Cross-layer Adaptable Contention Window for High-definition Map QoS Enhancement

The adoption of High-Definition (HD) mapping applications represents a critical step towards achieving Level-5 autonomous driving, revolutionizing road safety and paving the way for unprecedented advancements in transportation technology. However, HD mapping imposes significant computational demands in processing the raw data generated by autonomous vehicle sensors. To mitigate this issue, researchers have opted to offload the data reducing the processing time. Unfortunately, the current de-facto standard IEEE802.11p in Vehicular Ad-hoc Network (VANET) does not provide the best latency or throughput for applications with low latency and heavy data transfer requirements. This is because of the fixed Contention Window (CW). To address this problem, solutions have been developed to dynamically allocate the CW nowadays with the help of Machine Learning (ML) paradigms. Nonetheless, these solutions do not include a strategy to dynamically allocate an optimal CW per service type. Instead, they focus on sharing the wireless channel fairly. In this paper, we have developed a cross-layer Reinforcement Learning (RL) algorithm between the application and Medium Access Control (MAC) layer that allocates CW per service type. Results showed improvement with a different gap in the latency Cumulative Distribution Function (CDF) of 181%, 120%, 107%, and 119% for Voice, Video, HD Map, and Best-effort respectively compared to other different approaches.