Performance of electromagnetic communication in underwater wireless sensor networks

Underwater wireless sensor networks (WSNs) composed of a number of sensor nodes that are deployed to conduct a collaborative monitoring task. Wireless signals are used for communication between the sensor nodes. Acoustic signals are the dominant signals used as a wireless communication medium in underwater WSNs due to the relatively low absorption in the underwater environments. Acoustic signals face a lot of challenges such as ambient noise, manmade noise, limited bandwidth, multipath and low propagation speed. Some of these challenges become more severe in shallow water environment where a high level of ambient and mankind noise, turbidity and multipath propagation are available. Therefore, electromagnetic signals can be applied as an alternative communication signal for underwater WSNs in the shallow water. In this project, the performance of EM communication in underwater WSNs is investigated for the shallow water environment. Theoretical calculations and practical experiments are conducted in fresh and seawater. It is shown that signals propagate for longer ranges in freshwater comparing to seawater. Theoretical results show that attenuation of electromagnetic communication in seawater is much higher than in fresh water. The attenuation is increasing with the increasing of frequency. In addition, velocity of the signal is increasing as the frequency is increasing while loss tangent is decreasing as the frequency increasing. Based on practical experiments, freshwater medium permits short ranges EM communication that does not exceed 25.1 cm for 2.4 GHz frequency. On the other hand, communication in seawater is very difficult to achieve for the same high frequency. Path loss exponent was estimated for freshwater environment based on logdistance path loss model. The estimation was achieved through a comparison between theoretical calculations and practical measurements. The path loss exponent for EM communication in fresh water was estimated to be in the range of 2.3 to 2.4

Artifact paths removal algorithm for ultra-wideband channels

Ultra-wideband (UWB) is a promising technology for achieving high data rate communications. When UWB channel measurements are conducted, channel impulse responses (CIRs) are extracted from measured UWB waveforms using CLEAN deconvolution algorithm. However, artifact paths that represent unreal received multipath components (MPCs) are generated during this process. These artifact paths are registered as part of the measured CIRs representing a reflected signal from a scatterer. In reality, these paths do not represent a real scattering environment and this affects accurate channel modeling. Therefore, removal of the artifact paths is important to conserve better and have a more real scattering environment. In this work, an algorithm was developed to remove artifact paths from measured CIRs. The algorithm development was achieved based on the concept of geometric elliptical modeling applied to wideband channels, where the effective path in each ellipse is utilized to represent the channel response of the ellipse. Several UWB channel measurements were conducted to obtain the measured UWB waveforms. In addition, the characteristics of the UWB channels were analyzed in terms of CIRs properties and their stationarity regions. The algorithm performance was evaluated by comparing the single-template CLEAN CIRs with the CIRs result from the application of the developed algorithm on single-template CLEAN CIRs. Results showed that the developed algorithm can successfully remove the artifact paths. Besides that, an enhancement in the received power was achieved. For a specific measured channel, the received power enhancement obtained was more than 5%. The algorithm is beneficial for enhancing accuracy of CIRs extracted from a single-template CLEAN algorithm. Consequently, more accurate channel characteristics are gained leading to improved channel modelling and different parameter extractions

A New Beamforming Approach Using 60 GHz Antenna Arrays for Multi-Beams 5G Applications

Recent studies and research have centred on new solutions in different elements and stages to the increasing energy and data rate demands for the fifth generation and beyond (B5G). Based on a new-efficient digital beamforming approach for 5G wireless communication networks, this work offers a compact-size circular patch antenna operating at 60 GHz and covering a 4 GHz spectrum bandwidth. Massive Multiple Input Multiple Output (M−MIMO) and beamforming technology build and simulate an active multiple beams antenna system. Thirty-two linear and sixty-four planar antenna array configurations are modelled and constructed to work as base stations for 5G mobile communication networks. Furthermore, a new beamforming approach called Projection Noise Correlation Matrix (PNCM) is presented to compute and optimise the fed weights of the array elements. The key idea of the PNCM method is to sample a portion of the measured noise correlation matrix uniformly in order to provide the best representation of the entire measured matrix. The sampled data will then be utilised to build a projected matrix using the pseudoinverse approach in order to determine the best fit solution for a system and prevent any potential singularities caused by the matrix inversion process. The PNCM is a low-complexity method since it avoids eigenvalue decomposition and computing the entire matrix inversion procedure and does not require including signal and interference correlation matrices in the weight optimisation process. The suggested approach is compared to three standard beamforming methods based on an intensive Monte Carlo simulation to demonstrate its advantage. The experiment results reveal that the proposed method delivers the best Signal to Interference Ratio (SIR) augmentation among the compared beamformers

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Prospects of 5G Communications

The next generation of wireless communication is going to meet human demands beyond today’s trend. This study sets the frame on the future of wireless communication that requires real time responses which pushes this technology towards lower latency and conflicting required band for whole system. Moreover, the challenges for this hot topic are highlighted. So this study had shed light on the important studies that had been done to get to the bottom of these issues

In-band device to device (D2D) communication and device discovery: A survey

Device to device (D2D) communication is one of the potentials to achieve the established standards for 5G. It represents a direct communication between two devices located in the vicinity of each other. In D2D communication, user’s data traffic can be offloaded without passing through the base transceiver system (BTS) and the core network. In order to initiate the D2D communication, device discovery is needed, and it is a major design issue for D2D communication. To achieve a potential discovery, there are some requirements such as energy efficient discovery capability, supporting a large number of devices and proximity discovery in the network assisted underlay D2D communication network. In this paper, device discovery processes and methods are presented and assessed using different approaches. In addition, a device discovery technique is proposed for single cell and a multi cell which capable to provide accurate and fast discovery with energy efficient and optimized routed path. D2D discovery techniques are evaluated, its main challenges are analyzed and potential solutions are also suggested

Device discovery signal design for proximal devices in D2D communication

To initiate Device to Device (D2D) communication, proximal device discovery is the most important problem in the contemporary cellular networks. By using reference signal (RS), devices can be discovered for a D2D companion through the proximal discovery process. We propose an RS model for proximal device discovery and discovery resources multiplexing using Zadoff–Chu (ZC) sequence for RS transmission and detection. The proposed proximal device discovery model is robust for collision avoidance, and provides rapid and accurate discovery in a dense area. The performance of proposed discovery procedure is verified numerically and by simulation. Random search and the smart random search algorithms are applied for evaluation of proposed model in terms of discovery time and discovered devices

Measurement and parameter description of time-varying ultra-wideband infostation channel

In this article, we present the measurement and description of channel parameters for the time-varying Infostation UWB channel. We also consider how such parameters can be used to improve system performance in terms of optimally combating inter-symbol interference (ISI) and inter-channel interference (ICI) in the case of multiband OFDM

Variable step-size l0-norm NLMS algorithm for sparse channel estimation

Wireless communication systems often require accurate channel state information (CSI) at the receiver side. Typically, the CSI can be obtained from channel impulse response (CIR). Measurements have shown that the CIR of wideband channels are often sparse. To this end, the least mean square (LMS)-based algorithms have been used to estimate the CIR at the receiver side, which unfortunately is not able to accurately estimate sparse channels. In this paper, we propose a variable step-size l0-norm normalized LMS (NLMS) algorithm. The step-size is varied with respect to changes in the mean square error (MSE), allowing the filter to track changes in the system as well as produce smaller steady-state errors. We present simulation results and compare the performance of the new algorithm with the invariable step-size NLMS (ISS-NLMS), variable step-size NLMS (VSS-NLMS) and the invariable step-size l0-NLMS (ISS-l0-NLMS) algorithms. The results show that the proposed algorithm improves the identification of sparse systems