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arxiv: 2604.20466 · v1 · submitted 2026-04-22 · 📡 eess.SP · cs.SY· eess.IV· eess.SY

Adaptive Multi-UAV Relay Deployment Framework in Satellite Aerial Ground Integrated Systems

Bhola , Yu-Jia Chen , Ashutosh Balakrishnan , Swades De , Li-Chun Wang This is my paper

Pith reviewed 2026-05-10 00:07 UTC · model grok-4.3

classification 📡 eess.SP cs.SYeess.IVeess.SY
keywords UAV relaysatellite aerial ground integrated networkenergy efficiencycapacity optimizationfairnessamplify-and-forward6G coverage
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The pith

The adaptive multi-UAV relay deployment framework boosts total capacity, energy efficiency, and fairness in satellite-air-ground networks over satellite-ground systems alone.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces the adaptive multi-UAV deployment framework to place multiple amplify-and-forward UAV relays dynamically alongside LEO satellites. It sets up a joint optimization that targets higher total network capacity and energy efficiency while enforcing fairness across users and meeting their service needs. Simulations indicate gains in all three metrics compared with LEO satellite plus ground base station operation. This matters for 6G scenarios with blockages or sudden demand spikes because the UAV layer can fill coverage gaps without new fixed infrastructure.

Core claim

Dynamically deploying multiple UAV relays in satellite aerial ground integrated networks, using an optimization that jointly maximizes total capacity and energy efficiency subject to fairness and user requirements, produces measurable improvements in all three quantities over static LEO satellite and ground base station configurations.

What carries the argument

The joint optimization problem that selects UAV relay positions and power levels to maximize a combined objective of total capacity and energy efficiency while constraining minimum per-user rates and fairness index.

If this is right

  • UAV relays can extend reliable coverage into non-line-of-sight urban and disaster zones without additional ground infrastructure.
  • Total network capacity rises because relays offload congested satellite or base-station links.
  • Energy efficiency improves by using shorter, lower-power relay hops instead of direct long-range transmissions.
  • Capacity fairness increases because the framework can steer resources toward underserved users.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same adaptive placement logic could be tested with other relay types such as reconfigurable intelligent surfaces to see whether similar gains appear.
  • Real deployments would need to account for UAV battery limits and regulatory flight constraints that the current formulation leaves aside.
  • Integration with existing terrestrial 5G base stations could further reduce the number of UAVs required for a target performance level.

Load-bearing premise

The optimization can be solved fast enough for real-time repositioning of UAVs as user locations change, and UAVs can be moved without prohibitive delay, energy drain, or positioning limits.

What would settle it

A set of simulations or field measurements in which the proposed framework shows no statistically significant increase in total capacity, energy efficiency, or fairness relative to the LEO-GBS baseline under realistic time-varying user distributions.

Figures

Figures reproduced from arXiv: 2604.20466 by Ashutosh Balakrishnan, Bhola, Li-Chun Wang, Swades De, Yu-Jia Chen.

Figure 1
Figure 1. Figure 1: System Model: Hybrid Network Overview. A. Motivation Recent interest has focused on integrating terrestrial net￾works with space-based LEO satellites to provide seamless connectivity and high-speed broadband access for 6G communi￾cations. LEO satellite networks consist of a mega-constellation of satellites and wireless backbones. Traditionally, satellites have been used primarily for communication in rural… view at source ↗
Figure 2
Figure 2. Figure 2: Similarly, the visibility variable from the user to the [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of the UAVr mobility model. Each UAVr provides circular coverage with radius 𝑅𝑗 (blue disk), while the rectangular grid represents logical subdivisions of the GBS area for hotspot tracking and UAVr displacement planning. The grid does not imply rectangular coverage but is used for section-wise traffic analysis and decision￾making. The UAVr selects its next location within its circular reach ba… view at source ↗
Figure 4
Figure 4. Figure 4: The flowchart of the AMUD framework. VI. THE PROPOSED AMUD FRAMEWORK This section presents the main concept and an overview of the proposed AMUD method. Following this, we will describe the AMUD procedure in detail. Finally, we will discuss the key aspects of our proposed design. A. Main Idea The proposed AMUD framework maximizes energy effi￾ciency (33) while ensuring user fairness (32) and satisfying mini… view at source ↗
Figure 5
Figure 5. Figure 5: The AMUD framework is visualized in 2D and 3D, showcasing phases: (a) Initial phase, (b) Re-association phase, and (c) User association and power optimization phase. Users are depicted as blue dots, GBS centers as black triangles, UAVr centers as green triangles, hotspots as orange-shaded areas, GBS coverage as blue circles, and UAVr coverage as orange-shaded circles. Arrows indicate user associations. For… view at source ↗
Figure 6
Figure 6. Figure 6: Performance comparison of the proposed AMUD–SAGIN with other approaches, including EGC–SAGIN, LEO–GBS, and GBS– only, in terms of energy efficiency with varying numbers of excess users. mitigate congestion. Moreover, AMUD–SAGIN surpasses EGC– SAGIN by 53%, demonstrating superior adaptability under high-demand conditions. Although the energy efficiency trends of AMUD–SAGIN and LEO–GBS appear similar, AMUD– … view at source ↗
Figure 7
Figure 7. Figure 7: Performance comparison of the proposed AMUD–SAGIN with other approaches, including EGC–SAGIN, LEO–GBS, and GBS–only, in terms of energy efficiency with varying LEO satellite transmission power levels. The simulation ( [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Performance comparison of the proposed AMUD–SAGIN with other approaches, including EGC–SAGIN, LEO–GBS, and GBS–only, in terms of energy efficiency with varying LEO satellite altitudes [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: Performance comparison of the proposed AMUD–SAGIN with other approaches, including EGC–SAGIN, LEO–GBS, and GBS– only, in terms of capacity fairness with varying excess user counts. D. The Fairness Analysis [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 9
Figure 9. Figure 9: Performance comparison of the proposed AMUD–SAGIN with other approaches, including EGC–SAGIN, LEO–GBS, and GBS– only, in terms of total capacity with varying excess user counts. C. Network Capacity Analysis [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
read the original abstract

The sixth generation (6G) communication networks are expected to provide high data rates, ultra-reliable communication, and massive connectivity, especially in challenging environments such as dense urban areas and disaster-affected regions. However, traditional terrestrial-only networks face significant challenges in these scenarios, including signal blockages from high-rise buildings, traffic congestion, and dynamic user distributions. To address these limitations, we propose the adaptive multi-UAV deployment (AMUD) framework within satellite air-ground integrated networks (SAGINs). The AMUD framework dynamically deploys amplify-and-forward multiple unmanned aerial vehicle relay (UAVr) in with low Earth orbit (LEO) satellites to improve coverage, alleviate congestion, and ensure reliable communication in non-line-of-sight and high-demand conditions. We formulate an optimization problem that aims to jointly maximize the energy efficiency of the total network and the total capacity while ensuring the fairness of the total capacity and satisfying the users' requirements. The simulation results demonstrate that AMUD improves the total capacity of the network, improves the total energy efficiency, and increases the fairness of the capacity compared to traditional LEO satellite and ground base station (LEO-GBS) only systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The manuscript proposes the adaptive multi-UAV deployment (AMUD) framework for satellite aerial ground integrated networks (SAGINs). It dynamically deploys amplify-and-forward UAV relays alongside LEO satellites to improve coverage, alleviate congestion, and ensure reliable communication in non-LoS and high-demand scenarios. The central contribution is a joint optimization problem that maximizes total network energy efficiency and sum capacity subject to fairness and per-user QoS constraints. Simulation results are presented claiming improvements in capacity, energy efficiency, and fairness relative to LEO satellite plus ground base station baselines.

Significance. If the simulation gains are reproducible and the solution method is reliable, the work addresses a timely problem in 6G SAGIN design by coupling UAV 3D positioning with resource allocation under dynamic conditions. The emphasis on fairness alongside EE and rate is a positive feature. However, the absence of explicit models, quantitative deltas, and solver details in the provided text limits assessment of practical significance.

major comments (3)
  1. [Optimization Problem] The optimization jointly maximizes EE and sum-rate while coupling UAV 3D positions (affecting path loss and LoS probability) with power allocation and relay gains. This formulation is non-convex, and the paper relies on an iterative algorithm (likely SCA or alternating optimization). Without a convergence proof to a global optimum or comparison against exhaustive search on a discretized grid, the reported improvements in capacity and EE could be artifacts of local solutions or favorable initializations rather than framework superiority.
  2. [Simulation Results] The abstract asserts that simulations demonstrate improvements in total capacity, energy efficiency, and fairness, yet supplies no channel model, energy consumption model, simulation parameters, baseline definitions, or quantitative results. This omission prevents verification of the central claim that AMUD outperforms LEO-GBS systems and makes the magnitude of any gains impossible to evaluate.
  3. The framework assumes the optimization can be solved in real time for dynamic user distributions and that UAV relays can be repositioned adaptively without prohibitive delays or energy overhead. No complexity analysis, latency bounds, or practical positioning constraints are provided to support this assumption, which is load-bearing for the claimed adaptability.
minor comments (1)
  1. [Abstract] The abstract contains a grammatical error ('deploy amplify-and-forward multiple unmanned aerial vehicle relay (UAVr) in with low Earth orbit') that should be corrected for readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and insightful comments on our manuscript describing the AMUD framework for SAGINs. We address each major comment point by point below, providing clarifications and noting the revisions incorporated into the updated manuscript.

read point-by-point responses

  1. Referee: [Optimization Problem] The optimization jointly maximizes EE and sum-rate while coupling UAV 3D positions (affecting path loss and LoS probability) with power allocation and relay gains. This formulation is non-convex, and the paper relies on an iterative algorithm (likely SCA or alternating optimization). Without a convergence proof to a global optimum or comparison against exhaustive search on a discretized grid, the reported improvements in capacity and EE could be artifacts of local solutions or favorable initializations rather than framework superiority.

    Authors: We appreciate the referee's observation on the non-convexity of the joint optimization. The solution method alternates between UAV 3D positioning (using SCA to convexify the path-loss and LoS terms) and power/relay-gain allocation. In the revised manuscript we have added a formal convergence analysis: the objective is shown to be monotonically non-decreasing at each iteration and upper-bounded, guaranteeing convergence to a stationary point. Global optimality is intractable to prove for this problem class; exhaustive search on a discretized 3D grid is computationally prohibitive given the continuous positioning variables and high dimensionality. To mitigate concerns about local optima we now report results from multiple random initializations, all of which yield consistent gains over the LEO-GBS baseline. revision: partial

  2. Referee: [Simulation Results] The abstract asserts that simulations demonstrate improvements in total capacity, energy efficiency, and fairness, yet supplies no channel model, energy consumption model, simulation parameters, baseline definitions, or quantitative results. This omission prevents verification of the central claim that AMUD outperforms LEO-GBS systems and makes the magnitude of any gains impossible to evaluate.

    Authors: We regret that these details were not presented with sufficient prominence. The full manuscript already contains the channel models (free-space loss plus Rician fading for LEO links, probabilistic LoS model for UAV relays), energy-consumption models (UAV propulsion plus communication power), a complete parameter table (carrier frequency, bandwidth, noise density, UAV altitude bounds, user density), explicit baseline definitions (LEO satellites coexisting with ground base stations without UAV relays), and quantitative results (percentage improvements in capacity, EE, and Jain's fairness index). The revised version reorganizes the simulation section to present these elements first, with an expanded parameter table and numerical deltas clearly stated in the text and figure captions. revision: yes

  3. Referee: The framework assumes the optimization can be solved in real time for dynamic user distributions and that UAV relays can be repositioned adaptively without prohibitive delays or energy overhead. No complexity analysis, latency bounds, or practical positioning constraints are provided to support this assumption, which is load-bearing for the claimed adaptability.

    Authors: We agree that practical deployment considerations must be addressed. The revised manuscript now includes a complexity analysis of the alternating SCA algorithm (polynomial per iteration). We also add latency bounds derived from realistic UAV cruise speeds (10–20 m/s) and repositioning distances, and we clarify that the optimization is solved periodically rather than continuously. Practical constraints—maximum UAV velocity, energy budget for mobility, and minimum repositioning interval—have been incorporated into the system model and problem formulation to support the adaptability claims. revision: yes

Circularity Check

0 steps flagged

No circularity: optimization formulation evaluated via independent simulations

full rationale

The paper states an optimization problem that jointly maximizes network energy efficiency and total capacity subject to fairness and QoS constraints, then reports simulation outcomes comparing the resulting AMUD deployment against LEO-GBS baselines. No equation reduces by construction to its own inputs, no fitted parameter is relabeled as a prediction, and no load-bearing premise rests solely on self-citation. The derivation chain consists of standard non-convex optimization followed by numerical evaluation; the reported gains are therefore falsifiable against external benchmarks and do not collapse into self-reference.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. The optimization is presumed to rest on standard wireless channel models and energy consumption equations drawn from prior literature.

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