A First Look at FEC Code Rate Determination from a Computational Cost Perspective

Forward Error Correction (FEC) is the preferred method for recovering lost packets in time-sensitive applications. However, setting the code rate to adapt to changing network environment is still a significant but challenging problem. Recently, researchers explored deep learning (DL) methods to solve this problem, but DL-based methods come with increased computation overhead and decision-making latency compared to traditional methods, which may make these methods infeasible or less effective. Thus, real-time network (RTN) providers usually have trouble with these two questions when considering whether to adopt a DL-based method: (1). How much additional computing resources are required by each DL-based method? (2). If resource competition occurs, can these DL-based methods be able to make timely code rate decisions? In this paper, we evaluate existing proposed DL-based methods for code rate determination in terms of computational cost to answer these two questions. Particularly, we measure the computational resources and the time required by these DL-based methods for making one code rate decision. We find that among existing DL-based methods, some methods require several times or even tens of times more computational resources to achieve timely decision-making. Furthermore, we identify that three methods are prone to resource competition with existing FEC schemes, which indicates that RTN providers need to configure their computing resources carefully when selecting these methods. We further analyze the reasons behind this phenomenon and provide some design suggestions for RTN nroviders.