High-Level Requirements and Link Budget Analysis

Maritime high-speed over-the-horizon wireless communication is realizable through evaporation ducts. Detailed measurement, analysis, and modeling of duct channels are essential for application of this communication technique. In this paper, X-band electromagnetic (EM) wave propagation systems were developed and deployed for a 133-km over-the-horizon microwave link in coastal areas of the Yellow Sea. The propagation length was 7.7 times the line-of-sight length. Measurement results including the path loss (PL) and meteorological data were obtained during a 54-day period in autumn 2021. The long-term channel results were analyzed on the basis of statistical analysis and model simulations. Results showed that our measurement system, with a maximum measurable power loss of 200 dB, had connected with a probability of 56.2% during the measurement period. Model simulation showed that evaporation duct environments are not ideal in autumn, with an average evaporation duct height (EDH) of 10.6 m. The land breeze in autumn introduced dry and cold air to the link, which could promote evaporation of seawater and reduce PL by approximately 40 dB. Annual spatiotemporal characteristics of EDH showed that evaporation ducts are most suitable for over-the-horizon communication in spring, especially May.

show abstract

“…The PL at 120 km is about 142 dB, which is only 13-dB higher than that at 20 km. The PL exponent (Rouphael, 2009) is about 1.709.…”

Section: Figurementioning

confidence: 98%

Long-term over-the-horizon microwave channel measurements and statistical analysis in evaporation ducts over the Yellow Sea

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Using a combination of computational algorithms [24] and approximations based on empirical measurements obtained from channel-sounding experiments, path loss models are created. In general, propagation path loss rises with both frequency and distance, shown as follows [25]:…”

Section: Path Loss In Wireless Communicationsmentioning

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

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

“…The wavelength of free space is defined as the ratio between the speed of light in meters per second and the carrier frequency in Hz, as indicated in Equations ( 2) and ( 3). The work [24] is the foundation for the equations.…”

Section: Environment Modelmentioning

confidence: 99%

Reinforcement Learning Environment for Advanced Vehicular Ad Hoc Networks Communication Systems

Teixeira

Huszák

2022

Sensors

View full text Add to dashboard Cite

Ad hoc vehicular networks have been identified as a suitable technology for intelligent communication amongst smart city stakeholders as the intelligent transportation system has progressed. However, in a highly mobile area, the growing usage of wireless technologies creates a challenging context. To increase communication reliability in this environment, it is necessary to use intelligent tools to solve the routing problem to create a more stable communication system. Reinforcement Learning (RL) is an excellent tool to solve this problem. We propose creating a complex objective space with geo-positioning information of vehicles, propagation signal strength, and environmental path loss with obstacles (city map, with buildings) to train our model and get the best route based on route stability and hop number. The obtained results show significant improvement in the routes’ strength compared with traditional communication protocols and even with other RL tools when only one parameter is used for decision making.

show abstract

High-Level Requirements and Link Budget Analysis

Cited by 12 publications

References 4 publications

Long-term over-the-horizon microwave channel measurements and statistical analysis in evaporation ducts over the Yellow Sea

Long-term over-the-horizon microwave channel measurements and statistical analysis in evaporation ducts over the Yellow Sea

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

Reinforcement Learning Environment for Advanced Vehicular Ad Hoc Networks Communication Systems

Contact Info

Product

Resources

About