Outage probability analysis of EH relay-assisted non-orthogonal multiple access (NOMA) systems over Block Rayleigh Fading Channel

Non-orthogonal multiple access (NOMA) has been identified as a promising multiple access technique for the fifth generation (5G) mobile networks due to its superior spectral efficiency. In this paper, we propose and investigate a Non-Orthogonal Multiple Access (NOMA) of energy harvesting (EH) relay assisted system over Block Rayleigh Fading Channel. In order to evaluate the performance of the proposed system, the integral expression of the outage probability is analyzed and derived. Numerical results confirm that our derived analytical results match well with the Monte Carlo simulations in connection with all possible system parameter

Half-duplex power beacon-assisted energy harvesting relaying networks: system performance analysis

In this work, the half-duplex (HF) power beacon-assisted (PB) energy harvesting (EH) relaying network, which consists of a source (S), Relay (R), destination (D) and a power beacon (PB) are introduced and investigated. Firstly, the analytical expressions of the system performance in term of outage probability (OP) and the system throughput (ST) are analyzed and derived in both amplify-and-forward (AF) and decode-and-forward (DF) modes. After that, we verify the correctness of the analytical analysis by using Monte-Carlo simulation in connection with the primary system parameters. From the numerical results, we can see that all the analytical and the simulation results are matched well with each other

Performance analysis for power-splitting energy harvesting based two-way full-duplex relaying network over nakagami-m fading channel

Energy harvesting relay network is considered as the promising solution for a wireless communication network in our time. In this research, we present and demonstrate the system performance of the energy harvesting based two-way full-duplex relaying network over Nakagami-m fading environment. Firstly, we propose the analytical expressions of the achievable throughput and outage probability of the proposed system. In the second step, the effect of various system parameters on the system performance is presented and investigated. In the final step, the analytical results are also demonstrated by Monte-Carlo simulation. The numerical results demonstrated and convinced the analytical and the simulation results are agreed with each other

Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications

This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts: (i) the active-transmit message transmitted by the transmitter using conventional RF signals and (ii) the backscatter message transmitted by an ambient backscatter tag that backscatters upon the active signals emitted by the transmitter. Notably, the backscatter tag does not generate its own signal, making it difficult for an eavesdropper to detect the backscattered signals unless they have prior knowledge of the system. Here, we assume that without decoding/knowing the backscatter message, the eavesdropper is unable to decode the original message. Even in scenarios where the eavesdropper can capture both messages, reconstructing the original message is a complex task without understanding the intricacies of the message-splitting mechanism. A challenge in our proposed framework is to effectively decode the backscattered signals at the receiver, often accomplished using the maximum likelihood (MLK) approach. However, such a method may require a complex mathematical model together with perfect channel state information (CSI). To address this issue, we develop a novel deep meta-learning-based signal detector that can not only effectively decode the weak backscattered signals without requiring perfect CSI but also quickly adapt to a new wireless environment with very little knowledge. Simulation results show that our proposed learning approach, without requiring perfect CSI and complex mathematical model, can achieve a bit error ratio close to that of the MLK-based approach. They also clearly show the efficiency of the proposed approach in dealing with eavesdropping attacks and the lack of training data for deep learning models in practical scenarios

Sustainability analysis of methane-to-hydrogen-to-ammonia conversion by integration of high-temperature plasma and non-thermal plasma processes

The Covid era has made us aware of the need for resilient, self-sufficient, and local production. We are likely willing to pay an extra price for that quality. Ammonia (NH3) synthesis accounts for 2 % of global energy production and is an important point of attention for the development of green energy technologies. Therefore, we propose a thermally integrated process for H2 production and NH3 synthesis using plasma technology, and we evaluate its techno-economic performance and CO2 footprint by life cycle assessment (LCA). The key is to integrate energy-wise a high-temperature plasma (HTP) process, with a (low-temperature) non-thermal plasma (NTP) process and to envision their joint economic potential. This particularly means raising the temperature of the NTP process, which is typically below 100 °C, taking advantage of the heat released from the HTP process. For that purpose, we proposed the integrated process and conducted chemical kinetics simulations in the NTP section to determine the thermodynamically feasible operating window of this novel combined plasma process. The results suggest that an NH3 yield of 2.2 mol% can be attained at 302 °C at an energy yield of 1.1 g NH3/kWh. Cost calculations show that the economic performance is far from commercial, mainly because of the too low energy yield of the NTP process. However, when we base our costs on the best literature value and plausible future scenarios for the NTP energy yield, we reach a cost prediction below 452 $/tonne NH3, which is competitive with conventional small-scale Haber-Bosch NH3 synthesis for distributed production. In addition, we demonstrate that biogas can be used as feed, thus allowing the proposed integrated reactor concept to be part of a biogas-to-ammonia circular concept. Moreover, by LCA we demonstrate the environmental benefits of the proposed plant, which could cut by half the carbon emissions when supplied by photovoltaic electricity, and even invert the carbon balance when supplied by wind power due to the avoided emissions of the carbon black credits

Awareness and preparedness of healthcare workers against the first wave of the COVID-19 pandemic: A cross-sectional survey across 57 countries.

BACKGROUND: Since the COVID-19 pandemic began, there have been concerns related to the preparedness of healthcare workers (HCWs). This study aimed to describe the level of awareness and preparedness of hospital HCWs at the time of the first wave. METHODS: This multinational, multicenter, cross-sectional survey was conducted among hospital HCWs from February to May 2020. We used a hierarchical logistic regression multivariate analysis to adjust the influence of variables based on awareness and preparedness. We then used association rule mining to identify relationships between HCW confidence in handling suspected COVID-19 patients and prior COVID-19 case-management training. RESULTS: We surveyed 24,653 HCWs from 371 hospitals across 57 countries and received 17,302 responses from 70.2% HCWs overall. The median COVID-19 preparedness score was 11.0 (interquartile range [IQR] = 6.0-14.0) and the median awareness score was 29.6 (IQR = 26.6-32.6). HCWs at COVID-19 designated facilities with previous outbreak experience, or HCWs who were trained for dealing with the SARS-CoV-2 outbreak, had significantly higher levels of preparedness and awareness (p<0.001). Association rule mining suggests that nurses and doctors who had a 'great-extent-of-confidence' in handling suspected COVID-19 patients had participated in COVID-19 training courses. Male participants (mean difference = 0.34; 95% CI = 0.22, 0.46; p<0.001) and nurses (mean difference = 0.67; 95% CI = 0.53, 0.81; p<0.001) had higher preparedness scores compared to women participants and doctors. INTERPRETATION: There was an unsurprising high level of awareness and preparedness among HCWs who participated in COVID-19 training courses. However, disparity existed along the lines of gender and type of HCW. It is unknown whether the difference in COVID-19 preparedness that we detected early in the pandemic may have translated into disproportionate SARS-CoV-2 burden of disease by gender or HCW type