{C31} J. Jang and H. J. Yang, “Learning-Based Distributed Resource Allocation in Asynchronous Multicell Networks,” in Proc. International Conference on ICT Convergence (ICTC), 2018.

{C31} J. Jang and H. J. Yang, “Learning-Based Distributed Resource Allocation in Asynchronous Multicell Networks,” in Proc. International Conference on ICT Convergence (ICTC), 2018.

A resource allocation problem is tackled in asynchronous multicell downlink LTE-LAA networks to improve the proportional fairness by assuming limited channel state information (CSI). Previous studies solve the resource allocation problem by relaxing the problem into fractional frequency resource allocation problems. Specifically, the binary resource allocation indicators are relaxed to real values, and the per-resource block (RB) signal-to-interference ratio (SINR) is averaged over all the RBs. However, the performance of such an approach is far beyond the optimality in frequency selective channels. We propose a learning-based resource allocation framework only with limited CSI in frequency selective channels. Without any additional CSI, we build a fully connected neural network architecture, based on which a distributed reinforcement learning algorithm is proposed. The proposed algorithm is implemented using the TensorFlow library (Version 1.3.0 GPU) and python (Version 2.7). Numerical results show that the proposed learning-based algorithm shows enhanced proportional fairness performance compared to existing algorithms even with the same CSI assumption.