---
title: 'Aerial-STAR: UAV-Mounted STAR-RIS Systems'
url: https://www.emergentmind.com/topics/aerial-star
type: topic
---

# Aerial-STAR: UAV-Mounted STAR-RIS Systems

Aerial-STAR denotes a **UAV-mounted Simultaneous Transmit And Reflect Reconfigurable Intelligent Surface (STAR-RIS)** system for wireless communications. In this line of work, the system integrates the **360° coverage** and reconfigurability of STAR-RIS with the mobility and LoS adaptability of unmanned aerial vehicles, so that transmission, reflection, trajectory design, beamforming, and resource allocation are optimized jointly in air-ground networks [2509.13328]. Closely related formulations appear under the names **STAR-RIS-UAV**, **UAV-mounted STAR-RIS**, **airborne STAR-RIS**, and **aerial STAR-RIS-assisted** systems, covering downlink multi-user communications, coordinated multipoint cellular systems, MEC, emergency networking, IoT NOMA, covert satellite-terrestrial links, and comparative 3D deployment studies [2305.12778].

## 1. Terminology, scope, and canonical system models

The literature uses several closely related names for the same architectural idea. One paper studies a **STAR-RIS-UAV aided coordinated multipoint cellular system for multi-user networks**, in which a UAV is equipped with a STAR-RIS and hovers between two base stations to serve a **cell-edge user** in the transmission sector and **cell-center users** in the reflective sector [2305.12778]. Another studies **STAR-RIS assisted UAV communication systems**, where one UAV at constant altitude is aided by a fixed STAR-RIS to serve multiple ground users via NOMA [2209.03777]. A later work explicitly names the UAV-mounted STAR-RIS system **Aerial-STAR** and frames it as a downlink multi-user communication system with a **coupled TRC phase shift model** [2509.13328].

| Formulation in the literature | Core setting | Source |
|---|---|---|
| Aerial-STAR | UAV-mounted STAR-RIS for downlink multi-user communications | [2509.13328] |
| STAR-RIS-UAV | UAV equipped with STAR-RIS for CoMP multi-user networks | [2305.12778] |
| STAR-RIS assisted UAV communication systems | UAV, fixed STAR-RIS, multiple ground users via NOMA | [2209.03777] |

Across these formulations, STAR-RIS is introduced as distinct from conventional RIS because it can **simultaneously transmit and reflect** incoming signals, thereby providing **full-space coverage** and additional degrees of freedom [2209.03777]. In the UAV-mounted setting, this is combined with aerial mobility, dynamic placement, and improved line-of-sight conditions. This suggests that “Aerial-STAR” is best understood not as a single protocol, but as a family of airborne STAR-RIS architectures whose common feature is the co-design of intelligent-surface control and UAV motion.

## 2. Electromagnetic model, transmission/reflection control, and energy-aware deployment

A central modeling distinction is between **independent** and **coupled** transmission and reflection coefficients (TRCs). Much of the earlier literature assumes that STAR-RIS transmission and reflection coefficients can be tuned independently, but later work emphasizes that this is unrealistic for **passive** surfaces because transmission and reflection are mutually coupled and constrained by energy conservation and electromagnetic properties [2509.13328]. In the coupled model used for Aerial-STAR, the amplitude and phase constraints are written as
\[
\beta_n^\mathcal{R} = \sqrt{1 - (\beta_n^\mathcal{T})^2},
\qquad
|\theta_n^\mathcal{R} - \theta_n^\mathcal{T}| = \frac{\pi}{2},
\]
with the TRC matrix
\[
\boldsymbol{\Phi}_c = \mathrm{diag}(\beta_1^c e^{j\theta_1^c}, \ldots, \beta_N^c e^{j\theta_N^c}), \; c \in \{\mathcal{R}, \mathcal{T}\}
\]
[2509.13328]. A related 3D comparison study uses the constraints
\[
\beta_{t,n}^2 + \beta_{r,n}^2 = 1,\qquad \cos(\phi_{t,n} - \phi_{r,n}) = 0
\]
for aerial STAR-RIS elements [2512.08755].

Control protocols in the broader STAR-RIS-UAV literature typically include **energy splitting (ES)** and **mode switching (MS)**. Under ES, each element splits its received signal energy into transmitted and reflected fractions, with \(\beta_n^t + \beta_n^r = 1\), while under MS each element is set to either transmission or reflection mode [2305.12778]. Earlier UAV-assisted STAR-RIS studies report that **the energy split between reflection and transmission modes is adaptive** and highly dependent on the real-time UAV trajectory [2209.03777].

In airborne deployment, propulsion cannot be treated independently of surface size. Aerial-STAR work introduces an explicit RIS-drag term in the UAV energy model:
\[
P_{\text{total}}(t) = P_{UAV} + P_{\text{RIS-drag}},
\qquad
P_{\text{RIS-drag}} = \frac{1}{2} \rho A_{RIS} C_d V_\perp^3,
\]
with
\[
A_{RIS} = (N_x-1)^2 \frac{\lambda^2}{\mu^2}
\]
and communication efficiency
\[
\eta_{EE}(t) = \frac{\sum_{j=1}^{J}R_j(t)}{P_{\text{total}}(t)}
\]
[2509.13328]. This makes Aerial-STAR fundamentally different from fixed STAR-RIS deployment: increasing the number of elements improves beamforming flexibility but also increases drag and energy demand.

## 3. Optimization objectives and mathematical formulations

The dominant design pattern is **joint optimization**. In passive Aerial-STAR downlink systems, the objective is to optimize **UAV trajectory**, **passive STAR-RIS TRCs**, and **active beamforming vectors at the base station** subject to QoS, power, speed, and coupling constraints [2509.13328]. In one canonical formulation, the objective is
\[
\max_{\mathbf{T}, \mathbf{\Phi}, \mathbf{W}_B} \sum_{t=1}^{T} \eta_{EE}(t)
\]
with minimum user-rate, UAV-speed, phase-range, and coupled-phase constraints [2509.13328].

Other formulations target **sum rate** rather than communication efficiency. In STAR-RIS assisted UAV communication systems, the goal is to maximize the sum rate of all users by jointly optimizing the STAR-RIS passive beamforming vectors, the UAV trajectory, and power allocation [2209.03777]. In the coordinated multipoint cellular setting, the objective is to maximize the system sum rate by jointly optimizing BS beamforming vectors and STAR-RIS transmission and reflection coefficient matrices under BS power and minimum QoS constraints [2305.12778]. In active STAR-RIS IoT NOMA networks, the objective is the **system sum rate maximization problem** for the joint optimization of **active STAR-RIS beamforming**, **UAV trajectory design**, and **power allocation**, where the beamforming variables include amplification, power-splitting, and phase shift [2501.02488].

A second major objective class is **energy minimization**. In aerial STAR-RIS empowered MEC, the formulated problem minimizes the total energy consumption of both IoT devices and the aerial STAR-RIS-UAV while optimizing task offloading, aerial STAR-RIS trajectory, amplitude and phase shift coefficients, and transmit power under latency, MEC resource, STAR-RIS energy split, and UAV mobility constraints [2312.08714]. A third class adds **covertness**. In aerial active STAR-RIS-assisted satellite-terrestrial covert communications, the objective is to maximize the sum of the fair channel capacity for all ground users while satisfying a covert constraint derived from the minimal detection error probability of a Warden [2504.16146].

This diversity of objectives implies that Aerial-STAR is not tied to a single network utility. Depending on the application, the same airborne STAR-RIS substrate can be tuned for sum rate, energy efficiency, offloading efficiency, fairness, or covert performance.

## 4. Solution methods: alternating optimization, convexification, and deep reinforcement learning

Model-based optimization is prominent in the earlier literature. For STAR-RIS assisted UAV communication systems, the non-convex problem is decomposed into three subproblems—**STAR-RIS beamforming optimization**, **UAV trajectory optimization**, and **power allocation optimization**—which are solved alternately using convex reformulations, iterative optimization, semidefinite programming relaxations, and successive convex approximation [2209.03777]. In STAR-RIS-UAV coordinated multipoint systems, a **joint penalty-based iterative algorithm** is proposed instead of alternating optimization, so that all variables are optimized in every iteration for both ES and MS protocols [2305.12778]. For active STAR-RIS IoT NOMA, an **alternating optimization (AO)** algorithm decouples the original problem into three subproblems, with a penalty-based method for the rank-one constraint and successive convex optimization for UAV trajectory and power allocation [2501.02488]. In the 3D performance comparison between aerial RIS and STAR-RIS, the sum-rate problem is addressed via **weighted minimum mean square error** and **block coordinate descent**, with **Penalty Dual Decomposition** for amplitude-phase coupling [2512.08755].

Dynamic environments and high-dimensional action spaces have motivated reinforcement learning. In aerial STAR-RIS empowered MEC, the joint problem is modeled as an MDP and solved using **proximal policy optimization (PPO)** because of its sample efficiency and stability [2312.08714]. In STAR-RIS assisted UAV NOMA emergency communications, the long-term constrained optimization is cast as a CMDP and solved by **Lagrange based reward constrained proximal policy optimization (LRCPPO)**, with an inner penalized-reward PPO layer and an outer Lagrange-multiplier update layer [2307.14345]. In aerial active STAR-RIS-assisted covert communications, a **generative deterministic policy gradient (GDPG)** algorithm uses a **generative diffusion model (GDM)** as the policy representation and an **action gradient mechanism** for policy improvement [2504.16146]. For coupled-phase Aerial-STAR, the hybrid action space motivates a **Dual Actor Deep Deterministic Policy Gradient (DA-DDPG)** algorithm with one actor for continuous actions and one actor for discrete actions [2509.13328].

These solution choices correspond closely to modeling assumptions. Deterministic convexification is favored when the channel model and constraints are explicit and tractable, whereas DRL is favored when the state-action space is high-dimensional, the objective is long-horizon, or the coupled trajectory–beamforming control is difficult to solve directly within polynomial time.

## 5. Performance findings and design trade-offs

Aerial-STAR studies repeatedly report gains over conventional RIS baselines, but the gains are qualified by geometry, altitude, hardware constraints, and the chosen objective. In STAR-RIS assisted UAV communication systems, simulations show that **the STAR-RIS achieves a higher sum rate than traditional RIS**, that **the UAV's trajectory is closer to STAR-RIS than that of RIS**, and that **the energy splitting for reflection and transmission highly depends on the real-time trajectory of UAV** [2209.03777]. In coordinated multipoint multi-user networks, the **STAR-RIS-UAV aided wireless communication system has a much higher sum rate than the system with conventional RIS or without RIS**, and the proposed structure is described as more flexible than a fixed STAR-RIS [2305.12778].

For the practically constrained coupled-phase Aerial-STAR model, one study reports that **DA-DDPG** outperforms conventional **DDPG** and **DQN-based** solutions by **24%** and **97%**, respectively, in accumulated reward; **three-dimensional UAV trajectory optimization** achieves **28%** higher communication efficiency compared to two-dimensional and altitude optimization; and the **HFI based reward function** provides **41% lower QoS denial rates** compared to other benchmarks [2509.13328]. The same work states that the **mobile Aerial-STAR system shows superior performance over fixed deployed counterparts**, and that the **coupled phase STAR-RIS** outperforms **dual Transmit/Reflect RIS** and **conventional RIS** setups [2509.13328].

RIS size is not monotone with airborne efficiency. In the Aerial-STAR energy-aware model, a larger RIS provides higher throughput but also increases drag, so efficiency can peak and then decrease as \(N\) increases [2509.13328]. In active STAR-RIS IoT NOMA, numerical results indicate that the UAV-mounted active STAR-RIS enhances the channel gain from the BS to the IoT devices by high-quality channel construction and power compensation, and that the performance gap widens as the number of STAR-RIS elements increases [2501.02488]. In a broader low-altitude wireless network setting, the average transmission rate of the overall system scales positively with both UAV count and STAR-RIS element numbers [2510.22108].

A more nuanced result emerges from direct aerial RIS versus aerial STAR-RIS comparison in 3D wireless environments. That study concludes that **STAR-RIS outperforms RIS in low-altitude scenarios due to its full-space coverage capability**, whereas **RIS delivers better performance near the base station at higher altitudes** [2512.08755]. This qualifies any blanket claim of STAR-RIS dominance and indicates that deployment altitude and orientation are system-level variables, not merely implementation details.

## 6. Variants, misconceptions, and broader research directions

One common misconception is that all Aerial-STAR work assumes physically realizable STAR-RIS hardware. The literature itself distinguishes sharply between **independent** TRC models and **coupled** phase-shift models. The latter are presented as more accurate for passive surfaces, while the former offer greater flexibility and often higher upper-bound performance [2509.13328]. A related practical distinction is between **passive** and **active** STAR-RIS. Active STAR-RIS-equipped UAVs introduce amplification coefficients and power compensation, and they are studied for IoT NOMA and covert satellite-terrestrial communications, but they also require additional per-element and total power constraints [2501.02488].

A second misconception is that the aerial platform is only a placement mechanism. Multiple papers make the UAV state part of the optimization variables: trajectory, speed, altitude, orientation, propulsion energy, and even RIS-induced drag directly affect the achievable utility [2312.08714]. In some formulations, the UAV must satisfy initial and final location constraints; in others, geofencing, velocity transition mechanisms, or collision-avoidance-style formation constraints appear. This suggests that Aerial-STAR is as much an aerial robotics control problem as it is an intelligent-surface communication problem.

The broader research frontier extends beyond single-UAV downlink sum-rate maximization. Reported directions include **MEC** energy minimization with task offloading [2312.08714], **emergency communication networks** with minimum average rate and maximum energy constraints [2307.14345], **collaborative beamforming** in low-altitude wireless networks using UAV swarms and STAR-RIS omnidirectional reconfigurable beamforming [2510.22108], and **satellite-terrestrial covert communications** assisted by **AASTAR-RIS** [2504.16146]. The STARS-enabled ISAC literature, although not specific to UAV mounting, also contributes a relevant systems perspective by framing STARS as a full-space platform for integrated sensing and communications, with both independent and coupled phase-shift models [2207.10748].

Taken together, these results indicate that Aerial-STAR has evolved from a throughput-oriented extension of RIS-assisted UAV communications into a broader design space centered on **full-space coverage**, **air-ground mobility**, **hybrid continuous-discrete control**, and **physically constrained intelligent surfaces**. A plausible implication is that future Aerial-STAR systems will be evaluated less by isolated sum-rate gains and more by how well they balance communication efficiency, fairness, mobility cost, hardware realizability, and deployment geometry.

Source: https://www.emergentmind.com/topics/aerial-star