Ray Tracing Analysis of Sub-6 GHz and mmWave Indoor Coverage with Reflecting Surfaces

Ozturk, Ender; Erden, Fatih; Du, Kairui; Anjinappa, Chethan Kumar; Özdemir, Özgür; Güvenç, İsmail

doi:10.1109/rws53089.2022.9719917

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…In indoor scenarios, the PL estimation is challenging because of the complexity of the environment and the impact of multipath. One option is to use heuristic models [10] or models based on ray-tracing (RT) techniques [11].…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…The different shapes and size reflectors can be made part of the interior/exterior design. Earlier simulation and measurement studies, such as [3], [7], [9], have shown that objects such as metal act as perfect reflectors, enabling strong reflections for directional NLoS communication. Thus, the deployment of the passive metallic reflectors can create a favorable propagation environment by introducing new multipath components (MPCs) to the channel and increasing the overall spatial diversity of the MPCs.…”

Section: Introductionmentioning

confidence: 99%

Indoor Propagation Measurements with Sekisui Transparent Reflectors at 28/39/120/144 GHz

Anjinappa¹,

Ganesh²,

Özdemir³

et al. 2022

Preprint

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One of the critical challenges of operating with the terahertz or millimeter-wave wireless networks is the necessity of at least a strong non-line-of-sight (NLoS) reflected path to form a stable link. Recent studies have shown that an economical way of enhancing/improving these NLoS links is by using passive metallic reflectors that provide strong reflections. However, despite its inherent radio advantage, metals can dramatically influence the landscape's appearance -especially the indoor environment. A conceptual view of escaping this is by using transparent reflectors. In this work, for the very first time, we evaluate the wireless propagation characteristics of passive transparent reflectors in an indoor environment at 28 GHz, 39 GHz, 120 GHz, and 144 GHz bands. In particular, we investigate the penetration loss and the reflection characteristics at different frequencies and compare them against the other common indoor materials such as ceiling tile, clear glass, drywall, plywood, and metal. The measurement results suggest that the transparent reflector, apart from an obvious advantage of transparency, has a higher penetration loss than the common indoor materials (excluding metal) and performs similarly to metal in terms of reflection. Our experimental results directly translate to better reflection performance and preserving the radio waves within the environment than common indoor materials, with potential applications in controlled wireless communication.

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“…This type of material has relatively low penetration losses in mmWaves [170], so it will allow very important transmission contributions to be considered. According to these results it is observed that the transmission contributions are very important in this particular scenario, so omitting this information as many scientific studies have done [160][161][162][163][164][165][166][167][168] may lead to inaccurate results, although it also depends on the type of scenario simulated.…”

Section: Calibration Of Ray-tracing Algorithm With Measurementsmentioning

confidence: 96%

“…The Ray-Tracing algorithm used so far has only taken into account the physical phenomena of specular reflection and edge diffraction. These two physical phenomena are the most important to consider according to the literature [160][161][162][163][164][165][166][167][168], because they are the ones that contribute the most energy to the received power, although this statement depends on the materials of the scenario where the mmWaves propagation channel is being evaluated [169][170][171][172][173].…”

Section: Calibration Of Ray-tracing Algorithm With Measurementsmentioning

confidence: 99%

Human Body Scattering Effects at Millimeter Waves Frequencies for Future 5G Systems and Beyond

Peña¹

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Future mobile communications are expected to experience a technical revolution that goes beyond Gbps data rates and reduces data rate latencies to levels very close to a millisecond. New enabling technologies have been researched to achieve these demanding specifications. The utilization of mmWave bands, where a lot of spectrum is available, is one of them.Due to the numerous technical difficulties associated with using this frequency band, complicated channel models are necessary to anticipate the radio channel characteristics and to accurately evaluate the performance of cellular systems in mmWave. In particular, the most accurate propagation models are those based on deterministic ray tracing techniques. But these techniques have the stigma of being computationally intensive, and this makes it difficult to use them to characterize the radio channel in complex and dynamic indoor scenarios. The complexity of characterizing these scenarios depends largely on the interaction of the human body with the radio environment, which at mmWaves is often destructive and highly unpredictable.On the other hand, in recent years, the video game industry has developed powerful tools for hyper-realistic environments, where most of the progress in this reality emulation has to do with the handling of light. Therefore, the graphic engines of these platforms have become more and more efficient to handle large volumes of information, becoming ideal to emulate the radio wave propagation behavior, as well as to reconstruct a complex interior scenario. Therefore, in this Thesis one has taken advantage of the computational capacity of this type of tools to evaluate the mmWave radio channel in the most efficient way possible. This Thesis offers some guidelines to optimize the signal propagation in mmWaves in a dynamic and complex indoor environment, for which three main objectives are proposed.The first objective has been to evaluate the scattering effects of the human body when it interacts with the propagation channel. Once evaluated, a simplified mathematical and geometrical model has been proposed to calculate this effect in a reliable and fast way. Another objective has been the design of a modular passive reflector in mmWaves, which optimizes the coverage in indoor environments, avoiding human interference in the propagation, in order to avoid its harmful scattering effects. And finally, a real-time predictive beam steering system has been designed for the mmWaves radiation system, in order to avoid propagation losses caused by the human body in dynamic and complex indoor environments.

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Ray Tracing Analysis of Sub-6 GHz and mmWave Indoor Coverage with Reflecting Surfaces

Cited by 4 publications

References 7 publications

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

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

Indoor Propagation Measurements with Sekisui Transparent Reflectors at 28/39/120/144 GHz

Human Body Scattering Effects at Millimeter Waves Frequencies for Future 5G Systems and Beyond

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