Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

Abstract

The rapid development of information and wireless communication technologies together with the large increase in the number of end-users have made the radio spectrum more crowded than ever. Besides, providing a stable and reliable service is challenging, as electromagnetic environments are evolving and becoming more sophisticated. Accordingly, there is an urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency and the quality of service to provide agile management of network resources, so as to better meet the needs of future wireless users. Specifically, Automatic Modulation Recognition (AMR) plays an essential role in most intelligent communication systems especially with the emergence of Software Defined Radio (SDR). AMR is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Thanks to the significant advancements in Deep Learning (DL) applications, new and powerful tools have been provided which can tackle problems in this space. Thus, today, integrating DL models into AMR has gained the attention of many researchers. This work aims to provide a comprehensive state-of-the-art review of the most recent Machine Learning (ML) based AMR methods for Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. Furthermore, the architecture of each model will be identified along with a detailed comparison in terms of specifications and performance. Finally, an outline of the open problems, challenges, and potential research directions is provided along with discussion and conclusion.

Robust Automatic Modulation Recognition Through Joint Contribution of Hand-Crafted and Contextual Features

“…Extensive literature studies revealed that most existing FB-AMR methods employ different types of HCFs such as wavelet feature [21], spectral-based features [22], timefrequency features [23][24] [25], instantaneous features [1] and statistical features [1]. The derivations of these HCFs are further explained in the following subsections.…”

“…Automatic modulation recognition (AMR) is a popular technique used 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].…”

“…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]. In contrary to LB approaches, FB approaches are considered as the more practical solutions due to their ease of implementation for real-life scenario and ability to produce sub-optimal solutions.…”

“…For instance, CNN is widely used as an image recognition method to solve multiple-object detection problems due to its promising ability to automatically extract multiple levels of features from inputs. Motivated by the successes of DL in different areas, numerous DL-based AMR techniques have been proposed based on CNN, RNN, DNN and other network architectures in recent years and they have been observed to achieve significant results [1].…”

“…Jdid et al [1] presented a comprehensive survey on existing AMR methods developed with different machine learning (ML) and DL models. They compared these MLbased and DL-based AMR methods in terms of their network structures, training and testing hyperparameters, noise conditions, modulation pools and performances.…”