Content Service Oriented Resource Allocation for Space–Air–Ground Integrated 6G Networks: A Three-Sided Cyclic Matching Approach

Abstract

With the evolution of artificial intelligence-generated content (AIGC) techniques and the development of space-airground 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.

Blockchain-based 6G task offloading and cooperative computing resource allocation study

“…Subsequently, it verifies the signature and MAC of the transaction. The computational cost of the sub-node during the pre-prepare process is (13 In the third step, each sub-node dispatches a prepare message to the rest of the sub-nodes. This node has to verify 2f (f = (D − 1)/3) signatures and MACs sent from the other sub-nodes, and the sub-node has to generate 1 signature and D − 1 MACs.…”

“…They have additionally devised a dynamic nested neural network aimed at facilitating online adaptation of the learning model structure to effectively address the evolving demands of dynamic resource allocation. Qin et al [13] introduced a novel 6G resource allocation framework centered around air-heavenairspace integration. They addressed the intricate issue of triple matching among equipment, content sources, and users within air-heaven-airspace integrated networks through content-centric and client-focused resource allocation techniques.…”

Unmanned Aerial Vehicle Assisted Post-Disaster Communication Coverage Optimization Based on Internet of Things Big Data Analysis

“…The research on UAV communication coverage in emergency areas is mainly concentrated in several aspects, including resource allocation [ 30 , 31 ], height optimization [ 32 ], trajectory optimization [ 33 , 34 ], and multi-UAV collaborative coverage [ 35 ]. Liu et al [ 36 ] investigated post-disaster multi-UAV collaborative control strategies and proposed a distributed multi-UAV control solution based on deep reinforcement learning.…”

Matching-Based Resource Allocation for Satellite–Ground Network

“…In social and commercial domains, three-sided matching problems are particularly prevalent [ 29 , 30 ]. Reference [ 31 ] utilized a three-sided matching algorithm to find the optimal matching between space–air–ground network facilities, content sources, and users. Paper [ 32 ] studied the problem of three-sided matching between spectrum, equipment, and user in wireless network virtualization.…”