Rural Macrocell Path Loss Measurements for 5G Fixed Wireless Access at 26 GHz

Saba, Norshahida; Mela, Lauri; Sheikh, Muhammad Usman; Salo, J.; Ruttik, Kalle; Jäntti, Riku

doi:10.1109/5gwf52925.2021.00064

Cited by 7 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They trained this model with 28 GHz path loss data and compared it to four existing empirical models, revealing its strong performance, particularly in high-band frequencies [63]. Additionally, a comparison between the single-frequency FI model and the single-frequency CI models indicated their similar performance across all frequencies under line-of-sight scenarios [64][65][66]. However, it is important to note that the measurement did not account for material properties, which may have contributed to the symmetrical behavior of the data due to potential reflection and diffraction effects.…”

Section: Reviewed Papers On Empirical-based Path Loss Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review

Shaibu,

Onwuka,

Salawu

et al. 2023

Future Internet

View full text Add to dashboard Cite

The rapid development of 5G communication networks has ushered in a new era of high-speed, low-latency wireless connectivity, as well as the enabling of transformative technologies. However, a crucial aspect of ensuring reliable communication is the accurate modeling of path loss, as it directly impacts signal coverage, interference, and overall network efficiency. This review paper critically assesses the performance of path loss models in mid-band and high-band frequencies and examines their effectiveness in addressing the challenges of 5G deployment. In this paper, we first present the summary of the background, highlighting the increasing demand for high-quality wireless connectivity and the unique characteristics of mid-band (1–6 GHz) and high-band (>6 GHz) frequencies in the 5G spectrum. The methodology comprehensively reviews some of the existing path loss models, considering both empirical and machine learning approaches. We analyze the strengths and weaknesses of these models, considering factors such as urban and suburban environments and indoor scenarios. The results highlight the significant advancements in path loss modeling for mid-band and high-band 5G channels. In terms of prediction accuracy and computing effectiveness, machine learning models performed better than empirical models in both mid-band and high-band frequency spectra. As a result, they might be suggested as an alternative yet promising approach to predicting path loss in these bands. We consider the results of this review to be promising, as they provide network operators and researchers with valuable insights into the state-of-the-art path loss models for mid-band and high-band 5G channels. Future work suggests tuning an ensemble machine learning model to enhance a stable empirical model with multiple parameters to develop a hybrid path loss model for the mid-band frequency spectrum.

show abstract

Section: Reviewed Papers On Empirical-based Path Loss Modelsmentioning

confidence: 99%

“…A thorough measuring campaign at 26 GHz was conducted over the course of the summer in two remote areas of southern Finland, according to Saba et al [66]. The collected data demonstrate that the mean path loss increased from 4 to 6 dB for every 20 m increase in antenna height.…”

Section: Los and Nlos Indoormentioning

confidence: 99%

Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review

Shaibu,

Onwuka,

Salawu

et al. 2023

Future Internet

View full text Add to dashboard Cite

show abstract

“…Traditional PL predictions are primarily categorized into deterministic methods [7][8][9][10][11][12][13] and empirical methods [14][15][16][17][18][19][20][21][22]. For deterministic methods, PL is typically modeled through the ray tracing (RT) technique combined with environmental information.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [17] measured the PL in a citrus plantation, and analyzed the measurement results based on the two-ray model, close-in (CI) free space reference distance PL model, and so on. For A2G scenarios, the authors in [20] investigated the effects of different transmission antenna heights on PL in rural scenarios at 26 GHz using a crane as a research platform, and analyzed the fitting performance of different empirical models, such as the CI and alpha-beta-gamma models. The authors in [21,22], respectively, studied channel parameters such as the received signal strength indicator of ZigBee and the WiFi link in farmland scenarios based on UAVs.…”

Section: Introductionmentioning

confidence: 99%

Air-to-Ground Path Loss Model at 3.6 GHz under Agricultural Scenarios Based on Measurements and Artificial Neural Networks

Li,

Mao,

et al. 2023

Drones

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) have found expanding utilization in smart agriculture. Path loss (PL) is of significant importance in the link budget of UAV-aided air-to-ground (A2G) communications. This paper proposes a machine-learning-based PL model for A2G communication in agricultural scenarios. On this basis, a double-weight neurons-based artificial neural network (DWN-ANN) is proposed, which can strike a fine equilibrium between the amount of measurement data and the accuracy of predictions by using ray tracing (RT) simulation data for pre-training and measurement data for optimization training. Moreover, an RT pre-correction module is introduced into the DWN-ANN to optimize the impact of varying farmland materials on the accuracy of RT simulation, thereby improving the accuracy of RT simulation data. Finally, channel measurement campaigns are carried out over a farmland area at 3.6 GHz, and the measurement data are used for the training and validation of the proposed DWN-ANN. The prediction results of the proposed PL model demonstrate a fine concordance with the measurement data and are better than the traditional empirical models.

show abstract

Millimeter-Wave Radio Link Analysis for 5G FWA by Combining Measurements and Geospatial Data

Saba

Mela

Ruttik

et al. 2022

2022 IEEE Future Networks World Forum (FNWF)

View full text Add to dashboard Cite

Rural Macrocell Path Loss Measurements for 5G Fixed Wireless Access at 26 GHz

Cited by 7 publications

References 12 publications

Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review

Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review

Air-to-Ground Path Loss Model at 3.6 GHz under Agricultural Scenarios Based on Measurements and Artificial Neural Networks

Millimeter-Wave Radio Link Analysis for 5G FWA by Combining Measurements and Geospatial Data

Contact Info

Product

Resources

About