Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach

2026-04-15

AI Summary

This work addresses the energy efficiency challenges in 6G high-traffic scenarios by proposing an aerial platform that integrates autonomous flying vehicles with an augmented multifunctional reconfigurable intelligent surface (AM-RIS). Within a fluid antenna-assisted full-duplex network, the approach jointly optimizes base station beamforming, user transmit power, AM-RIS configuration, and antenna placement. The paper innovatively designs an AM-RIS architecture capable of simultaneous reflection, amplification, and energy harvesting, and introduces a self-optimizing hybrid deep reinforcement learning (SOHRL) framework. This framework synergistically combines multi-agent DQN and PPO algorithms, attention mechanisms, and meta-learning-driven hyperparameter optimization to handle both continuous and discrete decision variables. Simulation results demonstrate that the proposed method significantly outperforms existing solutions in terms of system energy efficiency and coverage performance, with AM-RIS achieving optimal gains under full-duplex operation.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 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.

Problem

• Energy Efficiency
• Full-Duplex Networks
• Reconfigurable Intelligent Surface
• Fluid Antennas
• 6G Networks

Innovation

• Reconfigurable Intelligent Surface
• Fluid Antenna
• Full-Duplex
• Deep Reinforcement Learning
• Energy Efficiency