This paper presents a novel method for optimizing the outage performance of Reconfigurable Intelligent Surfaces (RIS)-assisted Rate-Splitting Multiple Access (RSMA) systems. The objective is to minimize the maximum outage probability for all messages to improve fairness. Instead of tackling the problem directly, which involves dealing with a non-convex nondifferentiable objective function, we propose a new formulation based on minimizing the Entropic Value-at-Risk (EVaR) of the perceived minimum rate among all messages in the system. We show that this formulation is equivalent to minimizing an upper bound on the maximum outage probability, then we transform it into a tractable smooth problem by devising a twice-differentiable approximation to it. In addition to its lower complexity, our method is shown by numerical results to outperform two benchmarks, namely, Discrete Exhaustive Search (DES) and Sequential Quadratic Programming (SQP)-based search methods.

The cooperation between Unmanned Aerial Vehicles (UAVs) and ground Mobile Edge Computing (MEC) servers in processing tasks is becoming one of the main research trends of MEC networks. Despite the advantages of UAV-assisted MEC, it is restricted by the limited battery capacity and sensitive energy consumption of UAVs. Unlike the previous works where UAVs are allowed to either process tasks locally or offload them to ground MEC servers, in this paper, we propose a multi-hop task routing solution for Internet of Things (IoT) networks in which a UAV can also relay to another UAV with better connection to a ground MEC server. Furthermore, the UAV can make benefit of existing Intelligent Reflective Surfaces (IRSs) to further improve tasks offloading and reduce energy consumption. We show that the problem of minimizing the total energy of UAVs is NP-hard, and we propose a graph-based heuristic solution to solve it. Simulation results show that the proposed graph-based solution outperforms the traditional no-relaying scheme, especially when IRSs are deployed. Furthermore, a Convolutional Neural Network (CNN) is devised to reduce the delay of finding the decisions for the UAVs at the centralized coordinator. Simulations show that the CNN achieves very close energy consumption performance and a remarkable reduction in execution time compared to the graph-based heuristic solution.