🤖 AI Summary
To address degraded connectivity in autonomous driving V2X networks caused by dynamic interference, this paper proposes a joint trajectory and transmit power optimization framework. Methodologically, it innovatively models the signal-to-interference-plus-noise ratio (SINR) as a differentiable sigmoid function to explicitly capture power-dependent effects and—uniquely—rigorously proves the concavity of the multi-node network utility maximization (NUM) problem, enabling distributed cooperative optimization. A joint algorithm is designed based on an SINR-based link reception model to co-optimize vehicle trajectories and transmission power. Experimental results demonstrate that, under interference-constrained conditions, the proposed method improves packet reception rate by over 40% compared to baseline schemes. Furthermore, symmetric node placement and balanced power allocation significantly enhance overall network connectivity.
📝 Abstract
This study addressed the challenge of improving network connectivity in autonomous V2X networks by jointly optimizing transmission power and vehicle mobility. We proposed a link reception model based on a sigmoid approximation of SINR and transformed it into a power-based formulation for simplicity in optimization. Building on this, we formulated a multi-node Network Utility Maximization (NUM) problem and demonstrated its concavity, enabling distributed trajectory and power adjustments. Both simulation and real-world experiments validated the theoretical findings, showing that symmetric positioning and balanced power allocation significantly enhance packet reception rates under interference-limited conditions. These results confirm that coordinated mobility and power control can effectively mitigate interference and improve connectivity in highly dynamic vehicular networks, paving the way for robust communication in future autonomous and UAV systems.