Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks

• URL: http://arxiv.org/abs/2605.02416v2
• Date: Sat, 09 May 2026 08:16:50 GMT
• Title: Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks
• Abstract summary: We propose a dueling double deep Q-network (DDQN)-based adaptive multi-objective handover framework for low Earth orbit (LEO) satellite networks.<n>The proposed method enables dynamic trade-off learning among throughput, blocking probability, and switching cost under time-varying network conditions.
• Score: 0.10499611180329803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a dueling double deep Q-network (DDQN)-based adaptive multi-objective handover framework for low Earth orbit (LEO) satellite networks. The proposed method enables dynamic trade-off learning among throughput, blocking probability, and switching cost under time-varying network conditions. Simulation results demonstrate that the proposed approach consistently outperforms conventional baselines, achieving up to 10.3% throughput improvement and near-zero blocking under typical operating conditions.

Related papers

• Reinforcement Learning-Guided Evolutionary Policy Optimization for Preference-Adjustable Heterogeneous Agile Earth Observation Satellite Scheduling [50.176885352114674]
  Heterogeneous agile Earth observation satellite (AEOS) scheduling requires task selection, satellite assignment, and observation sequencing.<n>This paper proposes an evolutionary policy optimization framework for heterogeneous AEOS scheduling with preference-adjustable weighted objectives.
  arXiv  Detail & Related papers  (2026-08-25T12:16:28Z)
• Multi-Timescale Latent-Action DRL for Joint Optimization in Edge-Cloud Networks [48.421359451414155]
  Load imbalance across edge cloud and layers degrades latency performance in.<n> hierarchical edge-cloud computing systems under dynamic task arrivals and heterogeneous resources.<n>We propose a two-time deep learning study with an action space (2MDRL-LA) to jointly optimize service placement.<n>It achieves a 20.8% reduction in average e2e latency and a 13% improvement in resource utilization over the scheme.
  arXiv  Detail & Related papers  (2026-06-30T11:56:15Z)
• Spatial-Temporal Learning-Based Distributed Routing for Dynamic LEO Satellite Networks [0.2994333558834796]
  We propose a spatial-temporal learning-based distributed routing framework for Low Earth Orbit (LEO) satellite networks.<n>The proposed approach maintains low computational overhead with negligible carbon emissions, demonstrating its efficiency from a Green AI perspective.
  arXiv  Detail & Related papers  (2026-05-04T10:05:43Z)
• Reinforcement Learning for Opportunistic Routing in Software-Defined LEO-Terrestrial Systems [16.049887428366333]
  Large-scale low Earth orbit (LEO) satellite constellations are driving the need for intelligent routing strategies.<n>We introduce opportunistic routing, which aims to minimize delivery delay by forwarding packets to any currently available ground gateways rather than fixed destinations.
  arXiv  Detail & Related papers  (2026-01-20T07:01:14Z)
• QoS-Aware Hierarchical Reinforcement Learning for Joint Link Selection and Trajectory Optimization in SAGIN-Supported UAV Mobility Management [52.15690855486153]
  A space-air-ground integrated network (SAGIN) has emerged as an essential architecture for enabling ubiquitous UAV connectivity.<n>This paper formulates UAV mobility management in SAGIN as a constrained multiobjective joint optimization problem.
  arXiv  Detail & Related papers  (2025-12-17T06:22:46Z)
• Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization [61.55616421408666]
  Low-Altitude Economy Networks (LAENets) have enabled a variety of applications, including aerial surveillance, environmental sensing, and semantic data collection.<n> onboard vision (VLMs) offer inference for real-time inference but limited onboard dynamic network conditions.<n>We propose a UAV-enabled LAENet system that improves communication efficiency under dynamic LAENet conditions.
  arXiv  Detail & Related papers  (2025-10-11T05:11:21Z)
• Joint AoI and Handover Optimization in Space-Air-Ground Integrated Network [48.485907216785904]
  Low Earth orbit (LEO) satellite constellations offer promising solutions with global coverage and reduced latency.<n>Yet struggle with intermittent coverage and intermittent communication windows due to orbital dynamics.<n>Our three-layer design employs hybrid free-space optical (FSO) links for high-capacity satellite-to-ground communication and reliable radio frequency (RF) links for HAP-to-ground transmission.
  arXiv  Detail & Related papers  (2025-09-16T06:16:56Z)
• Decentralized Rank Scheduling for Energy-Constrained Multi-Task Federated Fine-Tuning in Edge-Assisted IoV Networks [5.034703123469061]
  In Internet of Vehicles (IoV) systems, enabling efficient and low-latency multi-task adaptation is challenging due to client mobility, heterogeneous resources, and intermittent connectivity.<n>This paper proposes a hierarchical federated fine-tuning framework that coordinates roadside units (RSUs) and vehicles to support resource-aware and mobility-resilient learning across dynamic IoV scenarios.
  arXiv  Detail & Related papers  (2025-08-13T06:29:00Z)
• Adaptive Optimization of Latency and Throughput with Fidelity Constraints in Quantum Networks Using Deep Neural Networks [3.1856756516735936]
  We propose a semi-supervised adaptive purification approach employing Deep Neural Networks (DNNs) to optimize the balance between latency, throughput, entanglement resource utilization and fidelity thresholds.<n>Our results underline the potential of deep learning techniques for achieving adaptive, optimized performance in future quantum networking applications.
  arXiv  Detail & Related papers  (2025-05-18T15:23:18Z)
• Adaptive Genetic Selection based Pinning Control with Asymmetric Coupling for Multi-Network Heterogeneous Vehicular Systems [8.454856509502733]
  This paper proposes an optimized pinning control approach for heterogeneous multi-network vehicular ad-hoc networks (VANETs) We first establish a rigorous theoretical foundation by proving the stability of pinning control strategies under both single and multi-network conditions. Building on this theoretical groundwork, we propose an adaptive genetic algorithm tailored to select optimal pinning nodes.
  arXiv  Detail & Related papers  (2024-11-05T11:49:26Z)
• A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode [53.71516191515285]
  The low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system. We propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks.
  arXiv  Detail & Related papers  (2024-08-29T06:57:45Z)
• Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learning [113.48727062141764]
  We propose a distributed collaborative beamforming (DCB)-based uplink communication paradigm for enabling ground-space direct communications. DCB treats the terminals that are unable to establish efficient direct connections with the low Earth orbit (LEO) satellites as distributed antennas. We propose an evolutionary multi-objective deep reinforcement learning algorithm to obtain the desirable policies.
  arXiv  Detail & Related papers  (2024-04-11T03:13:02Z)
• Multi Agent DeepRL based Joint Power and Subchannel Allocation in IAB networks [0.0]
  Integrated Access and Backhauling (IRL) is a viable approach for meeting the unprecedented need for higher data rates of future generations. In this paper, we show how we can use Deep Q-Learning Network to handle problems with huge action spaces associated with fractional nodes.
  arXiv  Detail & Related papers  (2023-08-31T21:30:25Z)
• Fidelity-Guarantee Entanglement Routing in Quantum Networks [64.49733801962198]
  Entanglement routing establishes remote entanglement connection between two arbitrary nodes. We propose purification-enabled entanglement routing designs to provide fidelity guarantee for multiple Source-Destination (SD) pairs in quantum networks.
  arXiv  Detail & Related papers  (2021-11-15T14:07:22Z)
• Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA [78.60275748518589]
  Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks. A novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.
  arXiv  Detail & Related papers  (2021-01-02T15:21:08Z)
• Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles [52.79089414630366]
  We develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL) We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner.
  arXiv  Detail & Related papers  (2020-07-27T12:38:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.