Construction of Polar Codes with Reinforcement Learning
This addresses the suboptimality of existing polar-code constructions for advanced decoders, offering a novel sequential approach that improves error performance in communication systems.
The paper tackles the problem of constructing polar codes for the successive-cancellation list decoder by formulating it as a maze-traversing game solvable with reinforcement learning, resulting in code constructions that achieve lower frame-error rates compared to standard methods across various code lengths and decoders.
This paper formulates the polar-code construction problem for the successive-cancellation list (SCL) decoder as a maze-traversing game, which can be solved by reinforcement learning techniques. The proposed method provides a novel technique for polar-code construction that no longer depends on sorting and selecting bit-channels by reliability. Instead, this technique decides whether the input bits should be frozen in a purely sequential manner. The equivalence of optimizing the polar-code construction for the SCL decoder under this technique and maximizing the expected reward of traversing a maze is drawn. Simulation results show that the standard polar-code constructions that are designed for the successive-cancellation decoder are no longer optimal for the SCL decoder with respect to the frame error rate. In contrast, the simulations show that, with a reasonable amount of training, the game-based construction method finds code constructions that have lower frame-error rate for various code lengths and decoders compared to standard constructions.