Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management

Abstract: In this paper, an interference-aware path planning scheme for a network of cellular-connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV aims at achieving a tradeoff between maximizing energy efficiency and minimizing both wireless latency and the interference level caused on the ground network along its path. The problem is cast as a dynamic game among UAVs. To solve this game, a deep reinforcement learning algorithm, based on echo state network (ESN) cells, is proposed. The introduced deep ESN architecture is trained to allow each UAV to map each observation of the network state to an action, with the goal of minimizing a sequence of time-dependent utility functions. Each UAV uses ESN to learn its optimal path, transmission power level, and cell association vector at different locations along its path. The proposed algorithm is shown to reach a subgame perfect Nash equilibrium (SPNE) upon convergence. Moreover, an upper and lower bound for the altitude of the UAVs is derived thus reducing the computational complexity of the proposed algorithm. Simulation results show that the proposed scheme achieves better wireless latency per UAV and rate per ground user (UE) while requiring a number of steps that is comparable to a heuristic baseline that considers moving via the shortest distance towards the corresponding destinations. The results also show that the optimal altitude of the UAVs varies based on the ground network density and the UE data rate requirements and plays a vital role in minimizing the interference level on the ground UEs as well as the wireless transmission delay of the UAV.

• The paper introduces a deep reinforcement learning algorithm that optimizes UAV path planning to balance energy efficiency, latency, and interference in 5G networks.
• A dynamic game-theoretic approach enables each UAV to independently adjust trajectories and radio parameters, achieving a subgame perfect Nash equilibrium.
• Simulation results demonstrate the method outperforms heuristics by adapting UAV altitudes based on network density, enhancing ground user data rates and reducing latency.

Overview of "Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management"

The paper presents a sophisticated methodology for managing the pathways of cellular-connected unmanned aerial vehicles (UAVs) within the paradigm of 5G networks. Specifically, the authors propose an interference-aware path planning protocol, aimed at balancing energy efficiency while minimizing delays and the interference incurred by UAV activities on ground networks. The framework employs a deep reinforcement learning (RL) approach, leveraging echo state networks (ESNs) to manage UAV actions concerning path determination, transmission power, and network cell associations.

Technical Contributions and Results

The UAV path planning problem is addressed through a dynamic game-theoretic approach, with each UAV independently optimizing its trajectory and radio parameters using a deep RL framework. The game is structured as a finite dynamic noncooperative game with UAVs acting as independent agents. The core component of the solution is a novel deep ESN-based RL algorithm, which addresses the challenges associated with dynamic environments and limited information sharing amongst UAVs.

Key mathematical models in the work include derivations for UAV wireless latency, SINR, and interference formulations, where the UAV trajectory is modeled as a dynamic graph problem. A primary outcome of this approach is the establishment of a subgame perfect Nash equilibrium (SPNE) upon algorithm convergence, optimizing system-wide objectives such as latency and interference management.

Strong simulation results demonstrate that the proposed framework outperforms heuristic path planning methodologies such as the shortest path algorithm, by yielding increased data rates for ground users and reduced latency for UAV missions. The numerical evaluation highlights the adaptability of UAV altitudes in response to the network density of ground users, showcasing critical dependencies on interference management and latency objectives.

Implications and Future Directions

The implementation of deep RL for UAV path optimization in cellular networks introduces several practical and theoretical advancements for the integration of aerial and terrestrial network components. On the practical side, the framework enables real-time UAV adjustments, supporting applications requiring low latency and high data rates. The incorporation of ESNs defies the conventional practice of offline optimization, enabling UAVs to learn and adapt to the network state dynamically.

Theoretically, this work suggests strong potential for extending RL techniques to other domains within wireless communications where real-time interference management is essential. Future work could explore adaptive trade-offs in reward structures to dynamically align with evolving network density and UAV energy constraints.

Furthermore, considering 5G's penetration and evolution, further research might explore multi-band operations beyond sub-6 GHz, analyzing the impact of such frameworks in mmWave frequencies. The exploration of cooperative strategies between UAVs might also offer insights into further reducing interference, thus potentially enhancing network throughput and efficiency.

In conclusion, the paper lays a foundation for advanced interference management strategies in UAV networks, presenting a viable solution to a fundamental challenge in future wireless network architectures. Its integration of game theory with RL, specifically via ESNs, presents a promising pathway forward in the harmonization of UAV operations within cellular systems.