Parameter Training Efficiency Aware Resource Allocation for AIGC in Space-Air-Ground Integrated Networks

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Veröffentlicht in:	arXiv.org (Jun 19, 2024), p. n/a
1. Verfasser:	Qian, Liangxin
Weitere Verfasser:	Zhao, Jun
Veröffentlicht:	Cornell University Library, arXiv.org
Schlagworte:	Wireless networks Semidefinite programming Graph theory Mathematical programming Quadratic programming Optimization Edge computing Efficiency Resource allocation Mobile computing Artificial intelligence Constraints Parameters
Online-Zugang:	Citation/Abstract Full text outside of ProQuest
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Beschreibung
Abstract:	With the evolution of artificial intelligence-generated content (AIGC) techniques and the development of space-air-ground integrated networks (SAGIN), there will be a growing opportunity to enhance more users' mobile experience with customized AIGC applications. This is made possible through the use of parameter-efficient fine-tuning (PEFT) training alongside mobile edge computing. In this paper, we formulate the optimization problem of maximizing the parameter training efficiency of the SAGIN system over wireless networks under limited resource constraints. We propose the Parameter training efficiency Aware Resource Allocation (PARA) technique to jointly optimize user association, data offloading, and communication and computational resource allocation. Solid proofs are presented to solve this difficult sum of ratios problem based on quadratically constrained quadratic programming (QCQP), semidefinite programming (SDP), graph theory, and fractional programming (FP) techniques. Our proposed PARA technique is effective in finding a stationary point of this non-convex problem. The simulation results demonstrate that the proposed PARA method outperforms other baselines.
ISSN:	2331-8422
Quelle:	Engineering Database