Enhanced Robustness
W2RP was further developed in multiple directions, focused on improving robustness under various scenarios and channel conditions.
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Enabling Multi-Link Data Transmissions
Sending fragments periodically with a static shaping time can reach its limits in case the channel utilization is extremely high. Obviously, it is not possible to support unlimited numbers of high-volume V2X collaborative sensing links within limited channel capacity. Instead, link access to resources has to be managed.
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Reliable Multicast for Large Samples
Unicast data dissemination in cooperative perception and similar applications is inefficient as it can be expected that data may be needed by multiple vehicles/nodes. Hence, W2RP has been extended to support multicast resulting in WiMEP. As errors can be individual per receiver, efficient backward error correction (BEC) is a challenging task. For this purpose, WiMEP supports bundling of BEC for receivers with similar error patterns and offers means to prioritize receivers based on arbitrary conditions. The following figure visualizes the prioritized retransmission scheme of WiMEP. The decision on which fragment to retransmit first depends on the priority-based reader selector that chooses the most important receiver (reader) and prioritizes its fragment retransmissions. Nevertheless, retransmissions continue to be send via multicast, hence all other receivers can also benefit from the retransmission. This procedure is repeated until a) all readers received the sample completely or b) the sample deadline elapses.
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Robustness to Burst Errors
So far, previous works on W2RP (and WiMEP) assumed uniformly distributed (bit) errors. However, real-world scenarios can be subject to more sophisticated error conditions. For example, burst errors can occur that put additional strain on the backward error correction (BEC) mechanism of W2RP.
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Improving Robustness by Means of Data Optimization
Despite previous works on improving reliability and robustness for large data transmissions, the high data rates produced by (high definition) sensor data are still a challenge. With the data rates ranging from 10-1000 Mbit/s per application, depending on the type of sensor and whether raw or preprocessed data is used, transmission of such data while adhering to stringent timing and safety constraints via state-of-the-art wireless technologies is not viable. While commodity 802.11ac/be and 5G cellular solutions can achieve multi-Gbit/s data rates, there are no appropriate mechanism to ensure reliability for large data streams. On the other end of the spectrum there are dedicated V2X protocols and ultra reliability and low latency solutions (URLLC and wireless TSN) only manage small data rates that are insufficient for large data streams. Still, such high-definition are deemed essential in enabling higher levels of automations in the future. Consequently, different solutions for robust sensor streaming and collaboration, such as loss-less application-centric data optimization schemes, are required.
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