A Survey of Blind Modulation Classification Techniques for OFDM Signals

Abstract

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed.

A Review of Research on Signal Modulation Recognition Based on Deep Learning

“…Therefore, it is of great significance to study the modulation and identification methods of OFDM signals. However, due to the diversification of modulation types, the channels of multi-carrier systems are very complex, resulting in the identification of sub-carrier modulation of OFDM signals and the identification between OFDM signals and single-carrier signals becoming very difficult [117]. This brings great challenges to future wireless communication.…”

An Improved Modulation Recognition Algorithm Based on Fine-Tuning and Feature Re-Extraction

“…Modulation recognition is a two-step process: pre-processing the communication signals and using the appropriate classifier to recognize the modulation types [8]. The modulation recognition algorithms for communication signals can be divided into three categories at present [9], which are likelihood-based, feature-based and deep learning-based algorithms.…”

TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GAN

“…For OFDM modulation signals, the existing conventional blind estimation algorithms mainly estimate the modulation mode [9], carrier frequency [10], chip time width [11], cycle prefix length [12], etc. However, blind parameter estimation of OFDM signals in a complex electromagnetic environment still has limitations, such as sensitivity to noise and fuzzy estimation [13], complicated estimation steps, high computational complexity, and large parameter estimation errors [14].…”