Polar code decoding with soft decision algorithms

Hitoshi Tokushige

Yong-Geol Shim, Choong Woong Lee

Elke Offer

Mónica Liberatori, Leonardo J. Arnone, Jorge Castiñeira Moreira +1 more

Abhijeet N. Chavan, Makarand Mohan Jadhav

The most important thing in any communication system is reliable transmission of data. To transmit the data through noisy channel, a redundancy is added before transmission which is known as channel coding. This transmitted data is decoded at the receiver with the help of soft decision decoding or hard decision decoding. Soft decision decoding can be classified as reliability based and code structure based decoding. Reliability in coding theory implies the probability of decoding the bit correctly. Most reliable bits give correct decoding output while less reliable bits are more prone to create errors. Iterative decoder accepts soft inputs-including a priori values-and delivers soft outputs that can he split into: the soft channel and a priori inputs, and the extrinsic value. The extrinsic value is used as an a priori value for the next iteration. The commonly used iterative decoder is sum product belief propagation algorithm. We are extending this analysis to the min-sum algorithm for polar codes. Using polar codes, the sum-product and min-sum algorithms are compared and simulation results are demonstrated.

Vera Miloslavskaya, Peter Trifonov

The problem of efficient soft-decision decoding of polar codes with any binary kernel is considered. The proposed approach represents a generalization of the sequential decoding algorithm introduced recently for the case of polar codes with Arikan kernel. Numeric results show that the proposed algorithm enables near-ML decoding of polar codes with BCH kernel.