Abstract
Antenna selection in Multiple-Input Multiple-Output (MIMO) systems has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity. Recently, deep learning based methods have achieved promising performance in many application fields. This paper proposed a deep learning (DL) based antenna selection technique. First, we generated the label of training antenna systems by maximizing the channel capacity. Then, we adopted the deep convolutional neural network (CNN) on the channel matrices to explicitly exploit the massive latent cues of attenuation coefficients. Finally, we used the adopted CNN to assign the class label and then select the optimal antenna subset. Experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based antenna selection.
Highlights
To evaluate the performance of the proposed method, we randomly generated 500000 channel matrix samples by i. i. d. sampling from a complex Gaussian distribution with mean 0 and variance 1
For wireless communication, a suboptimal antenna selection scheme is still acceptable if the channel capacity loss is low
Experimental results demonstrated that convolutional neural network (CNN) outperforms k-nearest neighbors (KNN) and support vector machine (SVM) for antenna selection
Summary
This work proposed a data-driven receiving antenna selection approach through deep CNN and channel capacity criterion. We adopted deep CNN on channel matrix to extract rich features for antenna selection. The trained deep CNN can assign the best antenna subset selection solution to test samples. The adopted deep CNN is used to classify the test channel matrix and select the optimal antenna subset.
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