An intelligent method for resource management in wireless networks

In wireless cellular network, resource constraint has become a critical and important issue. Users always and everywhere expect telecommunication systems with the best quality. They also need visual and multimedia communication. So, an intelligent wireless network that has the ability to adapt to environment in different network traffics is needed. The intelligent network has the capability to decide and modify itself. One of these intelligent methods is the use of multi-agent system. The main criteria considered in resource management of cellular networks are rate of dropped calls and blocked calls. Blocked calls include new calls and dropped calls include calls which are made by transition a mobile from a cell to another cell. In this project, we take a look at former techniques and then we will propose solutions to reduce the two mentioned criteria based on intelligent agents. The main purposes of this article are: reducing dropped calls, reducing blocked calls and decreasing traffic load variance between several cells and balancing among them. The results show that implementing multi-agnet system concerning intelligence and using agnets in proposed method has noticeable improvement than other methods in decreasing blocked calls and dropped calls.

[1]  Rajeev Babbar Agent based resource management in 3G wireless networks , 2005 .

[2]  Parag C. Pendharkar,et al.  A Multi-Agent Distributed Channel Allocation Approach for Wireless Networks , 2006, IEEE Vehicular Technology Conference.

[3]  Symeon Papavassiliou,et al.  Mobile agent-based approach for efficient network management and resource allocation: framework and applications , 2002, IEEE J. Sel. Areas Commun..

[4]  Dan Keun Sung,et al.  An adaptive channel reservation scheme for soft handoff in cellular DS-CDMA systems , 1999, 1999 IEEE 49th Vehicular Technology Conference (Cat. No.99CH36363).

[5]  J.-S. Chen,et al.  Fixed channel allocation scheme performance enhancement for cellular mobile systems , 2002 .

[6]  Shamila Makki Traffic congestion control in communication networks , 2007 .

[7]  K. Al Agha,et al.  A communicating scheme for adaptive resource allocation in wireless networks , 1998, ICUPC '98. IEEE 1998 International Conference on Universal Personal Communications. Conference Proceedings (Cat. No.98TH8384).

[8]  T. C. Jannett,et al.  A comparison of agent paradigms for resource management in distributed sensor networks , 2007 .

[9]  Y. Ahmet Sekercioglu,et al.  A Traveling Distance Prediction Based Method to Minimize Unnecessary Handovers from Cellular Networks to WLANs , 2008, IEEE Communications Letters.

[10]  Dorothy Okello,et al.  Future-Generation Wireless Networks: Opportunities and Challenges , 2004 .

[11]  Neelam Soundarajan,et al.  On Distributed Dynamic Channel Allocation in Mobile Cellular Networks , 2002, IEEE Trans. Parallel Distributed Syst..

[12]  Peng Jiang,et al.  Intelligent Control Of Cellular Network Coverage Using Semi-Smart Antennas For Load Balancing , 2008, 2008 International Conference on Signal Processing, Communications and Networking.

[13]  Nupur Giri,et al.  Resource Management for Cellular Network Using Socially Intelligent Multi Agent System , 2010, 2010 International Conference on Recent Trends in Information, Telecommunication and Computing.