Source-rate planning in self-powered wireless multi-hop D2D settings under stochasticity: A scenario-based iterative optimization approach

Abstract

Multi-hop Device-to-Device (D2D) communications are emerging as the foundation for numerous compelling 6G applications, enabling seamless information flow between distributed nodes. In the context of such uncertain wireless multi-hop D2D settings, jointly optimizing source data rates, routing, and transmission power decisions is both an essential task and a highly complex problem, particularly due to uncertainties introduced by the wireless channel states and the energy harvesting processes on the nodes. In the current literature, this problem is mostly tackled in a future agnostic sense, and/or using specific distributions to model the uncertainties. In contrast, in this paper, we compute a future energy and resource allocation plan of the network’s operation, using scenario-based optimization techniques to account for stochasticities. Scenarios can model generic distributions of uncertain quantities in a tractable manner. The formulated problem is inherently non-convex and to solve it, we propose CoNetPlan-E, a heuristic iterative method that at each iteration solves appropriately parameterized convex approximations of the original problem. We prove that CoNetPlan-E converges under realistic assumptions, while ensuring that the obtained solution at convergence is feasible for the original non-convex problem. Numerical evaluations showcase the effectiveness of the proposed method compared to existing baseline solutions, while considering three levels of increasing network topology complexity. Importantly, CoNetPlan-E is superior with respect to scalability and runtime while leading to close-to-optimal solutions as these are determined by the standard non-convex solver Ipopt.