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Constrained DRL for Energy Efficiency Optimization in RSMA-Based Integrated Satellite Terrestrial Network.

Qingmiao Zhang1, Lidong Zhu1, Yanyan Chen2

  • 1National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)
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Summary

This study introduces an energy-efficient resource allocation method for integrated satellite-terrestrial networks using rate splitting multiple access (RSMA). The proposed constrained soft actor-critic (SAC) algorithm optimizes performance while meeting quality of service (QoS) demands.

Keywords:
constrained deep reinforcement learningenergy efficiencyintegrated satellite terrestrial networkrate splitting multiple accesssoft actor-critic

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Area of Science:

  • Communications Engineering
  • Artificial Intelligence
  • Network Optimization

Background:

  • 6G communications demand extensive coverage and ubiquitous connectivity, increasing satellite roles.
  • Rapid growth in users and devices necessitates advanced multiple access technologies like rate splitting multiple access (RSMA).
  • Satellites, being power-limited, require energy-efficient resource allocation strategies in integrated networks.

Purpose of the Study:

  • To investigate energy-efficient resource allocation in integrated satellite terrestrial networks (ISTN) employing RSMA.
  • To address the challenges of non-convex optimization problems with Quality of Service (QoS) requirements and continuous state/action spaces.
  • To develop a novel algorithm that maximizes reward while ensuring QoS constraints are met.

Main Methods:

  • Utilizing a constrained soft actor-critic (SAC) algorithm, a policy-gradient method.
  • Incorporating Lagrangian relaxation to transform the constrained problem into a penalized unconstrained optimization task.
  • Implementing an on-off mechanism to optimize learning efficiency by avoiding unnecessary updates based on network state changes.

Main Results:

  • The proposed constrained SAC algorithm demonstrates superior energy efficiency compared to benchmark algorithms.
  • The algorithm successfully satisfies the Quality of Service (QoS) constraints.
  • The integrated on-off mechanism significantly reduces the overall time consumption of the learning process.

Conclusions:

  • The constrained SAC algorithm offers an effective solution for energy-efficient resource allocation in ISTN with RSMA.
  • The approach balances performance optimization with critical QoS requirements.
  • The on-off mechanism enhances the practical applicability of the algorithm by improving computational efficiency.