Learned Soft MIMO Detection via Search Path Selection and Symbol Distribution Fitting

We propose a learning-aided soft multi-input multi-output (MIMO) detection method, consisting of a search path selection module and a symbol distribution fitting module. The search path selection module uses a neural network to generate a set of candidate paths. Different from traditional tree search algorithms, we utilize the soft estimates output by a neural network to calculate and cancel the symbol interference. The symbol distribution fitting module aims to solve the missing counter-hypotheses problem, where an additional neural network is employed to estimate the marginal posterior probability distribution of each symbol. Numerical results demonstrate the effectiveness of the proposed method especially when the allowed computational complexity is highly limited.