Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium

Castellanos, German; Beelde, Brecht De; David, Paul A.; Martens, Luc; Joseph, Wout; Deruyck, Margot

doi:10.3390/s23031056

Cited by 3 publications

(4 citation statements)

References 42 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This work is an extension of our previous work [ 17 ]. Compared to [ 17 ], a different FWA network architecture is envisioned, and different network parameters are considered, i.e., in this paper, we compare different technologies and frequency bands.…”

Section: Introductionmentioning

confidence: 71%

“…Compared to the FWA network planning solution presented in [ 17 ], where the cpe devices have no routing capabilities, very limited edge devices are required to enable network routing from all cpe devices towards the pop device. The advantage of the current approach is that fewer edge devices are required, which makes the network easier to deploy, as the placement of radio equipment on public buildings and street furniture requires significant administrative work.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…This makes the cpe devices more expensive compared to cpe devices that only need a single wireless connection towards an edge device. In [ 17 ], 116 edge devices are required to cover 100 cpe devices (without routing capabilities) in the village of Leest. As such, the total installation cost of the cpe and edge devices for a telecom operator will be lower using our proposed network architecture.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Different FWA architectures exist. In [ 17 ], cpe devices do not have networking capabilities and they can only connect to edge devices. The benefit of this architecture is that the cpe devices are cheaper to deploy, but the number of required edge devices increases.…”

Section: Background Theory and Modelingmentioning

confidence: 99%

See 3 more Smart Citations

MmWave Physical Layer Network Modeling and Planning for Fixed Wireless Access Applications

Beelde

Vantorre

Castellanos

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

The large bandwidths that are available at millimeter-wave frequencies enable fixed wireless access (FWA) applications, in which fixed point-to-point wireless links are used to provide internet connectivity. In FWA networks, a wireless mesh is created and data are routed from the customer premises equipment (CPE) towards the point of presence (POP), which is the interface with the wired internet infrastructure. The performance of the wireless links depends on the radio propagation characteristics, as well as the wireless technology that is used. The radio propagation characteristics depend on the environment and on the considered frequency. In this work, we analyzed the network characteristics of FWA networks using radio propagation models for different wireless technologies using millimeter-wave (mmWave) frequencies of 28 GHz, 60 GHz, and 140 GHz. Different scenarios and environments were considered, and the influence of rain, vegetation, and the number of subscribers was investigated. A network planning algorithm is presented that defines a route for each CPE towards the POP based on a predefined location of customer devices and considering the available capacity of the wireless links. Rain does not have a considerable effect on the system capacity. Even though the higher frequencies exhibit a larger path loss, resulting in a lower power of the received signal, the larger bandwidths enable a higher channel capacity.

show abstract

Section: Introductionmentioning

confidence: 71%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Background Theory and Modelingmentioning

confidence: 99%

See 2 more Smart Citations

MmWave Physical Layer Network Modeling and Planning for Fixed Wireless Access Applications

Beelde

Vantorre

Castellanos

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

5G mmWave Network Planning Using Machine Learning for Path Loss Estimation

Santana,

Martinez Alonso,

Guillen Nieto

et al. 2024

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

New frequency bands, such as the mmWave at 28 GHz for fifth Generation networks in Frequency Range 2, are being introduced to accomplish the required throughput for new services such as remote surveillance, object tracking, and factory automation. These high-frequency bands are sensible to reflections and diffraction. Therefore, accurate path loss calculation relies on advanced models and ray tracing. However, this is time-consuming, and evaluating a large set of candidate solutions is no longer possible when planning the optimal number and location of base stations in the network planning process. This paper investigates the use of machine learning to approximate a complex mmWave ray-tracingbased path loss model in indoor scenarios. The much lower calculation time allows us to approximate the ray-tracer path loss estimation well and to apply a genetic algorithm for realizing network planning. The machine learning model is trained and validated for two buildings and tested with another, with an average of the Mean Absolute Error of 2.8 dB over all cases. It is shown that the combination of Machine Learning and Genetic Algorithm is able to find a network deployment in the FR2 band accounting for the minimum number of access points and minimum electromagnetic exposure, while still providing a predefined coverage percentage inside the building.

show abstract

Propagation Modeling of Unmanned Aerial Vehicle (UAV) 5G Wireless Networks in Rural Mountainous Regions Using Ray Tracing

Ali,

Abu-Samah,

Abdullah

et al. 2024

Drones

View full text Add to dashboard Cite

Deploying 5G networks in mountainous rural regions can be challenging due to its unique and challenging characteristics. Attaching a transmitter to a UAV to enable connectivity requires a selection of suitable propagation models in such conditions. This research paper comprehensively investigates the signal propagation and performance under multiple frequencies, from mid-band to mmWaves range (3.5, 6, 28, and 60 GHz). The study focuses on rural mountainous regions, which were empirically simulated based on the Skardu, Pakistan, region. A complex 3D ray tracing method carefully figures out the propagation paths using the geometry of a 3D environment and looks at the effects in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The analysis considers critical parameters such as path loss, received power, weather loss, foliage loss, and the impact of varying UAV heights. Based on the analysis and regression modeling techniques, quadratic polynomials were found to accurately model the signal behavior, enabling signal strength predictions as a function of distances between the user and an elevated drone. Results were analyzed and compared with suburban areas with no mountains but more compact buildings surrounding the Universiti Kebangsaan Malaysia (UKM) campus. The findings highlight the need to identify the optimal height for the UAV as a base station, characterize radio channels accurately, and predict coverage to optimize network design and deployment with UAVs as additional sources. The research offers valuable insights for optimizing signal transmission and network planning and resolving spectrum-management difficulties in mountainous areas to enhance wireless communication system performance. The study emphasizes the significance of visualizations, statistical analysis, and outlier detection for understanding signal behavior in diverse environments.

show abstract

Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium

Cited by 3 publications

References 42 publications

MmWave Physical Layer Network Modeling and Planning for Fixed Wireless Access Applications

MmWave Physical Layer Network Modeling and Planning for Fixed Wireless Access Applications

5G mmWave Network Planning Using Machine Learning for Path Loss Estimation

Propagation Modeling of Unmanned Aerial Vehicle (UAV) 5G Wireless Networks in Rural Mountainous Regions Using Ray Tracing

Contact Info

Product

Resources

About