Experimental evaluation of the precision of received signal strength based visible light positioning

In this work, the experimental evaluation of the distance estimation variance is executed for received signal strength based visible light positioning. It is shown that based on the signal to noise ratio at the matched filter output, an accurate determination of the precision is achieved. In order to suppress dc ambient light which contains no information regarding the distance between the LED and the receiver, matched filtering with the dc-balanced part of the transmitted signal is required. As a consequence, the theoretical lower bound for the precision can not be achieved

マクロ-フェムトセルシステムの最適化モデルに関する研究

早大学位記番号:新8266早稲田大

Battle of the bands: a long-term analysis of frequency band and channel distribution development in WLANs

In this article, we present the results of a long-term analysis ofWireless Local Area Network (WLAN) frequency band and channel distribution development. To the best of our knowledge, no similar research has been published in recent academic publications. Overcrowding of the limited frequency space on the 2.4 GHz band has become a significant issue in WLAN networking. Due to the overabundance of devices operating at 2.4 GHz, avoiding network performance degrading interference has become impossible in densely populated environments. Although the latest 802.11 WLAN standard amendments have shifted their emphasis toward the wider and less congested 5 GHz band, the 2.4 GHz band has stayed as the dominant frequency band. To observe the evolvement of WLAN frequency band and channel utilisation, data collected on nine WLAN surveys conducted between May 2019 and January 2022 was analysed. Furthermore, a simple linear regression model was produced to forecast the future development of WLAN frequency band utilisation. It was hypothesised that there would be an increase in 5 GHz frequency band utilisation as devices compliant with the latest 802.11 standard amendments become widely adopted. The survey results show a significant increase in 5 GHz frequency band utilisation. While the number of networks operating at 2.4 GHz saw a modest 42% increase, the number of networks operating at 5 GHz over doubled during the survey period. At the end of the study, 35% of all detected networks operated at 5 GHz, compared to 25% at the beginning of the study. Based on the produced linear regression model, the portion of 5 GHz networks in the survey area is expected to reach the level of 2.4 GHz networks by the autumn of 2025.</p

Line-of-Sight Detection for 5G Wireless Channels

With the rapid deployment of 5G wireless networks across the globe, precise positioning has become essential for many vertical industries reliant on 5G. The predominantly non-line-of-sight (NLOS) propagation instigated by the obstacles in the surrounding environment, especially in metro city areas, has made it particularly difficult to achieve high estimation accuracy for positioning algorithms that necessitate direct line-of-sight (LOS) transmission. In this scenario, correctly identifying the line-of-sight condition has become extremely crucial in precise positioning algorithms based on 5G. Even though numerous scientific studies have been conducted on LOS identification in the existing literature, most of these research works are based on either ultra-wideband or Wi-Fi networks. Therefore, this thesis focuses on this hitherto less investigated area of line-of-sight detection for 5G wireless channels. This thesis examines the feasibility of LOS detection using three widely used channel models, the Tapped Delay Line (TDL), the Clustered Delay Line (CDL), and the Winner II channel models. The 5G-based simulation environment was constructed with standard parameters based on 3GPP specifications using MATLAB computational platform for the research. LOS and NLOS channels were defined to transmit random signal samples for each channel model where the received signal was subjected to Additive White Gaussian Noise (AWGN), imitating the authentic propagation environment. Variable channel conditions were simulated by randomly alternating the signal-to-noise ratio (SNR) of the received signal. The research mainly focuses on machine learning (ML) based LOS classification. Additionally, the threshold-based hypothesis was also deployed for the same scenarios as a benchmark. The main objectives of the thesis were to find the statistical features or the combination of statistical features of the channel impulse response (CIR) of the received signal, which provide the best results and to identify the most effective machine learning method for LOS/NLOS classification. Furthermore, the results were verified through actual measurement samples obtained during the NewSense project. The results indicate that the time-correlation feature of the channel impulse response used in isolation would be effective in LOS identification for 5G wireless channels. Additional derived features of the CIR do not significantly increase the classification accuracy. Positioning Reference Signals (PRS) were found to be more appropriate than Sounding Reference Signals (SRS) for LOS/NLOS classification. The study reinforced the significance of selecting the most suitable machine learning algorithm and kernel function as relevant for the task of obtaining the best results. The medium Gaussian support vector machines ML algorithm provided the overall highest precision in LOS classification for simulated data with up to 98% accuracy for the Winner II channel model with PRS. The machine learning algorithms proved to be considerably more effective than conventional threshold-based detection for both simulated and real measurement data. Additionally, the Winner II model with the richest features presented the best results compared with CDL and TDL channel models

Internet of Things and Sensors Networks in 5G Wireless Communications

The Internet of Things (IoT) has attracted much attention from society, industry and academia as a promising technology that can enhance day to day activities, and the creation of new business models, products and services, and serve as a broad source of research topics and ideas. A future digital society is envisioned, composed of numerous wireless connected sensors and devices. Driven by huge demand, the massive IoT (mIoT) or massive machine type communication (mMTC) has been identified as one of the three main communication scenarios for 5G. In addition to connectivity, computing and storage and data management are also long-standing issues for low-cost devices and sensors. The book is a collection of outstanding technical research and industrial papers covering new research results, with a wide range of features within the 5G-and-beyond framework. It provides a range of discussions of the major research challenges and achievements within this topic

Internet of Things and Sensors Networks in 5G Wireless Communications

This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors

Internet of Things and Sensors Networks in 5G Wireless Communications

This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors