Journal
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 68, Issue 5, Pages 4262-4274Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2019.2907589
Keywords
Vehicle-to-vehicle communications; Markov decision process; stochastic games; Markov perfect equilibrium; oblivious equilibrium; reinforcement learning
Categories
Funding
- Finnish Funding Agency for Innovation (TEKES) under the Project Wireless for Verticals (WIVE)
- Academy of Finland [319759, 319758, 289611]
- Telecommunications Advanced Foundation
- US MURI AFOSR MURI [18RT0073]
- National Science Foundation [1717454, 1731424, 1702850, 1646607]
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1731424, 1702850] Funding Source: National Science Foundation
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1717454, 1646607] Funding Source: National Science Foundation
- Academy of Finland (AKA) [319759, 319758, 319759, 289611, 319758, 289611] Funding Source: Academy of Finland (AKA)
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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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