Indoor Millimeter-Wave Propagation Prediction by Measurement and Ray Tracing Simulation at 38 GHz

Hossain, Ferdous; Geok, Tan Kim; Rahman, Tharek Abd; Hindia, Mohammad Nour; Dimyati, Kaharudin; Abdaziz, Azlan

doi:10.3390/sym10100464

Cited by 24 publications

(11 citation statements)

References 35 publications

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“…Therefore, the RSSI predictions using RT are more accurate than MW. Some previous studies on ray-tracing propagation models have succeeded in obtaining results of MAE ranging from 3 to 8.52 [64][65][66][67]. The results of this study showed an average MAE of 2.9-A much better result than the results of the above studies.…”

Section: The Development Of An Automatic Radio Map Using Ray-tracingcontrasting

confidence: 42%

Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

Firdaus

Ahmad

Sahibuddin

2019

Sensors

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Wireless local area networks (WLAN)-fingerprinting has been highlighted as the preferred technology for indoor positioning due to its accurate positioning and minimal infrastructure cost. However, its accuracy is highly influenced by obstacles that cause fluctuation in the signal strength. Many researchers have modeled static obstacles such as walls and ceilings, but few studies have modeled the people’s presence effect (PPE), although the human body has a great impact on signal strength. Therefore, PPE must be addressed to obtain accurate positioning results. Previous research has proposed a model to address this issue, but these studies only considered the direct path signal between the transmitter and the receiver whereas multipath effects such as reflection also have a significant influence on indoor signal propagation. This research proposes an accurate indoor-positioning model by considering people’s presence and multipath using ray-tracing, we call it (AIRY). This study proposed two solutions to construct AIRY: an automatic radio map using ray tracing and a constant of people’s effect for the received signal strength indicator (RSSI) adaptation. The proposed model was simulated using MATLAB software and tested at Level 3, Menara Razak, Universiti Teknologi Malaysia. A K-nearest-neighbor (KNN) algorithm was used to define a position. The initial accuracy was 2.04 m, which then reduced to 0.57 m after people’s presence and multipath effects were considered.

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Section: The Development Of An Automatic Radio Map Using Ray-tracingcontrasting

confidence: 42%

Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

Firdaus

Ahmad

Sahibuddin

2019

Sensors

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“…The calculation complexity and simulation time of shooting and bouncing rays (SBR) method was proportional to the number of devices and objects presented in an environment. Because of the higher reflection and diffraction of mmWave signals in an urban environment, the use of highly directional horn antenna was preferred to achieve an acceptable output at the user end [64]. Therefore, these measurements were performed using the directional narrow-beam Rx antenna with a 3D transmitter-receiver separation distance varying between 20 and 50 m. In order to create a LOS link between the transmitter and the receiver for the LOS scenario, both antennas were boresight aligned with high directivity to each other and no obstacle between them.…”

Section: Measurement Setupmentioning

confidence: 99%

Investigation of Future 5G-IoT Millimeter-Wave Network Performance at 38 GHz for Urban Microcell Outdoor Environment

et al. 2019

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The advent of fifth-generation (5G) systems and their mechanics have introduced an unconventional frequency spectrum of high bandwidth with most falling under the millimeter wave (mmWave) spectrum. The benefit of adopting these bands of the frequency spectrum is two-fold. First, most of these bands appear to be unutilized and they are free, thus suggesting the absence of interference from other technologies. Second, the availability of a larger bandwidth offers higher data rates for all users, as there are higher numbers of users who are connected in a small geographical area, which is also stated as the Internet of Things (IoT). Nevertheless, high-frequency band poses several challenges in terms of coverage area limitations, signal attenuation, path and penetration losses, as well as scattering. Additionally, mmWave signal bands are susceptible to blockage from buildings and other structures, particularly in higher-density urban areas. Identifying the channel performance at a given frequency is indeed necessary to optimize communication efficiency between the transmitter and receiver. Therefore, this paper investigated the potential ability of mmWave path loss models, such as floating intercept (FI) and close-in (CI), based on real measurements gathered from urban microcell outdoor environments at 38 GHz conducted at the Universiti Teknologi Malaysia (UTM), Kuala Lumpur campus. The measurement data were obtained by using a narrow band mmWave channel sounder equipped with a steerable direction horn antenna. It investigated the potential of the network for outdoor scenarios of line-of-sight (LOS) and non-line-of-sight (NLOS) with both schemes of co-(vertical-vertical) and cross (vertical-horizontal) polarization. The parameters were selected to reflect the performance and the variances with other schemes, such as average users cell throughput, throughput of users that are at cell-edges, fairness index, and spectral efficiency. The outcomes were examined for various antenna configurations as well as at different channel bandwidths to prove the enhancement of overall network performance. This work showed that the CI path loss model predicted greater network performance for the LOS condition, and also estimated significant outcomes for the NLOS environment. The outputs proved that the FI path loss model, particularly for V-V antenna polarization, gave system simulation results that were unsuitable for the NLOS scenario. IntroductionIn a 5G communication network, mobile operators are forced to use a high-frequency spectrum due to the limited amount of channel bandwidth. The 5G boosts the overall performance and enhances user experience offered by the present generation mobile technologies. Some of the most hyped features include higher data-rates, lower end-to-end delays, and minimal energy consumption [1]. In order to meet a common goal, several technologies need to be implemented in a fashion that allows the smooth operation of the overall network as a singular body. Nevertheless, vast technologies and communication...

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“…Our method is faster, as our calculation phase helps to overcome the computational limitation of the site specific methods. The RL phase optimization is a good contribution in proposed RT [45][46][47][48]. The appropriate use of RL phase in the proposed method makes it more efficient.…”

Section: Validation Of Ray Tracing Resultsmentioning

confidence: 99%

An Efficient 3-D Ray Tracing Method: Prediction of Indoor Radio Propagation at 28 GHz in 5G Network

et al. 2019

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Millimeter wave technology will be dominating the fifth-generation networks due to the clear advantage of higher frequency bands and hence wider spectrum. In this paper, the indoor radio wave propagation at 28 GHz is studied by developing an efficient three-dimensional ray tracing (ETRT) method. The simulation software based on the ETRT model has been verified by measurement data. The received signal strength indication and path loss have shown significant agreement between simulation and measurement. Compared with the conventional shooting bouncing ray tracing method, the proposed ETRT method has better agreement with measurement data.

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Indoor Millimeter-Wave Propagation Prediction by Measurement and Ray Tracing Simulation at 38 GHz

Cited by 24 publications

References 35 publications

Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

Investigation of Future 5G-IoT Millimeter-Wave Network Performance at 38 GHz for Urban Microcell Outdoor Environment

An Efficient 3-D Ray Tracing Method: Prediction of Indoor Radio Propagation at 28 GHz in 5G Network

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