Channel-Aware Throughput Maximization for Cooperative Data Fusion in CAV

--Connected and autonomous vehicles (CA Vs) have garnered significant attention due to their extended perception range and enhanced sensing coverage. T o address challenges such as blind spots and obstructions, CA Vs employ vehicle-to-vehicle (V2V) communications to aggregate sensory data from surrounding vehicles. However, cooperative perception is often constrained by the limitations of achievable network throughput and channel quality. In this paper, we propose a channel-aware throughput maximization approach to facilitate CA V data fusion, leveraging a self-supervised autoencoder for adaptive data compression. We formulate the problem as a mixed integer programming (MIP) model, which we decompose into two sub-problems to derive optimal data rate and compression ratio solutions under given link conditions. An autoencoder is then trained to minimize bitrate with the determined compression ratio, and a fine-tuning strategy is employed to further reduce spectrum resource consumption. Experimental evaluation on the OpenCOOD platform demonstrates the effectiveness of our proposed algorithm, showing more than 20.19% improvement in network throughput and a 9.38% increase in average precision (AP@IoU) compared to state-of-the-art methods, with an optimal latency of 19.99 ms. Index T erms --Cooperative perception, throughput optimization, connected and autonomous driving (CA V). Recently, autonomous driving has emerged as a promising technology for smart cities. By leveraging communication and artificial intelligence (AI) technologies, autonomous driving can significantly enhance the performance of a city's transportation system. This improvement is achieved through real-time perception of road conditions and precise object detection from onboard sensors (such as radars, LiDARs, and cameras), thereby improving road safety without human intervention [1]. Moreover, the ability of autonomous vehicles to adapt to dynamic environments and communicate with surrounding infrastructure and vehicles is crucial for maintaining the timeliness and accuracy of collected data, thereby enhancing the overall system performance [2]-[9]. Joint perception among connected and autonomous vehicles (CA Vs) is a key enabler to overcome the limitations of individual agent sensing capabilities [10].