scholarly journals Utilization of an OLED-Based VLC System in Office, Corridor, and Semi-Open Corridor Environments

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6869
Author(s):  
Zahra Nazari Chaleshtori ◽  
Zabih Ghassemlooy ◽  
Hossien B. Eldeeb ◽  
Murat Uysal ◽  
Stanislav Zvanovec

Organic light emitting diodes (OLEDs) have recently received growing interest for their merits as soft light and large panels at a low cost for the use in public places such as airports, shopping centers, offices, and train or bus stations. Moreover, the flexible substrate-based OLEDs provide an attractive feature of having curved or rolled lighting sources for the use in wearable devices and display panels. This technology can be implemented in visible light communications (VLC) for several applications such as visual display, data communications, and indoor localization. This article aims to investigate the use of flexible OLED-based VLC in indoor environments (i.e., office, corridor and semi-open corridor in shopping malls). We derive a two-term power series model to be match with the root-mean-square delay spread and optical path loss (OPL). We show that, for OLED positioned on outer-wall of shops, the channel gain is enhanced in contrast to them being positioned on the inner-wall. Moreover, the channel gain in empty environments is higher compare with the furnished rooms. We show that, the OPL for a 10 m link span are lower by 4.4 and 6.1 dB for the empty and semi-open corridors compared with the furnished rooms, when OLED is positioned on outer-wall of shops. Moreover, the channel gain in the corridor is higher compared with the semi-open corridor. We also show that, in furnished and semi-open corridors the OPL values are 55.6 and 57.2 dB at the center of corridor increasing to 87.6 and 90.7 dB at 20 m, respectively, when OLED is positioned on outer-wall of shops.

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1653
Author(s):  
Ahmed Al-Saman ◽  
Michael Cheffena ◽  
Olakunle Elijah ◽  
Yousef A. Al-Gumaei ◽  
Sharul Kamal Abdul Rahim ◽  
...  

The millimeter-wave (mmWave) is expected to deliver a huge bandwidth to address the future demands for higher data rate transmissions. However, one of the major challenges in the mmWave band is the increase in signal loss as the operating frequency increases. This has attracted several research interests both from academia and the industry for indoor and outdoor mmWave operations. This paper focuses on the works that have been carried out in the study of the mmWave channel measurement in indoor environments. A survey of the measurement techniques, prominent path loss models, analysis of path loss and delay spread for mmWave in different indoor environments is presented. This covers the mmWave frequencies from 28 GHz to 100 GHz that have been considered in the last two decades. In addition, the possible future trends for the mmWave indoor propagation studies and measurements have been discussed. These include the critical indoor environment, the roles of artificial intelligence, channel characterization for indoor devices, reconfigurable intelligent surfaces, and mmWave for 6G systems. This survey can help engineers and researchers to plan, design, and optimize reliable 5G wireless indoor networks. It will also motivate the researchers and engineering communities towards finding a better outcome in the future trends of the mmWave indoor wireless network for 6G systems and beyond.


2018 ◽  
Vol 189 ◽  
pp. 03017
Author(s):  
Junhui Mei ◽  
Juntong Xi

Indoor positioning systems have attracted increasing interests for the emergency of location based service in indoor environments. Wi-Fi fingerprint-based localization scheme has become a promising indoor localization technique due to the availability of access point (AP) and its low cost. However, the received signal strength (RSS) values are easily fluctuated by the interference of multi-path effects, which introduce propagation errors into localization results. In order to address the issue, a fingerprint-based autoencoder network scheme is proposed to learn the essential features from the measured coarse RSS values and extract the trained weight parameters of autoencoder network as refined fingerprints. The extracted fingerprints are able to represent the environmental properties and display strong robustness with fluctuated signals. The proposed scheme is further implemented in complex indoor scenes, which substantiate the effectiveness and accuracy improvement compared with other RSS-based schemes.


Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3431
Author(s):  
Mateusz Groth ◽  
Krzysztof Nyka ◽  
Lukasz Kulas

In this paper, we present a novel, low-cost approach to indoor localization that is capable of performing localization processes in real indoor environments and does not require calibration or recalibration procedures. To this end, we propose a single-anchor architecture and design based on an electronically steerable parasitic array radiator (ESPAR) antenna and Nordic Semiconductor nRF52840 utilizing Bluetooth Low Energy (BLE) protocol. The proposed algorithm relies on received signal strength (RSS) values measured by the receiver equipped with the ESPAR antenna for every considered antenna radiation pattern. The calibration-free concept is achieved by using inexpensive BLE nodes installed in known positions on the walls of the test room and acting as reference nodes for the positioning algorithm. Measurements performed in the indoor environment show that the proposed approach can successfully provide positioning results better than those previously reported for single-anchor ESPAR antenna localization systems employing the classical fingerprinting method and relying on time-consuming calibration procedures.


2010 ◽  
pp. 9-15
Author(s):  
Andreas Fink ◽  
Helmut Beikirch ◽  
Matthias Voss

Distance estimation by the evaluation of RSSI measurements is a simple and well-known technique to predict the position of an unknown node. Therefore the infrastructure does not have to be extended by expensive hardware for synchronization or direction approximation. However, with the localization based on RSSI measurements common and proven systems can be used for the infrastructure. For indoor environments the distance-pending path loss is affected by strong variations, especially appearing as frequency specific signal dropouts. A diversity concept with redundant data transmission in different frequency bands can reduce the dropout probability. If also space diversity and plausibility filtering are used, the Location Estimation Error can be reduced significantly. The investigations show that a good performance for precision and availability can also be reached with low infrastructural costs.


Author(s):  
Nidal Qasem

<span>The 60 GHz band has been selected for short-range communication systems to meet consumers’ needs for high data rates. However, this frequency is attenuated by obstacles. This study addresses the limitations of the 60 GHz band by modifying indoor environments with ring Frequency Selective Surfaces (FSSs) wallpaper, thereby increasing its utilization. The ring FSS wallpaper response at a 61.5 GHz frequency has been analyzed using both MATLAB and Computer Simulation Technology (CST) Microwave Studio (MWS) software. ‘Wireless InSite’ is also used to demonstrate enhanced wave propagation in a building modified with ring FSSs wallpaper. The demonstration is applied to Single Input Single Output (SISO) and Multiple Input Multiple Output (MIMO) systems to verify the effectiveness of FSSs on such systems’ capacity. The effectiveness of the suggested modification over delay spread has been studied for the MIMO scenario, as well as the effect of the human body on capacity. Simulation results presented here show that modifying a building using ring FSS wallpaper is an attractive scheme for significantly improving the indoor 60 GHz wireless communications band. This paper also presents and compares two large-scale indoor propagation Path Loss Models (PLMs), the Close-In (CI) free space reference distance model and the Floating Intercept (FI) model. Data obtained from ‘Wireless InSite’ over distances ranging from 4 to 14.31 m is analyzed. Results show that the CI model provides good estimation and exhibits stable behavior over frequencies and distances, with a solid physical basis and less computational complexity when compared to the FI model. </span>


Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2857
Author(s):  
Simon Tomažič ◽  
Igor Škrjanc

Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost Bluetooth localization with the least data acquisition and network configuration overhead. The proposed system incorporates a sophisticated visual-inertial localization algorithm for a fully automated collection of Bluetooth signal strength data. A suitable collection of measurements can be quickly and easily performed, clearly defining which part of the space is not yet well covered by measurements. The obtained measurements, which can also be collected via the crowdsourcing approach, are used within a constrained nonlinear optimization algorithm. The latter is implemented on a smartphone and allows the online determination of the beacons’ locations and the construction of path loss models, which are validated in real-time using the particle swarm localization algorithm. The proposed system represents an advanced innovation as the application user can quickly find out when there are enough data collected for the expected radiolocation accuracy. In this way, radiolocation becomes much less time-consuming and labor-intensive as the configuration time is reduced by more than half. The experiment results show that the proposed system achieves a good trade-off in terms of network setup complexity and localization accuracy. The developed system for automated data acquisition and online modeling on a smartphone has proved to be very useful, as it can significantly simplify and speed up the installation of the Bluetooth network, especially in wide-area facilities.


Author(s):  
Shweta Jain ◽  
Christian Barona ◽  
Nicholas Madamopoulos

This paper presents a ray-tracing technique to model the multi-path fading effect in indoor spaces. Random set P of points on all surfaces inside a given hypothetical indoor space are chosen. Each pi ∈ P is considered to be a point from which the transmitted signal reflects just before reaching the receiver. The received signal is the vector sum of various reflections that arrive at the receiver. The received signal strength (RSS) is then computed from the signal envelope. This technique provides RSS statistics that are similar to the models of signal propagation developed after extensive measurements in multi-path environments. In addition, this technique captures the spatial correlation of signal impairment. For example, path loss computed with this technique shows that co-moving receivers experience correlated signal fades while those moving in different spaces see un-correlated fading. The technique presented here is a low cost, first principle approach to simulate channel impairments due to multi-path effect and interference. It benefits any wireless simulation study that needs the signal-space mapping and context such as indoor localization. This randomized ray-tracing technique does not compete with or replace other, more accurate ray-tracing techniques that use either brute force or geometric optics to obtain site-specific signal-to-space mapping.  


Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3406
Author(s):  
Jie Jiang ◽  
Yin Zou ◽  
Lidong Chen ◽  
Yujie Fang

Precise localization and pose estimation in indoor environments are commonly employed in a wide range of applications, including robotics, augmented reality, and navigation and positioning services. Such applications can be solved via visual-based localization using a pre-built 3D model. The increase in searching space associated with large scenes can be overcome by retrieving images in advance and subsequently estimating the pose. The majority of current deep learning-based image retrieval methods require labeled data, which increase data annotation costs and complicate the acquisition of data. In this paper, we propose an unsupervised hierarchical indoor localization framework that integrates an unsupervised network variational autoencoder (VAE) with a visual-based Structure-from-Motion (SfM) approach in order to extract global and local features. During the localization process, global features are applied for the image retrieval at the level of the scene map in order to obtain candidate images, and are subsequently used to estimate the pose from 2D-3D matches between query and candidate images. RGB images only are used as the input of the proposed localization system, which is both convenient and challenging. Experimental results reveal that the proposed method can localize images within 0.16 m and 4° in the 7-Scenes data sets and 32.8% within 5 m and 20° in the Baidu data set. Furthermore, our proposed method achieves a higher precision compared to advanced methods.


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