3D RT adaptive path sensing Method: RSSI modelling validation at 4.5 GHz, 28 GHz, and 38 GHz

Geok, Tan Kim; Hossain, Ferdous; Rahim, Sharul Kamal Abdul; Elijah, Olakunle; Eteng, Akaa Agbaeze; Loh, Chun Theng; Li, Lim Li; Tso, C.P.; Rahman, Tharek Abd; Hindia, Mohammad Nour

doi:10.1016/j.aej.2022.04.033

Cited by 13 publications

(4 citation statements)

References 52 publications

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“…However, the authors stated that a new deployment (the existing one does not meet the coverage requirements) is required due to the complexity of the environment for an Ultra Dense Network at 28 GHz. In [26], the authors developed an Adaptive Path Sensing Method (APSM) for indoor radio wave propagation prediction at 4.5 GHz, 28 GHz, and 38 GHz. Measurement campaigns, which cover indoor line-of-sight (LOS), non-line-of-sight (NLOS), and different room scenarios were conducted.…”

Section: B Path Loss Estimation Using Ray-tracing Methodsmentioning

confidence: 99%

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

Santana,

Martinez Alonso,

Guillen Nieto

et al. 2024

IEEE Open J. Commun. Soc.

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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.

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Section: B Path Loss Estimation Using Ray-tracing Methodsmentioning

confidence: 99%

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

Santana,

Martinez Alonso,

Guillen Nieto

et al. 2024

IEEE Open J. Commun. Soc.

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“…On the contrary, ray tracing models can provide more accurate path loss predictions in exchange for more intensive computational efforts [7]. An example of using 3D ray tracing method for indoor propagation loss prediction is presented in [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning for Path Loss Prediction at 7 GHz in Urban Environment

2023

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In the 6 GHz spectrum sharing band, unlicensed devices are managed by automated frequency coordination (AFC) systems to protect incumbent services from interference. Thus, it is important to select accurate propagation models for interference calculation and analysis. This paper utilizes a modelaided deep learning technique for path loss prediction at 7 GHz, as a representative frequency within the 6 GHz band, in an urban environment. The proposed model is a hybrid model, which leverages both domain expert knowledge from a physics-based general-purposed channel model as well as the learningbased capability from a neural network, for path loss prediction. The model is trained and tested using sufficient-quantity and high-quality real propagation measurement data collected in four locations in an urban environment. Numerical results show that the deep learning model provides a better prediction performance than most empirical models. Furthermore, the feasibility of proposed model generalization to new locations after fine-tuning is examined.

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“…However, because of the long computation time required for ray-tracing calculations, more efficient computation is required to predict propagation environments considering moving objects, as repeated simulations are necessary [10,11,12,13]. Some studies introduced methods for extracting data from a preprocessed database for ray-tracing acceleration [14,15,16]. 3GPP Technical Report Release 14 is a hybrid channel model that combines a stochastic model with a ray tracing model [17].…”

Section: Introductionmentioning

confidence: 99%

Path Clustering for Radio Propagation Prediction With Moving Object in Indoor Environment

Shirakawa,

Furukawa,

Ching

et al. 2024

IEEE Access

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To simulate radio propagation in a dynamic indoor environment such as a factory operating with moving people and automated guided vehicles (AGV), we devised a technique to determine shielding losses by moving objects based on ray tracing. For a moving object environment, conventional ray-tracing methods require simulation of each snapshot of a moving object. This conventional technique requires too much time for real-time predictions. The proposed method first calculates the static environment using the ray-tracing method without the moving object, and then the shielding loss caused by a moving object is included through post-processing. To enhance efficiency, 3D path clustering was employed, which significantly reduced the total number of paths and computation time. For comparison, we measured the radio-propagation environment of an existing Japanese factory. The root-mean-square errors (RMSEs) and correlation coefficients of the proposed and conventional methods were compared based on the experimental results, wherein the indicators of the proposed method were comparable to those of the conventional method even though the number of paths was reduced. Furthermore, the proposed method reduces the computation time to approximately 5% of that of the conventional method.

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3D RT adaptive path sensing Method: RSSI modelling validation at 4.5 GHz, 28 GHz, and 38 GHz

Cited by 13 publications

References 52 publications

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

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

Deep Learning for Path Loss Prediction at 7 GHz in Urban Environment

Path Clustering for Radio Propagation Prediction With Moving Object in Indoor Environment

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