Recovery of Information Lost by the Least Squares Estimation in Real-Time Network Environment

Abstract

Feedback mechanism based algorithms are frequently used to solve network optimization problems. These schemes involve users and network exchanging information to achieve convergence towards an optimal solution. However, in implementation, these algorithms do not guarantee messages will be delivered to the destination, especially when network congestion occurs, which in turn often results in packet drops. This condition may lead to algorithm failing to converge. To prevent this, we propose least square (LS) estimation algorithm to recover the missing information when packets are dropped from the network. Our results from several scenarios demonstrate that LS estimation can provide the convergence for feedback mechanism based algorithm.