Stochastic Geometry and Performance Analysis of Large Scale Wireless Networks

Stochastic Geometry has attained massive growth in modelling and analysing of wireless network. This suits well for analysing the performance of large scale wireless network with random topologies. Analytical framework is established to evaluate the performance of the network. Here we have created a mathematical model for uplink analysis and the gain of uplink and downlink is obtained. Then ad-hoc network architecture is designed and the performance of the network is compared with the traditional method. Finally, a new scheduling algorithm is developed for cellular network and the gain parameter is quantified with the help of Stochastic Geometry tool. The accuracy is acquired from extensive Monte Carlo simulator.

Developing Monte Carlo Simulator of Reinforcement Learning Type

Monte Carlo methods are a way to solve the reinforcement learning problem based on average test results. To ensure that well-defined results are available, Monte Carlo methods are used only for episodic tasks. The Monte Carlo term is often used more widely in any valuation method whose operation involves significant participation on a random basis. Here it is specifically used for methods based on the average of full results (as opposed to methods that are learned from incomplete results). The paper describes a simulator for estimating raindrops in a specific area using the package matlib. Keywords: Monte Carlo, reinforcement learning, simulation, matlib, python

ASMODEUS Meteor Simulation Tool

<p><span class="fontstyle0">We describe a universal meteor simulation tool set named A</span><span class="fontstyle0">SMODEUS </span><span class="fontstyle0">and present several of its possible use cases. The toolset consists of a Monte-Carlo simulator of meteoroids entering the Earth</span><span class="fontstyle2">’</span><span class="fontstyle0">s atmosphere, functions for transformation to observer-centred coordinate frames representing virtual views of the sky, application of observational bias effects and a number of statistical tools for analyses of produced data sets and comparison to real-world data. The simulation has already been used in several areas of research, most notably estimates of meteoroid </span><span class="fontstyle3">fl</span><span class="fontstyle0">ux and de-biasing of real-world meteor observations and in investigation of how varying the initial properties of meteoroids affects the resulting meteors. It lends itself to many more possible applications, such as assessment of selection bias in ground-based observing systems, investigation of models of meteor </span><span class="fontstyle3">fl</span><span class="fontstyle0">ight and ablation, and evaluation of mass and population indices of meteor showers.</span></p>