The signal power in wireless communication systems is influenced by various factors, including the environment. These factors include path differences, operational frequency, and environmental conditions. Consequently, designing a communication system that generates a stronger signal is highly challenging. To address this, large-scale path-loss models are employed to estimate the path loss and signal power across different frequencies, distances, and environments. In this paper, we focused on the urban micro, urban macro, and rural macro environments to estimate path loss and signal power at millimeter wave frequencies. We compared the path loss and received power among different path-loss models developed by standard organizations. Simulation results indicate that the fifth-generation channel model provides enhanced path loss and signal power in urban micro environments, while the third-generation partnership project model performs well in urban macro and rural macro environments when compared to other path-loss models.
Keywords: 5G networks; millimeter wave; path-loss models; rural environment; urban environment
The wireless communication field is one of the most successful and rapidly expanding industries due to the rising demand for mobile devices, which is followed by network expansion. By the end of 2025, it is estimated that 20 billion devices will be connected to the mobile network, increasing the amount of wireless data traffic [[
Several studies found that mmWave frequencies have implementation problems, especially with relation to the path loss (PL) and received power (RP) imposed by various parameters such as weather and atmospheric conditions and obstacles in the surroundings [[
In this paper, the effectiveness of different existing propagation models such as 5GCM, 3GPP, METIS, and mmMAGIC at mmWave frequencies between 1 and 100 GHz are considered and compared. The urban micro (UMi), urban macro (UMa), and rural macro (RMa) environments were taken into consideration for line-of-sight (LOS) and non-line-of-sight (NLOS) situations. Because the urban microcellular network provides low latency, high capacity, improved coverage, and improved network reliability, urban and rural macrocellular networks provide extended coverage, improved energy efficiency, and cost effective deployments. Out of these three cellular networks, specific deployment strategies can be selected based on factors such as population density, geographic characteristics, and infrastructure availability. The main goals of this paper are to determine the path loss and received power in urban and rural environments, as well as to estimate the PL and RP at different mmWave frequencies using the various path-loss models that are currently available. An optimized path-loss model can be selected based on the estimated path loss and received power in a given environment. The selected optimized model can be used by the service providers to enhance their network capacity and coverage and energy efficiency. The remainder of the paper is structured as follows: Section 2 explains related work, Section 3 discusses path-loss models, Section 4 contains the results and discussions, and Section 5 contains the conclusion.
The alpha-beta-gamma (ABG) model, the floating intercept (FI) model, and the close-in-free space with distance (CI) model were the three basic path-loss models [[
Single-frequency CI and FI models and multi-frequency ABG and CIF models were used to evaluate the propagation characteristics of two indoor stairwell environments [[
In Ref. [[
In mmWave communication systems, beam management was the major problem due to dense network deployment and directional transmission. Many authors addressed beam management algorithms in the literature to enhance the wireless communication system performance. In Ref. [[
For accurate design and comparison of wireless networks and for their deployment, wireless channel models are necessary, which will simulate signal propagation accurately and efficiently. In this paper, we considered the existing four path-loss models that are introduced by the four major organizations: (i) 5G channel model (5GCM) [[
The UMi path-loss model, PL expression, and parameters range, like shadow fading, carrier frequency, distance, and antenna heights, are listed in Table 2. In the UMi environment, the propagation path is divided into two types: street canyon (SC) and open square (OS).
The large-scale CI with reference distance and ABG models are considered to estimate the PL and RP in LOS and NLOS scenarios. In 5GCM, urban micro-street canyon (UMi-SC) and urban micro-open square (UMi-OS) environments are considered, and PL and RP for these models under LOS and NLOS scenarios are estimated at a frequency range of 6 GHz to 100 GHz. The PL equations of the 5GCM model are shown in Table 1.
In this model, the distance
(
where
Large scale ABG model is used to estimate the PL of UMi-NLOS scenario [[
LOS PL of METIS model depends on break point distance (
(
(
The large-scale ABG model is used to estimate the PL in UMI-NLOS scenario.
This model uses large-scale ABG model to estimate the PL in UMi-LOS and UMi-NLOS scenarios.
The large-scale PL of an urban macro environment is measured using 5GCM, 3GPP, and METIS models. The PL model, PL expression, standard deviation, and various parameters like carrier frequency, distance, and antenna heights are listed in Table 3.
CI and ABG large-scale path-loss models are used to measure the PL in an urban macro environment [[
The LOS UMa PL is measured based on the break point distance and the ABG large-scale path-loss model. Similarly, UMa NLOS PL is measured using the ABG model for a standard deviation of 7.8 dB [[
In this model, LOS and NLOS PL is measured using the large-scale ABG model [[
The PL in a rural macro environment is measured using ITU-RM.2135/3GPP TR 38.901 [[
The large-scale CI model is used to measure the LOS RMa path loss [[
(
The received signal power in UMi, UMa and RMa environments is estimated by [[
(
where
The path loss and received power are influenced by a wide range of variables since each model is unique in its design and features depending on the kind of operating environment it is used in. In this paper, PL and RP are estimated for urban and rural environments, respectively. The simulation results of the UMi, UMa, and RMa environments for mmWave frequencies are presented and analyzed in this section. PL and RP are estimated using the various path-loss models proposed by the standard organizational bodies. Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8 and Figure 9 compare the PL of UMi, UMa, and RMa environments using various models at underutilized mmWave frequency bands of 28 GHz, 38 GHz, 60 GHz and 75 GHz, respectively. It has been found that the measured PL is affected by the distance (R), carrier frequency (f), and height of the BS antenna
Path loss in urban micro, urban macro, and rural macro environments are estimated using the equations shown in Table 1, Table 2 and Table 3. Figure 1, Figure 2, Figure 3 and Figure 4 present the measurement and comparison of path loss in the UMi scenario across different frequencies: 28 GHz, 38 GHz, 60 GHz, and 75 GHz. Among the models, the line-of-sight 5GCM-OS model demonstrates lower path loss, as shown by the findings in Figure 1, Figure 2, Figure 3 and Figure 4. Notably, the line-of-sight mmMAGIC model closely resembles the path loss curve observed in the 5GCM-OS model. In non-line-of-sight propagation, the path loss of 5GCM-OS is slightly higher than that of the 3GPP-SC model for distances less than 150 m, equal at a distance of 150 m, and higher than 3GPP-SC for distances greater than 150 m. Consequently, 5GCM-OS exhibits the lowest path loss beyond 150 m, while 3GPP-SC yields the lowest path loss up to that point. The mmMAGIC-SC model generates the highest path loss in the NLOS scenario.
It can be seen from Figure 1, Figure 2, Figure 3 and Figure 4 that each model's path loss gradually rises with distance and frequency. The 5GCM-OS model exhibits the lowest path loss in both LOS and NLOS scenarios for mmWave frequencies at various distances when compared to other models. Among them, the 3GPP-SC and mmMAGIC-SC models yield the highest path loss in LOS and NLOS scenarios, respectively. In practical applications, network providers seek higher signal power and lower path loss. In the UMi scenario, the 5GCM model produces the least path loss in LOS and NLOS scenarios at variable frequencies and distances. Therefore, the 5GCM model is considered to be an optimal path-loss model, with an optimal distance of 150 m in urban micro scenarios. Table 5 displays the PL values for each model at various frequencies.
Figure 5, Figure 6, Figure 7 and Figure 8 measure and compare the PL of the urban macro environment at 28 GHz, 38 GHz, 60 GHz, and 75 GHz, respectively. Compared to other models, the line-of-sight 5GCM model produces less path loss, which can be observed in Figure 5, Figure 6, Figure 7 and Figure 8. The line-of-sight 3GPP path loss curve closely resembles the path loss in the LOS 5GCM model. In LOS propagation, the path loss of 5GCM is slightly higher than the path loss of 3GPP model if the distance is less than 300 m, it is equal at a distance of 300 m, and if the distance is greater than 300 m, the path loss of 3GPP is higher than the path loss of 5GCM. Therefore, 5GCM creates the lowest path loss after 300 m while 3GPP generates the lowest path loss up to that point. The highest path loss is produced by the 5GCM model in the NLOS scenario.
It can be seen from Figure 5, Figure 6, Figure 7 and Figure 8 that each model's path loss gradually rises with distance and frequency. In comparison to the other models, the 5GCM and 3GPP model generates the lowest path loss in LOS and NLOS scenarios, respectively, and these two models are assumed as optimal path-loss models and the optimal distance is 300 m in urban macro scenarios. The detailed PL values at various frequencies are listed in Table 6.
Figure 9 measure and compare the PL of the rural macro environment at 28 GHz, 38 GHz, 60 GHz, and 75 GHz. It can be seen from Figure 9 and Table 4 that path loss gradually rises with distance and frequency.
Path loss in urban micro cells and urban and rural macro cells can be affected by various factors such as distance, obstacles, interference, base station antenna height, and frequency band. Observations from Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8 and Figure 9 are that the PL in urban micro cells is generally higher than in urban macro and rural macro cells due to the lower base station antenna height. The base station antenna height in urban and rural macro cells is generally higher than that of an urban micro cell due to the difference in their coverage areas and signal propagation characteristics.
Macro cells generally possess a larger coverage area compared to micro cells, requiring base stations to transmit signals over greater distances to cover equivalent regions. By placing the antenna at a considerable distance from the ground, signals can traverse longer distances and cover wider areas. Nevertheless, signal loss can occur in both cell site types due to interference and obstructions, with the extent of loss contingent on the specific deployment and environmental conditions. Therefore, 5GCM and 3GPP models are considered to be optimal path-loss models in urban micro and macro, rural macro environments at an optimal distance of 150 m and 300 m, and 1000 m, respectively. From the simulation results, the advantages and disadvantages of each path-loss model are listed in Table 6.
Received power in urban micro, urban macro, and rural macro environments are estimated using Equation (
It can be seen from Figure 10, Figure 11, Figure 12 and Figure 13 that each model's received power gradually reduces with distance and frequency. In comparison to the other models, the 5GCM-OS model generates the highest power in LOS and NLOS scenarios. The minimum power is produced by the 3GPP-SC and mmMAGIC-SC models, respectively. Table 7 displays the RP values for each model at various distances and frequencies.
Figure 14, Figure 15, Figure 16 and Figure 17 measure and compare the received power of an urban macro environment at 28 GHz, 38 GHz, 60 GHz, and 75 GHz, respectively. Compared to other models, the line-of-sight 5GCM model produces the highest signal power, which will be observed from Figure 14, Figure 15, Figure 16 and Figure 17. The line-of-sight 3GPP curve closely resembles the LOS 5GCM model. In LOS propagation, the RP of 5GCM is slightly higher than the RP of 3GPP model if the distance is less than 300 m, it is equal at a distance of 300 m, and if the distance is greater than 300 m, the power of 3GPP is higher than the power of 5GCM. Therefore, 5GCM creates the lowest signal power after 300 m while 3GPP generates the highest power up to that point. The minimum power is produced by the 5GCM model in the NLOS scenario at all frequencies.
It can be seen from Figure 14, Figure 15, Figure 16 and Figure 17 that each model's received power gradually reduces with distance and frequency. In comparison to the other models, the 5GCM and 3GPP model generates the highest signal power in LOS and NLOS scenarios, respectively. Table 5 displays the RP values for each model at various distances and frequencies.
Figure 18 measure and compare the RP of rural macro environment at 28 GHz, 38 GHz, 60 GHz, and 75 GHz. It can be seen from Figure 18 and Table 5 that received power gradually reduces with distance and frequency. received power in urban micro cells, urban and rural macro cells can be affected by various factors such as distance, obstacles, interference, base station antenna height, and frequency band. Observations from Figure 10, Figure 11, Figure 12, Figure 13, Figure 14, Figure 15, Figure 16, Figure 17 and Figure 18 show that the RP in urban micro cells is generally lower than in urban macro and rural macro cells due to the base station antenna height. The base station antenna height in urban and rural macro cells is generally higher than that of urban micro cells due to the difference in their coverage area and signal propagation characteristics.
This paper investigates various existing path-loss models for mmWave frequency bands to estimate path loss and received power. The results demonstrate that path loss and received power are influenced by factors such as operating frequency, the distance between transmitter and receiver antennas, antenna location, antenna height, and their respective positions. Among the models considered, the 5GCM model is found to minimize path loss and maximize receiver power specifically in the urban micro environment. On the other hand, the 3GPP model is suitable for both urban and rural macro environments, surpassing other models by producing the lowest path loss in those respective environments. These models are recognized as optimal choices for enhancing system performance in terms of path loss. Service providers can leverage these models to improve the quality of service in both indoor and outdoor 5G mmWave wireless networks.
Path loss and signal power in urban and rural environments can be affected by various factors such as distance, obstacles, interference, antenna height, and frequency band. The actual amount of loss will depend on the specific deployment and environmental factors. In this paper, mmWave frequency band, large-scale path-loss models and UMi, UMa, and RMa scenarios are considered to estimate the path loss and signal power. PL and RP are estimated for 28 GHz, 38 GHz, 60 GHz, and 75 GHz using various path-loss models. From the results, it is predicted that the path loss is lower and the signal power is higher in an urban micro scenario than the in urban macro and rural macro scenarios, and out of all four models used, the 5GCM model achieves lower path loss and higher signal power in all environments. In the future, we want to estimate and compare the path loss of urban micro, macro, and rural macro environments using optimization algorithms like GA, PSO, and GWO.
Graph: Figure 1 Path loss of an Urban Micro environment at 28 GHz.
Graph: Figure 2 Path loss of an Urban Micro environment at 38 GHz.
Graph: Figure 3 Path loss of an Urban Micro environment at 60 GHz.
Graph: Figure 4 Path loss of an Urban Micro environment at 75 GHz.
Graph: Figure 5 Path loss of an Urban Macro environment at 28 GHz.
Graph: Figure 6 Path loss of an Urban Macro environment at 38 GHz.
Graph: Figure 7 Path loss of an Urban Macro environment at 60 GHz.
Graph: Figure 8 Path loss of an Urban Macro environment at 75 GHz.
Graph: Figure 9 Path loss of Rural Macro environment.
Graph: Figure 10 Received power of an Urban Micro environment at 28 GHz.
Graph: Figure 11 Received power of an Urban Micro environment at 38 GHz.
Graph: Figure 12 Received power of an Urban Micro environment at 60 GHz.
Graph: Figure 13 Received power of an Urban Micro environment at 75 GHz.
Graph: Figure 14 Received power of an Urban Macro environment at 28 GHz.
Graph: Figure 15 Received power of an Urban Macro environment at 38 GHz.
Graph: Figure 16 Received power of an Urban Macro environment at 60 GHz.
Graph: Figure 17 Received power of an Urban Macro environment at 75 GHz.
Graph: Figure 18 Received power of a Rural Macro environment.
Table 1 Summary of Related Works.
Author & Ref. No. Frequency Environment Observations Future Scope Oladimeji, T.T. et al. [ 24 GHz & 38 GHz Indoor Environment An experimental setup was used to estimate the mean prediction error and standard deviation error. These two errors were used to identify the efficiency of improved path-loss models. Path loss was estimated for a small indoor region, and the authors want to extend it for an outdoor scenario. Zhang, Y. et al. [ 2.02 GHz & 5.8 GHz Urban Scenario Estimated the path loss using machine learning models. Collection of training data, incremental learning, and hyper-parameter optimization are the major problems in path loss estimation using machine learning techniques. Thrane, J. et al. [ 2.6 GHz Urban Macro Scenario Path loss was estimated and compared using traditional channel models and deep learning channel models, and it was observed that the deep learning models provided better path loss compared to the traditional models. Prediction of path loss using deep learning models is more complex compared to the traditional model. Nguyen, C. et al. [ 0.8 GHz to 70 GHz Urban and Suburban environments The authors estimated the path loss at mmWave frequencies using a feed-forward deep neural network model. The proposed model enhanced the mean square error and prediction accuracy. Random search approach was considered for tuning the hyper-parameters. Hasan, R. et al. [ mmWave Bands UMi, UMa, and RMi environments MIMO uniform antenna arrays are used to estimate the angle of arrival, angle of departure, and power delay profiles at mmWave frequency bands. Path loss for different transmitter and receiver distances needs to be calculated for mmWave frequency bands. Lodro, M.M. et al. [ 28 GHz UMi, UMa, and RMa environments Directional and unidirectional antennas are considered in the urban microcell region, and the angle of arrival, angle of departure, and power delay profiles are estimated. The authors wanted to extend it for large-scale path-loss models. Aldhaibani, A.O. et al. [ 26 GHz, 28 GHz, 32 GHz & 38 GHz Indoor Stairwell Environment Path loss, path loss exponent, and standard deviations were estimated for the considered stairwell environments in the indoor region. Path loss estimation for multi-stair buildings is in the future's scope. MacCartney, G.R. et al. [ 28 GHz & 38 GHz Urban microcellular environment The authors estimated path loss in LOS and NLOS scenarios by considering steerable transmitting and receiving antennas in the outdoor environment. Need to implement it for the mmWave and THz frequency bands. Roslee, M.B. et al. [ 400 MHz to 1800 MHz suburban environment Existing Hata model was considered to optimize the path loss. System performance is estimated using the relative error. Path loss estimation and comparison using different path-loss models is needed. Obot, A. et al. [ 150 MHz to 1500 MHz Urban macrocellular environment Path loss was estimated and compared using the free space, Hata and Egli path-loss models. The authors observed that the Hata model is the best path-loss model out of three models. Need to implement it for the mmWave and THz frequency bands. This paper mmWave Frequency band Urban Microcell, Urban Macrocell, and Rural Macrocell In this paper, we estimated and compared path loss using the existing standard path-loss models in the mmWave frequency bands of all possible environments. Wanted to extend the same in the THz frequency region in the future.
Table 2 UMi Path Loss Models.
Model PL [dB] Shadow Fading [dB] Parameters 5GCM SC LOS = 3.76 GHz GHz 5GCM SC NLOS CI Model: = 8.09 GHz GHz ABG Model: = 7.82 5GCM OS LOS = 4.2 GHz GHz 5GCM OS NLOS CI Model: = 7.1 GHz GHz ABG Model: = 7.0 3GPP SC LOS = 4.0 GHz GHz, m m = 10 m where is a break point distance, which is given by Equation (1). 3GPP SC NLOS GHz GHz m m, m m, = 10 m METIS SC LOS = 3.1 GHz GHz, m m = 10 m where and are given by Equations (2) and (3) METIS SC NLOS GHz GHz m m, m m, = 10 m mmMAGIC SC LOS = 2.0 GHz GHz mmMAGICSC NLOS = 7.82 GHz GHz
Table 3 UMa Path Loss Models.
Model PL [dB] Shadow Fading [dB] Parameters 5GCM LOS = 4.1 GHz GHz 5GCM NLOS CI Model: = 6.8 GHz GHz ABG Model: = 6.5 3GPP LOS = 6.0 GHz GHz, m m = 25 m where is a break point distance, which is given by Equation (1). 3GPP NLOS GHz GHz m m, m m, = 25 m METIS LOS = 4 GHz GHz, m m = 25 m where is given by Equation (1) METIS NLOS GHz GHz m m, m m, = 25 m, w = 20 m, h = 20 m
Table 4 RMa Path Loss Models.
Model PL [dB] Shadow Fading [dB] Parameters 3GPP LOS GHz GHz, m m = 4.0 = 35 m, h = 5 m where is a break point distance, which is given by Equation (4). 3GPP NLOS GHz GHz m m, m m, mm, m
Table 5 Path Loss of UMi, UMa and RMa environments at 28 GHz, 38 GHz, 60 GHz and 75 GHz.
Model PL [dB] at R = 100 m PL [dB] at R = 500 m PL [dB] at R = 1000 m 28 38 60 75 28 38 60 75 28 38 60 75 Urban Micro Environment 5GCM-SC LOS 106.7 108.6 112.6 114.5 119.2 121.9 125.9 127.8 125.2 127.8 131.8 133.7 5GCM-OS LOS 104.2 106.0 109.9 111.9 115.6 118.3 123.3 124.2 121.1 123.8 127.7 129.7 3GPP-SC LOS 108.2 110.9 114.9 116.8 122.2 124.8 128.8 130.8 128.4 131.1 135.1 136.9 mmMAGIC-SC LOS 104.8 107.0 111.1 113.1 116.9 119.7 123.9 125.9 122.6 125.1 129.6 131.8 5GCM-SC NLOS 134.2 136.9 140.8 142.8 155.2 157.9 161.9 163.8 164.7 167.3 171.3 173.2 5GCM-OS NLOS 127.3 129.8 133.9 136.0 146.7 149.3 153.3 155.3 156.4 157.9 161.9 163.8 3GPP-SC NLOS 126.9 129.7 133.9 136.0 150.4 153.2 157.2 159.4 160.8 163.7 167.8 169.9 mmMAGIC-SC NLOS 159.7 162.4 166.4 168.3 189.6 192.3 196.2 198.2 203.4 205.6 209.6 211.5 5GCM LOS 106.1 108.9 112.9 114.5 119.6 122.2 126.2 128.2 125.5 128.2 132.1 134.1 3GPP LOS 105.9 108.5 112.5 114.4 120.5 123.2 127.2 129.1 127.0 129.7 133.7 135.6 5GCM NLOS 129.2 132.0 136.0 137.9 19.3 152.0 155.9 157.9 158.3 160.9 164.9 166.8 3GPP NLOS 115.7 118.3 122.3 124.3 141.8 144.4 148.4 150.3 153.4 156.0 159.9 161.9 3GPP LOS 120.9 123.6 127.5 129.5 127.7 130.3 134.3 136.2 135.2 137.9 141.8 143.8 3GPP NLOS 136.5 139.1 143.1 145.0 147.9 150.6 154.6 156.5 159.5 162.1 166.1 168.0
Table 6 Summary of Path loss models.
Parameter 5GCM Model 3GPP Model METIS Model mmMAGIC Model Path Loss in UMi This model provides the least path loss in LOS and NLOS Scenarios This model provides highest path loss in LOS scenario This model provides highest path loss in NLOS Scenario Path Loss in UMa This model provides the least path loss in LOS This model provides least path loss in NLOS scenario Path Loss in RMa Only the 3GPP model is used to estimate the path loss in rural macro cellular networks Advantages This model is specifically designed for 5G networks, incorporating unique characteristics associated with 5G frequency bands and technologies This model utilizes a standardized approach for path loss estimation in cellular networks and also 5G networks This model is specifically designed to estimate spectral efficiency, energy efficiency, improved coverage, and path loss in 5G networks This model is specifically designed for 5G networks and it addresses challenges and opportunities of mmWave frequency bands. This model accurately estimates the path loss in urban micro and macro environments This model estimates the path loss for a wide range of propagation environments and scenarios. This model accurately estimates the path loss in urban micro and macro environments This model accurately estimates the path loss in urban micro and indoor environments Disadvantages It does not support non-mmWave frequency Bands This model does not consider all the real-world problems while estimating the path loss operating frequency range is very small and above 6 GHz it does not support It does not support non-mmWave frequency Bands This model is not well-suited for rural environments This model lacks accuracy in predicting path loss within real-world environments This model is not well-suited for urban macro rural macro environments This model is not well-suited for urban macro rural macro environments
Table 7 Received Power of UMi, UMa, and RMa environments at 28 GHz, 38 GHz, 60 GHz, and 75 GHz.
Model PL [dB] at R = 100 m PL [dB] at R = 500 m PL [dB] at R = 1000 m 28 38 60 75 28 38 60 75 28 38 60 75 5GCM-SC LOS −48.9 −51.6 −55.6 −57.5 −62.2 −64.9 −68.8 −70.8 −68.2 −70.8 −74.8 −76.7 5GCM-OS LOS −46.4 −49.0 −53.0 −54.9 −58.6 −61.3 −65.3 −67.2 −64.1 −66.8 −70.7 −72.7 3GPP-SC LOS −51.2 −53.9 −57.9 −59.8 −65.2 −67.9 −71.8 −73.8 −71.4 −74.1 −78.1 −80.0 mmMAGIC-SC LOS −47.2 −50.0 −53.1 −56.1 −59.9 −62.7 −66.8 −68.9 −65.7 −68.4 −72.6 −74.6 5GCM-SC NLOS −77.2 −79.9 −83.8 −85.8 −98.3 −100.9 −104.9 −106.8 −107.7 −110.3 −114.3 −116.2 5GCM-OS NLOS −69.9 −73.3 −76.9 −79.0 −89.7 −92.3 −96.3 −98.2 −98.3 −100.9 −104.9 −106.8 3GPP-SC NLOS −70.5 −72.7 −76.9 −79.0 −93.4 −96.2 −100.4 −102.5 −103.8 −106.6 −110.9 −112.9 mmMAGIC-SC NLOS −102.7 −105.4 −109.3 −111.3 −132.4 −135.3 −139.2 −141.2 −145.9 −148.6 −152.6 −154.5 Urban Macro Environment 5GCM LOS −49.3 −51.9 −55.9 −57.8 −62.6 −65.2 −69.2 −71.2 −68.5 −71.2 −75.2 −77.1 3GPP LOS −48.9 −51.5 −55.5 −57.4 −63.5 −66.1 −70.1 −72.1 −70.0 −72.7 −76.7 −78.6 5GCM NLOS −72.4 −75.0 −79.0 −80.9 −92.4 −95.0 −98.9 −100.9 −101.3 −103.9 −107.9 −110.0 3GPP NLOS −58.7 −61.4 −65.3 −67.2 −84.5 −87.4 −91.4 −93.3 −96.4 −99.0 −102.9 −104.9 Rural Macro Environment R = 500 m R = 1000 m R = 2000 m 3GPP LOS −60.9 −63.9 −64.2 −72.5 −70.7 −73.3 −77.3 −79.2 −78.2 −80.9 −84.8 −86.6 3GPP NLOS −79.5 −82.1 −86.1 −88.0 −90.9 −93.6 −97.5 −99.5 −102.5 −105.1 −109.0 −111.0
Conceptualization, C.S.; methodology, C.S.; software, C.S.; validation, M.R. and L.L.C.; writing—original draft preparation, C.S. and M.R.; reviewing, editing, and supervision, M.R., A.W., A.F.O. and M.H.J.; funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript.
Not applicable.
The authors declare that there is no conflict of interest in this paper.
By Chilakala Sudhamani; Mardeni Roslee; Lee Loo Chuan; Athar Waseem; Anwar Faizd Osman and Mohamad Huzaimy Jusoh
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