The consultative committee for space data system recommended the 4-dimension 8-ary phase shift keying trellis coded modulation (4D-8PSK-TCM). The 4D-8PSK-TCM has the advantage of low decoding latency over iterative error correction codes. The T-algorithm, which makes feasible to eliminate unnecessary additions and comparisons, can be applided to the 4D-8PSK-TCM to lower the decoding complexity. In this paper, we design the 4D-8PSK-TCM simulator with Hybrid T-algorithm based on deep learning to lower decoding complexity. The deep neural network predicts threshold of branch metric and path metric. Simulation results validate that the designed 4D-8PSK-TCM has lower complexity than the ideal 4D-8PSK-TCM while it maintain bit error rate performance of the ideal 4D-8PSK-TCM.
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2020-2018-0-01424) supervised by the IITP(Institute for Information and communications Technology Promotion). All authors have equal contributions.ACKNOWLEDGMENT This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2020-2018-0-01424) supervised by the IITP(Institute for Information and communications Technology Promotion).
