No abstract

Integrated access and bachhaul (IAB), introduced in 3GPP Release 16, is expected to be one of the main enablers for dense deployments in mmWave spectrum. The IAB architecture brings in topology changes, which impact network-related aspects, such as ensuring robust routing of traffic and load-balancing. While an IAB network should be transparent to the UE, its topology design may affect the end-user performance. Of particular importance in these first releases, hence, is to determine and evaluate optimal IAB topologies. This paper addresses optimal organisation of IAB nodes by considering a number of inter-connected aspects, including maximum number of hops, path-length, load balancing between the IAB-donors and traffic demand in the access network. In that, we focus on one of the parameters most sensitive to the architectural changes in the network - delay. Our analysis relies on the connectivity graph obtained by considering some practical aspects of sectorised beamforming using real mmWave antenna patterns.

The growing complexity of wireless networks has sparked an upsurge in the use of artificial intelligence (AI) within the telecommunication industry in recent years. In network slicing, a key component of 5G that enables network operators to lease their resources to third-party tenants, AI models may be employed in complex tasks, such as short-term resource reservation (STRR). When AI is used to make complex resource management decisions with financial and service quality implications, it is important that these decisions be understood by a human-in-the-loop. In this paper, we apply state-of-the art techniques from the field of Explainable AI (XAI) to the problem of STRR. Using real-world data to develop an AI model for STRR, we demonstrate how our XAI methodology can be used to explain the real-time decisions of the model, to reveal trends about the model’s general behaviour, as well as aid in the diagnosis of potential faults during the model’s development. In addition, we quantitatively validate the faithfulness of the explanations across an extensive range of XAI metrics to ensure they remain trustworthy and actionable.