Robustness of Deep Learning enabled IoT Applications Utilizing Higher Order QAM in OFDM Image Communication System
Published in IEEE International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2023
Abstract
In a standard Orthogonal Frequency Division Multiplex (OFDM)-based communication system, higher order M-ary Quadrature Amplitude Modulation (M-QAM) increases throughput and link capacity significantly due to the larger number of bits per symbol. Such a system is suitable to transmit large volumes of image data for real-time Internet of Things (IoT) applications. However, higher order M-QAM increases the noise margin and Bit Error Rate (BER) in the received signal. Although the error bits corrupt the individual pixels values, the global contents of the image may remain intact and can be interpreted by Deep Learning (DL) models. Therefore, the current study analyzes the robustness of DL models for image communication systems utilizing higher order M-QAM. The performance analysis of the system is presented in terms of the BER and image quality measures. In the analysis, distortions in the reconstructed images decrease significantly when using higher Eb/N0 across all M-QAM. For higher Eb/N0 in the 1024- QAM system, the performance of the model improved by 11% and obtained an accuracy of 86%, lagging by 6% compared to the accuracy obtained on the original images. Overall, the model maintained a high accuracy range between 83% and 90% at Eb/N0 10 dB across all M-QAM.