Proposed New Framework Scheme for Path Loss in Wireless Body Area Network

Recent technical developments in wi-fi networking, microelectronic integration and programming, sensors and the Internet have enabled us to create and enforce a range of new framework schemes to fulfil the necessities of healthcare-related wireless body area network (WBAN). WBAN sensors continually screen and measure patients’ indispensable signs and symptoms, and relay them to scientific monitoring for diagnosis. WBAN has a range of applications, the most necessary of which is to help patients suffering diseases to stay alive. The quality instance is the coronary heart implant sensor, whose video display unit monitors coronary heart sign and continuously transmits it. This setup eliminates the need for patients to visit the medical doctor frequently. Instead, they can take a seat at home and acquire an analysis and prescription for the disease. Today, a sizable effort is being made to increase low-power sensors and gadgets for utility in WBAN. A new framework scheme that addresses route loss in WBAN and discusses its penalties in depth is endorsed in this paper. The new framework scheme is applied to three case scenarios to obtain parameters by measuring vital information about the human body. On-body and intrabody conversation simulations are conducted. On-body conversation findings show that the route loss between transmitter and receiver rises with growing distance and frequency

A Study on Propagation Models for 60 GHz Signals in Indoor Environments

The millimeter-waves band will enable multi-gigabit data transmission due to the large available bandwidth and it is a promising solution for the spectrum scarcity below 6 GHz in future generations of mobile networks. In particular, the 60 GHz band will play a crucial role in providing high-capacity data links for indoor applications. In this context, this tutorial presents a comprehensive review of indoor propagation models operating in the 60 GHz band, considering the main scenarios of interest. Propagation mechanisms such as reflection, diffraction, scattering, blockage, and material penetration, as well as large-scale path loss, are discussed in order to obtain a channel model for 60 GHz signals in indoor environments. Finally, comparisons were made using data obtained from a measurement campaign available in the literature in order to emphasize the importance of developing accurate channel models for future wireless communication systems operating in millimeter-waves bands.

Enhancing energy efficiency for cellular-assisted vehicular networks by online learning-based mmWave beam selection

Millimeter Wave (mmWave) technology has been regarded as a feasible approach for future vehicular communications. Nevertheless, high path loss and penetration loss raise severe questions on mmWave communications. These problems can be mitigated by directional communication, which is not easy to achieve in highly dynamic vehicular communications. The existing works addressed the beam alignment problem by designing online learning-based mmWave beam selection schemes, which can be well adapted to high dynamic vehicular scenarios. However, this kind of work focuses on network throughput rather than network energy efficiency, which ignores the consideration of energy consumption. Therefore, we propose an Energy efficiency-based FML (EFML) scheme to compensate for this shortfall. In EFML, the energy consumption is reduced as far as possible under the premise of meeting the basic data rate requirements of vehicle users, and the users requesting the same content in close proximity can be organized into the same receiving group to share the same mmWave beam. The simulation results demonstrate that, compare with the comparison method with best energy efficiency, the proposed EFML improves energy efficiency by 17–41% in different scenarios.

New method for route efficient energy calculations with mobile-sink for wireless sensor networks

<p>Despite proposing a number of algorithms and protocols, especially those related to routing, for the purpose of reducing energy consumption in wireless sensor networks, which is one of the most important issues facing this type of network. In this research paper, energy consumption and cost are calculated taking into account energy consumption and the amount of data transferred to a thousand nodes through specific paths towards the mobile sink. The proposed model simulated by sending various amounts of data with specific path to know the energy consumption of each track and the network life time with 250, 500, and 1000 bits. Cost calculated using various weight for each track of these paths and the coefficient of movement time and path loss factor and others related to the transmission and receiving circuits. And finally, the results compared with a previous method it showed the efficiency of our method used and calculating 1000 nodes with various amount of bits to show the experimental results. Deep learning used to remember each and every path of each position or nearby to avoid calculation cost later.</p>

Modelling Multilayer Communication Channel in Terahertz Band for Medical Applications

In this work we present a multi-layer channel model for terahertz communication that incorporates both layers of human body tissues and textile layers. Many research works tackled communication channel modelling in human body alone while some other research focused on textile characterization/modelling alone. There is a real gap in connecting these different models. To investigate this, a multi-layer channel model for terahertz communication is developed, this model assumes external textile layer stacked over layers of human body tissues. The electromagnetic properties of the different layers are extracted from previous works that used time domain spectroscopy (TDS) in the terahertz band to characterize each of the considered layers. The model is implemented as a flexible MATLAB/Octave program that enables the simulation of layers with either fixed or random depths. This paper aims to pave the way to connecting patients’ in-body nano-nodes with off-body (on-cloth) nano-nodes by building such a combined channel model. This helps in many applications especially in the medical field. For example, having connected nano-nodes can help in diagnosing diseases, monitoring health by sending information to the external environment, treatment (e.g., increasing or decreasing a certain dose depending on the monitoring), etc. The obtained results show how the THz signal can be affected when it propagates through heterogeneous mediums (i.e., human body tissues and textile). Various types of path-loss has been calculated for this purpose and verified by comparison with results from previous research on separate models of human body and textile.

Unmanned Aerial Vehicle Propagation Channel over Vegetation and Lake Areas: First- and Second-Order Statistical Analysis

The use of unmanned aerial vehicles (UAV) to provide services such as the Internet, goods delivery, and air taxis has become a reality in recent years. The use of these aircraft requires a secure communication between the control station and the UAV, which demands the characterization of the communication channel. This paper aims to present a measurement setup using an unmanned aircraft to acquire data for the characterization of the radio frequency channel in a propagation environment with particular vegetation (Caatinga) and a lake. This paper presents the following contributions: identification of the communication channel model that best describes the characteristics of communication; characterization of the effects of large-scale fading, such as path loss and log-normal shadowing; characterization of small-scale fading (multipath and Doppler); and estimation of the aircraft speed from the identified Doppler frequency.

Dynamic spectrum management using frequency selection at licensed and unlicensed bands for efficient vehicle-to-vehicle communication

Background: The exponential increase in pattern of vehicles on the roads demands a need to develop a vehicular infrastructure that may not only ease congestions and provide a better experience but also pivot the levels of safety among users. The development of wireless technology has made it convenient for machines, devices and vehicles to interact with one another. The efficacy of this wireless communications relies on utilising current and available technology to enable information to be shared efficiently. In the wake of the available advancement in wireless technology, a new dynamic spectrum management (DSM) in vehicle-to-vehicle (V2V) communication that coexists with the existing Long-Term Evolution (LTE) network to increase the throughput in V2V communication is proposed. This will provide some solutions to enable a more efficient vehicular infrastructure. Methods: This paper focuses on the utilization of DSM in V2V communications by selecting an appropriate frequency band through the selection of available licensed and unlicensed frequency bands for vehicles. Further investigations are done to identify the effect of interference in the dynamic spectrum by observing the path loss, SINR, and the throughput with various interfering users. Results: The results show that the performance of the proposed DSM augments a significant improvement in the overall throughput and the signal-to-interference-plus-noise ratio (SINR) value is reduced by up to 60% when compared to the fixed spectrum allocation. Conclusions: Although the dynamic spectrum is still affected by the interference from the existing cellular users, the throughput of the dynamic spectrum remains sufficient to transmit the information to other vehicles.

Investigating Automated Hyper-Parameter Optimization for a Generalized Path Loss Model

This work aims at developing a generalized and optimized path loss model that considers rural, suburban, urban, and urban high rise environments over different frequencies, for use in the Heterogenous Ultra Dense Networks in 5G. Five different machine learning algorithms were tested on four combined datasets, with a sum of 12369 samples in which their hyper-parameters were automatically optimized using Bayesian optimization, HyperBand and Asynchronous Successive Halving (ASHA). For the Bayesian optimization, three surrogate models (the Gaussian Process, Tree Structured Parzen Estimator and Random Forest) were considered. To the best of our knowledge, few works have been found on automatic hyper-parameter optimization for path loss prediction and none of the works used the aforementioned optimization techniques. Differentiation among the various environments was achieved by the assignment of the clutter height values based on International Telecommunication Union Recommendation (ITU-R) P.452-16. We also included the elevation of the transmitting antenna position as a feature so as to capture its effect on path loss. The best machine learning model observed is K Nearest Neighbor (KNN), achieving mean Coefficient of Determination (R2), average Mean Absolute Error (MAE) and mean Root Mean Squared Error (RMSE) values of 0.7713, 4.8860dB, and 6.8944dB, respectively, obtained from 100 different samplings of train set and test set. Results show that machine learning can also be used to develop path loss models that are valid for a certain range of distances, frequencies, antenna heights, and environment types. HyperBand produced hyper-parameter configurations with the highest accuracy in most of the algorithms.