Abstract:
IEEE 802.11ax, popularly known as WiFi 6, introduces OFDMA (Orthogonal Frequency Division Multiple Access) that allows multiple users to transmit or receive frames concurrently via a more flexible utilization of the available bandwidth. Due to its ability to perform concurrent transmissions, prior studies suggest that OFDMA will provide reduced latency and increased throughput compared to OFDM. As our first work, we investigate this claim under various downlink traffic loads using the latest 802.11ax models supplied by the widely used open-source network simulator ns-3 (version 3.34). Our simulation results show that the actual benefits of OFDMA over OFDM can only be extracted under intermediate traffic loads. Motivated by this finding, we compare the performance of OFDMA and OFDM by simulating various application settings involving intermediate traffic rates. We find that OFDMA provides considerable improvements over OFDM under such traffic conditions for different application-specific parameters of interest, e.g., OFDMA delivers a more consistent bitrate for live video streaming applications and reduced jitter for video conferencing applications. Furthermore, for applications involving small payloads such as web-based applications and factory IoT-based motion control applications, OFDMA results in 7 − 10× lower average latency when compared to OFDM.
We observe from our experiments that OFDMA experiences a substantial increase in latency as the payload size becomes small (90-30 B). In an IoT-based factory setting, applications frequently exchange small payloads with stringent deadline constraints; missing these deadlines for certain critical applications such as human-safety monitoring and equipment control have a huge impact or penalty. Our experiments make it evident that such constraints cannot be met by simple schedulers such as round-robin. Thus there is a need for intelligent scheduling techniques in such deadline-based environments. As our second work, we propose a deadline- aware scheduler for factory environments that maximizes the number of packet deadlines met for critical applications and show that our scheduler significantly reduces the overall penalty incurred due to missed deadlines compared to other deadline-based schedulers.
