Sairam Subramanian

Demand modeling and growth planning for TDMA-based wireless networks

Wireless service providers are facing the need to plan and rapidly evolve their networks to meet subscriber demands. This includes adding switching and radio equipment. At the same time, providers want to maximize the return on their investment by yielding the most subscriber capacity from their network. As networks evolve, large numbers of base transceiver stations (BTSs) along with base station controllers (BSCs) and mobile switching centers (MSCs) are added to support subscriber growth and demand. How to deploy this equipment and maximize performance is a complex problem, which calls for sound and useful methodologies. In order to assess the performance of the network, the subscribers impacts must be known. Without the understanding of how the subscribers will tax the network resources, the providers will not be able to make the most of their investment and ensure quality and grade of service. This paper proposes techniques for modeling subscriber demand and behavior, which is the key in determining how networks will perform as the subscriber base grows and the network evolves. These scientific techniques were developed to generate accurate representations of subscriber behavior to be used in the planning of wireless networks.

Wireless networks self engineering engine

Each wireless network system has its own unique operating conditions. The geographical characteristics, RF conditions, call traffic and subscriber mobility of one network are different from others. In order to achieve maximum capacity and optimal performance of a network, the system parameters of the network must be engineered to fit the networks operating conditions. However, because of the complexity of wireless network system engineering, there has not been much success in developing effective engineering rules that are applicable to all systems. This lack of accurate universal engineering rules makes the process of wireless network engineering laborious, expensive and customized. A self engineering engine presented in this paper overcomes the difficulties in the current wireless network engineering practice. The adoption of the self engineering engine in the wireless network system will enable the system to monitor and engineer itself with the goal of achieving maximum capacity and optimal performance.