## 2 Related Work
### OFDM-Based Image Communication System
They evaluated the effect of various channel conditions (AWGN, Rayleigh), modulation techniques (BPSK, QPSK, 8-PSK, 16-PSK, 32-PSK, 64-PSK), antenna configurations, number of users, and transformation techniques (FFT, Fractional Fourier Transform, DWT, DCT) on image communication in Fifth Generation (5G) networks. The results from the former study indicated that using the DCT transformation technique improved the BER compared to FFT and increasing the number of received antennas led to higher quality recovered images. The later study indicated that DWT outperformed FRFT and FFT in terms of BER; however, as the number of users continued to increase in the Massive MIMO system, the quality of the recovered image decreased irrespective of the transformation technique used. Additionally, both studies showed that lower-order PSK modulation produced better results overall. There have been several studies of image transmission across OFDM communication system with DL based channel coding and decoding techniques for CV applications. For instance, [21] first proposed deep joint source-channel coding (JSCC) for wireless image transmission by combining the source and channel coding into a single auto-encoder structure. It optimized source compression and error correction coding through back-propagation, which outperformed conventional schemes (separate source and channel coding) under AWGN and Rayleigh flat fading channels. This scheme was further extended to larger neural networks [22], feedback network [23], progressive transmission [24], and additional attention modules [25; 26]. Motivated by model-based deep learning, DL-based JSCC were also extended to OFDM image communication system with fading channels in [27; 28]. The former study fed the neural network-encoded image as frequency domain OFDM baseband symbols and used adversarial measures to further improve the quality of reconstructed images. The latter utilized double attention mechanism to better map image features to subchannels and ensure important features were transmitted over the high-quality subchannels. Both [27; 28] outperformed conventional separate coding schemes. The aforementioned research works focused on OFDM-based image communication systems; however, they have not considered, multi-tier analysis of the blocks in the OFDM physical layer (PHY) that encompasses higher-order modulation schemes, various channel models and channel estimation. Additionally, it is not yet practical to adapt PHY as a deep learning architecture. Furthermore, DL application of the received images for a specific task has not been considered, only system performance and image quality has been studied. The authors in [29; 30; 31] attempted to mitigate this gap and considered DL-based applications, and analyzed the performance of DL models on received images from various OFDM communication systems. Authors in [29] determined that images from FFT-based OFDM were better for image transmission for cloud-based DL applications, as opposed to DCT-based systems. Authors [30] investigated the impact of channel correction on DL models in the OFDM-based image communication system and showed that channel correction had a major impact on the quality of recovered images, although deep learning models still produced acceptable results without it (with a lesser degree of improvement). Authors in [31] evaluated the impact of higher order M-QAM on the performance of the DL model. Their results showed that the DL model accuracy was lower on images from higher order M-QAM compared to lower order M-QAM, but the overall accuracy was still relatively high and suitable for applications that are unaffected by small fluctuations in DL accuracy. The studies have investigated the DL-based applications for image communication systems; however, they have not considered multi-tier analysis of the OFDM communication system, including fading channels, modulation techniques, and channel estimation methods.