Efficient Power Allocation in Coded MIMO Systems

Multiple-input multiple-output (MIMO) and low-density parity check (LDPC) codes are two of the fundamental technologies in the fifth-generation (5G) networks, where an efficient power allocation scheme is desired to minimize the bit error rate (BER) of the LDPC-coded MIMO system. However, the conventional power allocation methods do not take into account the constraint of modulation and coding scheme (MCS), which may degrade the BER performance. To solve this issue, we propose a deep learning based method to predict the efficient power allocation scheme in coded MIMO systems. Specifically, a neural network is built to learn the complex BER-SNR function to derive the power allocation ratio between the parallel MIMO streams, where the training label is acquired based on the exhaustive searching algorithm. Simulation results show that our proposed method could achieve better BER performance than its conventional counterparts.