VoIP Call Optimization in Diverse Network Scenarios Using Learning Based State-Space Search Technique

TL;DR

This paper presents a learning-based state-space search method for adaptive VoIP call quality optimization across diverse network scenarios, effectively managing QoS mechanisms in real-time.

Contribution

It introduces a novel approach that models QoS management as a state-space problem and applies incremental heuristic search combined with learning strategies for dynamic adaptation.

Findings

Effective QoS optimization in diverse network conditions

Improved call quality through adaptive mechanism sequencing

Enhanced performance with learning-based refinement

Abstract

A VoIP based call has stringent QoS requirements with respect to delay, jitter, loss, MOS and R-Factor. Various QoS mechanisms implemented to satisfy these requirements must be adaptive under diverse network scenarios and applied in proper sequence, otherwise they may conflict with each other. The objective of this paper is to address the problem of adaptive QoS maintenance and sequential execution of available QoS implementation mechanisms with respect to VoIP under varying network conditions. In this paper, we generalize this problem as state-space problem and solve it. Firstly, we map the problem of QoS optimization into state-space domain and apply incremental heuristic search. We implement the proposed algorithm under various network and user scenarios in a VoIP test-bed for QoS enhancement. Then learning strategy is implemented for refinement of knowledge base to improve the performance of call quality over time. Finally, we discuss the advantages and uniqueness of our approach.