<table><caption>TABLE 8. Summary of (RNN, DNN & AE) based AMR methods.</caption><thead><tr><td>Algorithm</td><td>Network configuration</td><td>Modulation pool</td><td>Remarks</td></tr></thead><tbody><tr><td>OTA-DL-based radio signal classification [39]</td><td>Input layer, 6 residual stacks, 2 fully connected layers, and softmax output layer</td><td>RadioML 2018.01A</td><td>• ResNet performance under channel impairments &amp; versus depth was studied. • Training parameters: 236,344</td></tr><tr><td>CNN-RNN-Based AMR [46]</td><td>Input layer, 3 LSTM layers, 3 fully connected layers and softmax output layer</td><td>{BFSK, DQPSK, 16QAM, 4PAM, MSK and GMSK}</td><td>Refer to Table 7</td></tr><tr><td>SSAE-AF [71]</td><td>Input layer, hidden layer and output layer</td><td>{ASK, PSK, QAM, FSK, MSK, LFM, and OFDM}</td><td>• Training data: (6*7)k • Testing data: (1*7)k • Batch size: 100 • Epochs: 100 • Unsupervised pre-training and supervised fine-tuning.</td></tr><tr><td>LSTM-IQFOC [89]</td><td>Input layer, 3 convolution layers, 1 LSTM layer, 1 fully connected layer and softmax output layer</td><td>Refer to Table 7</td><td>• LSTM-IQFOC performance was around higher 2% than CNN-IQFOC (Table 7) for SNR&gt;-.5dB. • Tanh &amp; RELU activation functions were used.</td></tr><tr><td>LSTM-based AMR [102]</td><td>Input layer, 2 LSTM layers, one fully connected layer and softmax output layer</td><td>RadioML2016.10a</td><td>• Training data: 110-440k • Testing data: 110k • Batch size: 128 • Epochs: 80 • Combine both rotation and flip methods can provide further improvement.</td></tr><tr><td>DC-LSTM AMR [103]</td><td>Input layer, 2 dual-channel LSTM layers and softmax output layer</td><td>RadioML2016.10a</td><td>• Training data: 110k • Testing data: 110k • Batch size: 256 • Epochs: 70 • Center loss and weibull distribution.</td></tr><tr><td>DL-based Radio Modulation Classification [104]</td><td>Input layer, 2 LSTM layers, 2 fully connected layers and softmax output layer</td><td>RadioML2016.10a</td><td>• Training data: 88k • Testing Data: 11k • Batch size: 128 • Epochs: 150 • Validation Data: 11k • Adam optimizer was employed.</td></tr><tr><td>PSO-DNN [108]</td><td>Input layer, 2 hidden layers and softmax output layer</td><td>{BPSK, QPSK, 8PSK} and {16QAM, 64QAM, 256QAM}</td><td>• No. PS: (10-20-30-50) • Iteration number of training/optimizing: 300/5 • SGD method used for training the hidden layers and MSE function to calculate the output error.</td></tr><tr><td>DNN [109]</td><td>Input layer, 2 hidden layers and softmax output layer</td><td>{2FSK, 4FSK, 2PSK, 4PSK, 2ASK, 4ASK}</td><td>• Epochs: 278 • Loss function: cross-entropy</td></tr><tr><td>DNN-based MFI [110]</td><td>Input layer, 2 hidden layers and softmax output layer</td><td>{QPSK, 16QAM, 64QAM}</td><td>• Training data: 135*3 • Testing data: 60*3 • Unsupervised pre-training and supervised fine-tuning.</td></tr><tr><td>DNN-based AMR [111]</td><td>Input layer, 3 hidden layers and softmax output layer</td><td>{BPSK, QPSK, 8PSK, 16QAM, 64QAM}</td><td>• Batch size: 50 • Epochs: 15k • Training data: (15*5)k • Validation Data: (5*5)k • Cost function: negative-log- likelihood.</td></tr><tr><td>DNNG [112]</td><td>Input layer, 3 hidden layers and softmax output layer [48]</td><td>{16QAM, 4QAM, 8PSK, 16PSK, BPSK, 8QAM, 32QAM}</td><td>• The best performance results was found to be at epoch equal to 2M.</td></tr><tr><td>DBN-SCF-based AMR [114]</td><td>5 hidden layers and an output layer</td><td>{4FSK, 16QAM, BPSK, QPSK, and OFDM}</td><td>• Training data: 6k • Testing data: 500 • Verification data: 2k • Semi-supervised learning</td></tr><tr><td>SSAE [118]</td><td>Input layer, hidden layer and output layer</td><td>{FSK, PSK, ASK, QAM and MSK}</td><td>• Training data: (2.5*5)k • Testing data: (1*5)k • Batch size: 100 • Epochs: 100 • Unsupervised learning.</td></tr><tr><td>SCAE-HOC [120]</td><td>Input layer, six hidden layers (3 convolutional layers, 2 pooling layers and one fully connected layer)</td><td>{BPSK, QPSK, 16-QAM, and 64-QAM}</td><td>• Training data: 120k • Testing data: 60k • Unsupervised pre-training and fine-tuning were performed. • SGD was performed to reduce the training error.</td></tr></tbody></table>

from CAE to the feature layer of CNN in order to cope with ineffective training and over-fitting, which is caused by using limited labeled samples. However, it is worth mentioning that the modulation schemes used in all of these researches were PSK, QPSK, 8PSK, and 16QAM. Besides, the CNN structure was also the same including 2 convolutional layers, 3 dense layers, and a softmax output layer. Furthermore, the CAE in [125] had the same structure as in CNN with three deconvolution layers.

## 1) DISCUSSION AND LEARNED LESSONS

DL models play an important role in AMR and the performance demonstrated by recent prototypes is remarkable. This is because DL can prove advantageous with regard to feature learning, performance, complexity, and generalization capabilities [2]. On one hand, training DL models on a wide range of SNR can eliminate the requirement of SNR estimation and generalize the classification process in the different environmental conditions [39]. However, maintaining low computational complexity, in such models, remains challenging as well as constructing and gathering a large dataset of signals in order to train the model and to guarantee the performance

<table><caption>TABLE 9. Summary of DL-AMR method for MIMO systems.</caption><thead><tr><td>Paper</td><td>Training &amp; Testing parameters</td><td>Remarks</td></tr></thead><tbody><tr><td>[123]</td><td>• Training data: 14k<sup>18</sup> • Validation data: 6k<sup>18</sup> • Testing data: 10k<sup>18</sup></td><td>• ReLU &amp; dropout were used. • Antennas number: N<sub>r</sub> × N<sub>t</sub> ∈ {4×1, 4×2, 4×4}. • Benchmark: HOC and ANN-based AMR. • Classification accuracy: ≈100%, >91%, >87% for SNR&gt;-.0dB when N<sub>r</sub> × N<sub>t</sub> ∈ {4×1,4×2,4×4}, respectively.</td></tr><tr><td>[124]</td><td>• Training data: 20k<sup>18</sup> • Testing data: 10k<sup>18</sup> • Maximum epoch, early-stopping epoch, batch size were set as 100, 20 and 500, respectively</td><td>• ReLU and Dropout were used. • Antennas number: N<sub>r</sub> × N<sub>t</sub> ∈ {4×1, 4×2, 4×4}. • Benchmark: HOC and ANN-based AMR. • Classification accuracy: >90%, >83%, >81% for SNR&gt;-.5dB when N<sub>r</sub> × N<sub>t</sub> ∈ {4×1,4×2,4×4}, respectively.</td></tr><tr><td>[125]</td><td>• Training data: 20k<sup>18</sup> • Testing data: 10k<sup>18</sup> • Maximum epoch, and batch size were set as 500, and 500, respectively.</td><td>• ReLU used for all layers, while softmax and PReLU were used for the output layer in CNN and the last deconvolution layer in CAE. • Dropout were used. • Antennas number: N<sub>r</sub> × N<sub>t</sub> ∈ {4×1, 4×2, 4×4}. • Benchmark: CNN/ZF-AMR [125]. • Classification accuracy: >90%, >82%, and >80% for SNR&gt;-.5dB when N<sub>r</sub> × N<sub>t</sub> ∈ {4×1,4×2,4×4}, respectively.</td></tr></tbody></table>

¹⁸per type per SNR

under varying noise regimes. For example, the CNN model reported in [42] enjoys a high classification accuracy but suffers from a large number of training parameters and, hence,
high complexity. On the other hand, training a DL model on a narrow range of SNR [46] can obtain high accuracy. However, to target a wide SNR range, it is needed to use multiple DL models for each SNR value, which will result in high storage requirements. Fundamentally, the main difference between RNN and CNN is the nature of their input data. While CNN is specialized in modeling spatial data, RNN is very effective in modeling temporal data. In fact, weight sharing is a key advantage in CNN as well as the auto-learning of deep spatial features. Alternatively, RNN can remember each information through time and, hence, very appropriate for sequenced data applications such as speech-to-text applications and video processing applications. However, both CNN and RNN suffer from vanishing and exploding gradient problems and require large amounts of data. It is worth mentioning that training RNN is a complex process and requires additional effort. AEs can be used effectively for unsupervised hand-crafted features learning [118], [120], or even for features extraction and learning [71].

However, RNN and CNN models can be used together to form a hybrid DL model and extend the effective pixel neighbourhood [57], [89]. Due to the advancements in the field of image recognition, image processing techniques are being extended to AMR domain. For this purpose, some kind of transformation algorithms have to be employed in order to adapt the techniques previously used for imaging to AMR [68], [72], [74]. However, this kind of data manipulation requires additional time which can complicate the AMR problem in time-sensitive applications. Likewise, several imaging techniques are being extended to AMR domain such as data augmentation [77] transfer learning [39], [86], and PSO algorithm [108], yet additional efforts are required to achieve the required performance and enable confident decision making.

## V. CHALLENGES AND FUTURE RESEARCH DIRECTIONS

Although the advancement of DL-AMR models has been witnessed in the past few years, there are many issues that need to be addressed in future researches. Generally speaking, the design of an ideal and robust classifier remains an open-ended challenge, despite the significant improvements and blooming results of the previous works. For example, some algorithms need prior information of the signal (e.g. carrier frequency, baud rate, offset timing, etc.) [52], while others are limited to a small number of modulation schemes [47], [48]. Besides, some algorithms have considerable computational complexity [42] and cannot be used in real-time applications. Others are based on ideal cases with ideal assumptions and cannot be used in practical applications. Some classifiers need high SNR values [6] which is different from realistic scenarios. Consequently, if the current researches on DL-AMR are to be more successful, they need to focus on the key feature extraction [69] and the selection of classification criteria at low SNRs. Specifically, artificially choosing features, however, is a complicated and difficult process. Nowadays, there is an increasing need to enhance

the flexibility of AMR in the current and future wireless communications to provide reliable low latency services. The rest of this section is devoted to point out some potential research directions and open challenges.

### A. ELECTROMAGNETIC ENVIRONMENT

As found in the present literature, the use of DL technologies can simplify the signal processing steps and improve the AMR performance. Moreover, it can provide more practical, robust, and efficient AMR methods with high classification accuracy. Although DL-AMR methods outperform the traditional modulation recognition ones [119], but there is still a long way for signal recognition in real electromagnetic environments and practical applications. The performance of FB-based AMR methods tends to degrade badly with channel impairment effects such as the high-speed mobility and impulsive nature of noise [20], multipath fading effects [32], PO [41], CFO [52], and heavy noises and interferences [88]. However, the quality of service in the complex communication environment under low SNRs is hard to be guaranteed and, therefore, further investigation is required for the robustness of DL classifiers in a larger range of SNR [43].

### B. SIGNALS DE-NOISING BEFORE AMR PROCESS

AMR at low SNR becomes more challenging due to the complex channel environment and the interference of multiple noises. Therefore, eliminating such noises and enhancing the SNR can improve the performance of AMR methods. Therefore, signals de-noising algorithms can be applied before AMR methods. For example, LRR algorithm was applied in [88] for de-noising the cyclic spectrum images before the AMR process. Similarly, image de-noising algorithms can be adopted in order to remove the different kind of noises [79], [106]. Moreover, DL models can be employed directly on the received raw signals in order to perform de-noising process before AMR method [126].

### C. VARIABLE LENGTH SIGNALS

A large amount of researches found in the literature consider the input of the proposed DL model as a fixed-length vector, which is not the real scenario where the signal frame length is diverse [42], [89]. For instance, inclusive investigation and enhanced models based on fully convolutional networks [127], NiN [128], RNN [129], LSTM [102], [130] and signal segmentation algorithms [49] can obtain DL-AMR methods that can deal with variable-length inputs. Moreover, several techniques in the field of computer vision can be considered to resolve such issues. For instance, the following techniques can be used for this target:

* Resizing the input images to the same input size [79],

* Crop out certain regions of the input image when the images are large,

* Pad the images with a suitable background.

However, DL-AMR methods with variable-length input can contribute to the problem of short-time classification.
high complexity. On the other hand, training a DL model on a narrow range of SNR [46] can obtain high accuracy. However, to target a wide SNR range, it is needed to use multiple DL models for each SNR value, which will result in high storage requirements. Fundamentally, the main difference between RNN and CNN is the nature of their input data. While CNN is specialized in modeling spatial data, RNN is very effective in modeling temporal data. In fact, weight sharing is a key advantage in CNN as well as the auto-learning of deep spatial features. Alternatively, RNN can remember each information through time and, hence, very appropriate for sequenced data applications such as speech-to-text applications and video processing applications. However, both CNN and RNN suffer from vanishing and exploding gradient problems and require large amounts of data. It is worth mentioning that training RNN is a complex process and requires additional effort. AEs can be used effectively for unsupervised hand-crafted features learning [118], [120], or even for features extraction and learning [71].

However, RNN and CNN models can be used together to form a hybrid DL model and extend the effective pixel neighbourhood [57], [89]. Due to the advancements in the field of image recognition, image processing techniques are being extended to AMR domain. For this purpose, some kind of transformation algorithms have to be employed in order to adapt the techniques previously used for imaging to AMR [68], [72], [74]. However, this kind of data manipulation requires additional time which can complicate the AMR problem in time-sensitive applications. Likewise, several imaging techniques are being extended to AMR domain such as data augmentation [77] transfer learning [39], [86], and PSO algorithm [108], yet additional efforts are required to achieve the required performance and enable confident decision making.

## V. CHALLENGES AND FUTURE RESEARCH DIRECTIONS

Although the advancement of DL-AMR models has been witnessed in the past few years, there are many issues that need to be addressed in future researches. Generally speaking, the design of an ideal and robust classifier remains an open-ended challenge, despite the significant improvements and blooming results of the previous works. For example, some algorithms need prior information of the signal (e.g. carrier frequency, baud rate, offset timing, etc.) [52], while others are limited to a small number of modulation schemes [47], [48]. Besides, some algorithms have considerable computational complexity [42] and cannot be used in real-time applications. Others are based on ideal cases with ideal assumptions and cannot be used in practical applications. Some classifiers need high SNR values [6] which is different from realistic scenarios. Consequently, if the current researches on DL-AMR are to be more successful, they need to focus on the key feature extraction [69] and the selection of classification criteria at low SNRs. Specifically, artificially choosing features, however, is a complicated and difficult process. Nowadays, there is an increasing need to enhance

the flexibility of AMR in the current and future wireless communications to provide reliable low latency services. The rest of this section is devoted to point out some potential research directions and open challenges.

### A. ELECTROMAGNETIC ENVIRONMENT

As found in the present literature, the use of DL technologies can simplify the signal processing steps and improve the AMR performance. Moreover, it can provide more practical, robust, and efficient AMR methods with high classification accuracy. Although DL-AMR methods outperform the traditional modulation recognition ones [119], but there is still a long way for signal recognition in real electromagnetic environments and practical applications. The performance of FB-based AMR methods tends to degrade badly with channel impairment effects such as the high-speed mobility and impulsive nature of noise [20], multipath fading effects [32], PO [41], CFO [52], and heavy noises and interferences [88]. However, the quality of service in the complex communication environment under low SNRs is hard to be guaranteed and, therefore, further investigation is required for the robustness of DL classifiers in a larger range of SNR [43].

### B. SIGNALS DE-NOISING BEFORE AMR PROCESS

AMR at low SNR becomes more challenging due to the complex channel environment and the interference of multiple noises. Therefore, eliminating such noises and enhancing the SNR can improve the performance of AMR methods. Therefore, signals de-noising algorithms can be applied before AMR methods. For example, LRR algorithm was applied in [88] for de-noising the cyclic spectrum images before the AMR process. Similarly, image de-noising algorithms can be adopted in order to remove the different kind of noises [79], [106]. Moreover, DL models can be employed directly on the received raw signals in order to perform de-noising process before AMR method [126].

### C. VARIABLE LENGTH SIGNALS

A large amount of researches found in the literature consider the input of the proposed DL model as a fixed-length vector, which is not the real scenario where the signal frame length is diverse [42], [89]. For instance, inclusive investigation and enhanced models based on fully convolutional networks [127], NiN [128], RNN [129], LSTM [102], [130] and signal segmentation algorithms [49] can obtain DL-AMR methods that can deal with variable-length inputs. Moreover, several techniques in the field of computer vision can be considered to resolve such issues. For instance, the following techniques can be used for this target:

* Resizing the input images to the same input size [79],

* Crop out certain regions of the input image when the images are large,

* Pad the images with a suitable background.

However, DL-AMR methods with variable-length input can contribute to the problem of short-time classification.
This kind of classification is required in many systems in real-world scenarios. For instance, scanning a receiver need to have a swift decision without consuming much time to acquire more data to increase certainty. Besides, it could be unavoidable when short signal bursts in the environment. Moreover, the classification on short observations can be very challenging but yet very needed in several systems where the delay is a very crucial parameter. Therefore, there is an urgent need to provide effective methods to deal with the variable-length input.

## D. SIGNALS DATASETS

Even though DL-AMR can provide significant performance, it requires a large amount of training data. In practice, collecting a sufficient amount of reliable training samples is usually costly and difficult. Therefore, the used large-scale training dataset should be constructed adequately and carefully with more realistic signals under a wide range of SNR [39]. Furthermore, data augmentation methods can make the classification of radio modulation schemes more successful by using shorter radio samples [77], [102], which will provide a simplified DL model with a smaller classification response time.

## E. ENHANCED DL MODELS

As mentioned before, the DL-based methods can operate under a wide range of SNR. Hence, large-scale datasets will be needed in order to achieve the expected results. This may result in a high number of training parameters and, hence, high computational complexity along with high storage requirements. Besides, the training process may consume a long time and suffers from over-fitting issues. Therefore, optimized and hybrid DL methods [46] can be studied in order to avoid over-fitting and reduce the computational complexity as well as to improve the classification accuracy [25], [108]. Moreover, the samples-labeling process in the training phase in supervised learning methods is very difficult to accomplish in addition to be time-consuming. Hence, the idea of semi-supervised [114] and unsupervised learning methods is being adopted [119]. These methods can meet the rapid growth of AMR applications and enhance the overall performance.

## F. UNKNOWN MODULATION TYPES

In several military applications such as intelligence and surveillance systems, the AMR process should be able to detect the modulation type of unknown signals. For example, jamming devices are deployed in the communication channel between adversary units to prevent communication among them. More specifically, they transmit noise or dummy signals modulated by the matching modulation type. Therefore, the modulation scheme of the adversary communication signals should be detected in order to allow the jamming devices to transmit jamming signals modulated using the corresponding modulation scheme.

However, as stated in the existing literature, the type of modulation schemes to be classified is assumed to be within a pool of known ones [110], [111]. More specifically, seldom works are capable of coping with unknown signals. Therefore, a comprehensive set of modulation types or a more reasonable model design is needed to overcome the lack of universality for the unknown signal recognition.

## G. REAL HARDWARE IMPLEMENTATION

In order to design an AMR method feasible to be implemented on real hardware, further investigation is required to achieve high classification accuracy along with low computational complexity. For example, the methods presented in [40], [50] suffer from a large number of learnable parameters. Conversely, the method proposed in [56] achieve low complexity with relatively low classification accuracy. Besides, the proposed DL methods found in the literature are still in the simulation phase and seldom works are devoted to implementing the proposed AMR method on real hardware. Hence, deploying DL-AMR algorithm to real powerful computing hardware (e.g. field-programmable gate arrays (FPGA) and beyond) can be very challenging [81], especially when it comes to timing and validation issues.

## H. DL-AMR IN MIMO SYSTEMS

DL-AMR models are scarcely explored for MIMO and massive MIMO communication systems. In this direction, further investigation can be done in order to design new methods or even extend prior works which were dedicated for SISO systems. Although some signals datasets already exist in the literature for SISO systems, but yet there are seldom datasets for MIMO and massive MIMO systems. Hence, it is very important for future researches to build robust and sufficient signals datasets for such systems.

## VI. CONCLUSION

Machine learning and deep learning are becoming increasingly popular in AMR domain. Research in this area is still incipient but it has however shown outstanding results. In this article, we provided a comprehensive survey of recent work lying at the junction of AMR, machine learning and deep learning. We reviewed both ML and DL models in AMR area for both SISO and MIMO communication systems. We discussed the architecture of various deep learning models and pointed out the key advantages of each model. We warped up this paper by presenting potential research directions and open challenges, which may result in significant future research results.

## REFERENCES

[1] Z. Zhu and A. K. Nandi, *Automatic Modulation Classification: Principles, Algorithms and Applications*, 1st ed. Hoboken, NJ, USA: Wiley, 2015.

[2] C. Zhang, P. Patras, and H. Haddadi, "Deep learning in mobile and wireless networking: A survey," *IEEE Commun. Surveys Tuts.*, vol. 21, no. 3, pp. 2224-2287, 3rd Quart., 2019, doi: 10.1109/COMST.2019.2904897.

[3] Y. A. Eldemerdash, O. A. Dobre, and M. Oner, "Signal identification for multiple-antenna wireless systems: Achievements and challenges," *IEEE Commun. Surveys Tuts.*, vol. 18, no. 3, pp. 1524-1551, 3rd Quart., 2016, doi: 10.1109/COMST.2016.2519148.
This kind of classification is required in many systems in real-world scenarios. For instance, scanning a receiver need to have a swift decision without consuming much time to acquire more data to increase certainty. Besides, it could be unavoidable when short signal bursts in the environment. Moreover, the classification on short observations can be very challenging but yet very needed in several systems where the delay is a very crucial parameter. Therefore, there is an urgent need to provide effective methods to deal with the variable-length input.

### D. SIGNALS DATASETS

Even though DL-AMR can provide significant performance, it requires a large amount of training data. In practice, collecting a sufficient amount of reliable training samples is usually costly and difficult. Therefore, the used large-scale training dataset should be constructed adequately and carefully with more realistic signals under a wide range of SNR [39]. Furthermore, data augmentation methods can make the classification of radio modulation schemes more successful by using shorter radio samples [77], [102], which will provide a simplified DL model with a smaller classification response time.

### E. ENHANCED DL MODELS

As mentioned before, the DL-based methods can operate under a wide range of SNR. Hence, large-scale datasets will be needed in order to achieve the expected results. This may result in a high number of training parameters and, hence, high computational complexity along with high storage requirements. Besides, the training process may consume a long time and suffers from over-fitting issues. Therefore, optimized and hybrid DL methods [46] can be studied in order to avoid over-fitting and reduce the computational complexity as well as to improve the classification accuracy [25], [108]. Moreover, the samples-labeling process in the training phase in supervised learning methods is very difficult to accomplish in addition to be time-consuming. Hence, the idea of semi-supervised [114] and unsupervised learning methods is being adopted [119]. These methods can meet the rapid growth of AMR applications and enhance the overall performance.

### F. UNKNOWN MODULATION TYPES

In several military applications such as intelligence and surveillance systems, the AMR process should be able to detect the modulation type of unknown signals. For example, jamming devices are deployed in the communication channel between adversary units to prevent communication among them. More specifically, they transmit noise or dummy signals modulated by the matching modulation type. Therefore, the modulation scheme of the adversary communication signals should be detected in order to allow the jamming devices to transmit jamming signals modulated using the corresponding modulation scheme.

However, as stated in the existing literature, the type of modulation schemes to be classified is assumed to be within a pool of known ones [110], [111]. More specifically, seldom works are capable of coping with unknown signals. Therefore, a comprehensive set of modulation types or a more reasonable model design is needed to overcome the lack of universality for the unknown signal recognition.

### G. REAL HARDWARE IMPLEMENTATION

In order to design an AMR method feasible to be implemented on real hardware, further investigation is required to achieve high classification accuracy along with low computational complexity. For example, the methods presented in [40], [50] suffer from a large number of learnable parameters. Conversely, the method proposed in [56] achieve low complexity with relatively low classification accuracy. Besides, the proposed DL methods found in the literature are still in the simulation phase and seldom works are devoted to implementing the proposed AMR method on real hardware. Hence, deploying DL-AMR algorithm to real powerful computing hardware (e.g. field-programmable gate arrays (FPGA) and beyond) can be very challenging [81], especially when it comes to timing and validation issues.

### H. DL-AMR IN MIMO SYSTEMS

DL-AMR models are scarcely explored for MIMO and massive MIMO communication systems. In this direction, further investigation can be done in order to design new methods or even extend prior works which were dedicated for SISO systems. Although some signals datasets already exist in the literature for SISO systems, but yet there are seldom datasets for MIMO and massive MIMO systems. Hence, it is very important for future researches to build robust and sufficient signals datasets for such systems.

## VI. CONCLUSION

Machine learning and deep learning are becoming increasingly popular in AMR domain. Research in this area is still incipient but it has however shown outstanding results. In this article, we provided a comprehensive survey of recent work lying at the junction of AMR, machine learning and deep learning. We reviewed both ML and DL models in AMR area for both SISO and MIMO communication systems. We discussed the architecture of various deep learning models and pointed out the key advantages of each model. We warped up this paper by presenting potential research directions and open challenges, which may result in significant future research results.

## REFERENCES

[1] Z. Zhu and A. K. Nandi, *Automatic Modulation Classification: Principles, Algorithms and Applications*, 1st ed. Hoboken, NJ, USA: Wiley, 2015.

[2] C. Zhang, P. Patras, and H. Haddadi, "Deep learning in mobile and wireless networking: A survey," *IEEE Commun. Surveys Tuts.*, vol. 21, no. 3, pp. 2224-2287, 3rd Quart., 2019, doi: 10.1109/COMST.2019.2904897.

[3] Y. A. Eldemerdash, O. A. Dobre, and M. Oner, "Signal identification for multiple-antenna wireless systems: Achievements and challenges," *IEEE Commun. Surveys Tuts.*, vol. 18, no. 3, pp. 1524-1551, 3rd Quart., 2016, doi: 10.1109/COMST.2016.2519148.
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<figure><img src="image_9.png" /></figure>

**BACHIR JDID** received the B.Eng.Sc. degree in communications engineering from the Higher Institute for Applied Science and Technology, Damascus, Syria, in 2013. He is currently pursuing the M.Phil. degree with the Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia. His research interests include wireless communications, signal processing, image processing, embedded systems, and artificial intelligence and its application in wireless communications.

<figure><img src="image_10.png" /></figure>

**KAIS HASSAN** was born in 1975. He graduated in telecommunications engineering from the Higher Institute for Applied Science and Technology (HIAST), Syria, in 2000. He received the M.S. degree in telecommunications systems and the Ph.D. degree in telecommunications from the University of Valenciennes, France, in 2009 and 2012, respectively. From 2000 to 2008, he was a Research Engineer with HIAST. From 2013 to 2014, he was an Assistant Professor with the University of Western Brittany, France.

Since September 2014, he has been an Associate Professor with Le Mans University, France. He is currently with the Acoustics Laboratory of Le Mans University (LAUM). His research interests include wireless communications, signal processing, and cognitive radio.

<figure><img src="image_11.png" /></figure>

**IYAD DAYOUB** (Senior Member, IEEE) received the B.Eng. degree in telecommunications and electronics from Syria, in 1993, the M.A.Sc. degree in electrical engineering from the National Polytechnic Institute of Lorraine (INPL), and the Ph.D. degree from the Institute of Electronics, Microelectronics and Nanotechnology (IEMN), University of Valenciennes, in 2001. He has worked as a System Engineer with Siemens (Middle East) and as a Researcher with Alcatel Business Systems

Alcatel, Colombes, Paris. From 2007 to 2014, he was a member of the National Council of Universities (CNU), France, in the area of electrical engineering, electronics, photonics and systems. From 2010 to 2014, he was an Adjunct Professor with Concordia University, Montreal. He is currently a Professor of communications engineering. His current research activities at the IEMN of Université Polytechnique Hauts de France (UPHF) & INSA H-d-F are focused on wireless communications, high-speed communications, cognitive radio, and hybrid radio-optic technologies. He is a member of several International Conference Advisory Committees, Technical Program Committees and Organization Committees, such as VTC, GLOBECOM, ICC, PIMRC, WWC, and so on.

<figure><img src="image_12.png" /></figure>

**WEI HONG LIM** (Senior Member, IEEE) received the B.Eng. degree (Hons.) in mechanical engineering and the Ph.D. degree in computational intelligence from the Universiti Sains Malaysia, Penang, Malaysia, in 2011 and 2014, respectively. From 2015 to 2017, he was a Post-doctoral Researcher with the Intelligent Control Laboratory, National Taipei University of Technology, Taiwan, where he was a Visiting Researcher, in 2019. He is currently an Assistant Professor

with the Faculty of Engineering, Technology and Built Environment, UCSI University. He is also working with three national research grants awarded by the Ministry of Education, Malaysia, and five internal grant projects supported by UCSI University. He has published more than 50 research articles in research areas related to computational intelligence, optimization algorithms, energy management, and digital image processing. He has been actively involved in various professional bodies and registered as the European Engineer (EUR ING) with the Fédération Européenne d'Associations Nationales d'Ingénieurs (FEANI), a Chartered Engineer (CEng) with the Engineering Council, U.K., (ECUK) and a Professional Technologist (PTech) with the Malaysia Board of Technologist (MBOT). He is also an Active Reviewer for various reputable journals, such as IEEE Access, Complexity, Mathematical Problem in Engineering, and Computational Intelligence and Neuroscience.

<figure><img src="image_13.png" /></figure>

**MASTANEH MOKAYEF** received the master's degree from the Faculty of Engineering, University Technology Malaysia, in 2009, and the Ph.D. degree from the Wireless Communication Centre, Faculty of Electrical Engineering, University Technology Malaysia (UTM), in 2014. Since 2015, she has been working with UCSI University, Malaysia, where she is currently works as an Assistant Professor with the Faculty of Engineering and Built Environment (FETBE). Her research

interests include wireless communications, spectrum sharing method, spectrum management, cellular communication systems and antenna design. Since 2017, she has been a member of Board of Engineers Malaysia (BEM).
[121] V. Bhatia, M. R. Tripathy, and P. Ranjan, “Deep learning for massive MIMO: Challenges and future prospects,” in Proc. *IEEE 9th Int. Conf. Commun. Syst. Netw. Technol. (CSNT)*, Apr. 2020, pp. 26–31, doi: 10.1109/CSNT48778.2020.9115783.

[122] J. Xia, K. He, W. Xu, S. Zhang, L. Fan, and G. K. Karagiannidis, “A MIMO detector with deep learning in the presence of correlated interference,” *IEEE Trans. Veh. Technol.*, vol. 69, no. 4, pp. 4492–4497, Apr. 2020, doi: 10.1109/TVT.2020.2972806.

[123] Y. Wang, J. Wang, W. Zhang, J. Yang, and G. Gui, “Deep learning-based cooperative automatic modulation classification method for MIMO systems,” *IEEE Trans. Veh. Technol.*, vol. 69, no. 4, pp. 4575–4579, Apr. 2020, doi: 10.1109/TVT.2020.2976942.

[124] Y. Wang, J. Gui, Y. Yin, J. Wang, J. Sun, G. Gui, H. Gacanin, H. Sari, and F. Adachi, “Automatic modulation classification for MIMO systems via deep learning and zero-forcing equalization,” *IEEE Trans. Veh. Technol.*, vol. 69, no. 5, pp. 5688–5692, May 2020, doi: 10.1109/TVT.2020.2981995.

[125] Y. Wang, G. Gui, H. Gacanin, T. Ohtsuki, H. Sari, and F. Adachi, “Transfer learning for semi-supervised automatic modulation classification in ZF-MIMO systems,” *IEEE J. Emerg. Sel. Topics Circuits Syst.*, vol. 10, no. 2, pp. 231–239, Jun. 2020, doi: 10.1109/JETCAS.2020.2992128.

[126] Y. Liu and Y. Liu, “Modulation recognition with pre-denoising convolutional neural network,” *Electron. Lett.*, vol. 56, no. 5, pp. 255–257, Mar. 2020, doi: 10.1049/el.2019.3586.

[127] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proc. *IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)*, Jun. 2015, pp. 3431–3440, doi: 10.1109/CVPR.2015.729896.

[128] M. Lin, Q. Chen, and S. Yan, “Network in network,” 2013, arXiv:1312.4400. [Online]. Available: http://arxiv.org/abs/1312.4400

[129] A. Murad and J.-Y. Pyun, “Deep recurrent neural networks for human activity recognition,” *Sensors*, vol. 17, no. 11, p. 2556, Nov. 2017, doi: 10.3390/s17112556.

[130] F. Karim, S. Majumdar, H. Darabi, and S. Chen, “LSTM fully convolutional networks for time series classification,” *IEEE Access*, vol. 6, pp. 1662–1669, 2018, doi: 10.1109/ACCESS.2017.2779939.

<figure><img src="image_10.png" /></figure>

**BACHIR JDID** received the B.Eng.Sc. degree in communications engineering from the Higher Institute for Applied Science and Technology, Damascus, Syria, in 2013. He is currently pursuing the M.Phil. degree with the Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia. His research interests include wireless communications, signal processing, image processing, embedded systems, and artificial intelligence and its application in wireless communications.

<figure><img src="image_11.png" /></figure>

**KAIS HASSAN** was born in 1975. He graduated in telecommunications engineering from the Higher Institute for Applied Science and Technology (HIAST), Syria, in 2000. He received the M.S. degree in telecommunications systems and the Ph.D. degree in telecommunications from the University of Valenciennes, France, in 2009 and 2012, respectively. From 2000 to 2008, he was a Research Engineer with HIAST. From 2013 to 2014, he was an Assistant Professor with the University of Western Brittany, France.

Since September 2014, he has been an Associate Professor with Le Mans University, France. He is currently with the Acoustics Laboratory of Le Mans University (LAUM). His research interests include wireless communications, signal processing, and cognitive radio.

<figure><img src="image_12.png" /></figure>

**IYAD DAYOUB** (Senior Member, IEEE) received the B.Eng. degree in telecommunications and electronics from Syria, in 1993, the M.A.Sc. degree in electrical engineering from the National Polytechnic Institute of Lorraine (INPL), and the Ph.D. degree from the Institute of Electronics, Microelectronics and Nanotechnology (IEMN), University of Valenciennes, in 2001. He has worked as a System Engineer with Siemens (Middle East) and as a Researcher with Alcatel Business Systems

Alcatel, Colombes, Paris. From 2007 to 2014, he was a member of the National Council of Universities (CNU), France, in the area of electrical engineering, electronics, photonics and systems. From 2010 to 2014, he was an Adjunct Professor with Concordia University, Montreal. He is currently a Professor of communications engineering. His current research activities at the IEMN of Université Polytechnique Hauts de France (UPHF) & INSA H-d-F are focused on wireless communications, high-speed communications, cognitive radio, and hybrid radio-optic technologies. He is a member of several International Conference Advisory Committees, Technical Program Committees and Organization Committees, such as VTC, GLOBECOM, ICC, PIMRC, WWC, and so on.

<figure><img src="image_13.png" /></figure>

**WEI HONG LIM** (Senior Member, IEEE) received the B.Eng. degree (Hons.) in mechanical engineering and the Ph.D. degree in computational intelligence from the Universiti Sains Malaysia, Penang, Malaysia, in 2011 and 2014, respectively. From 2015 to 2017, he was a Post-doctoral Researcher with the Intelligent Control Laboratory, National Taipei University of Technology, Taiwan, where he was a Visiting Researcher, in 2019. He is currently an Assistant Professor

with the Faculty of Engineering, Technology and Built Environment, UCSI University. He is also working with three national research grants awarded by the Ministry of Education, Malaysia, and five internal grant projects supported by UCSI University. He has published more than 50 research articles in research areas related to computational intelligence, optimization algorithms, energy management, and digital image processing. He has been actively involved in various professional bodies and registered as the European Engineer (EUR ING) with the Fédération Européenne d'Associations Nationales d'Ingénieurs (FEANI), a Chartered Engineer (CEng) with the Engineering Council, U.K., (ECUK) and a Professional Technologist (PTech) with the Malaysia Board of Technologist (MBOT). He is also an Active Reviewer for various reputable journals, such as IEEE Access, Complexity, Mathematical Problem in Engineering, and Computational Intelligence and Neuroscience.

<figure><img src="image_14.png" /></figure>

**MASTANEH MOKAYEF** received the master's degree from the Faculty of Engineering, University Technology Malaysia, in 2009, and the Ph.D. degree from the Wireless Communication Centre, Faculty of Electrical Engineering, University Technology Malaysia (UTM), in 2014. Since 2015, she has been working with UCSI University, Malaysia, where she is currently works as an Assistant Professor with the Faculty of Engineering and Built Environment (FETBE). Her research

interests include wireless communications, spectrum sharing method, spectrum management, cellular communication systems and antenna design. Since 2017, she has been a member of Board of Engineers Malaysia (BEM).