실시간 차량 밀도에 대응하는 심층강화학습기반 C-V2X 분산혼잡제어
Deep Reinforcement Learning-Based C-V2X Distributed Congestion Control for Real-Time Vehicle Density Response
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초록
분산혼잡제어는 높은 밀도의 차량 네트워크에서 채널 혼잡을 완화하고, 통신 성능을 개선하는 기술이다. 기존 분산혼잡제어 기술은 quality of service (QoS) 요구사항을 고려하지 않은 채 채널 혼잡을 줄이는 방향으로 동작한다. 이러한 분산혼잡제어 알고리즘설계는 과도한 DCC 동작으로 인하여 다른 QoS를 저하시킬 수 있다. 이와 같은 문제를 해결하기 위해 심층강화학습 기반 QoS 적응형 DCC 알고리즘을 제안한다. 시뮬레이션은 준 실환경 시뮬레이터를 기반으로 동적인 차량 밀도를 생성하여 평가하였으며, 시뮬레이션 결과 기존 DCC 알고리즘 보다 목표 QoS에 더 근접한 결과를 확인하였다.
Abstract
Distributed congestion control (DCC) is a technology that mitigates channel congestion and improves communicationperformance in high-density vehicular networks. Traditional DCC techniques operate to reduce channel congestionwithout considering quality of service (QoS) requirements. Such design of DCC algorithms can lead to excessiveDCC actions, potentially degrading other aspects of QoS. To address this issue, we propose a deep reinforcementlearning-based QoS-adaptive DCC algorithm. The simulation was conducted using a quasi-real environment simulator,generating dynamic vehicular densities for evaluation. The simulation results indicate that our proposed DCCalgorithm achieves results closer to the targeted QoS compared to existing DCC algorithms.
참고문헌 (10)
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