Searching for good fast recovery convolutional codes using importance sampling methods

In this paper, we study importance sampling (IS) methods to construct long convolutional codes with fast recovery capability. For codes with long memory length (m⩾12), the simulation method based on IS can avoid the excessive amount of computation required by other methods like analytical computation and standard Monte Carlo simulation. We establish a suboptimal IS method to search for good fast recovery convolutional codes with long memory lengths. The speed-up factors of two to six orders of magnitude over standard Monte Carlo simulation are obtained. Finally, we outline the code search procedures, present the code search results and give some applications of the codes