Mehdi Bennis

TL;DR: In this paper, a coordination-based and context-aware mobility management (MM) procedure for small cell networks using tools from reinforcement learning is proposed, where macro and pico BSs jointly learn their long-term traffic loads and optimal cell range expansion, and schedule their UEs based on their velocities and historical rates (exchanged among tiers).

TL;DR: This paper shows that the overall efficiency of the system can be improved by sharing different resources in the network between several operators by using a physical layer cellular model with idealistic resource management.

TL;DR: This work study the downlink (DL) controller-to-actuator scheduling problem in a wireless industrial network such that the outage probability is minimized and proposes an online data-driven approach to jointly schedule the DL transmissions and learn the channel distributions in an online manner.

TL;DR: The developed spatiotemporal model is leveraged to formulate a joint optimization problem of the content caching and spectrum partitioning in order to minimize the average service delay andumerical results highlight the superiority of the proposed scheme over conventional caching schemes under equal and optimized bandwidth allocations.

TL;DR: In this article, the main factors that influence the environmental footprint of federated learning policies compared with classical CL/Big-Data algorithms running in data centers are analyzed. And the proposed analytical framework takes into account both learning and communication energy costs, as well as the incurred greenhouse gas or carbon equivalent, emissions.