Deep Reinforcement Learning based Resource Allocation for V2V Communications (1805.07222v1)

Abstract: In this paper, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communications based on deep reinforcement learning, which can be applied to both unicast and broadcast scenarios. According to the decentralized resource allocation mechanism, an autonomous agent', a V2V link or a vehicle, makes its decisions to find the optimal sub-band and power level for transmission without requiring or having to wait for global information. Since the proposed method is decentralized, it incurs only limited transmission overhead. From the simulation results, each agent can effectively learn to satisfy the stringent latency constraints on V2V links while minimizing the interference to vehicle-to-infrastructure (V2I) communications.

• The paper employs a decentralized multi-agent DRL framework to achieve autonomous resource allocation in V2V networks.
• It models resource allocation as a partially observable Markov decision process using DQN to select power levels and spectrum bands.
• Simulation results show improved V2I capacity and reduced latency, making the method suitable for high-mobility vehicular networks.

Deep Reinforcement Learning for Resource Allocation in V2V Communications

The paper "Deep Reinforcement Learning based Resource Allocation for V2V Communications" presents a decentralized approach to resource allocation using deep reinforcement learning (DRL) tailored for vehicle-to-vehicle (V2V) communications. This paper addresses both unicast and broadcast scenarios within V2V networks, emphasizing the reduction of latency and interference.

Key Contributions

The core contribution of the paper lies in employing a decentralized framework that utilizes multi-agent DRL to enable autonomous decision-making by individual V2V components, referred to as agents. This method eschews the need for global information often required in centralized systems, thereby reducing overheads and potentially increasing scalability in expansive networks.

Problem Context

V2V communication has stringent Quality-of-Service (QoS) demands, particularly concerning ultra-low latency and reliability, which are fundamental for enhancing road safety. Traditional centralized resource allocation methods can be cumbersome due to increased transmission overheads needed for the collection of local information in high mobility settings. The paper's approach of using DRL seeks to circumvent these limitations by enabling distributed decision-making that adapts to rapidly changing network conditions.

Methodology

The methodology involves modeling the resource allocation problem as a decentralized partially observable Markov decision process (POMDP). Each V2V link acts as an independent agent that continuously updates its choice of sub-band and power level based solely on localized observations, harnessing DRL for spectrum and power allocation:

1. State Space: The state incorporates channel information, interference levels, and QoS metrics pertinent to V2V links.
2. Action Space: This involves discrete selections of power levels and spectrum bands.
3. Reward Function: It aggregates three components—capacity of vehicle-to-infrastructure (V2I) links, capacity of V2V links, and penalties for latency constraint violations.

Deep Q-Networks (DQN) are employed to approximate the action-value function due to their capability to manage large state-action spaces effectively.

Findings

Simulation results indicate that the proposed DRL mechanism significantly enhances the probability that V2V links meet latency constraints while minimizing interference to V2I communications. The results benchmarked against standard methods underscore improved V2I capacities and compliance with V2V latency requirements. For broadcast communications, the methodology also demonstrated substantial improvements in resource allocation efficiency.

Implications and Future Directions

From a practical perspective, this work facilitates the deployment of resource-efficient, reliable vehicular networks without necessitating extensive centralized control, well-suited for the evolving demands of autonomous and connected vehicle systems. Theoretically, it paves the way for integrating advanced DRL models in communication networks, opening avenues for adaptive, robust wireless communication strategies.

Future developments could explore more sophisticated DRL architectures like proximal policy optimization (PPO) or attention-based mechanisms to further refine decision-making under uncertainty. Moreover, real-world vehicular conditions such as variable traffic densities and complex urban environments could be incorporated to test the robustness of this approach.

In conclusion, this work represents a meaningful step towards achieving the dual goals of efficient resource usage and meeting strict latency constraints in next-generation vehicular communication networks. By integrating intelligent, decentralized agents into these networks, the authors bridge a critical gap between theoretical research and practical vehicular communication solutions.