发表机构
National Chung Cheng University; Feng Chia University（中正大学; 逢甲大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对空-地一体化异构网络中D2D通信的上行资源分配问题，提出低复杂度MCRF算法，支持共享复用并集成干扰避免策略，联合优化吞吐量、公平性和能量效率，显著提升性能并改善公平性。
AI 中文摘要
本研究探讨了一种空-地一体化异构网络（AGIHN）架构，该架构结合了地面宏基站和作为空中基站的无人机（UAV），以增强宏小区用户的上行接入。针对多对设备到设备（D2D）通信对的上行资源分配这一复杂挑战，我们提出了一种低复杂度的多信道速率公平（MCRF）算法。与传统的独占分配方法不同，MCRF支持共享复用，允许多个D2D对在同一资源块上同时复用，从而显著提高频谱效率。为了管理这种非正交共享引起的严重层内干扰，我们集成了一种启发式干扰避免（IA）策略，以确保D2D用户的传输质量。所提出的框架联合优化系统吞吐量、用户公平性和能量效率，而无需计算密集的离线训练。仿真结果展示了不同复用模式下的显著性能优势：与传统的单信道独占复用方案相比，MCRF实现了巨大增益，D2D能量效率和吞吐量分别提高了约397%和542%。此外，相对于多信道基准（例如MCRR），所提出的算法优化了效率-公平性权衡，在干扰密集环境中将公平性指数提高了7.43%，同时保持了超过0.6的稳健公平性得分。
英文摘要
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.
Comments15 pages, 5 figures. Accepted for publication in IEEE Transactions on Vehicular Technology. ©2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses
