Automatic Modulation Classification through clustering in the I/Q plane

Abstract

Automatic Modulation Classification (AMC) refers to a process where the modulation scheme of a signal of interest is determined. It is not a requirement of an AMC system to recover the original data contained in the signal. AMC has several applications for both military and civilian use. The military may use AMC to determine the modulation scheme of a signal in order to demodulate or more efficiently jam the signal. AMC can also be used for threat identification as the modulation scheme of a sig-nal can reveal additional information about the hardware being used. In civilian use, AMC can be used to create cognitive radios capable of sensing the electromagnetic spectrum in order to use the optimal modulation scheme in a co-operative environment. Regulatory agencies that are required to enforce band allocations can use AMC to ensure/determine compliance with regulations. Most AMC methods typically use either Maximum Likelihood approaches with hypothesis testing or Feature-based approaches in conjunction with a decision tree or some form of machine learning. These techniques require training or threshold calibration that will need to be re-set if the operating environment changes significantly. Several of these techniques also have large computational complexity and long execution times. Most AMC methods are only evaluated in an Additive White Gaussian Noise (AWGN) channel and are not described to the extent that it can be replicated. Several methods may not function at all in a multipath environment. In 2000 B.G. Mobasseri [1] investigated using the location of symbol levels in the In-phase and Quadrature (I/Q) plane as a robust feature for AMC. This feature is, how-ever, rarely used. We use this feature to develop an AMC method that does not re-quire retraining a machine learning algorithm or extensive reconfiguration for different channel conditions. A literature study was first conducted to find publications that utilise the location of I/Q samples as a feature. A simple multi-stage AMC method was then developed. The Classification Accuracy, execution time and scalability of k-means, k-medoids, fuzzy c-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points To Identify the Clustering Structure (OPTICS) and hierarchical clustering was evaluated for each stage in an AWGN channel. The best performing clustering algorithm (k-means) was then used to develop a new AMC method. The proposed method was then evaluated in an AWGN channel and compared to popular Feature-based and Likelihood-based techniques. The proposed method was improved by extracting a parameter that we define as the Classification Quality. We use this parameter to detect and reject incorrect classifications. The proposed method was then re-evaluated in an AWGN channel while re-jecting classifications with a low Classification Quality in order to prevent any incorrect classifications. The proposed method was then evaluated in a simulated multipath channel with the European Telecommunications Standards Institute (ETSI) Tap Delay Line (TDL) models. The proposed method does not rely on machine learning, is deterministic in the num-ber of operations and has low algorithmic complexity, resulting in low execution times while maintaining a large pool of possible modulation schemes. The proposed method addresses several of the drawbacks of the more popular methods in the literature, among others that minimal reconfiguration will be required with a significant change in the operating environment. The low execution time, linear scalability and the deter-ministic nature of the proposed method is also highly advantageous.