{'fraction_non_alphanumeric': 0.02920443101711984, 'fraction_numerical': 0.0, 'mean_word_length': 5.371794871794871, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
MIMO Radar Waveform Optimization By Deep Learning Method

The signal-to-interference plus noise ratio (SINR) maximization with constant modulus (CM) constraint is a key issue in Multiple-Input-Multiple-Ouput (MIMO) radar system. This problem is hard to solve, due to the SINR function and CM constraint both are nonconvex. Usually, the existing methods indirectly optimize the problem by relaxing SINR function or CM constraint to a more tractable form. These methods usually degrade the performance due to relaxation. To address this issue, the deep learning (DL) based method is proposed, by using the strong and robust nonlinear fitting capabilities of the DL. Firstly, the CM constraint problem was converted into an unconstrained phase optimization problem. Then, we construct an optimization training network (OTN) directly sloving this nonconvex problem without relaxation. Simulation results show that our proposed method acheived better performance compared with the existing methods.