A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Shankar, T.
- Coverage and Connectivity Analysis of ADHOC Networks in Presence of Channel Randomness
Authors
1 CEG, Anna University, Chennai, Tamil Nadu, IN
2 VIT University, Vellore, Tamil Nadu, IN
3 Department of Electronics and Communication Engineering, CEG, Anna University Chennai, Tamil Nadu, IN
Source
Networking and Communication Engineering, Vol 3, No 3 (2011), Pagination: 152-157Abstract
This paper is to analyze the coverage and connectivity of Ad Hoc Networks and to enhance the connectivity of the network by using Rician Fading, for this to compute the node isolation probability and coverage with respect to shadowing and fading phenomena in an ad hoc network in the presence of channel randomness. The concentrate on Rayleigh fading and Rician fading for finding the node isolation probability and also MIMO (multiple input multiple output) technique was used to enhance network coverage and connectivity. Further this paper considering Lognormal Shadowing, Rayleigh Fading and Rician Fading to simulates the graphs between the node isolation probabilities versus node density.Keywords
MIMO, Channel Randomness, Node Isolation.
- Resource Reservation Based on Mobility Prediction in Personal Communication Systems
Authors
1 VIT University, Vellore, Tamil Nadu, IN
Source
Networking and Communication Engineering, Vol 2, No 3 (2010), Pagination: 109-116Abstract
IEEE 802.11 Mobility of the users in Personal Communication systems gives rise to the problem of mobility management. Predictive reservation allows the reservation of resources for an ongoing call in the next cell, so that the call is sustained when the Mobile Station (MS) moves to the next cell. Mobility management covers the methods for storing and updating the location information. of the mobile users served by them. Mobility prediction thus becomes an inevitable process in mobility management. Mobility prediction is defined as the prediction of the mobile user’s next movement where the user is traveling between the cells of the network. By using the predicted movement, the system can effectively allocate resources to the most probable-to move cell instead of blindly allocating resources in the entire neighborhood of the cell. Mobility prediction based on data mining method to predict the mobile user’s next movement is implemented in this project. The method is based on mining the User Actual Paths to discover the regularities in the patterns, extracting mobility rules from these patterns and finally, the matching rule, having the highest confidence plus support value corresponding to the current trajectory of the user, is used to predict the mobile user’s next cell movement. Through accurate prediction, the system can reserve resources in an efficient manner, thus leading to improved resource utilization. The performance of the method is evaluated through simulation. The results obtained in each phase leading to more accurate prediction of the mobile user’s next cell movement have been presented.