Network Utility Maximization based on Incentive Mechanism for Truthful Reporting of Local Information
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Abstract (arXiv)
Classic network utility maximization problems are usually solved assuming all information is available, implying that information not locally available is always truthfully reported. This may not be practical in all scenarios, especially in distributed/semi-distributed networks. In this paper, incentive for truthful reporting in network optimizations with local information is studied. A novel general model for extending network utility maximization (NUM) problems to incorporate local information is proposed, which allows each user to choose its own objective locally and/or privately. Two specific problems, i.e., a user-centric problem (UCP) and a network-centric problem (NCP), are studied. In the UCP, a network center aims to maximize the collective benefit of all users, and truthful reporting from the users regarding their local information is necessary for finding the solution. We show that the widely-adopted dual pricing cannot guarantee truthful information reporting from a user unless the resource is over-supplied or the price is too high for this user to afford. In the NCP, the network center has its own objective and preferred solution, and incentive needs to be provided for the users to adopt the solution it prefers. Truthful reporting from users is necessary for the center to determine the incentives and achieve its solution. For two-user and multiuser cases, we propose two mechanisms to motivate truthful reporting from users while guaranteeing nonnegative utility gains for both the users and the center. A case study on underlay D2D communication illustrates the application of the UCP and NCP. Simulations are conducted for the D2D application to validate the analytical results and demonstrate the proposed mechanisms.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Sep 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2021Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspectivecited by 14
- 2023Holistic Network Virtualization and Pervasive Network Intelligence for 6Gcited by 14
- 2023A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowdcited by 0
- 2026Gradient Manipulation in Distributed Stochastic Gradient Descent with Strategic Agents: Truthful Incentives with Convergence Guaranteescited by 0
- 2026Gradient Manipulation in Distributed Stochastic Gradient Descent with Strategic Agents: Truthful Incentives with Convergence Guarantees30 Mar 2026
- 2023A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd17 Jan 2023
- 2023Holistic Network Virtualization and Pervasive Network Intelligence for 6G2 Jan 2023
- 2021Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective22 Jun 2021