In a device-to-device wireless multi-hop communication scenario with resource-constrained devices, the energy efficiency of communication plays a significant role in network design and operation. Based on the recently proposed Glossy network flooding approach, we introduce cooperation between transmitting nodes and formulate a robust multicast beamforming problem with imperfect channel state information (CSI) and analyze its performance. The level of cooperation is dependent on the number of limited feedback bits from receivers to transmitters. First, the impact of the number of limited feedback bits B on energy efficiency is studied, and the programming problem for finding the optimal B is formulated subject to a maximum outage constraint of 5%. Numerical simulations show that there exists an optimal number of feedback bits that maximizes energy efficiency. Second, the effect of the number of cooperating transmitters on energy efficiency is investigated. Results show that an optimum group of cooperating transmit nodes, also known as a transmit coalition, can be formed in order to maximize energy efficiency. Results show that the investigated techniques including optimum feedback bits and transmit coalition formation can achieve a 100% increase in energy efficiency when compared to the state-of-the-art Glossy under the same operation requirements in very dense networks.

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Cognitive autonomous networks imply that networks will independently derive and execute intelligent decisions, and thereby elevating the human operator's role to a higher level of abstraction. At that level, the operator only specifies the desired outcomes, called intents, which must then be supported by corresponding intent-driven capabilities in the network or its management functions. Although, Intent-Driven Management (IDM) has been published in multiple works, there is still no globally agreed end?to?end view of such IDM systems, let alone a globally agreed definition of intents. This paper provides a comprehensive discussion on the core aspects of IDM systems and combines them into an end-to-end system view with the related example solutions. Contrasting against a short review of related scientific and standards literature, the paper introduces a flexible, generic definition of intents and an end-to-end IDM system architecture as well as the related modeling of intents to support their standardization. The paper also introduces implementation examples fitting the architecture and discusses advanced IDM features that need to be provided, including the ability to detect and resolve conflicts between intents.