Generative AI-enabled Wireless Communications for Robust Low-Altitude Economy Networking

Changyuan Zhao; Jiacheng Wang; Ruichen Zhang; Dusit Niyato; Geng Sun; Hongyang Du; Dong In Kim; Abbas Jamalipour

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Generative AI-enabled Wireless Communications for Robust Low-Altitude Economy Networking

Changyuan Zhao, Jiacheng Wang, Ruichen Zhang, Dusit Niyato, Geng Sun, Hongyang Du, Dong In Kim, Abbas Jamalipour

arXiv:2502.18118v2Feb 25, 2025eess.SP

TL;DR

LAENets require robust wireless networking because dynamic operations and uncertain environments threaten QoS. The paper analyzes robustness requirements and QoS metrics, reviews GenAI approaches, and proposes a diffusion-based framework with an MoE-transformer actor network. In robust beamforming, the framework improves worst-case achievable secrecy rate by more than 15% over four learning baselines.

Problem
LAENets face wireless-layer uncertainty, while conventional numerical and learning methods depend heavily on training data and remain susceptible to unknown environments.
Method
The paper proposes a diffusion-based reinforcement framework with an MoE-transformer actor network for robust LAENet optimization.
Results
Approximately 15% ASR improvement over four learning baselines is achieved in robust optimization.
Takeaways & Limitations
The results highlight GenAI's potential for enhancing LAENet robustness under uncertainty.
Takeaways & Limitations
Energy efficiency remains a critical constraint requiring future energy-aware GenAI models for power-limited LAENet nodes.

Abstract

from arXiv · show

Low-Altitude Economy Networks (LAENets) have emerged as significant enablers of social activities, offering low-altitude services such as the transportation of packages, groceries, and medical supplies. Owing to their control mechanisms and ever-changing operational factors, LAENets are inherently more complex and vulnerable to security threats than traditional terrestrial networks. As applications of LAENet continue to expand, the robustness of these systems becomes crucial. In this paper, we propose a generative artificial intelligence (GenAI) optimization framework that tackles robustness challenges in LAENets. We conduct a systematic analysis of robustness requirements for LAENets, complemented by a comprehensive review of robust Quality of Service (QoS) metrics from the wireless physical layer perspective. We then investigate existing GenAI-enabled approaches for robustness enhancement. This leads to our proposal of a novel diffusion-based optimization framework with a Mixture of Experts (MoE)-transformer actor network. In the robust beamforming case study, the proposed framework demonstrates its effectiveness by optimizing beamforming under uncertainties, achieving a more than 15% increase over four learning baselines in the worst-case achievable secrecy rate. These findings highlight the significant potential of GenAI in strengthening LAENet robustness.

I. INTRODUCTION

LAENets face uncertainty from dynamic environments, while conventional numerical and learning methods remain data-dependent and vulnerable to unknown conditions. The paper analyzes robustness requirements and proposes GenAI-based optimization, including an MoE-transformer actor network within a diffusion-based framework.

Motivation: Dynamic weather, air traffic, user demand, and emergencies create uncertainties that make robust LAENet design necessary.Robustness is treated as maintaining QoS despite these uncertainties.
Motivation: Traditional numerical algorithms and learning methods depend heavily on training data and can be susceptible to unknown environments.These limitations hinder uncertainty handling at the wireless physical layer.
Contributions: The paper analyzes LAENet robustness and introduces GenAI models, applications, and benefits for wireless communications.The analysis covers robustness requirements and GenAI-enabled wireless networking.
Contributions: Robustness requirements are incorporated into optimization using stochastic, chance-constrained, and robust optimization paradigms.These paradigms represent different levels of robustness.
Contributions: The proposed method integrates an MoE-transformer actor network with a diffusion-based optimization framework for robust beamforming.A case study evaluates how GenAI supports robust beamforming in LAENets.

II. THE OVERVIEW OF ROBUST LAENET

LAENets combine low-altitude transportation, communication, and sensing to support social activities. Their overview organizes these applications across three primary aspects.

Overview: LAENets rely on cooperation between edge equipment and low-altitude communication networks to facilitate social activities.The overview introduces the network before describing its application areas.
Low-Altitude Transportation: Low-altitude transportation includes drone delivery of packages, groceries, and medical supplies.Examples include deployments by Amazon, Walmart, and Wing.
Low-Altitude Communication: Low-altitude communication uses aerial platforms such as drones with portable mobile base stations to extend connectivity.One example supports remote forestry machinery control over 5G networks.
Low-Altitude Sensing: Low-altitude sensing uses drones for high-resolution environmental mapping and data collection.Applications include monitoring deforestation, glacier melting, and wildlife habitats.

B. The Significance of Robust LAENet

LAENets are more complex and vulnerable than terrestrial networks because of automatic control and changing operating conditions. Robustness therefore requires preserving QoS across safety, security, confidentiality, accuracy, and integrity concerns.

Robustness Motivation: Automatic control mechanisms and shifting operational factors make LAENets more complex and vulnerable than traditional terrestrial networks.This complexity is especially relevant as aerial-delivery applications expand.
Robustness Motivation: Robustness means maintaining LAENet QoS despite uncertainties affecting operations.The paper treats robustness as essential for reliable network service.
Safety: Safety robustness protects equipment, operators, and service recipients under adverse weather, interference, and unexpected obstacles.Safety measures must continue performing effectively under these conditions.
Security and Confidentiality: Security and confidentiality robustness preserves reliable communications and QoS while resisting attacks, data breaches, and unauthorized access.These protections must not compromise application performance.
Accuracy and Integrity: Accuracy and integrity robustness enables precise instruction transmission, execution, and operational feedback despite environmental and transmission uncertainties.These properties support QoS maintenance during unpredictable conditions.

C. Robustness Considerations for LAENet

Robustness considerations map LAENet applications to QoS indicators and optimization problems under uncertainty. The paper distinguishes optimization frameworks by uncertainty level and emphasizes robust optimization for severe or adversarial conditions.

QoS Metrics: LAENet applications are mapped to suitable QoS indicators and transformed into optimization problems under uncertainty.This mapping provides a basis for evaluating and enhancing robustness.
QoS Metrics: Sensing accuracy is measured through indicators such as time of arrival, channel state information, and received signal strength.These indicators support reliable environmental perception.
QoS Metrics: Communication-rate robustness uses outage probability, bit error rate, and spectral efficiency to address failures, errors, and throughput.The indicators reflect performance across varying channel conditions.
QoS Metrics: Computation-resource robustness is evaluated through computation success rate, overload tolerance, and resource availability probability.These indicators address efficiency, signaling overhead, and processing in dynamic networks.
QoS Metrics: Control stability is assessed through trajectory tracking accuracy, disturbance rejection, and stability margin index.These measures address device speed, trajectory, and state under dynamic channel conditions.
Optimization under Uncertainty: Deterministic, stochastic, chance-constrained, and robust optimization provide progressively different treatments of uncertainty and constraint risk.Robust optimization targets worst-case performance under severe uncertainties or adversarial conditions.

III. GENAI-ENABLED WIRELESS PHYSICAL LAYER FOR ROBUST LAENET

This section introduces GenAI and reviews its use in wireless physical-layer communications to improve robust QoS metrics for LAENets.

The reviewed wireless physical-layer methods use GenAI to improve robust QoS metrics for LAENets.
GenAI uses unsupervised or self-supervised learning to generate results resembling learned data.Typical models include GANs, VAEs, diffusion models, and LLMs.
GenAI models data distributions to support detailed analysis, threat detection, and synthetic simulation of spoofing attacks or signal interference.

B. Comparison of GenAI and discriminative AI

GenAI differs from discriminative AI by modeling data distributions and uncertainties rather than focusing primarily on deterministic decision boundaries. The section also reviews GenAI-based channel modeling, channel estimation, and beam training for aerial communications.

Comparison of GenAI and discriminative AI: GenAI models underlying data distributions, enabling probabilistic representations of uncertainty for stochastic, chance-constrained, and robust optimization.
Channel Modeling: A two-stage GenAI model combines link-state prediction with a conditional VAE to generate air-to-ground path parameters across LOS, NLOS, and outage conditions.
Channel Estimation: A GAN-based OTFS channel estimator learns from 10,000 high-speed datasets with receiver velocities from 100 to 500 km/h.
Channel Estimation: The GAN-based estimator reduces susceptibility to Doppler effects and multipath distortion, supporting more accurate channel estimation.
Beam Training: A DR-VAE predicts strongest beam-pair probabilities and beam-training feedback to reduce overhead and maximize frame spectral efficiency.

D. Lessons Learned

The lessons learned emphasize GenAI’s distributional modeling and adaptability for complex aerial wireless scenarios, while motivating an MoE-transformer actor network to stabilize diffusion-based optimization under uncertainty.

Lessons Learned: GenAI maps latent distributions to communication parameters such as path loss, delay, and angle measurements in complex aerial scenarios.
Lessons Learned: Diffusion-based reinforcement learning generates actions through multistep denoising and adapts to multiscale wireless-channel fading.
Lessons Learned: Diffusion generation can produce variable actions for the same state, affecting convergence during uncertainty optimization.
Lessons Learned: The proposed MoE-transformer actor combines multi-head attention for fine-grained dependencies with selective expert activation for dynamic uncertainties.
Lessons Learned: The actor network is intended to enable more stable and robust action generation within diffusion reinforcement learning.

V. CASE STUDY: GENAI-ENABLED ROBUST BEAMFORMING FOR SECURE COMMUNICATION

The case study considers robust 3D beamforming for secure LAENet communication, adapting beam directions and power allocations to environmental variation and UAV mobility while addressing eavesdropping risks.

3D beamforming adapts beam directions and power allocations to environmental variations and UAV mobility for reliable signal transmission.
The secure communication scenario uses a terrestrial BS with a UPA to transmit confidential signals and artificial noise to legitimate UAVs while evading eavesdroppers.

C. Optimization Design

The paper formulates secure beamforming as an optimization problem under uncertainty, evaluating stochastic, chance-constrained, and robust optimization levels. The MoE-transformer method achieves the strongest learning and worst-case robustness results while maintaining low per-iteration latency and lower reward variance.

Optimization Formulation: Secure beamforming maximizes achievable secrecy rate while constraining eavesdropping-channel capacity below Ceve through a penalized reinforcement-learning reward.The reward equals ASR minus the amount by which eavesdropping capacity exceeds Ceve.
Optimization Formulation: Three robustness levels are evaluated: stochastic optimization maximizes expected ASR, chance-constrained optimization enforces constraint satisfaction with probability Peve, and robust optimization maximizes a worst-case reward lower bound.Monte Carlo methods approximate chance-constraint satisfaction and worst-case performance.
Experimental Setup: The experiment models a terrestrial BS with a 4 × 4 UPA, a legitimate UAV, and an eavesdropper, each using Nb = Ne = 6 antennas, with position and channel uncertainties.The eavesdropping threshold is Ceve = 3bps/Hz, and chance-constrained optimization uses Peve = 70%.
Numerical Results: 13% improvement over SAC and GNN and over 200% improvement over GDM are reported for stochastic-optimization learning curves.The proposed method also maintains relatively fast convergence in chance-constrained optimization.
Numerical Results: 15.8% higher reward than transformer and about 44% higher than SAC are reported for robust optimization, where all returns become negative.The MoE-transformer remains the least degraded among the compared methods.
Numerical Results: 0.01906 s per-iteration wall-clock time is reported for MoE-Transformer, versus 0.01813 s for transformer, 0.01625 s for SAC and GDM, and 0.01875 s for GNN.The paper states that this latency gap is below the 100 ms control-loop budget typical for LAE edge nodes.
Numerical Results: 16.99 is the MoE-transformer’s lowest reported inference-reward variance in robust optimization, indicating steadier worst-case behavior than the compared methods.The other methods show larger variances and more fluctuation in worst-case returns.

E. Open Challenges

The paper identifies data requirements, hyper-parameter sensitivity, and limited reliability guarantees as open challenges for scaling GenAI-enabled robust beamforming to large LAENets.

Data Requirement: Greater uncertainty increases the data required for GenAI to learn policy distributions before identifying the optimal LAENet strategy.The paper identifies data requirements as a barrier to large-scale deployment.
Hyper-parameter Sensitivity: Performance depends on the noise schedule, denoising depth, and MoE expert count, requiring continual field retuning across latency, energy, and robustness.The paper labels this issue hyper-parameter sensitivity.
Reliability Guarantees: Current solutions justify reliability with empirical Monte Carlo simulations but lack formal worst-case guarantees for end-to-end robustness.The paper identifies this as a limitation for safety-critical aerial links.

A. Adaptive GenAI for Dynamic Environments

LAENets require GenAI approaches that adapt to dynamic conditions, detect anomalies, and recover from disruptions while respecting constrained energy resources. The proposed diffusion-based reinforcement framework with an MoE-transformer actor network addresses robustness under uncertainty, while energy-aware designs remain important for sustained operation.

Dynamic adaptation: GenAI adaptation should account for changing network topology, environmental conditions, and mission requirements in real time.Suggested techniques include online learning, real-time model adaptation, and context-aware optimization.
Anomaly recovery: Multimodal GenAI anomaly detection can support recovery from interference, equipment malfunctions, and unexpected obstacles.Potential recovery actions include dynamic path adjustments and alternate resource allocations.
Energy-aware optimization: Energy-aware GenAI models must balance computational demands with limited drone-battery power while maintaining network robustness.Lightweight architectures, efficient inference, and collaborative processing are identified as possible approaches.
Robust beamforming: The diffusion-based reinforcement framework with an MoE-transformer actor network learned robust beam patterns under various uncertainties.The case study reported approximately 13% ASR improvement in stochastic optimization and around 15% in robust optimization over four learning baselines.
Implications: The reported results support GenAI as a promising approach for enhancing LAENet robustness and motivate further application-oriented exploration.The conclusion connects the case-study findings to continued investigation of GenAI for robust LAENets.