Archive Browsing VOLUME 8 ISSUE 03 OCTOBER 2019

Load Prediction using Deep Reinforced Learning

Authors: Prashant Dutta

File Name: Prashant_80300-19-103.pdf
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Study of Radio Resources Allocation in LTE for QoS
Abstract: In this article we are interested in an important task of the eNodeB in the LTE network architecture, it is the RRM (Radio Resource Management) its goal is to accept or reject requests for connection to the network, ensuring an optimal distribution of radio resources between the UEs (Users Equipments). It consists mainly of two elements AC (Admission Control) and PS (Packet Scheduling). In this work we will focus on the PS, which realizes an efficient allocation of radio resources in both directions i.e. Uplink (considered in our case) and Downlink. Several approaches and algorithms have been proposed in the literature to meet this need (allocate resources efficiently), this diversity and multitude of algorithms are linked to the factors considered allowing the optimal management of radio resource, specifically the type of traffic and QoS requested by the UE. In this article, a study of several proposed scheduling algorithms for LTE (uplink and downlink) is made. Therefore, we offer our evaluation and reviews.

Authors: Gajanan Uttam Patil, Dr. Girish Ashok Kulkarni

File Name: Gajanan_80300-19-102.pdf
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Impact of Cognitive Radio in Spectrum Allocation
Abstract: Cognitive radio is born as a method that proposes a solution to the problem, dynamically managing the resource. One of the stages that integrate this technology is the spectrum decision, in which the frequency bands are selected and assigned based on the quality of service requirements of the cognitive users. This paper analyses the fundamental elements that make up the cognitive radio and the allocation of spectral bands. Among the main problems in the insertion of new applications and wireless technologies, there is a lack of radio spectrum for its allocation, due to the inefficient way of the distribution of the available radio spectrum, which is currently assigned statically. In addition, a synthesis of the development platforms most used in the research on this technology and of the activities developed in order to find guidelines for the adaptable use of the spectrum

Authors: Anilkumar Dulichand Vishwakarma, Dr. Girish Ashok Kulkarni

File Name: Anil_80300-19-101.pdf
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Comparative Analysis of LEACH, DEEC and DDEEC
Abstract: This paper is based on wireless sensor networks, in conjunction with an appropriate management methodology that allows us to analyze and verify the behaviour of these wireless networks. To make WSN energy efficient and to increase the lifetime of the network, this research work presents Distributed Energy-Efficient Clustering (DEEC), Developed DEEC (DDEEC) and Low-Energy Adaptive Clustering Hierarchy (LEACH). Performance of this approach is evaluated using certain evaluation parameters; Throughput and Network Lifetime.

Authors: Sonali Sopan Patil, Prof. Sarang Dagajirao Patil

File Name: Sonali_80300-19-104.pdf
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