K. Haneda et al ., “Indoor 5G 3GPP-like channel models for UMi OS LOS Scenario: office and shopping mall environments,” 2016 IEEE CI: PL 𝑔 , 𝑒 = 32.4 + 19 𝑚𝑚𝑚 10 𝑒 + 20 𝑚𝑚𝑚 10 𝑔 , 𝜏 = 4.2 dB International Conference on Communications Workshops (ICCW), May 2016. [Online]. Available: http://arxiv.org/abs/1603.04079 UMi OS NLOS Scenario: ABG: PL 𝑔 , 𝑒 = 3.7 + 41 𝑚𝑚𝑚 10 𝑒 + 24 𝑚𝑚𝑚 10 𝑔 , 𝜏 = 7.0 dB CI: PL 𝑔 , 𝑒 = 32.4 + 29 𝑚𝑚𝑚 10 𝑒 + 20 𝑚𝑚𝑚 10 𝑔 , 𝜏 = 7.1 dB 2

Multi-Frequency Path Loss Model Performance Using Results from Industry White Paper InH Office LOS Scenario: CI: PL 𝑔 , 𝑒 = 32.4 + 17 𝑚𝑚𝑚 10 𝑒 + 20 𝑚𝑚𝑚 10 𝑔 , 𝜏 = 3.0 dB InH Office NLOS Scenario: Single-Slope Models: ABG: PL 𝑔 , 𝑒 = 17.3 + 38 𝑚𝑚𝑚 10 𝑒 + 25 𝑚𝑚𝑚 10 𝑔 , 𝜏 = 8.0 dB CIF: PL 𝑔 , 𝑒 = 32.4 + 32 ∗ (1 + 0.06 ∗ ( 𝑔 − 24.2)/24.2) ∗ 𝑚𝑚𝑚 10 𝑒 + 20 𝑚𝑚𝑚 10 𝑔 , 𝜏 = 8.3 dB InH Office NLOS Scenario: Dual-Slope Models: 33.0 + 17 𝑚𝑚𝑚 10 𝑒 + 25 𝑚𝑚𝑚 10 𝑔 , 1 m < 𝑒 < 6.9 m ABG: PL 𝑔 , 𝑒 = � 33.0 + 17 𝑚𝑚𝑚 10 6.9 + 25 𝑚𝑚𝑚 10 𝑔 + 42 𝑚𝑚𝑚 10 𝑒 /6.9 , 𝑒 > 6.9 m 𝜏 = 7.8 dB CIF: PL 𝑔 , 𝑒 = 32.4 + 25 ∗ (1 + 0.12 ∗ ( 𝑔 − 24.1)/24.1) ∗ 𝑚𝑚𝑚 10 𝑒 + 20 𝑚𝑚𝑚 10 𝑔 , 1 m < 𝑒 < 7.8 m 32.4 + 25 ∗ (1 + 0.12 ∗ ( 𝑔 − 24.1)/24.1) ∗ 𝑚𝑚𝑚 10 7.8 + 20 𝑚𝑚𝑚 10 𝑔 + 43 ∗ (1 + 0.04 ∗ ( 𝑔 − 24.1)/24.1) ∗ 𝑚𝑚𝑚 10 𝑒 /7.8 , 𝑒 > 7.8 m 𝜏 = 7.7 dB � Note: f is in GHz and d is in meters. These forms are in 3GPP/ITU style. K. Haneda et al ., “5G 3GPP-like channel models for outdoor urban microcellular and macrocellular environments,” 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring) , May 2016. [Online]. Available: http://arxiv.org/abs/1602.07533. K. Haneda et al ., “Indoor 5G 3GPP-like channel models for office and shopping mall environments,” 2016 IEEE International Conference on Communications Workshops (ICCW), May 2016. [Online]. Available: http://arxiv.org/abs/1603.04079 3

Multi-Frequency Path Loss Model Performance Using Results from Industry White Paper InH Shopping Mall LOS Scenario: CI: PL 𝑔 , 𝑒 = 32.4 + 17 𝑚𝑚𝑚 10 𝑒 + 20 𝑚𝑚𝑚 10 𝑔 , 𝜏 = 2.0 dB InH Shopping Mall NLOS Scenario: Single-Slope Models: ABG: PL 𝑔 , 𝑒 = 18.1 + 32 𝑚𝑚𝑚 10 𝑒 + 22 𝑚𝑚𝑚 10 𝑔 , 𝜏 = 7.0 dB CIF: PL 𝑔 , 𝑒 = 32.4 + 26 ∗ (1 + 0.01 ∗ ( 𝑔 − 39.5)/39.5) ∗ 𝑚𝑚𝑚 10 𝑒 + 20 𝑚𝑚𝑚 10 𝑔 , 𝜏 = 7.4 dB InH Shopping Mall NLOS Scenario: Dual-Slope Models: 22.2 + 29 𝑚𝑚𝑚 10 𝑒 + 22 𝑚𝑚𝑚 10 𝑔 , 1 m < 𝑒 < 147.0 m ABG: PL 𝑔 , 𝑒 = � 22.2 + 29 𝑚𝑚𝑚 10 147.0 + 22 𝑚𝑚𝑚 10 𝑔 + 115 𝑚𝑚𝑚 10 𝑒 /147.0 , 𝑒 > 147.0m 𝜏 = 6.4 dB CIF: PL 𝑔 , 𝑒 = 32.4 + 24 ∗ (1 − 0.01 ∗ ( 𝑔 − 39.5)/39.5) ∗ 𝑚𝑚𝑚 10 𝑒 + 20 𝑚𝑚𝑚 10 𝑔 , 1 m < 𝑒 < 110 m � 32.4 + 24 ∗ (1 − 0.01 ∗ ( 𝑔 − 39.5)/39.5) ∗ 𝑚𝑚𝑚 10 110 + 20 𝑚𝑚𝑚 10 𝑔 + 84 ∗ (1 + 0.39 ∗ ( 𝑔 − 39.5)/39.5) ∗ 𝑚𝑚𝑚 10 𝑒 /110 , 𝑒 > 110 m 𝜏 = 6.3 dB Note: f is in GHz and d is in meters. These forms are in 3GPP/ITU style. K. Haneda et al ., “5G 3GPP-like channel models for outdoor urban microcellular and macrocellular environments,” 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring) , May 2016. [Online]. Available: http://arxiv.org/abs/1602.07533. K. Haneda et al ., “Indoor 5G 3GPP-like channel models for office and shopping mall environments,” 2016 IEEE International Conference on Communications Workshops (ICCW), May 2016. [Online]. Available: http://arxiv.org/abs/1603.04079 4

Multi-Frequency Path Loss Model Performance in the UMa Scenario The ABG model has noticeable errors at close-in distances, i.e., it predicts much less path loss compared with free space, as well as the CI and CIF models S. Sun et al ., ”Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications,” IEEE Transactions on Vehicular Technology , Mar. 2016. 5

Multi-Frequency Path Loss Model Performance in the UMa Scenario The ABG model over estimates path loss (i.e., underestimates interference) at large distances (e.g. 1 km) compared with the CI/CIF model The CI/CIF model is more conservative when analyzing interference-limited systems at large distances and more realistic when modeling signal strengths at close-in distances. S. Sun et al ., ”Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications,” IEEE 6 Transactions on Vehicular Technology , Mar. 2016.

Multi-Frequency Path Loss Model Prediction Accuracy and Sensitivity Analysis Parameters of the ABG, CI, and CIF path loss models for prediction in distance Shadow fading standard deviation of the ABG, CI, and CIF path loss models for prediction when the prediction set is closer to the transmitter in the UMa scenario in distance when the prediction set is closer to the transmitter in the UMa scenario ABG: Large and unstable shadow fading standard deviation; Significant variation of model parameters CI/CIF: Small and stable shadow fading standard deviation; Little variation of model parameters S. Sun et al ., ”Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications,” IEEE 7 Transactions on Vehicular Technology , Mar. 2016.

Multi-Frequency Path Loss Model Prediction Accuracy and Sensitivity Analysis Parameters of the ABG, CI, and CIF path loss models for prediction in distance Shadow fading standard deviation of the ABG, CI, and CIF path loss models for prediction when the prediction set is closer to the transmitter in the UMi scenario in distance when the prediction set is closer to the transmitter in the UMi scenario ABG: Large and unstable shadow fading standard deviation; Significant variation of model parameters CI/CIF: Small and stable shadow fading standard deviation; Little variation of model parameters S. Sun et al ., ”Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications,” IEEE 8 Transactions on Vehicular Technology , Mar. 2016.

Multi-Frequency Path Loss Model Prediction Accuracy and Sensitivity Analysis Shadow fading standard deviation of the ABG, CI, and CIF path loss models for prediction Parameters of the ABG, CI, and CIF path loss models for prediction in distance in distance when the prediction set is closer to the transmitter in the InH office scenario when the prediction set is closer to the transmitter in the InH office scenario ABG: Large and unstable shadow fading standard deviation; Significant variation of model parameters CI/CIF: Small and stable shadow fading standard deviation; Little variation of model parameters S. Sun et al ., ”Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications,” IEEE 9 Transactions on Vehicular Technology , Mar. 2016.