Deep learning

Channel (broadcasting)

Pattern recognition (psychology)

Quadrature amplitude modulation

Economic growth

Bit error rate

Modulation (music)

Aesthetics

Geodesy

Engineering

Philosophy

Benchmark (surveying)

Artificial intelligence

Economics

Geography

Electronic engineering

Convergence (economics)

Computer science

Quadrature (astronomy)

Telecommunications

Convolutional neural network

IEEE Wireless Communications Letters

Automatic modulation recognition (AMR) plays a vital role in modern communication systems. This letter proposes a novel three-stream deep learning framework to extract the features from individual and combined in-phase/quadrature (I/Q) symbols of the modulated data. The proposed framework integrates one-dimensional (1D) convolutional, two-dimensional (2D) convolutional and long short-term memory (LSTM) layers to extract features more effectively from a time and space perspective. Experiments on the benchmark dataset show the proposed framework has efficient convergence speed and achieves improved recognition accuracy, especially for the signals modulated by higher dimensional schemes such as 16 quadrature amplitude modulation (16-QAM) and 64-QAM.

A Spatiotemporal Multi-Channel Learning Framework for Automatic Modulation Recognition