AI/ML Based Encoder and Decoder Design for PUCCH HARQ-ACK Payload

Error control coding for the systems for the previous generations (2G to 5G) has assumed the inputs bits to be uniformly distributed. However, some sources such as downlink (DL) HARQ-ACK payload are inherently non-uniformly distributed. Hence, there is room for significant performance improvement by employing joint source channel coding design aided by deep learning based techniques. We focus on AI/ML based codebook learning and decoder design for HARQ-ACK payload in order to exploit the source distribution in the control channel codebook and perform Unequal Error Protection (UEP) for the source bits encoded in the codewords. Our results demonstrate 4 - 8 dB reduction in the transmit power required to achieve the desired ACK and NACK error rates, compared to the 5G NR compliant encoder and decoder design.