Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review
Abstract
:1. Introduction
- We compared the applicability of machine learning models against existing current 5G empirical models at the high-band frequency spectrum.
- We evaluated the applicability of these models in the context of coexistence studies in the selected mid-band and high-band spectrums.
- Considerations for potential scenarios involving additional environmental factors were considered when examining the study of frequency band propagation analysis that are candidates for 6G systems.
2. Channel Propagation Characteristics
2.1. Characteristics of Mid-Band and High-Band Frequency Spectra
2.1.1. Short Wavelength
2.1.2. Abundant Bandwidth
2.1.3. Propagation Loss
2.2. Path Loss in Wireless Communications
3. Path Loss Models
3.1. Empirical Models
3.1.1. Early Empirical Models
- A.
- Okumura Model
- B.
- Okumura–Hata Model
- C.
- COST-231 Hata Model
3.1.2. Current Empirical Models
- A.
- 3GPP TR 38.901 Model [39]
- i.
- ii.
- B.
- Close-In (CI) Free Space Reference Distance Path Loss Model [23]
- C.
- Alpha–Beta–Gamma (ABG) Model [23]
- D.
- FI Model [46]
3.2. Machine Learning Models
4. Performance Metrics for Path Loss Models
- A.
- Mean Square Error (MSE)
- B.
- Mean Absolute Error (MAE)
- C.
- Mean Error (ME)
- D.
- R2 Score
5. Reviewed Papers on Empirical-Based Path Loss Models
5.1. Performance Evaluation of Empirical Path Loss Models in Urban Environment
5.2. Performance Evaluation of Empirical Path Loss Models in Indoor Environment
6. Reviewed Papers on Machine-Learning-Based Path Loss Models
6.1. Assessment of Machine Learning Path Loss Models in Outdoor Urban Environments
6.2. Evaluation of High-Band Machine Learning Path Loss Models in Urban Environments
6.3. Evaluation of Indoor Machine Learning Path Loss Models
7. Open Research Issues
7.1. Research Gaps
- i.
- Dynamic urban environments: Urban environments are highly dynamic, with changes in building layouts, vegetation, and infrastructure occurring frequently. The current empirical models, such as the ABG, CI, FI, and 3GPP, may not be able to account for these dynamic changes adequately, resulting in inaccuracies in path loss predictions. Integrating real-time or dynamic elements into empirical models is a research gap that needs to be addressed. Hence, they need to be improved to fit the worst-case scenario of the urban environment and to make them reliable in any other urban environment. The current empirical models may not adequately capture or account for these interference and multipath effects, leading to inaccurate predictions.
- ii.
- Inadequate integration of machine learning techniques: Machine learning techniques, such as the random forest model and neural networks, have shown promise in improving path loss prediction accuracy across the considered mid-band and high-band frequency spectrums. However, limited research exists on effectively integrating these techniques into empirical models for path loss in the mid-band frequency spectrum in urban environments. Exploring the potential of machine learning-based approaches and their integration into existing models is an important research gap.
- iii.
- Lack of validation in real-world scenarios: Empirical models developed for predicting path loss in mid-band and high-band frequencies in urban and suburban environments may not have been extensively tested and validated in real-world scenarios. The absence of comprehensive field measurement and validation studies can introduce uncertainties and limit the reliability and accuracy of the models.
- iv.
- Inadequate consideration of complex environment: The empirical models that were used did not sufficiently account for factors such as high-rise buildings, vegetation, and diverse land use patterns, leading to inaccuracies in path loss estimation. Developing models that can accurately capture and incorporate the characteristics of complex urban and suburban environments is a significant research gap.
- v.
- Lack of relevant features on labeled training data: There is a shortage of relevant features on labeled datasets, as in the case of the random forest model that outperformed other chosen models [77,83]. This scarcity of relevant features that greatly impact the prediction poses a significant research gap as it hinders the development and deployment of an accurate model in these scenarios. The result should be modeled with multiple parameters and examined in a commercial environment with more obstructions to accurately determine the stability of the models.
7.2. Future Direction
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Abbreviation | Definition |
---|---|
Angle of elevation | |
3GPP | 3rd Generation Partnership Project |
5G | 5th Generation |
ABG | Alpha–Beta–Gamma |
CI | Close-In reference |
2D distance between Tx and Rx | |
3D distance between Tx and Rx | |
Reference distance | |
dB | Decibel |
Central frequency | |
FI | Float intercept |
FSPL | Free space path loss |
GHz | Gigahertz |
Antenna height for the base station | |
Antenna height for the user terminal | |
IMT | International mobile telecommunication |
ITU | International Telecommunication Union |
LOS | Line-of-sight |
MHz | Megahertz |
mm-Wave | Millimeter wave |
MIMO | Multiple-input multiple-output |
NLOS | Non-line-of-sight |
PL | Path loss |
Path loss for urban macro and line-of-sight scenario | |
Path loss for urban macro and non-line-of-sight scenario | |
Rx | Receiver |
T-R | Transmitter to receiver |
Tx | Transmitter |
User terminal | |
WRC | World Radio Communication Conference |
Country/Region | Frequency Band | Frequency | Auction Status |
---|---|---|---|
China | n41 | 2.515–2.675 GHz | Auctioned |
n78 | 3.4–3.6 GHz | Auctioned | |
n79 | 4.8–4.9 GHz | Auctioned | |
n258 | 24.75–27.5 GHz | Upcoming | |
Finland | n78 | 3.41–3.8 GHz | Auctioned |
n258 | 25.1–27.5 GHz | Auctioned | |
France | n78 | 3.4–3.8 GHz | Auctioned |
n257 | 26.5–27.5 GHz | Upcoming | |
Germany | n78 | 3.4–3.7 GHz | Auctioned |
n258 | 24.25–27.5 GHz | Upcoming | |
Ireland | n78 | 3.4–3.8 GHz | Auctioned |
n258 | 26 GHz | Upcoming | |
Italy | n78 | 3.6–3.8 GHz | Auctioned |
n258 | 26.5–27.5 GHz | Auctioned | |
- | 700 MHz | Auctioned | |
Russia | n40 | 2.3–2.4 GHz | Upcoming |
n79 | 4.4–4.99 | Auctioned | |
n248 | 24.25–27.5 GHz | Upcoming | |
Spain | n78 | 3.4–3.6 GHz | Auctioned |
n78 | 3.6–3.8 GHz | Upcoming | |
United Kingdom | n78 | 3.4–3.8 GHz | Auctioned |
n258 | 24.25–27.5 GHz | Upcoming | |
USA | n258 | 27.5–28.35 GHz | Auctioned |
n258 | 24–47 GHz | Auctioned | |
Japan | n78 | 3.6–3.8 GHz | Auctioned |
n79 | 4.4–4.9 GHz | Auctioned | |
n258 | 27.5–29.5 GHz | Upcoming | |
Republic of Korea | n78 | 3.4–3.7 GHz | Auctioned |
n258 | 26.5–29.5 GHz | Upcoming | |
n79 | 4.8–5.0 GHz | Auctioned |
Ref. | Freq. (GHz) | Model | Scenario | Environment | Area of Focus/Methodology | Important Results | Drawback |
---|---|---|---|---|---|---|---|
[61] | 3.5 | Model fit, FSPL, and ITU-R | LOS and NLOS | Indoor | Conduction of large-scale fading, using an omnidirectional antenna and a spectrum analyzer under two different scenarios. | The FSPL overestimates path loss whereas the ITU-R model performed better, therefore recommended to be used. | It will be necessary to conduct extensive measurement campaigns across similar structures to obtain a representative model. |
[62] | 28 | Grey model, 5GCM, 3GPP, METIS, and mmMAGIC | LOS and NLOS | Outdoor–urban | The suggested path loss model was tested against four 5G empirical models after being trained using measured path loss data. | In contrast to the linear regression model, it is discovered that the proposed model has a good prediction. | There is need to test the comparative analysis beyond the mean absolute error (MAE) |
[48] | 14, 18, and 22 | CI and FI | LOS and NLOS | Indoor | Measurement campaigns were carried out to examine the models and path loss exponent (PLE). | The LoS comparison demonstrates that for the chosen frequency bands, the two models produce precise estimates that fit the actual measured data. | The impact of the materials surrounding the symmetry of the environment were not considered. |
[47] | 28 | CI, FI, and RMSE | LOS and NLOS | Outdoor–urban | Two 5G models were employed to evaluate the best path loss model. Additionally, five distinct path loss scenarios were analyzed during this process. | The FI model performed better with the lowest value of RMSE. | A live measurement campaign should be carried out for proper investigation |
[66] | 26 | 3GPP, ABG, and CI | LOS and NLOS | Outdoor–rural | A comprehensive measurement campaign was conducted in two rural areas using a crane to assess path loss at transmite antenna heights of 30, 50, and 70 m. | The height of the cell site antenna appears to be a crucial design factor for network planning. | There is need to test the models in another frequency to know the stability at multiple frequencies. |
[67] | 28 | FSPL, CIB, and CI | LOS | Outdoor–urban | Based on the data collected during measurement campaigns, some selected models were employed to look into the channel loss for the 5G system. | The CIB path loss model is suitable for the LOS scenarios as it aligns with the data from the environment. | There is a need to investigate the effect of return and mismatch losses along the feed line. |
[68] | 28, 38, 60, 73, 100, and 120 | NYUSIM and CI | LOS and NLOS | Urban microcell | Large-scale simulation analysis on geometric parameters and environmental conditions for the proposed millimeter wave channels. | Geometric parameters and external factors affect the statistical channel modeling’s parameters. | For the suggested model’s performance to be verified and assessed, more experimental data are needed. |
[69] | 1.8, 3.5, and 28 | FI and CI | LOS | Outdoor–urban | Modeling of path loss from wideband measurement campaign. | A guiding effect was noticed in the 1.8 GHz frequency band, which is not observed in other bands. | To check the stability of the models, a non-line-of-sight (NLoS) scenario should be taken into account. |
[70] | PEF and Log-distance | LOS | Suburban | The “drive test” and “walk test” experiments were conducted to investigate propagation loss along distinct paths. | With an RMSE that was 1.4 dB lower, the suggested model performed better than the log-distance model. | The proposed model needs to be tested on other frequency bands to accurately determine the stability of the models. | |
[71] | 28 | CI, FI, and ZMS | LOS and NLOS | Indoor | Simulation for all possible polarization at NLoS and LoS scenarios per meter over 47 m. | The straightforward model that is suggested, which only has one parameter called ZMS, can forecast expansive path loss across distance. | To develop a model that is representative, a comprehensive measuring campaign across comparable buildings will be necessary. |
[42] | 1.8 | FSPL and COST-231 Hata | LOS | Urban | With the dataset gathered from drive tests, the proposed model was tuned using the magnetic optimization algorithm (MOA). | With a lower RMSE value, the proposed augmented model outperformed and was more representational of the data than its traditional counterparts. | The magnetic optimization algorithm that was used has deficiencies in handling non-linearity and may suffer from convergence issues to provide an accurate model. |
[72] | 2.5 | FSPL, SUI, Okumura, and COST-231 Hata. | LOS | Urban | Five different empirical models were tested with actual data measurements to find the most performed model to predict path loss. | The COST-231 Hata model proved to be more suitable than the other chosen models, with a minimal RMSE of 5.27 dB. | The COST-231 Hata model needs to be fine-tuned to suit the special scenario of the urban environment that comprises old and modern buildings. |
Reference | Freq. (GHz)/Scenario | ML. Algorithm | Input Features | Performance Indicators | Important Results | Limitations/Area of Improvement |
---|---|---|---|---|---|---|
[74] | 28/ Urban | CNN | Tx, Rx, floor plan image matrix | RMSE | The proposed model outperformed the chosen models, demonstrating a root mean square error of 8.59 dB. | To accurately determine the model stability, the result should be modeled with multiple parameters. |
[75] | 3.5/ Urban | D-CNN | Satellite image, pl exponent, and shadow factor | MSE | The results made public demonstrate a high degree of real-time channel parameter prediction accuracy. | In commercial environments with more impediments, the path loss model needs to be tested. |
[76] | 28/ Urban | RF | Tx power, Tx antenna gain, terrain profile, and site coordinates | RMSE and cost time | This study recommends employing the RF model, as it was proven to be reliable with high accuracy. | There is a need to compare the proposed model with any of the widely used models in the same environment to test its validity. |
[77] | 3.7/ Rural | ANN, RF, SVR, and B-kNN | Distance between Tx and Rx, Tx height, and Rx height | RMSE, ME, MAPE, MAE, and σ | The ML models outperformed the empirical ones with remarkably low RMSE on the order of 4.2 to 4.3 dB after a comparison between the proposed ML models and those of the chosen empirical models. | It only takes path loss and distance into consideration. Another important element that must be taken into consideration is frequency. |
[78] | 28/ Indoor | CNN | LAMS images | RMSE | The suggested model solved the few-shot data problem and implemented path loss prediction in a smart factory. | There is a need to test the proposed model in a non-line-of-sight scenario to deduce its stability in the indoor environment. |
[79] | 2.5/Suburban, Urban | ANN | 3D locations, frequency, transmitted and receiver power, antenna information, and feeder loss | AME, MAE, STD, and TR | These PL prediction models become more accurate and stable when environmental data are included, with unweighted rectangular environmental features performing better. | The suggested model’s viability needs to be examined in a commercial environment with a higher level of obstruction. |
[80] | 28/Suburban, Urban | AE—CNN | GPS Tx and Rx coordinates, DE-LAMS image size, and Google map image matrix | RMSE | Modern deterministic and empirical techniques cannot match the proposed innovative AE-CNN path loss model in suburban environments. | Improvements must be made to the suggested model’s performance in the line-of-sight (LoS) situation. |
[81] | 2.1/ Urban | KNN, SVR, RF, and AdaBoost | Vertical and horizontal coordinates (x,y) for FBS height | RMSE, MAE, and MAPE | The most accurate predictions came from the tree-based ensemble models, with AdaBoost achieving the lowest MAPE value of 2.72%. | Distinct scenarios should be examined for the comparative examination of the models. |
[82] | 2.2, 4.7, and 26.4/Urban | RNN | Path loss and gate layer | RMSE | With an RMSE of 2dB, the suggested method outperformed the standard method for predicting path loss using LSTM, a type of RNN utilized in time series prediction. | To verify the performance of the suggested model at various frequencies, more performance indicators should be implemented. |
[83] | 1.5/ Urban | XGBoost, CNN | Data in tabular form, pictures (Tr_to_R_area), and pseudo images. | MAE, MAPE, and RMSE | The proposed strategy produced superior results than prior fusion methods, with an MAE value of 3.07 dB as opposed to the 3.15 dB of the traditional bimodal approaches. | For the performance of the suggested model to be verified, more experimental data are needed. |
[84] | 3.5/ Urban | D-NN, ABG, and CI | Tx height, Tx-Rx pair separation, and path profile. | RMSE | According to simulation data, the proposed model performs better than traditional models and has an accuracy of 72%. | There is a need to investigate the applicability of the proposed model in a commercial environment. |
[85] | 2.5/ Urban | DL | 3D image | MAE, MAPE, and RMSE | It has been demonstrated that for lower transmitter heights, texture’s influence is more significant. The features are consistently provided by the SFTA algorithm. | It will be necessary to conduct an extensive measurement campaign to obtain a representable model. |
[86] | 28/ Urban | 3D—CNN | 3D—LAMS image | RMSE | When the data were extracted into a 40 m square, the best performance was attained. | To know the stability at multiple frequencies, the result should be modeled with more input features. |
[87] | 3.5/ Urban | CNN | Building height, image, and distance from Tx and Rx | RMSE | The region where the NN model’s estimation accuracy declined was concentrated close to Tx, according to the proposed CNN model, which was built using the same principle as the NN model. | To validate the performance of the suggested model at various frequencies, performance measures should go beyond RMSE. |
[88] | 5.9/ Urban | MLP, CNN, and RF | Coordinates for Tx and Rx, the number of buildings on the path, the distances covered inside and outside of structures, the widths of the streets where Tx and Rx are located, and the separations between Tx and Rx from side corners | MAE, MAPE, and RMSE | RF performed better than the MLP model, which had a maximum RMSE value of 11 ns, among the machine learning models used to characterize the impacts of radio wave propagation in dynamic vehicle situations. | Testing the proposed model in an environment with more obstacles, such as a commercial one, is necessary. |
[31] | 28/ Urban | LR, MLR1, and MLR2 | Distance between T and R, time delay, received power, RMS delay spread, azimuth and elevation AoDs, and elevation AoAs | MAE, MSE, RMSE, and R-squared | Prediction of the model for new communication scenarios with the reduction in the required number of measurements and complexity. | For the proposed model’s performance to be verified, more input features are needed. |
[89] | 60/ Urban | CNN and MLP | 3D image | MAE, RMSE, and RMSLE | The suggested model, which utilizes building footprint and top-view photos to forecast path loss, was presented. | Training features from a physical measurement campaign are required to validate the performance of the model. |
[90] | 3.5/ Urban | GLMs, NNs, and k-NN | Image, TX power, and coordinates of the transmitter | MAE | Simpler models with higher performance and lower computational cost are GLM and KNN. | More performance indicators should be used to validate each model’s performance for comparison. |
[91] | 2.5/ Indoor | LR and ANN | Reflections of the ground, ceiling, and walls | MSE and MAE | With the lowest MSE and MAE values, the ANN model outperformed the linear model in terms of performance. | It just takes into account the surface of the reflection area. Another important factor that needs to be taken into account is obstruction. |
[92] | 2.3/ Indoor | GPR, LG, and KNN | Received power, T-R separation distance, elevation, and azimuth AoD | MSE and MAE | A multi-dimensional GPR-based model that is capable of estimating path loss was proposed. | Varied environments need to be used to test the stability of the proposed model. |
[93] | 7/ Urban | NN, FI, CI, WINNER II, and 3GPP | 3D distance between Tx and Rx, center frequency, Tx height, Rx height, latitude, longitude, and satellite image | RMSE | When compared to the selected traditional models, the proposed model provided superior accuracy in predicting path loss. | The model needs to be improved in the context of the environment with more obstructions. |
[94] | 1.8/ Urban | RF | Cell distance, vertical angle, horizontal angle from Rx, total height of Tx, total height of Rx, road width, and height of nearby buildings | MAPE and RMSE | Employing hyperparameter tuning for the suggested model leads to enhanced predictive accuracy performance. | Exploratory data analysis (EDA) is important and should be improved on the available data. |
[95] | 3.5/ Urban | RF | Geographical coordinates, distance, azimuth, and antenna gain | MAE and RMSE | The use of an RF model with the given attributes presents better prediction accuracy | Enhancing the model’s performance requires the inclusion of supplementary input features. |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Shaibu, F.E.; Onwuka, E.N.; Salawu, N.; Oyewobi, S.S.; Djouani, K.; Abu-Mahfouz, A.M. Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review. Future Internet 2023, 15, 362. https://doi.org/10.3390/fi15110362
Shaibu FE, Onwuka EN, Salawu N, Oyewobi SS, Djouani K, Abu-Mahfouz AM. Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review. Future Internet. 2023; 15(11):362. https://doi.org/10.3390/fi15110362
Chicago/Turabian StyleShaibu, Farouq E., Elizabeth N. Onwuka, Nathaniel Salawu, Stephen S. Oyewobi, Karim Djouani, and Adnan M. Abu-Mahfouz. 2023. "Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review" Future Internet 15, no. 11: 362. https://doi.org/10.3390/fi15110362
APA StyleShaibu, F. E., Onwuka, E. N., Salawu, N., Oyewobi, S. S., Djouani, K., & Abu-Mahfouz, A. M. (2023). Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review. Future Internet, 15(11), 362. https://doi.org/10.3390/fi15110362