dynamic channel allocation using hopfield networks
DESCRIPTION
A presentation based on a research paper of Dynamic channel allocation using ANN(Artificial Neural Networks)TRANSCRIPT
Dynamic Channel Allocation for Mobile Communication
By:Kr. Rajeev Ranjan 07438Electronics and Comm. Engg.NIT Hamirpur (HP)
Introduction to Wireless Systems
Cellular Concept
Cellular Concept
Cell
Base Station (BS) or BTS
Mobile Station (MS)
Cellular Concept
BTS
MS
Forward Link
Reverse Link
GSM System Architecture
Channel Allocation
Channel Allocation
A
B
C
Interfering Cell
Non Interfering Cell
Interference Order
Channel Allocation
Channel Allocation
Fixed Channel Allocation (FCA)
Dynamic Channel Allocation (DCA)
Hard Condition
Soft Condition
Dynamic Chanel Allocation Technique
Why DCA ?
• FCA systems allocate specific channels to specific cells.
• Uneven Call Traffic -- available channels are not being used efficiently in FCA.
• In DCA systems, no set relationship exists between channels and cells.
Dynamic Chanel Allocation Technique
Energy Function
Dynamic Chanel Allocation Technique
Conventional Approach
• Channel Allocation algorithm, rearranges the assignment channels in the whole network every time a new call arrives.• Complex Algorithms for deciding which available channel is most efficient.• Algorithms computationally intensive.• Requires large computing resources (Time).
These drawbacks lead us to use of Hopfield Networks
Hopfield Neural Networks
Characteristics
•Association or Classification•Optimization •Restoration of Patterns
Mapping Network
Objective:Assign a channel to the cell effectively along with the decrease in computation cost.
Hopfield Neural Networks
Architecture
Mapping the problem into HNN
Input
• Call arrival or the termination in a particular cell.
Output
• Assignment of channel available in the network.
Association or Mapping Network is used here in order to allocate the channel
Output of Hopfield Network is given by
Energy Function is given by
E(x)= - ½ Xt W X - It X + Q X
Solution of HNN
Solution of HNN
Energy Function
wherex input vector (channel assignments) for which an optimal
solution is sought;b bias vector determined by constraints;W symmetric weight matrix for the neural network.
• Weight Update:• Bias Vector Update:
Simulation :
Simulation carried out •over a portion of 7x7 Hexagonal Cell•2 rings of interfering cell•70 channels
To evaluate the performance:• Blocking Probability --used as a performance Index .
Compared this with the existing systems of Dynamic channel allocation techniques.
Simulation Result:
Simulation Result:
Conclusion:
•The HNN is an effective channel allocation technique with lowerthe computational costs of the algorithm (Time).
•A valuable feature of the HNN allocation technique is itsinsensitiveness to local faults -- implies loss of elaborativecapacity of some neurons doesn’t mean that system will beblocked.
References:
• Principles of Wireless Communication : By Theoder S. Rappaport.
• Hopfield Neural Networks for DCA in Mobile Communication byEnrico DEL RE, Romano FANTACCI, Luca RONGA, Giovanni GIAMBENE.
IEEE Volume 3, Page(s):1664 - 1668 .
• Hopfield Networks for Finding Global Optimum by Xiaofei Huang.(Proceedings of International Joint Conference on Neural Networks,Montreal, Canada, July 31 - August 4, 2005).
