Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning (1905.02910v2)

Abstract: This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. Fast channel variations in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In response, we model the resource sharing as a multi-agent reinforcement learning problem, which is then solved using a fingerprint-based deep Q-network method that is amenable to a distributed implementation. The V2V links, each acting as an agent, collectively interact with the communication environment, receive distinctive observations yet a common reward, and learn to improve spectrum and power allocation through updating Q-networks using the gained experiences. We demonstrate that with a proper reward design and training mechanism, the multiple V2V agents successfully learn to cooperate in a distributed way to simultaneously improve the sum capacity of V2I links and payload delivery rate of V2V links.

• The paper presents a distributed multi-agent reinforcement learning framework for efficient spectrum sharing in high-mobility vehicular networks.
• It employs a fingerprint-based deep Q-network approach to address the nonstationarity challenges inherent in multi-agent reinforcement learning.
• Simulation results show that the proposed method significantly outperforms baseline approaches in both V2I capacity and V2V reliability metrics.

Multi-Agent Reinforcement Learning for Spectrum Sharing in Vehicular Networks

The paper "Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning" by Le Liang, Hao Ye, and Geoffrey Ye Li presents a paper on deploying advanced multi-agent reinforcement learning (MARL) techniques for efficient spectrum sharing in vehicular networks. This work focuses on vehicular communication scenarios where vehicle-to-vehicle (V2V) links reuse the spectrum initially assigned to vehicle-to-infrastructure (V2I) links. The high mobility of vehicles presents a challenge due to fast-changing channel conditions that make centralized spectrum management unsuitable, prompting the need for a distributed resource allocation approach.

Overview

In a high mobility vehicular network, the need for a robust spectrum sharing scheme is crucial to maintain quality communication both for V2I links, which support high data rate services, and V2V links, which facilitate reliable transmission of safety-critical messages. The authors propose a novel framework using MARL, where each V2V link is modeled as an agent trying to navigate the complex interference environment. Utilizing a fingerprint-based deep Q-network (DQN) method makes the MARL approach amenable to distributed implementation, thus addressing the nonstationary challenges stemming from having multiple learning agents.

Key Contributions

1. MARL Framework for Spectrum Sharing: The paper introduces a distributed algorithm where each V2V link uses a separate DQN to learn optimal spectrum sharing strategies. This approach focuses on maximizing the cumulative reward from V2I channel capacity while ensuring high reliability in V2V message delivery within time constraints.
2. Fingerprint-Based Stabilization: To counteract the nonstationarity in MARL, a fingerprint method is deployed that conditions the action-value function on low-dimensional indicators of other agents' policy changes, such as training iteration numbers and exploration rates.
3. Integrated Learning and Execution: The proposed system is executed in two phases: a centralized training phase to determine efficient strategy updates for each agent, and a distributed implementation phase where agents utilize trained networks to make instantaneous decisions.

Numerical Results

The results obtained from simulations designed according to the 3GPP TR 36.885 standard demonstrate that the proposed MARL approach significantly surpasses baseline methods in both the V2I sum capacity and the V2V payload delivery success rate, closely approaching theoretical performance upper bounds. Notably, even with varying payload sizes—detached from training conditions—the solution maintains robustness due to targeted reward design in the learning algorithm.

Implications and Future Directions

The MARL framework for spectrum allocation holds practical implications for future vehicular networks, particularly in scenarios where infrastructure-less communication and effective spectrum utilization are necessary. It implies substantial improvements over conventional approaches by enabling autonomous decision-making based on local observations, hence reducing signaling overhead.

The paper invites future exploration into extending MARL methods to more complex vehicular environments, such as MIMO systems or millimeter-wave based communications, possibly improving resilience and adaptability under varied vehicular mobility patterns and network topologies. Additionally, further studies might consider enhancing robustness and adaptability by refining the reward structures or incorporating alternative reinforcement learning paradigms that might handle different vehicular network scenarios more efficiently.

In conclusion, through the innovative use of MARL, this paper provides a strong foundation for the development of distributed spectrum sharing solutions in vehicular networks, paving the way for more advanced intelligent transportation system applications.