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.INDEX TERMS Distributed antenna systems, machine learning, power allocation schemes, spectral efficiency, energy efficiency, k-NN algorithm.
In recent years, a lot of power allocation algorithms have been proposed to maximize spectral efficiency (SE) and energy efficiency (EE) for the distributed antenna systems (DAS). However, the traditional iterative power allocation algorithms are difficult to be implemented in reality because of their high computational complexity. With the development of machine learning algorithms, it has been proved that the machine learning method has excellent learning ability and low computational complexity, which can approximate the traditional iterative power allocation well and be easily to be implemented in reality. In this paper, we propose a new deep neural network (DNN) model for DAS. From the perspective of machine learning, traditional iterative algorithms can be regarded as a nonlinear mapping between user channel realizations and optimal power allocation schemes. Therefore, we train the DNN to learn the nonlinear mapping between the user channel realizations and the corresponding power allocation schemes based on the traditional iterative algorithm. Then, a power allocation schemes based on DNN method is developed to maximize SE and EE for DAS. The simulation results show that the proposed scheme can not only obtain the almost similar performance as the traditional iterative algorithm, but also reduce much online computational time.
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