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Nash Soft Actor-Critic LEO Satellite Handover Management Algorithm for Flying Vehicles

Jinxuan Chen, Mustafa Ozger, Cicek Cavdar

TL;DR

The paper tackles the problem of maintaining continuous connectivity for flying vehicles and ground terminals in a high-midelity LEO satellite network, where rapid satellite motion induces frequent handovers and QoS degradation. It proposes Nash-SAC, a distributed handover strategy that blends multi-agent reinforcement learning with game-theoretic equilibrium to minimize handovers while optimizing network utility under CINR/INR constraints. The approach models a Walker Star LEO constellation with heterogeneous users, defines two optimization objectives (minimizing handovers and maximizing a network-utility metric), and demonstrates that Nash-SAC reduces handovers by over 16% and blocking by over 18%, with net utility improvements up to 48% relative to baselines. The results show that the method provides reliable, robust connectivity for both FVs and ground terminals, with the ability to prioritize FVs when necessary, making it suitable for next-generation aerial connectivity scenarios.

Abstract

Compared with the terrestrial networks (TN), which can only support limited coverage areas, low-earth orbit (LEO) satellites can provide seamless global coverage and high survivability in case of emergencies. Nevertheless, the swift movement of the LEO satellites poses a challenge: frequent handovers are inevitable, compromising the quality of service (QoS) of users and leading to discontinuous connectivity. Moreover, considering LEO satellite connectivity for different flying vehicles (FVs) when coexisting with ground terminals, an efficient satellite handover decision control and mobility management strategy is required to reduce the number of handovers and allocate resources that align with different users' requirements. In this paper, a novel distributed satellite handover strategy based on Multi-Agent Reinforcement Learning (MARL) and game theory named Nash-SAC has been proposed to solve these problems. From the simulation results, the Nash-SAC-based handover strategy can effectively reduce the handovers by over 16 percent and the blocking rate by over 18 percent, outperforming local benchmarks such as traditional Q-learning. It also greatly improves the network utility used to quantify the performance of the whole system by up to 48 percent and caters to different users requirements, providing reliable and robust connectivity for both FVs and ground terminals.

Nash Soft Actor-Critic LEO Satellite Handover Management Algorithm for Flying Vehicles

TL;DR

The paper tackles the problem of maintaining continuous connectivity for flying vehicles and ground terminals in a high-midelity LEO satellite network, where rapid satellite motion induces frequent handovers and QoS degradation. It proposes Nash-SAC, a distributed handover strategy that blends multi-agent reinforcement learning with game-theoretic equilibrium to minimize handovers while optimizing network utility under CINR/INR constraints. The approach models a Walker Star LEO constellation with heterogeneous users, defines two optimization objectives (minimizing handovers and maximizing a network-utility metric), and demonstrates that Nash-SAC reduces handovers by over 16% and blocking by over 18%, with net utility improvements up to 48% relative to baselines. The results show that the method provides reliable, robust connectivity for both FVs and ground terminals, with the ability to prioritize FVs when necessary, making it suitable for next-generation aerial connectivity scenarios.

Abstract

Compared with the terrestrial networks (TN), which can only support limited coverage areas, low-earth orbit (LEO) satellites can provide seamless global coverage and high survivability in case of emergencies. Nevertheless, the swift movement of the LEO satellites poses a challenge: frequent handovers are inevitable, compromising the quality of service (QoS) of users and leading to discontinuous connectivity. Moreover, considering LEO satellite connectivity for different flying vehicles (FVs) when coexisting with ground terminals, an efficient satellite handover decision control and mobility management strategy is required to reduce the number of handovers and allocate resources that align with different users' requirements. In this paper, a novel distributed satellite handover strategy based on Multi-Agent Reinforcement Learning (MARL) and game theory named Nash-SAC has been proposed to solve these problems. From the simulation results, the Nash-SAC-based handover strategy can effectively reduce the handovers by over 16 percent and the blocking rate by over 18 percent, outperforming local benchmarks such as traditional Q-learning. It also greatly improves the network utility used to quantify the performance of the whole system by up to 48 percent and caters to different users requirements, providing reliable and robust connectivity for both FVs and ground terminals.
Paper Structure (9 sections, 13 equations, 5 figures, 1 table, 1 algorithm)

This paper contains 9 sections, 13 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Design of Handover Management Strategy
  3. System model
  4. Problem formulation
  5. Nash-SAC
  6. Results AND Analysis
  7. Parameters Setting
  8. Simulation Results and Analysis
  9. Conclusions

Figures (5)

  • Figure 1: The architecture of the satellite network and the users, including the FVs and the ground terminals.
  • Figure 2: Number of accumulative average handovers versus time.
  • Figure 3: Blocking rate versus available channels when using different methods.
  • Figure 4: Network utility for different types of users when using different methods.
  • Figure 5: CDF of DL CINR for different types of users when using Nash-SAC.