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Feedback mechanism based algorithms are frequently used to solve networkoptimization problems. These schemes involve users and network exchanginginformation (e.g. requests for bandwidth allocation and pricing) to achieveconvergence towards an optimal solution. However, in the implementation, thesealgorithms do not guarantee that messages will be delivered to the destinationwhen network congestion occurs. This in turn often results in packet drops,which may cause information loss, and this condition may lead to algorithmfailing to converge. To prevent this failure, we propose least square (LS)estimation algorithm to recover the missing information when packets aredropped from the network. The simulation results involving several scenariosdemonstrate that LS estimation can provide the convergence for feedbackmechanism based algorithm.

Practical Recovery Solution for Information Loss in Real-Time Network Environment