Design and Development of an RF Coverage Optimization System using Spatio-Temporal Mobile User Densities and Autonomic Network Management Approach
|Budget||Rs. 9.08 million|
|Status||On Going Project|
|Progress Report||View Progress Report|
The proposed system consist of numerous functions comprising a SW application running on an open source mobile handset that collects real network data and signaling messages , its geographical location and the time stamp among other parameters. It also acts as an autonomic network element that is part of an autonomic network management systems comprising other end users mobile handsets collating real life management data and transmitting it to a centralized Network Node which is collecting all of that data and performs node authentication, data validation, data analysis functions and creates necessary reports determining the state of key performance indices and recommends actions, if any, that might be necessary. The proposed solution is different from the conventional approach in that it does not depend on the existing operator owned network infrastructure to gather key network performance indices and in that sense is a shift in the current paradigm of RF coverage optimization. It is also feasible since mobile handsets have now technologically evolved where availability of location information has become a matter of routine due to the push by players like Google who have taken some very bold initiatives in recent years and introduced open source operating systems that are very suitable for location based services offering in a drive to increase the applications that can be offered on a mobile cellular data network. We intend to exploit this extra dimension, becoming available now, for a more reliable RF coverage optimization solution than what have traditionally been available. There would be several benefits of the project: 1. The database would be an indication of the network performance. 2. Optimization procedures would require minimal drive tests activities, as majority testing would be covered by this project. 3. Resource allocation can be done efficiently, in accordance with the user density. 4. It can serve as a performance measurement tool for the regulatory authorities (for instance PTA). As the tool would work autonomic-ally, the end user would also get to know the signal strength of different network operators.