Marvell Asia Pte., Ltd.

Di Fan

Nedeljko Varnica

Xuanxuan Lu

The present disclosure describes apparatuses and methods for implementing a reinforcement learning-enabled low-density parity check (LDPC) decoder. In aspects, an RL-enabled LDPC decoder processes, as part of a first decoding iteration, data of a channel to generate LDPC state information and provides the LDPC state information to a machine learning (ML) algorithm of an RL agent. The RL-enabled LDPC decoder is then configured with LDPC decoding parameters obtained from the ML algorithm and processes, as part of a second decoding operation, the data using the decoding parameters to generate subsequent LDPC state information. The RL-enabled LDPC decoder provides decoded data of the channel based on the subsequent LDPC state information. By using the LDPC decoding parameters provided by the ML algorithm of the RL agent, the RL-enabled LDPC decoder may decode channel data in fewer decoding iterations or with a higher success rate, thereby improving LDPC decoding performance.

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Irvine, CA, United States of America

Di Fan