  It is well known that to fulfill their full potential, the design of polar
codes must be tailored to their intended decoding algorithm. While for
successive cancellation (SC) decoding, information theoretically optimal
constructions are available, the code design for other decoding algorithms
(such as belief propagation (BP) decoding) can only be optimized using
extensive Monte Carlo simulations. We propose to view the design process of
polar codes as a graph search problem and thereby approaching it more
systematically. Based on this formalism, the design-time complexity can be
significantly reduced compared to state-of-the-art Genetic Algorithm (GenAlg)
and deep learning-based design algorithms. Moreover, sequences of
rate-compatible polar codes can be efficiently found. Finally, we analyze both
the complexity of the proposed algorithm and the error-rate performance of the
constructed codes.
