Abstract / truncated to 115 words (read the full abstract)

Facing the diversity of communication needs of 5G networks and the future 6G, resource allocation is considered as a key enabler to increase the number of devices, the data rate or the reliability of the communication links. In machine-type communications networks, recent work has proposed to adapt the temporal resource allocation as a function of the underlying process driving the activity of the devices. This thesis firstly focuses on the impact of having only limited knowledge of the underlying process, and proposes methods to mitigate the bias induced by the lack of knowledge. Secondly, an algorithm for the joint optimization of the temporal resource allocation and the transmit power of the devices is proposed. The ... toggle 5 keywords

wireless networks – resource allocation – stochastic gradient descent – machine type communication – enhanced mobile broadband

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