Dependable Modulation Classifier Explainer (DMCE) with Measurable Explainability

Tejas Gaikwad^1*, Gaurav Duggal^1* and Bhupendra Sinha^1*

• ¹Reliance Industries (India), India

Internet of Things(IoT) plays a significant role in building Smart Cities worldwide. Smart cities use IoT devices to collect and analyze the data to provide better services and solutions. These IoT devices are heavily dependent on the network for communication. These new age networks have Artificial Intelligence(AI) playing a crucial role in reducing network roll-out and operation costs, improving entire system performance, enhancing customer services, and generating possibilities to embed a wide range of telecom services and applications. For IoT devices, it is essential to have a robust and trustable network for reliable communication among devices and service points. The signals sent between the devices or service points use modulation to send a signal over a bandpass frequency range. Our work focuses on modulation classification done using deep learning method(s), Adaptive Modulation Classification(AMC), which now has become an integral part of a communication system. We propose a Dependable Modulation Classifier Explainer(DMCE) that focuses on the explainability of modulation classification. Our work demonstrates how we can visualize and understand a particular prediction made by seeing highlighted data points crucial for modulation class prediction. We have also demonstrated a numeric Explainability Measurable Metric(EMM) to interpret the prediction. In the end, we have presented a comparative analysis with existing state-of-the-art methods.