Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach
Summary: arXiv:2604.14309v2 Announce Type: replace-cross
Abstract: 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 downlink (DL) beamforming at BS, uplink (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.
Key Findings
- SOHRL outperforms benchmarks of the case without attention mechanism and conventional hybrid/multi-agent/standalone DRL.
- AM-RIS in FD achieves the highest energy efficiency compared to half-duplex, conventional rigid antenna arrays, partial energy harvesting, and conventional RIS without amplification.
- This highlights the potential of AM-RIS as a compelling solution for energy efficiency-aware wireless networks.
Implications for Future Networks
The integration of AAVs and AM-RISs represents a significant advancement in wireless communication technologies, particularly as the demand for higher data rates and more efficient energy usage grows in the context of 6G networks. The proposed architecture not only addresses the challenges of high traffic demands but also proposes a sustainable solution through energy harvesting and intelligent resource management.
As the telecommunications industry moves towards the next generation of connectivity, innovations such as those presented in this study will be critical in shaping the future landscape of wireless networks. The self-optimizing capabilities of the SOHRL framework suggest that future networks may become increasingly autonomous, adapting dynamically to varying conditions and user demands.
Conclusion
This research paves the way for further exploration of hybrid deep reinforcement learning approaches in communication networks. The promising results indicate that the combination of AM-RIS and FA technologies could revolutionize the efficiency and effectiveness of next-generation networks, making them more responsive to the challenges posed by increasing data traffic and energy consumption.