Machine learning-based antenna selection for sparse array reconfiguration

The sparse array design for adaptive beamforming is usually formulated into combinatorial antenna selection problems, which belong to notorious NP-hard problems. As the commonly deployed convex relaxation algorithms are susceptible to local optima, several trials with different initial points are conducted for the global optima. Moreover, the high computational load of optimization techniques prohibits the real-time adaptive array reconfiguration. In this chapter, we consider to utilize machine learning algorithms, specifically support vector machine (SVM) and convolutional neural network (CNN), for solving combinatorial antenna selection problems.