Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition and remains challenging for traditional methods due to complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include the following: (1) a convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; (2) a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and (3) experimental results demonstrate that HDMF is very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network.

Automatic modulation classification is a challenging and critical task in the field of communication. Deep convolutional networks (ConvNets) have been recently applied in cognitive radio and achieved remarkable performance. However, existing ConvNet-based methods mainly focus on the first-order architecture design, while fail to explore feature correlations of radio signal, which are particularly significant for useful information extraction in low signal-to-noise ratios (SNRs). In this paper, we propose high-order convolutional attention networks (HoCANs) for radio signal expression and feature correlation learning, based on a novel high-order attention mechanism to rescale the convolutional features along channel and sequence dimensions. High-order convolutional layer and covariance matrix after nonlinear transformation are led for tenser filtering with more discriminative representations of radio signals. Experiments have been conducted to validate the superiority of HoCANs which achieve state-of-the-art accuracy for automatic modulation classification on RADIOML 2018.01A dataset.

With the promising techniques and mobile applications of the Internet of Things (IoT) in the fifth-generation (5G) wireless networks, the mobile edge computing (MEC) paradigm has been widely utilized to support the massive data processing and meet the computation demand of mobile devices. However, the service coverage is limited by the static servers. Therefore, the unmanned aerial vehicles (UAVs) with high mobility are deployed as aerial base stations (BS) to assist ground users to offload the task and process the data on edge. In this paper, we introduce a decentralized user allocation and dynamic service scheme to assign the UAV deployment for a multi-UAV MEC system, which incorporates the deep reinforcement learning (DRL) technology into a two-layer training framework. The continuous action, including the UAV trajectory and bit allocation of offloading, is first optimized in the lower layer, while the upper one concentrates on the discrete action space for the user allocation. In particular, we develop a constraint reward function that considers the communication quality in the lower layer to accomplish efficient communication. The well-trained neural network is embedded in the upper layer to provide rapid feedback for the upper environment. Experimental results demonstrate that the proposed approach achieves superior performance than the baseline methods in terms of the user energy consumption.

For modulation classification, hand-crafted approaches can generalize well from a few samples, yet deep learning algorithms require millions of samples to achieve the superior performance with purely data-driven manner. However for many practical problems only with small sample set (SSS) available, there still remains a challenge for deep learning. In this paper, we employ deep learning to solve the modulation classification task in a more practical setting, particularly suffering from the SSS problem and with low signal-to-noise ratios (SNRs). Novel modulated autocorrelation convolution networks (MACNs) are introduced to capture periodic representation for automatic modulation classification (AMC). In MACNs, modulated communication signals are classified with the periodic local features under an autocorrelation convolution criterion. Modulation filters are utilized to enhance the capacity of the convolution filters and compress the model. On a challenging SSS learning task in low SNRs, MACNs achieve state-of-the-art performance that outperforms the existing algorithms for AMC, while compressing the size of required storage space of convolutional filters by a factor of 8 compared with convolution neural networks (CNNs).