A Bayesian optimization framework for optimizing sliding window RLNC

Sliding Window Random Linear Network Coding (SW RLNC) is a promising approach for ensuring resilient communication in next-generation networks. In order to enhance both the coding efficiency and the overall performance of the coding scheme, the optimal configuration of the coding parameters should be addressed. Previous studies suggest that the trade-off between coding efficiency and overall performance in SW RLNC schemes is determined by two critical parameters, namely, the code rate and the coding window size. In this work, we address the joint optimization of the code rate and the coding window size by formulating it as a single-objective Sequential Model-Based Optimization problem and employ Bayesian Optimization (BO) models for efficiently solving it. To demonstrate proof-of-concept, we carry out an extensive evaluation campaign assessing the effectiveness of different BO models for this optimization problem. The results confirm that the optimization framework reliably finds optimal solutions with relatively few evaluations. Finally, we present a practical approach for leveraging the proposed optimization framework in real-world scenarios, allowing efficient adaptation to channel variations without requiring real-time re-optimization.