## 5 Conclusions and Future Work  
In this study, we have analyzed the performance of two DL models on images transmitted over various OFDM wireless communication systems for CV applications. Specifically, we have considered higher order M-QAM systems with different channel models and channel estimation techniques. The main objective was to achieve a higher data rate to enable real time CV applications while maintaining the overall communication system complexity. In general, the utilization of a higher order M-QAM in the fading channel environment leads to heavily distorted signals, regardless of the channel estimation techniques. However, our results have shown that the feature extractor of a DL model can be robust  
Figure 14: Grad-CAM visualizations of EfficientNetV2-B0 model (with Augmentation) on sample image recovered from different OFDM-based image communication systems under the Rayleigh Fading channel. The recovered image from different communication system (**top**), Grad-CAM heat map of the DL model’s attention region (**middle**) and superimposed heat map over the image (**bottom**) is shown. Images with green border are correctly predicted and images with red border are incorrectly predicted.  
against these distortions with suitable data augmentation techniques, thereby improving the model generalizability across the higher order M-QAM. In other words, this trained feature extractor can extract meaningful features even from very noisy images, which can be utilized for downstream tasks, such as traffic sign recognition in ITS.  
The present study implemented simple variable source coding technique and legacy OFDM PHY for communication system. As future work, we are interested in implementing PHY as a DL architecture and consider DL-based Joint Source-Channel coding for communication system and extending the application domain. Additionally, DL applications using advanced communication systems, such as MIMO and other hybrid systems, can be studied as an extension of this study.  
Conceptualization, N.I.; methodology, N.I.; software, N.I.; validation, S.S.; formal analysis, N.I.; investigation, N.I.; resources, N.I. and S.S.; data curation, N.I.; writing--original draft preparation, N.I.; writing--review and editing, S.S.; visualization, N.I.; supervision, S.S.; project administration, S.S.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This research is supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07048338).  
All the datasets used in this study are publicly available. The GTSRB dataset used for Traffic Sign Recognition is accessible at: [[https://sid.erda.dk/public/archives/daaeacd7](https://sid.erda.dk/public/archives/daaeacd7) ce1152 aea9b61d9f1e19370/published-archive.html]([https://sid.erda.dk/public/archives/daaeacd7](https://sid.erda.dk/public/archives/daaeacd7) ce1152 aea9b61d9f1e19370/published-archive.html) (accessed on 9 February 2022).  
The experiments in this paper were performed with GPU resources provided by the Korea NIPA (National IT Industry Promotion Agency).  
The authors declare no conflict of interest.