Abstract
Automatic modulation recognition (AMR) has become increasingly important in the field of signal processing, especially with the advancements of intelligent communication systems. Deep Learning (DL) technologies have been incorporated into the AMR field and they have shown outstanding performances against conventional AMR methods. The robustness of DL-based AMR methods under varying noise regimes is one of major concerns for the widespread utilization of this technology. Furthermore, most existing works have neglected the contributions of hand-crafted features (HCFs) in boosting the classification performances of DL-based AMR methods. In order to address the aforementioned technical challenges, a novel and robust DL-AMR method is proposed by leveraging the benefits of both contextual features (CFs) and HCFs for a specific range of signal-to-noise ratio (SNR). A novel feature selection algorithm is also proposed to search for the optimal sets of HCFs in order to reduce the dimensions of feature vectors without losing any important and relevant features. Simulation studies are performed to investigate the feasibility of proposed method in classifying 11 types of modulation schemes. Extensive performance analyses revealed the superiority of proposed method over baseline method in terms of the classification performance as well as the excellent capability of proposed feature selection algorithm in determining an optimal subset of HCFs.
Highlights
For the communication systems widely used in both military and civilian applications, the radio signals are encoded by predefined adaptive modulation schemes with respect to the specification of transmission channel
The performances of proposed hand-crafted features (HCFs) selection algorithm and proposed deep learning (DL)-based Automatic modulation recognition (AMR) method are compared with the methods used by O’Shea et al [4]
A Baseline (BL) AMR method presented in [4] is selected for AMR performance validation of the proposed AMR based on the proposed HCFs selection algorithm and signal-to-noise ratio (SNR) splitter, where the former method considers a total of 28 HCFs including Higher-Order Moments (HOMs), High-Order Cumulants (HOCs) and other features
Summary
For the communication systems widely used in both military and civilian applications, the radio signals are encoded by predefined adaptive modulation schemes with respect to the specification of transmission channel. Receiver needs to correctly identify the types of modulation scheme adopted in order to ensure successful demodulation. The receiver has no prior information about the modulation scheme of received signals in blind detection. Automatic modulation recognition (AMR) is a popular technique used. The associate editor coordinating the review of this manuscript and approving it for publication was Xiaofan He. to provide the blind recognition of the modulation scheme. In literatures, existing AMR techniques are designed and implemented based on two main approaches: (1) likelihoodbased (LB) and (2) feature-based (FB) [1]. Despite being able to achieve the optimum recognition rate, most LB approaches tend to suffer with technical drawbacks such as high computational complexity and strong dependency on the prior information of received signal [1]. It is notable that the performance of FB approaches relies tremendously on the hand-crafted features (HCFs) manually extracted from
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