Deep Learning based Cell-free Access Point Selection Algorithm in Power Internet of Things

To cope with the problem of inter-device interference and high energy consumption in traditional power Internet of things, a deep neural network (DNN) based access point (AP) selection algorithm was proposed. Firstly, it is considered that the AP without cellular was densely distributed around power equipment, so as to shorten the distance among the AP and equipments to reduce energy consumption. Secondly, based on the DNN the AP combination was effectively screened and labeled by training the large-scale fading coefficient of static random users to maximize the average spectral efficiency (SE). Finally, the test set was used to test. Simulation results show that the proposed algorithm can further improve the average spectral efficiency of the non-cellular network multi-device and reduce energy consumption compared with the traditional all-AP transmission and channel feature-based sorting algorithm.