University of Oulu

X. Chen, C. Wu, M. Bennis, Z. Zhao and Z. Han, "Learning to Entangle Radio Resources in Vehicular Communications: An Oblivious Game-Theoretic Perspective," in IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 4262-4274, May 2019. doi: 10.1109/TVT.2019.2907589

Learning to entangle radio resources in vehicular communications : an oblivious game-theoretic perspective

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Author: Chen, Xianfu1; Wu, Celimuge2; Bennis, Mehdi3;
Organizations: 1VTT Technical Research Centre of Finland, Oulu, Finland
2Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan
3Centre for Wireless Communications, University of Oulu, Oulu, Finland
4College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
5University of Houston, Houston, TX, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 6.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2019-09-18


In this paper, we investigate non-cooperative radio resource management in a vehicle-to-vehicle communication network. The technical challenges lie in high-vehicle mobility and data traffic variations. Over the discrete scheduling slots, each vehicle user equipment (VUE)-pair competes with other VUE-pairs in the coverage of a road side unit (RSU) for the limited frequency to transmit queued data packets, aiming to optimize the expected long-term performance. The frequency allocation at the beginning of each slot at the RSU is regulated by a sealed second-price auction. Such interactions among VUE-pairs are modeled as a stochastic game with a semi-continuous global network state space. By defining a partitioned control policy, we transform the original game into an equivalent stochastic game with a global queue state space of finite size. We adopt an oblivious equilibrium (OE) to approximate the Markov perfect equilibrium, which characterizes the optimal solution to the equivalent game. The OE solution is theoretically proven to be with an asymptotic Markov equilibrium property. Due to the lack of a priori knowledge of network dynamics, we derive an online algorithm to learn the OE solution. Numerical simulations validate the theoretical analysis and show the effectiveness of the proposed online learning algorithm.

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Series: IEEE transactions on vehicular technology
ISSN: 0018-9545
ISSN-E: 1939-9359
ISSN-L: 0018-9545
Volume: 68
Issue: 5
Pages: 4262 - 4274
DOI: 10.1109/TVT.2019.2907589
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the Finnish Funding Agency for Innovation (TEKES) under the project “Wirelessfor Verticals (WIVE)”, in part by the Academy of Finland under grants 319759, 319758 and 289611, in part by the Telecommunications Advanced Foundation, and in part by the US MURI AFOSR MURI 18RT0073 and the NSF grants1717454, 1731424, 1702850 and 1646607.
Academy of Finland Grant Number: 319758
Detailed Information: 319758 (Academy of Finland Funding decision)
289611 (Academy of Finland Funding decision)
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