A Scalable and Accurate Chessboard-Based AMC Algorithm With Low Computing Demands

Abstract

Automatic Modulation Classification (AMC) is a technique used to identify signal modulations in applications like IoT devices, cognitive radar, software-defined radio, and electronic warfare. These applications could be applied to IoT devices. With future wide applications of IoT devices, AMC algorithms need to be more compact yet suitable for embedded devices with limited resources and remain acceptable accuracy. Although current AMC algorithms deliver high accuracy, they require substantial computing power, making them unsuitable for IoT devices. This paper introduces the novel Chessboard-based Automatic Modulation Classification (CAMC) algorithm, which has dramatically high accuracy. Test results reveal that CAMC achieves 99%* accuracy under a 3dB SNR condition and 100% above 5dB SNR. Meanwhile, this algorithm is scalable and demands less computing power. It offers better accuracy results compared to state-of-the-art AMC algorithms, classifying mainstream modulations in IoT devices like BPSK, QPSK, 8PSK, and 16QAM, but requires less computing power than existing algorithms. Additionally, CAMC is hardware-friendly due to its inherent parallelism and scalability. The novelty of this paper is to classify 4 different modulations in a low-computation-loading required and hardware-friendly way and achieve a high accuracy of over 99%* above SNR of 3dB. (* Accuracy that most of the time could reach)

Keywords

• Communications technology
• classification algorithms
• modulation
• Internet of Things
• parallel algorithms
• image classification