In this paper, we propose a novel aerial backhaul architecture that employs an aerial active reconfigurable intelligent surface (RIS) to achieve energy-efficient, full 3D coverage including UAV-BSs and ground users in 6G wireless networks. Unlike prior aerial-RIS approaches limited to 2D coverage with only servicing ground users or passive operation, the proposed design integrates an active-RIS onto a high-altitude aerial platform, enabling reliable line-of-sight links and overcoming multiplicative fading through amplification. In a scenario with UAV-BSs deployed to handle sudden traffic surges in urban areas, the aerial-active-RIS both reflects and amplifies backhaul signals to overcome blockage. We jointly optimize the aerial platform placement, array partitioning, and RIS phase configuration to maximize UAV-BS energy-efficiency. Simulation results confirm that the proposed method significantly outperforms benchmarks, demonstrating its strong potential to deliver resilient backhaul connectivity with comprehensive 3D coverage in 6G networks.

This paper introduces a unified analytical and optimization framework for fluid antenna system-active reconfigurable intelligent surface (FAS-ARIS) communications in 6G. By combining the port reconfigurability of FAS with the signal amplification of ARIS, the proposed design enables more flexible control of the propagation environment and enhanced link reliability beyond what passive solutions can offer. We first derive the optimal ARIS amplification gain under a reflection power constraint to maximize the user’s signal-to-noise ratio (SNR). Using a block-diagonal matrix approximation, we obtain a tractable outage expression and a tight independent-antenna equivalent upper-bound. Building on this, we establish the monotonic relationship between outage and effective channel gain, which enables a closed-form solution for ARIS phase optimization under limited channel state information (CSI). To further improve spectral efficiency, we propose a region-partitioned throughput optimization framework that achieves near-optimal performance without exhaustive search, thereby verifying its low computational complexity. Extensive simulations confirm the accuracy of the analysis and demonstrate consistent gains in outage and throughput compared to baselines.

In this paper, we propose a novel wireless architecture, mounted on a high-altitude aerial platform, which is enabled by reconfigurable intelligent surface (RIS). By installing RIS on the aerial platform, rich line-of-sight and full-area coverage can be achieved, thereby, overcoming the limitations of the conventional terrestrial RIS. We consider a scenario where a sudden increase in traffic in an urban area triggers authorities to rapidly deploy unmanned-aerial vehicle base stations (UAV-BSs) to serve the ground users. In this scenario, since the direct backhaul link from the ground source can be blocked due to several obstacles from the urban area, we propose reflecting the backhaul signal using aerial-RIS so that it successfully reaches the UAV-BSs. We jointly optimize the placement and array-partition strategies of aerial-RIS and the phases of RIS elements, which leads to an increase in energy-efficiency of every UAV-BS. We show that the complexity of our algorithm can be bounded by the quadratic order, thus implying high computational efficiency. We verify the performance of the proposed algorithm via extensive numerical evaluations and show that our method achieves an outstanding performance in terms of energy-efficiency compared to benchmark schemes.

In this paper, we propose a novel wireless backhaul architecture, mounted on a high-altitude aerial platform, which is enabled by reconfigurable intelligent surface (RIS). We assume a sudden increase in traffic in an urban area, and to serve the ground users therein, authorities rapidly deploy unmanned-aerial-vehicle base-stations (UAV-BSs). In this scenario, since the direct backhaul link from the ground source can be blocked due to several obstacles from the urban area, we propose reflecting the backhaul signal using aerial-RIS and the phase of each RIS element, which leads to an increase in energy-efficiency ensuring the reliable backhaul link for every UAV-BS. We optimize the placement and array-partitioning strategy of aerial-RIS and the phase of each RIS element, which leads to an increase of energy-efficiency under guaranteeing the reliable backhaul link for every UAV-BS. We show that the complexity of our algorithm is upper-bounded by the quadratic order, thus implying high computational efficiency. We verify the performance of the proposed algorithm via extensive numerical evaluations and show that our method achieves an outstanding performance in terms of energy-efficiency compared to benchmark schemes.