Power Allocation Schemes Based on Machine Learning for Distributed Antenna Systems

In this paper, we investigate the great potential of the combination of machine learning technology and wireless communications. Currently, many researchers have proposed various optimization algorithms on resource allocation for distributed antenna systems (DASs). However, the existing methods are mostly hard to implement because of their high computational complexity. In this paper, a new system model for machine learning is considered for the scenario of DAS, which is more practical with its low computational complexity. We utilize the k-nearest neighbor (k-NN) algorithm based on the database of a traditional sub-gradient iterative method to get a power allocation scheme for DAS. The simulation results show that our k-NN algorithm can also obtain the power distribution scheme which is very similar to the results of the traditional algorithm.