Abstract: Aiming at the problem that the computation and time overhead of obtaining channel amplitude is very large when implementing network planning of 5G massive MIMO (multiple-input multiple-output) networks with the traditional system-level simulation methods, this paper proposes a BP (back propagation) network based adaptive neural network to predict the channel amplitude of massive MIMO systems. The adaptive neural network consists of a basic BP sub neural network and a feature-reduced BP sub neural network, which can realize self-adaption to the given training and test set and can quickly and accurately predict the user channel amplitude based on the ray-tracing data. Simulation results show that the proposed adaptive neural network can achieve close accuracy to the traditional system-level simulation methods when obtaining channel amplitude but with significantly reduced time overhead and can obviously reduce the training time, the amount of users with large prediction error and the average prediction error compared with the traditional BP neural network.

Key words: adaptive neural network, 5G network planning, massive MIMO, channel amplitude prediction, ray-tracing data