用智能飞艇和可重构表面提升6G网络能效，自适应优化信号与能耗。
Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach
- 混合深度强化学习框架协同优化飞行天线与智能表面的配置。
- 相比传统方案，能效提升显著，尤其在全双工模式下表现最优。
- 适合研究6G高效通信、智能反射表面及自适应算法的学者与工程师。
为应对第六代（6G）网络的高数据流量需求，本文提出一种新型架构，将自主飞行器（AAVs）与多功能可重构智能表面（MF-RISs）结合为流式天线（FA）辅助的全双工（FD）网络中的AM-RIS。AM-RIS具备信号反射、放大和能量收集（EH）的混合功能，可同时提升信号覆盖与系统可持续性。同时，流式天线实现基站（BS）的精细空间自适应，补足残余自干扰（SI）抑制。目标是通过联合优化基站下行波束成形、上行用户功率、AM-RIS配置以及流式天线与AM-RIS的位置，最大化整体能量效率（EE）。由于问题具有混合连续-离散参数且维度高，难以求解，本文提出自优化多智能体混合深度强化学习（SOHRL）框架，分别采用多智能体DQN处理离散动作、多智能体PPO处理连续动作。为增强自适应能力，引入注意力驱动的状态表示与元级超参数优化，使多智能体可自主调节学习参数。仿真结果验证了所提方法的有效性：与无注意力机制或传统混合/多智能体/独立强化学习基准相比，SOHRL性能更优。此外，全双工模式下的AM-RIS在能效上优于半双工、传统刚性天线阵列、部分能量收集及无放大的传统RIS，凸显其作为能效感知无线网络解决方案的巨大潜力。
原文摘要 · Abstract (English)
To address high data traffic demands of sixth-generation (6G) networks, this paper proposes a novel architecture that integrates autonomous aerial vehicles (AAVs) and multi-functional reconfigurable intelligent surfaces (MF-RISs) as AM-RIS in fluid antenna (FA)-assisted full-duplex (FD) networks. The AM-RIS provides hybrid functionalities, including signal reflection, amplification, and energy harvesting (EH), potentially improving both signal coverage and sustainability. Meanwhile, FA facilitates fine-grained spatial adaptability at FD-enabled base station (BS), which complements residual self-interference (SI) suppression. We aim at maximizing the overall energy efficiency (EE) by jointly optimizing transmit DL beamforming at BS, UL user power, configuration of AM-RIS, and positions of the FA and AM-RIS. Owing to the hybrid continuous-discrete parameters and high dimensionality of the intractable problem, we have conceived a self-optimized multi-agent hybrid deep reinforcement learning (DRL) framework (SOHRL), which integrates multi-agent deep Q-networks (DQN) and multi-agent proximal policy optimization (PPO), respectively handling discrete and continuous actions. To enhance self-adaptability, an attention-driven state representation and meta-level hyperparameter optimization are incorporated, enabling multi-agents to autonomously adjust learning hyperparameters. Simulation results validate the effectiveness of the proposed AM-RIS-enabled FA-aided FD networks empowered by SOHRL algorithm. The results reveal that SOHRL outperforms benchmarks of the case without attention mechanism and conventional hybrid/multi-agent/standalone DRL. Moreover, AM-RIS in FD achieves the highest EE compared to half-duplex, conventional rigid antenna arrays, partial EH, and conventional RIS without amplification, highlighting its potential as a compelling solution for EE-aware wireless networks.
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