Emerging use-cases like smart manufacturing and smart cities pose challenges in terms of latency, which cannot be satisfied by traditional centralized infrastructure. Edge networks, which bring computational capacity closer to the users/clients, are a promising solution for supporting these critical low latency services. Different from traditional centralized networks, the edge is distributed by nature and is usually equipped with limited compute capacity. This creates a complex network to handle, subject to failures of different natures, that requires novel solutions to work in practice. To reduce complexity, edge application technology enablers, advanced infrastructure and application orchestration techniques need to be in place where AI and ML are key players.
In this letter, the phase noise (PN) effect on orthogonal frequency division multiplexing (OFDM) based multi-node transmission for small cell backhaul is studied. Since each transmitter is equipped with an independent oscillator, the received signals are corrupted by all of these independent PNs (plus the PN at the receiver), which renders the conventional phase noise compensation schemes at the receiver ineffective. In this work, a phase noise compensation scheme for multi-node transmission is proposed, which can effectively mitigate the effect of the multiple phase noises.
Autonomous vehicles are predicted to have a large impact on the field of transportation and bring substantial benefits, but they present new challenges when it comes to ensuring safety. Today the standard ISO 26262:2011 treats each defined function, or item, as a complete scope for functional safety; the driver is responsible for anything that falls outside the items. With autonomous driving, it becomes necessary to ensure safety at all times when the vehicle is operating by itself. Therefore, we argue that the hazard analysis should have the wider scope of making sure the vehicle’s functions together fulfill its specifications for autonomous operation. The paper proposes a new iterative work process where the item definition is a product of hazard analysis and risk assessment rather than an input. Generic operational situation and hazard trees are used as a tool to widen the scope of the hazard analysis, and a method to classify hazardous events is used to find dimensioning cases among a potentially long list of candidates. The goal is to avoid dangerous failures for autonomous driving due to the specification of the nominal function being too narrow.
This paper investigates what challenges arise when extending the scope of functional safety for road vehicles to also include cooperative systems. Two generic alternatives are presented and compared with one another. The first alternative is to use a vehicle centric perspective as is the case in the traditional interpretation of ISO 26262 today. Here, an "item" (the top level system or systems for which functional safety is to be assured) is assumed to be confined to one vehicle. In the vehicle centric perspective inter-vehicle communication is not an architectural element and is therefore not a candidate for redundancy as part of the functional safety concept. The second alternative is to regard a cooperative system from a cooperative perspective. This implies that one item may span over several vehicles. The choice of perspective has implications in several ways. We investigate the implications for the cooperative item and in what ways the results may differ when going through the reference life cycle of ISO 26262. In particular we look at classification of hazardous events where severity is significantly higher since the cooperative system involves multiple rather than one single vehicle. We therefore suggest an additional severity class and as a consequence introduce a new automotive safety integrity level, ASIL E. The cooperative perspective includes the inter-vehicle communication as a candidate for redundancy. ASIL E can therefore be achieved using ASIL decomposition and the currently recommended product development phases for ASIL A to ASIL D. As an example for illustrating we use platooning.
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