In this paper, the traffic characteristics have been Hence, traffic engineering is required to support different studied by collecting traces from a CDMA2000 cellular applications as they have different service requirements. wireless network that provides services like messaging, For optimum performance, researchers and engineers must video streaming, e-mail, internet, song downloading. The devise efficient techniques for mobility management and traces record call activities including call initiation time, resource allocation to meet next generation demand. termination time, originating node identification number, To achieve the goal of designing robust and reliable packet size, home station id, foreign station id (when cellular wireless networks, understanding the roaming), handoffs. Traffic parameters namely, call intercharacteristics of traffic and mobility prediction [14] plays arrival times and call holding times were estimated using a very critical role [9]. Empirical studies of measured statistical methods. The results show that call inter-arrival traffic traces have led to the wide recognition of selftime distribution in this CDMA cellular wireless network is similarity in wired network traffic [2,3]. Multiclass heavy-tailed and can be modeled by gamma as well as Ethernet traffic exhibits dependencies over a long range of weibull distributions and are asymptotically long-range time scales [1,3]. This is to be contrasted with telephone dependent. It is also found that the call holding times are traffic which is Poisson in its arrival and exponential in best fitted with lognormal distribution and are not departure. In traditional Poisson traffic [16], the short-term correlated. An analytical model based on our observations fluctuations would average out, when it is integrated over a for performance measures of a circuit-switched cellular longer time domain and would come out with a constant wireless network with multiclass traffic sources is also mean value. The presence of multiclass traffic in cellular proposed.wireless networks brings it closer to the wired networks Keywords: Cellular wireless networks, multiclass traffic, and does not gurantee to behave the traditional way it is long-range dependence, heavy-tailed distribution, circuitmodelled for [15,4,5,12]. Multimedia application, switch multiplexing. messaging, internet applications,, e-commerce etc may cause the traffic to show self-similarity like wired networks I. INTRODUCTION [1] and hence, many of the previous assumptions upon which cellular wireless systems have been built may no With the advancement of technology and diminishing longer be valid in the presence of self-similarity [ 1]. cost, mobile phone is becoming the primary mode of To analyze the network performance and resource communication with anywhere anytime service. To cope up utilization, correct modeling of the network traffic is with the demand the future wireless networks will combine required. Most of the works in cellular wireless network high-speed mobile communications wit...
A photoredox-catalyzed direct arylation of quinoxalin-2-(1H)-ones using diaryliodonium triflates as the convenient, stable, and cheap aryl source is described. A broad variety of quinoxalin-2-(1H)-ones are shown to react with structurally and electronically diverse diaryliodonium triflates, allowing efficient access to a wide variety of pharmaceutically important 3-arylquinoxalin-2-(1H)-ones. The presented method is attractive with regard to operational simplicity, mild conditions, broad scope, scalability, and high functional group tolerance.
The effects of global warming are felt not only in the Earth’s climate but also in the geology of the planet. Modest variations in stress and pore-fluid pressure brought on by temperature variations, precipitation, air pressure, and snow coverage are hypothesized to influence seismicity on local and regional scales. Earthquakes can be anticipated by intelligently evaluating historical climatic datasets and earthquake catalogs that have been collected all over the world. This study attempts to predict the magnitude of the next probable earthquake by evaluating climate data along with eight mathematically calculated seismic parameters. Global temperature has been selected as the only climatic variable for this research, as it substantially affects the planet’s ecosystem and civilization. Three popular deep neural network models, namely, long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and transformer models, were used to predict the magnitude of the next earthquakes in three seismic regions: Japan, Indonesia, and the Hindu-Kush Karakoram Himalayan (HKKH) region. Several well-known metrics, such as the mean absolute error (MAE), mean squared error (MSE), log-cosh loss, and mean squared logarithmic error (MSLE), have been used to analyse these models. All models eventually settle on a small value for these cost functions, demonstrating the accuracy of these models in predicting earthquake magnitudes. These approaches produce significant and encouraging results when used to predict earthquake magnitude at diverse places, opening the way for the ultimate robust prediction mechanism that has not yet been created.
The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users. Due to this popularity, there has been a huge rise in mobile data volume, applications, types of services, and number of customers. Furthermore, due to the COVID‐19 pandemic, the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home. This massive increase in demand for multi‐class services has posed numerous challenges to wireless network frameworks. The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic, such as real‐time live streaming of videos, audios, text, images etc., at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers. Next‐generation wireless networks (NGWNs, i.e. 5G networks and beyond) are being developed to accommodate the service qualities mentioned above and many more. However, achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers. It requires the analysis of a huge volume of network data (structured and unstructured) received or collected from heterogeneous devices, applications, services, and customers and the effective and dynamic management of network parameters based on this analysis in real time. In the ever‐increasing network heterogeneity and complexity, machine learning (ML) techniques may become an efficient tool for effectively managing these issues. In recent days, the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain. This study discusses current wireless network research, brief discussions on ML methods that can be effectively applied to the wireless networking domain, some tools available to support and customise efficient mobile system design, and some unresolved issues for future research directions.
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