Load Balancing using Backpropagation in Mobile Networks
Mobile traffic is increasing rapidly as the number of mobile subscribers are increasing everyday. This results in uneven traffic in certain areas. Load balancing is a complex problem with dynamically varying inputs and outputs. This complex system is difficult to be modeled with conventional mathematical tools/methods.We use a multilayer recurrent neural network to model this problem. Backpropagation algorithm is used to train this multilayer recurrent neural network. Initially, the network is trained with previous mobile traffic data and tested for different traffic conditions.It is found to predict the loads in the base stations. This information helps in balancing the load on the base station by borrowing channels from an appropriate base station.
Sajal K. Das, Sanjoy K. Sen and Rajeev Jayaram,”A Novel Load Balancing Scheme for the Tele-Traffic Hot Spot Problem in Cellular Networks.
Sajal K. Das and Sanjoy K. Sen and Rajeev Jayaram,” A Structured Channel Borrowing Scheme for Dynamic Load Balancing in Cellular Networks,International Conference on Distributed Computing Systems,1997
Guohong Cao,”Integrating Distributed Channel Allocation and Adaptive Handoff Management for QoS-Sensitive Cellular Networks,Wireless Networks,2003, vol 9,
Sajal K. Das, Sanjoy K. Sen and Rajeev Jayaram,”A Dynamic Load Balancing Strategy for Channel Assignment Using Selective Borrowing in Cellular Mobile Environment,wireless networks, 1996,pp 1-25.
S.K. Das, S.K. Sen, R. Jayaram,”A structured channel borrowing scheme for dynamic load balancing in cellular networks,” 17th IEEE International Conference on Distributed Computing Systems (ICDCS '97),Baltimore, May 27-May 30,1997,pp 116
Ajay R Mishra, “Fundamentals of Cellular Network Planning and Optimization-2G/2.5G/3G… Evolution to 4G”, Wiley, 2004, England.
M.H. Willebeek-LeMair and A.P. Reeves,”Strategies for dynamic load balancing on highly parallel computers”, IEEE Trans. On parallel and distributed systems, vol 4, No. 9, Sept 1993.
M Duque-Anton, D. Knuz and B. Ruber, “ Channel Assignment for Cellular Radio using Simulated Annealing”, IEEE Trans. Vehicular Technology,vol VT-22, pp 218-222, Nov 1973.
S.M. Elnoubi, R. Singh, S.C. Gupta, “ A New Frequency Channel Assignment in High Capacity Mobile Communication Systems”, IEEE trans, Veh, technology, vol VT-31, no.3, August 1982
S.Tekinay and B. Jabbari, “ Handover and Channel Assignment in Mobile Cellular network”, IEEE Communication magazine, Nov 1991.
T. A. Dahlberg , J. Jung, “Load Sharing Protocols for Radio-level Resilience”, 16th International Teletraffic Congress, 1999
Simon Haykin, “Neural Networks, A Comprehensive foundation, 2nd Edn, Low Price Edition, Pearson Education,2005,New Delhi.
S.N.Sivanandam, M.Paul Raj, “Artificial Neural Networks”, Vikas Publishing House, New Delhi
Yegnanarayana, B , “ Artificial Neural Networks, PHI, NewDelhi, India
Pankaj Mehra, Benjamin Wah, “Loadbalancing: An automated Learning Approach”, world scientific publishing PTe Ltd, Singapore,1964.
P. Werbos, “Generalization of backpropagation with application to recurrent gas market model”, Neural Networks, vol.1,pp.339 – 356,1988.
N.K. Bose, P. Liang, “Neural Network Fundamentals with Graphs,Algorithms and Applications”, McGraw Hill Series in Electrical and Computer Engineering, International Editions, 1996.Singapore.
James A Freeman, David M Skapura, “Neural Networks Algorithms,Applications and Programming Techniques”, Pearson Education, 2004.
Leon O. Chua and Tam`as Roska, “Cellular Neural Networks and Visual Computing- Foundations and applications, Cambridge University press,2002.
J.Usha, Ajay Kumar, A.D. Shaligram:”New Reminder Service Scheme for Congestion Control in Mobile Networks, CIIT International Journal of Wireless Communications, vol1 , No.7, Oct 2009,pp 305-312.
Christos Stergiou, Dimitrios Siganos: “ Neural Networks”, surprise 96,journal Vol 4, 180 Queen’s Gate, London SW7 2BZ, UK.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.