Over-the-air (OTA) multiprobe setups provide an efficient way to characterize the performance of today's advanced wireless communication systems. In this paper, the measurement uncertainty of such a ...setup using a car as a test object is characterized through three experiments: measurement system analysis, channel sounder measurements, and probe coupling measurements. Four issues were in focus for the analysis: precision, realization of the wireless communication channel, coupling between the probes, and the influence of the test object size. The analysis shows that a large test object such as a car in an OTA multiprobe ring will affect the measurement uncertainty, but only to a small degree. The measurement uncertainty expressed as expanded uncertainty was below +/-1 dB, which is a level that would not violate best practice total uncertainty levels for comparable OTA methods.
With advancements in telecommunications, data transmission over increasingly harsher channels that produce synchronisation errors is inevitable. Coding schemes for such channels are available through ...techniques such as the Davey-MacKay watermark coding; however, this is limited to memoryless channel estimates. Memory must be accounted for to ensure a realistic channel approximation - similar to a Finite State Markov Chain or Fritchman Model. A novel code construction and three decoders are developed to correct synchronisation errors while considering the channel's correlated memory effects by incorporating ideas from the watermark scheme and memory modelling. Simulation results show that using the stationary distribution values as memoryless error probabilities in the proposed code construction and decoder is a viable solution, especially in cases with low memory between errors. Further tests imply that utilising the entire transition matrix in the decoding process may be better suited for cases with more significant memory between errors. The proposed system and decoder may prove helpful in fields such as free-space optics and possibly molecular communication, where harsh channels are used for communication.
•Presents a new synchronisation channel model that contains memory.•Adapts the watermark decoding scheme for use with the new proposed channel.•Provides three inner decoding algorithms to be used in the proposed framework.
VLC has been proposed as reliable high-capacity wireless optical communication system for indoor access networks. This technology comprises a high bandwidth unlicensed solution and has been suggested ...for ultra-reliable environments like hospitals. In this study, Machine Learning is used to investigate indoor wireless optical channels where ultra-reliable communications and signal coverage are required. Specifically, we assess the precision of different Decision Trees ML algorithms based on their mean absolute error metric in comparison to the anticipated simulated outcomes for optical channel estimation in various rooms that could be used in medical care. We demonstrate that Decision Trees models are capable of making fast and accurate predictions. It is shown that the Stack model is the most appropriate Decision Trees-based method, from the ones we studied, for predicting indoor VLC RSS values and constructing the corresponding REMs. These models are 300–65,000 times faster than traditional calculation methods, depending on the number of reflections bounces considered, and deliver accurate predictions, with less than 0.015 mean absolute percentage error, of the Radio Environment Map of a communication system that supports VLC. This is the first implementation of constructing optical REMs for Indoor VLC systems to the best knowledge of the authors.
Human Body Communication (HBC) is an alternative to radio wave-based Wireless Body Area Network (WBAN) because of its wide bandwidth leading to enhanced energy efficiency. Designing Modern HBC ...devices need the accurate electrical equivalent of the HBC channel for energy efficient communication. The objective of this paper is to present an improved lumped element-based detailed model of Galvanic HBC channel which can be used to explain the dependency of the channel behaviour on the internal body dependent parameters such as electrical properties of skin and muscle tissue layers along with the external parameters such as electrode size, electrode separation, geometrical position of the electrodes and return-path or parasitic capacitances. The model considers the frequency-dependent impedance of skin and muscle tissue layers and the effect of various coupling capacitances between the body and Tx/Rx electrodes to the Earth-Ground. A 2D planar structure of skin and muscle tissue layers is simulated using a Finite Element Method (FEM) tool to prove the validity of the proposed model. The effect of symmetry and asymmetry at the transmitter and receiver ends is also explained using the model. The model become very useful for fast calculation of Galvanic channel response without using any FEM tool. Experimental results show that the galvanic response is not only a function of channel length but also depends on the mismatch at the transmitter and receiver end. In case of a very high mismatch scenario, the channel behavior is dominated by the capacitive HBC, even for a galvanic excitation and termination.
Large-scale fading models play an important role in estimating radio coverage, optimizing base station deployments and characterizing the radio environment to quantify the performance of wireless ...networks. In recent times, multi-frequency path loss models are attracting much interest due to their expected support for both sub-6 GHz and higher frequency bands in future wireless networks. Traditionally, linear multi-frequency path loss models like the ABG model have been considered, however such models lack accuracy. The path loss model based on a deep learning approach is an alternative method to traditional linear path loss models to overcome the time-consuming path loss parameters predictions based on the large dataset at new frequencies and new scenarios. In this paper, we proposed a feed-forward deep neural network (DNN) model to predict path loss of 13 different frequencies from 0.8 GHz to 70 GHz simultaneously in an urban and suburban environment in a non-line-of-sight (NLOS) scenario. We investigated a broad range of possible values for hyperparameters to search for the best set of ones to obtain the optimal architecture of the proposed DNN model. The results show that the proposed DNN-based path loss model improved mean square error (MSE) by about 6 dB and achieved higher prediction accuracy R2 compared to the multi-frequency ABG path loss model. The paper applies the XGBoost algorithm to evaluate the importance of the features for the proposed model and the related impact on the path loss prediction. In addition, the effect of hyperparameters, including activation function, number of hidden neurons in each layer, optimization algorithm, regularization factor, batch size, learning rate, and momentum, on the performance of the proposed model in terms of prediction error and prediction accuracy are also investigated.
Coalescer are commonly utilized in the realm of deep gas drying field, and its performance is contingent upon the filter. The filter consists of a support structure, a coalescing layer, and a ...drainage layer. However, research focuses more on the impact of coalescing and drainage layers on coalescence performance. The impact of the support structure and the interlayer gap of the coalescing layer on coalescence performance during the production process remains unknown. To address the issue, the experiments were performed under 0.30 m/s surface velocity and 2.50 g/min liquid loading rate with coalescing layer ranging from 1 to 6 layers. Variation of pressure drop under different supports identified a statistically significant advantage for diamond mesh over orifice. The three placement methods' specific order of pressure drop is both-sided > front-side> no-support > back-side placement. Based on experimental evidence showing that liquid film interception of droplets dominates in the coalescence process, the liquid film interception theory was proposed. Further, based on the "liquid film interception theory", the effect of interlayer gaps on coalescence performance was researched. The results show that setting interlayer gaps corresponds to an increase in separation efficiency by 1–4 %, an increase in wet pressure drop by 34.63–63.86 %, and a decrease in quality factor by 208.76 %. Therefore, to achieve high-performance filter media, interlayer gaps should be avoided during the manufacturing process.
•Liquid film interception theory is proposed in the coalescence process.•The mechanism of interlayer gap leading to sudden increase in pressure drop is elaborated.•The coalescence performance of two support structures (orifice/ diamond mesh) was compared.•The optimal type and placement of the support structure is proposed.
The next generations of wireless networks will work in frequency bands ranging from sub-6 GHz up to 100 GHz. Radio signal propagation differs here in several critical aspects from the behaviour in ...the microwave frequencies currently used. With wavelengths in the millimetre range (mmWave), both penetration loss and free-space path loss increase, while specular reflection will dominate over diffraction as an important propagation channel. Thus, current channel model protocols used for the generation of mobile networks and based on statistical parameter distributions obtained from measurements become insufficient due to the lack of deterministic information about the surroundings of the base station and the receiver-devices. These challenges call for new modelling tools for channel modelling which work in the short-wavelength/high-frequency limit and incorporate site-specific details-both indoors and outdoors. Typical high-frequency tools used in this context-besides purely statistical approaches-are based on ray-tracing techniques. Ray-tracing can become challenging when multiple reflections dominate. In this context, mesh-based energy flow methods have become popular in recent years. In this study, we compare the two approaches both in terms of accuracy and efficiency and benchmark them against traditional power balance methods.
With the development of wireless communication technology, the fifth generation mobile communications system (5G) emerges at a historic moment and devotes itself to open the curtain of the ...information age. Recently, in order to satisfy the requirement of different applications, various advanced 5G technologies have been developed in full swing. However, before applying these 5G related technologies in practical systems, effective testing methods are needed to evaluate these technologies in a real, comprehensive, rapid and flexible manner. However, the testing methods are faced with new challenges along with the continuous development of the new 5G technologies. In this paper, we present a survey of 5G testing, including solutions and opportunities. In particular, two cases are considered, i.e., channel modelling and over- the-air (OTA) testing of antenna systems. Specifically, a non-stationary channel model is proposed to characterize and test massive multiple-input multiple-output (MIMO) channel. In addition, we propose two probe subset selection algorithms for three-dimensional (3D) OTA testing, which minimizes the number of probe antennas while ensuring the accuracy of the target channel emulation. Finally, future research directions and challenges on 5G testing are given.
The Internet of Things (IoT) has rapidly expanded for a wide range of applications towards a smart future world by connecting everything. As a result, new challenges emerge in meeting the ...requirements of IoT applications while retaining optimal performance. These challenges may include power consumption, quality of service, localization, security, and accurate modeling and characterization of wireless channel propagation. Among these challenges, the latter is critical to establishing point-to-point wireless communication between the sensors. Channel modeling also varies depending on the features of the surrounding area, which have a direct impact on the propagation of wireless signals. This presents a difficult task for network planners to efficiently design and deploy IoT applications without understanding the appropriate channel model to analyze coverage and predict optimal deployment configurations. As a result, this challenge has attracted considerable interest in academic and industrial communities in recent years. Therefore, this review presents an overview of current breakthroughs in wireless IoT technologies. The challenges in such applications are then briefly reviewed, focusing on wireless channel propagation modeling and characterization. Finally, the study gives a generalized form of commonly used channel models and a summary of recent channel modeling developments for wireless IoT technology. The outcome of this review is expected to provide a new understanding of the propagation behavior of present and future wireless IoT technologies, allowing network engineers to undertake correct planning and deployment in any environment. Additionally, the study may serve as a guideline for future channel modeling and characterization studies.
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