The application scenarios and requirements are more diverse in the fifth-generation (5G) era than before. In order to successfully support the system design and deployment, accurate channel modeling is important. Ray-tracing (RT) based deterministic modeling approach is accurate with detailed angular information and is a suitable candidate for predicting time-varying channel and multiple-input multiple-output (MIMO) channel for various frequency bands. However, the computational complexity and the utility of RT are the main concerns of users. Aiming at 5G and beyond wireless communications, this paper presents a comprehensive tutorial on the design of RT and the applications. The role of RT and the state-of-the-art RT techniques are reviewed. The features of academic and commercial RT based simulators are summarized and compared. The requirements, challenges, and developing trends of RT to enable the visions are discussed. The practices of the design of high-performance RT simulation platform for 5G and beyond communications are introduced, with the publicly available high-performance cloud-based RT simulation platform as the main reference. The hardware structure, networking, workflow, data flow and fundamental functions of a flexible high-performance RT platform are discussed. The applications of high-performance RT are presented based on two 5G scenarios, i.e., a 3.5 GHz Beijing vehicle-to-infrastructure scenario and a 28 GHz Manhattan outdoor scenario. The questions on how to calibrate and validate RT based on measurements, how to apply RT for mobile communications in moving scenarios, and how to evaluate MIMO beamforming technologies are answered. This tutorial will be especially useful for researchers who work on RT algorithms development and channel modeling to meet the evaluation requirements of 5G and beyond technologies.

The Design and Applications of High-Performance Ray-Tracing Simulation Platform for 5G and Beyond Wireless Communications: A Tutorial

A survey on data compression techniques: From the perspective of data quality, coding schemes, data type and applications

Glass fiber-reinforced composite laminates in polyetherimide resin have been studied via terahertz imaging and ultrasonic C-scans. The forced delamination is created by inserting Teflon film between various layers inside the samples prior to consolidating the laminates. Using reflective pulsed terahertz imaging, we find high-resolution, low-artifact terahertz C-scan and B-scan images locating and sizing the delamination in three dimensions. Furthermore, terahertz imaging enables us to determine the thicknesses of the delamination and of the layers constituting the laminate. Ultrasonic C-scan images are also successfully obtained; however, in our samples with small thickness-to-wavelength ratio, detailed ultrasonic B-scan images providing quantitative information in depth cannot be obtained by 5 MHz or 10 MHz focused transducers. Comparative analysis between terahertz imaging and ultrasonic C-scans with regard to spatial resolution is carried out demonstrating that terahertz imaging provides higher spatial resolution for imaging, and can be regarded as an alternative or complementary modality to ultrasonic C-scans for this class of glass fiber-reinforced composites.

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Nondestructive evaluation of forced delamination in glass fiber-reinforced composites by terahertz and ultrasonic waves

We discuss a fiber-integrated refractive index sensor with strongly improved detection performance. The resonator has been implemented by means of focused-ion beam milling of a step index fiber and shows a sensitivity of about 1.15µm/RIU. Coating the resonator walls led to a strongly improved mirror reflectivity by a factor of about 26. Design rules for device optimization and a detailed mathematical analysis are discussed, revealing that the sensor operates as an optimized Fabry-Perot resonator. We also show that the performance of such kind of Fabry-Perot sensors is, in general, limited by the detection limit function – a quantity depending on the cavitiy’s finesse and on the measurement capabilities used.

Reflectivity enhanced refractive index sensor based on a fiber-integrated Fabry-Perot microresonator.

Internet of Things (IoT) devices are typically powered by small batteries with a limited capacity. Thus, saving power as much as possible becomes crucial to extend their lifetime and therefore to allow their use in real application domains. Since radio communication is in general the main cause of power consumption, one of the most used approaches to save energy is to limit the transmission/reception of data, for instance, by means of data compression. However, the IoT devices are also characterized by limited computational resources which impose the development of specifically designed algorithms. To this aim, we propose to endow the lossless compression algorithm (LEC), previously proposed by us in the context of wireless sensor networks, with two simple adaptation schemes relying on the novel concept of appropriately rotating the prefix-free tables. We tested the proposed schemes on several datasets collected in several real sensor network deployments by monitoring four different environmental phenomena, namely, air and surface temperatures, solar radiation and relative humidity. We show that the adaptation schemes can achieve significant compression efficiencies in all the datasets. Further, we compare such results with the ones obtained by LEC and, by means of a non-parametric multiple statistical test, we show that the performance improvements introduced by the adaptation schemes are statistically significant.

Adaptive Lossless Entropy Compressors for Tiny IoT Devices

Energy is an important consideration in the design and deployment of wireless sensor networks (WSNs) since sensor nodes are typically powered by batteries with limited capacity. Since the communication unit on a wireless sensor node is the major power consumer, data compression is one of possible techniques that can help reduce the amount of data exchanged between wireless sensor nodes resulting in power saving. However, wireless sensor networks possess significant limitations in communication, processing, storage, bandwidth, and power. Thus, any data compression scheme proposed for WSNs must be lightweight. In this paper, we present an adaptive lossless data compression (ALDC) algorithm for wireless sensor networks. Our proposed ALDC scheme performs compression losslessly using multiple code options. Adaptive compression schemes allow compression to dynamically adjust to a changing source. The data sequence to be compressed is partitioned into blocks, and the optimal compression scheme is applied for each block. Using various real-world sensor datasets we demonstrate the merits of our proposed compression algorithm in comparison with other recently proposed lossless compression algorithms for WSNs.

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An Adaptive Lossless Data Compression Scheme for Wireless Sensor Networks

Fast and efficient lossless adaptive compression scheme for wireless sensor networks

The energy consumption of each wireless sensor node is one of critical issues that require careful management in order to maximize the lifetime of the sensor network since the node is battery powered. The main energy consumer in each node is the communication module that requires energy to transmit and receive data over the air. Data compression is one of possible techniques that can reduce the amount of data exchanged between wireless sensor nodes. In this paper, we proposed a simple lossless data compression algorithm that uses multiple Huffman coding tables to compress WSNs data adaptively. We demonstrate the merits of our proposed algorithm in comparison with recently proposed LEC algorithm using various real-world sensor datasets.

A Simple Data Compression Algorithm for Wireless Sensor Networks

This paper investigates the impacts that open-trench drains make on the accuracy of radio propagation prediction in an urban city environment. Compared with conventional prediction styles that assume the ground to be flat, in this paper, we have considered for the first time the real scenario in many Asian cities to make open-trench drains inclusive in radio propagation modeling. The aims of this paper are twofold. First, to scrutinize one narrow L-shaped structure, modeled after an open-trench drain, by means of comparing ray-tracing simulation results with the actual field measurement results at 2.4 and 5.8 GHz. Second, to compare one city model built without and with the inclusion of open-trench drains for running ray-tracing simulation in yielding radio propagation prediction results. The findings from this paper are especially beneficial to the improvement of mobile communications in extraordinary environments such as open-trench drains, caves, coal mines, underground passageway, and others. Besides, they provide a unique insight into how the presence of the open-trench drains may affect radio wave propagation in an urban city environment.

Towards a Comprehensive Ray-Tracing Modeling of an Urban City With Open-Trench Drains for Mobile Communications

Caves have been used as a natural habitat since ancient times, and our ancestors once resided in them. As time and age evolve, caves slowly lose their original functionality, yet never ever lose their significance as a place. In this letter, we present field measurement results taken from inside a beautiful cave-turned-into-a-wine-cellar site, which is the first of its kind for such tourist caves rich with stalagmites and stalactites. This cave, named Jeff's cellar operated by the Sunway Group, is one of the famous tourist hot spots in Malaysia, and it has never failed to attract tourists from near and far each year. Needless to say, ensuring that wireless communications are available inside this cave fondly frequented by tourists is important. In this letter, we present field measurement results taken from inside Jeff's cellar at three frequencies, namely, 900 MHz, 2.4 GHz, and 5.8 GHz in both line-of-sight and non-line-of-sight scenarios. These measured results are useful for the practical planning and implementation of wireless communications not only in the said cellar but also other tourist caves such as meditation cave, thermal steam cave, crystal cave, and others.

David Wee Gin Lim

Papers

An Adaptive Lossless Data Compression Scheme for Wireless Sensor Networks

Fast and efficient lossless adaptive compression scheme for wireless sensor networks

A Simple Data Compression Algorithm for Wireless Sensor Networks

Towards a Comprehensive Ray-Tracing Modeling of an Urban City With Open-Trench Drains for Mobile Communications

Propagation Measurement of a Natural Cave-Turned-Wine-Cellar