Machine Learning-Based Beamforming in Two-User MISO Interference Channels

As the demand for data rate increases, interference management becomes more important, especially in small cell environment of emerging wireless communication systems. In this paper, we investigate the machine learning-based beamforming design in two-user MISO interference channels. To see the possibilities of machine learning in beamforming design, we consider simple beamforming, where each user chooses one between two popular beamforming schemes, which are the maximum ratio transmission (MRT) beamforming and the zero-forcing (ZF) beamforming. We first propose a machine learning structure that takes transmit power and channel vectors as input and then recommends two users' choices between MRT and ZF as output. The numerical results show that our proposed machine learning-based beamforming design well finds the best beamforming combination and achieves the sum-rate more than 99.9% of the best beamforming combination.

[1]  Po-Chiang Lin,et al.  Machine-Learning-Based Adaptive Approach for Frame-Size Optimization in Wireless LAN Environments , 2009, IEEE Transactions on Vehicular Technology.

[2]  Björn E. Ottersten,et al.  Beamforming for MISO Interference Channels with QoS and RF Energy Transfer , 2013, IEEE Transactions on Wireless Communications.

[3]  Erik G. Larsson,et al.  Selfishness and altruism on the MISO interference channel: the case of partial transmitter CSI , 2009, IEEE Communications Letters.

[4]  S. Jayashri,et al.  Localization with beacon based support vector machine in Wireless Sensor Networks , 2015, 2015 International Conference on Robotics, Automation, Control and Embedded Systems (RACE).

[5]  Erik G. Larsson,et al.  Complete Characterization of the Pareto Boundary for the MISO Interference Channel , 2008, IEEE Transactions on Signal Processing.

[6]  Kentaro Ishizu,et al.  Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks , 2017, IEEE Access.

[7]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[8]  Walid Saad,et al.  Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective , 2017, IEEE Transactions on Wireless Communications.

[9]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[10]  Pierre Geurts,et al.  A machine learning approach to improve congestion control over wireless computer networks , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[11]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[12]  A. Forster,et al.  Machine Learning Techniques Applied to Wireless Ad-Hoc Networks: Guide and Survey , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.