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Modulation recognition using deep neural networks has shown promisingadvantages over conventional algorithms. However, most existing researchfocuses on single receive antenna. In this paper, two end-to-end featurelearning deep architectures are introduced for modulation recognition withmultiple receive antennas. The first is based on multi-view convolutionalneural network by treating signals from different receive antennas as differentviews of a 3D object and designing the location and operation of view-poolinglayer that are suitable for feature fusion of multi-antenna signals.Considering that the instantaneous SNRs could be different among receiveantennas in wireless communications, we further propose weight-learningconvolutional neural network which uses a weight-learning module toautomatically learn the weights for feature combing of different receiveantennas to perform end-to-end feature learning of multi-antenna signals.Results show that both end-to-end feature learning deep architecturesoutperform the existing algorithm, and the proposed weight-learningconvolutional neural network achieves the best performance.

Deep Modulation Recognition with Multiple Receive Antennas: An End-to-end Feature Learning Approach