Machine learning assisted adaptive LDPC coded system design and analysis

This paper proposes a novel machine learning (ML) assisted low-latency low density parity check (LDPC) coded adaptive modulation (AM) system, where short block-length LDPC codes are used. Conventional adaptive modulation and coding (AMC) system includes fixed look-up table method, which is also called inner loop link adaptation (ILLA) and outer loop link adaptation (OLLA). For ILLA, the adaptive capability is achieved by switching the modulation and coding modes based on a look-up table using signal-to-noise ratio (SNR) thresholds at the target bit error rate (BER), while OLLA builds upon the ILLA method by dynamically adjusting the SNR thresholds to further optimize the system performance. Although both improve the system overall throughput by switching between different transmission modes, there is still a gap to optimal performance as the BER is comparatively far away from the target BER. Machine learning (ML) is a promising solution in solving various classification problems. In this work, the supervised learning based k-nearest neighbours (KNN) algorithm is invoked for choosing the optimum transmission mode based on the training data and the instantaneous SNR. This work focuses on the low-latency communications scenarios, where short block-length LDPC codes are utilized. On the other hand, given the short block-length constraint, we propose to artificially generate the training data to train our ML assisted AMC scheme. The simulation results show that the proposed ML-LDPC-AMC scheme can achieve a higher throughput than the ILLA system while maintaining the target BER. Compared with OLLA, the proposed scheme can maintain the target BER while the OLLA fails to maintain the target BER when the block length is short. In addition, when considering the channel estimation errors, the performance of the proposed ML-LDPC-AMC maintains the target BER, while the ILLA system's BER performance can be higher than the target BER.