Algorithm improves energy efficiency and fairness in drone based networks
Joint Energy Efficiency and Fairness Optimization for D2D Communications in Aerial-Ground Integrated Heterogeneous Networks
Networking and Internet Architecture
Summary
Connecting devices directly in networks that mix ground stations and drones is tricky due to interference and resource sharing. The authors designed a new algorithm that allows many device pairs to share channels at once while avoiding interference. This method makes data transfer more energy-efficient and fair for users without needing complex training steps. Simulations show big improvements in throughput and fairness compared to traditional approaches.
What this means in practice
- •For network schedulers: Allocate uplink resources efficiently in networks combining ground base stations and drones to improve energy use and user fairness.
- •For wireless system engineers: Design interference-aware resource sharing schemes for device-to-device communication to boost throughput and fairness in aerial-ground integrated networks.
Authors
Chuan-Chi Lai, Ang-Hsun Tsai, Shang-Long Wu
Abstract
This study investigates an Aerial-Ground Integrated Heterogeneous network (AGIHN) architecture that combines terrestrial macro base stations and unmanned aerial vehicles (UAVs) serving as aerial base stations to enhance uplink access for macrocell users. To address the complex uplink resource allocation challenge for multiple device-to-device (D2D) communication pairs, we propose a low-complexity Multi-Channel Rate-Fair (MCRF) algorithm. Distinct from traditional exclusive allocation methods, MCRF supports shared reuse, enabling multiple D2D pairs to simultaneously multiplex on the same resource block, thereby significantly improving spectral efficiency. To manage the severe intra-tier interference arising from this non-orthogonal sharing, a heuristic Interference Avoidance (IA) strategy is integrated to ensure the transmission quality of D2D users. The proposed framework jointly optimizes system throughput, user fairness, and energy efficiency without requiring computationally intensive offline training. Simulation results demonstrate distinct performance advantages depending on the reuse mode: Compared to traditional single-channel exclusive reuse schemes, MCRF achieves massive gains, increasing D2D energy efficiency and throughput by approximately 397% and 542%, respectively. Furthermore, relative to multi-channel benchmarks (e.g., MCRR), the proposed algorithm optimizes the efficiency-fairness trade-off, enhancing the fairness index by 7.43% while maintaining a robust fairness score exceeding 0.6 in interference-prone environments.