# A Survey of Blind Modulation Classification Techniques for OFDM Signals
## 5 Challenges and Future Research Directions  
Based on an exhaustive literature review, this paper summarizes the two major MC approaches for the OFDM signal: statistics based and AI based, and also highlights their advantages and disadvantages. In the statistics-based approach, the LB approach provides optimal classification performance. As the number of unknown parameters increases, it becomes more computationally complex to find a desired analytical solution for the decision problem. If there is a closed-form solution made, it can be impractical because of its high computational complexity. A sub-optimal classifier is obtained from the optimal ML classifier to minimize computational complexity. In the FB algorithm, the expert domain feature needs to be extracted first, then decisions are made for the classification. FB algorithms are easier to implement, despite being sub-optimal. Many of the ML- and DL-based MC first use the signal pre-processing step, which includes noise reduction, parameter estimation, and making the signal synchronized, which enhances the quality of the received signal. After that, proper selection of classification model that can reduce the signal processing steps, increase the modulations classification accuracy and provide more reliable and effective methods of modulation classification, compared to conventional modulation methods. Nevertheless, several studies are mainly based on ideal hypotheses and rely on a large number of labeled signals. Most of the MC research is still focused on the simulation stage. The communication environment is more sophisticated, and signal frame lengths are varied in the realistic implementation scenario. However, with the increasing complexities of the communication environment and the increasing need for numerous particular tasks, it is difficult to make sure that a huge training data set is generated effectively for particular tasks. The development of semi-supervised algorithm systems is needed to solve this problem. Effective semi-supervised algorithms may be able to fulfill the increasing need for diverse signal processing demands by collecting a large amount of data, only a small fraction of which is labeled data. Another potential task is to figure out how to develop hardware platforms, implant applications, and evaluate algorithms employing measured data. Another challenge in the future is how to incorporate a DL-based transmission signal modulation identifier for OFDM signals on a field-programmable gate array (FPGA), which would necessitate further research into data quantization, model compression, and other related studies. Finally, DL techniques have a wide range of applications and growth potential as a powerful method for processing data and extracting features. In various fields, combining the DL model with other intelligent algorithms will yield more efficient results. Furthermore, traditional DL-based MC is challenging to implement in OFDM-based narrow-band (NB)-IoT devices, as it requires high computational complexity and more power as well as memory resources. However, implementing light-weight DL-based blind MC for NB-IoT devices that need less computational, space, and power requirements might be a difficult task for future adaptive transceiver systems. Another challenge in the future is modulation classification for QQPSK, \(\pi/4\)-QPSK, and MSK. Higher-order modulation classification for OFDM, MIMO-OFDM system, and adaptive OFDM systems over a randomized environment using a hybrid model need to be proposed in future wireless communication. In addition, we have to extend to a large number of modulation formats that work for all types of systems. MC can be implemented for massive MIMO systems, such as intelligent reflective surfaces, to reduce the distortion due to the non-line-of-sight (NLOS) component of the signal in future wireless communication. In OFDM-IM, the number of active subcarriers can be adjusted to achieve the desired spectral efficiency and BER performance. Thus, the MC algorithm for OFDM-IM needs to be explored.