## 1 Introduction  
The rapid advancement of technology in recent years has led to the development of vast network of interconnected devices and systems known as the Internet of Things (IoT) ecosystem in smart cities, which has transformed the way we interact with the physical world. From smart homes to connected vehicles, IoT technology has enabled smart devices to communicate with each other, share information, and make decisions based on the shared data. These smart devices, sensors, and actuators have enabled us to collect vast amounts of data from our surroundings to develop intelligent systems, such as Intelligent Transportation Systems (ITS), that can transform the way we commute. ITS leverages IoT technologies and mostly relies on image communication systems to use data from sensors and cameras to analyze traffic flow, predict congestion, and optimize traffic signals in real-time to create a smart and more efficient transportation system [1; 2; 3; 4]. The success of ITS relies heavily on the efficient and reliable transmission of data between devices, which requires high data rates and link capacities to handle the large amounts of data generated by the IoT devices in real-time. To achieve this, Orthogonal Frequency Division Multiplexing (OFDM) is widely used as an efficient communication system for high data rate and reliable wireless communication [5; 6]. The OFDM communication system uses both frequency and time domain multiplexing to transmit data over multiple sub-carriers. This division of the data stream into several subcarriers allows for parallel transmission, leading to more efficient use of the available bandwidth, and enabling high-speed data transmission with minimal interference and noise. Additionally, OFDM-based communication systems can benefit from using different modulation techniques to further improve data rate and link capacity. Among them, M-ary Quadrature Amplitude Modulation (M-QAM) is the most efficient technique, which can utilize higher order M-QAM to achieve greater bandwidth efficiency and link capacity with a greater number of bits per symbol [5; 7]. Visual data are crucial in ITS environment, and the ability to transmit and process a large number of images efficiently is essential for computer vision (CV) applications in such an environment. While high data rate OFDM-based image communication systems can facilitate the fast transmission of images, there are significant challenges in processing the vast amount of image data with added noise introduced by the utilization of different OFDM systems and the environment itself. To address these challenges, researchers are exploring the use of artificial intelligence (AI) and deep learning (DL) to improve the performance of CV applications in IoT ecosystem. AI and DL have emerged as powerful tools for processing and analyzing large amounts of data in real-time for various CV applications [8]. In the context of ITS, DL algorithms can be used for CV applications to extract meaningful information from image data to enable better decision-making for traffic management and safe driving. For instance, autonomous vehicles rely on AI algorithms to navigate traffic safely and efficiently, and AI algorithms can analyze data from vehicle sensors to identify potential safety hazards and alert drivers or emergency services, contributing to road safety. Additionally, traffic cameras equipped with DL algorithms can analyze traffic patterns and adjust traffic signals in real-time to reduce congestion and enhance road safety. Although both of these areas of OFDM-based wireless communication systems and DL-based CV applications (such as classification tasks) have been extensively studied in their respective fields, their combination has not been thoroughly explored as per literature review, mainly due to their differences. Therefore, our main objective in this paper is to combine the two and conduct a multi-tier analysis of OFDM-based image communication systems for DL applications while availing third-party computational resources.