Implementing Basic Designs And Architectures Of Self Organizing Networks

The last best practice evaluated is a new optimization and management services process that monitors, updates and implements third-generation (3G) data services. The article mentions two very important influences in the wireless cellular network industry are "the increasing complexity of wireless network services and the increasing customer demands and competitive pressures upon cellular service providers." (Buvaneswari, Ravishankar, Graybeal, Haner & Rittenhouse, 2004) This new process will not only increase cellular network quality, speed and flexibility but it will also allow these companies to "monitor, diagnose, and optimize their increasingly complex networks." (Buvaneswari, et al., 2004) All five best practices have strengths and weaknesses covered further on in the paper.

In this assignment, you will write a report detailing your observations on the transformation from early dial-up,

In terms of architecture, it has been very challenging to provide platform, and ability to render service with high availability from the providers of the first generation (1G) networks to 4G

The research paper will also include the analysis of mobility between systems working on 3GPP and non 3GPP standards. Mobile operators are working on Network Densification by deploying

When Cellular phones first came out the main use was to make phone calls. The first generation of the cell phone transmitted information the same analog format as a fixed line. The second generation (2G) transmission switched to digital which gave cell phones the ability to

History of mobile telecommunication started with the launching of first (IG), second (2G) and third (3G) generations telecommunication technologies. While the 1G is analogue, the 2G and 3G were digital with the

Most efforts have been focused on developing a stand-alone entity for each self-organizing mechanism, that will run in parallel with other, as well as designing coordination mechanisms in charge of stabilizing the network as whole. In this paper, a unified self-management mechanism based on Fuzzy Logic and Reinforcement Learning is proposed. Algorithm modifies handover parameters to optimize the Performance Indicators related to LB and HOO. Results show that it effectively provides better performance than independent entities running simultaneously in the

As mobile network operators undertake the transition from voice-oriented to data-oriented, they are becoming increasingly vulnerable to new types of data networking outages. This is due to the growing complexity of the mobile network as it becomes increasingly IP based.

Wireless communication has come a long way since weighing around 1.1 kg in 1983 to the myriad of smartphones available in the market today that are lightweight and loaded with endless features.

Further, the concepts of static/dynamic, labeled /unlabeled network structures are explained in depth on how the techniques are

“A model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.”

In summary, input and output devices are an essential part of business today. It is guaranteed that whether a company needs to print questionnaires, give telephone surveys, print checks, manage retail tags, or write long documents, that there are I/O devices running behind the scenes. Without these devices, users would not be able to communicate with computers in an information

The nature of the public network has changed. Demand for Internet Protocol (IP) data is growing at a compound annual rate of between 100% and 800%1, while voice demand remains stable. What was once a predominantly circuit switched network handling mainly circuit switched voice traffic has become a circuit-switched network handling mainly IP data. Because the nature of the traffic is not well matched to the underlying technology, this network is proving very costly to scale. User spending has not increased proportionally to the rate of bandwidth increase, and carrier revenue growth is stuck at the lower end of 10% to 20% per year. The result is that carriers

In machine learning, systems are trained to infer patterns from observational data. A particularly simple type of pattern, a mapping between input and output, can be learnt through a process called supervised learning. A supervised-learning system is given training data consisting of example inputs and the corresponding outputs, and comes up with a model to explain those data (a process called function approximation). It does this by choosing from a class of model specified by the system’s designer. [Nature. ANN 4]

This project report helps to understand the project on SIGNAL BOOSTING USING SMALL CELLS. The main aim of this project is to enhance the signal strength of a carrier network in areas where there will be low network coverage in and around MSU. This can be achieved using small cells which acts as network boosters. Thus, people can make use of this product despite of low network. Each small cell is able to boost signal of a particular carrier, so different small cells are used for different carriers. This project is planned not to work continuously but only when required. If there are events in university, then we can set up the equipment and run the service. For better understanding of the project, it is divided into smaller sections, each section is described clearly, which adds to the overall project. There are specific deliverables for each section which explains in depth about the planning, developing, controlling, and executing of project.