Improving Link Control For Enhancing Quality Of Service In Data Networks Using Kalman Filter Based Window Flow Technique

Subject Area: Engineering

The rapid proliferation of the internet and the heterogeneity of user equipment have led to complex challenges in managing network traffic and ensuring consistent quality of service. Researchers have addressed this issue through the introduction of sliding window link control mechanisms. The sliding window used by the existing ARQ has a fixed window size, as such does not adapt the window size to the dynamic network condition. This study focuses on enhancing network data link control to ensure reliable data transfer, given the increasing diversity in network applications and user base. The proposed approach utilizes a Kalman filter-based sliding window flow control scheme to improve link control in data link networks. Kalman filter estimation and prediction techniques are employed to compute link state statistics in Long Term Evolution (LTE) radio access networks, specifically focusing on Radio Link Control (RLC) acknowledgment request queuing (ARQ) traffic between RLC entities. The algorithm dynamically adjusts the window size based on Kalman filter estimates to optimize sliding window flow control. Results from the evaluation revealed significant improvements of 1.75% in spectral efficiency, 28.81% bit error rate reduction, 16% block error rate reduction, and 36.4% throughput improvement using the sliding window performance as the base line. The study's findings demonstrate the effectiveness of the proposed link control scheme in selecting efficient modulation and coding schemes for data transmission. In conclusion, the research contributes valuable insights to address the challenges posed by the evolving nature of network traffic and user requirements, paving the way for more robust and efficient data link control in modern data networks.