High Throughput Joint Error Detection and Correction Based On GRAND-MO and CRC

Guessing random additive noise decoding (GRAND) is a noise-centric universal algorithm that is suitable for linear block codes. Using the guessing decoding independence of GRAND-Markov order (GRAND-MO), in this paper, a serial-storing and parallel-crossing configured high throughput GRAND-MO decoding scheme is proposed. Subsequently, the key permutation generation module, the noise error patterns (NEPs) generation module and the guessing decoding module, are each elucidated through a comprehensive step-by-step approach. The NEPs generation module outputs patterns solely based on burst parameters. For the targeted cyclic redundancy check (CRC) codes, the error detection capability is jointly integrated with GRAND-MO for error correction. Compared to conventional linear block codes, simulation results show the error correction capability of CRC is significantly improved. Given a packet length of 128 bits and a clock frequency of 866 MHz, field programmable gate array (FPGA) implementation shows that the NEPs generation throughput can reach 14.6 Gbps under the 128 parallelism assumption. The throughput can be flexibly adjusted to satisfy different throughput requirements at the cost of increased hardware overhead. This work serves as a practical reference for future research in GRAND-MO.