iPAS: A deep Monte Carlo Tree Search-based intelligent pilot-power allocation scheme for massive MIMO system

Massive Multiple-Input-Multiple-Output (MIMO) is a promising technology to meet the demand for the connection of massive devices and high data capacity for mobile networks in the next generation communication system. However, due to the massive connectivity of mobile devices, the pilot contamination problem will severely degrade the communication quality and spectrum efficiency of the massive MIMO system. We propose a deep Monte Carlo Tree Search (MCTS)-based intelligent Pilot-power Allocation Scheme (iPAS) to address this issue. The core of iPAS is a multi-task deep reinforcement learning algorithm that can automatically learn the radio environment and make decisions on the pilot sequence and power allocation to maximize the spectrum efficiency with self-play training. To accelerate the searching convergence, we introduce a Deep Neural Network (DNN) to predict the pilot sequence and power allocation actions. The DNN is trained in a self-supervised learning manner, where the training data is generated from the searching process of the MCTS algorithm. Numerical results show that our proposed iPAS achieves a better Cumulative Distribution Function (CDF) of the ergodic spectral efficiency compared with the previous suboptimal algorithms.

Automated summary

Massive MIMO is a promising technology for the next generation communication system, but faces the challenge of pilot contamination. We propose an intelligent Pilot-power Allocation Scheme (iPAS) using deep reinforcement learning algorithm to maximize spectrum efficiency and address the pilot contamination issue.