Decentralized Deep Reinforcement Learning for Delay-Power Tradeoff in Vehicular Communications

2019-06-03 · Xianfu Chen, Celimuge Wu, Honggang Zhang, Yan Zhang, Mehdi Bennis, Heli Vuojala

This paper targets at the problem of radio resource management for expected long-term delay-power tradeoff in vehicular communications. At each decision epoch, the road side unit observes the global network state, allocates channels and schedules data packets for all vehicle user equipment-pairs (VUE-pairs). The decision-making procedure is modelled as a discrete-time Markov decision process (MDP). The technical challenges in solving an optimal control policy originate from highly spatial mobility of vehicles and temporal variations in data traffic. To simplify the decision-making process, we first decompose the MDP into a series of per-VUE-pair MDPs. We then propose an online long short-term memory based deep reinforcement learning algorithm to break the curse of high dimensionality in state space faced by each per-VUE-pair MDP. With the proposed algorithm, the optimal channel allocation and packet scheduling decision at each epoch can be made in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical simulations validate the theoretical analysis and show the effectiveness of the proposed online learning algorithm.

📄 PDF Abstract BibTeX arXiv:1906.00625

Code (0)

등록된 구현이 없습니다.

Tasks

Decision MakingDeep Reinforcement LearningManagement reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Scheduling

Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach

2018-12-16 · Zhao Chen, Xiaodong Wang

Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-limited mobile devices from computation-intensive tasks, which enables devices to offload workloads to nearby MEC servers and impro…

Deep Reinforcement Learning Edge-computing Reinforcement Learning Reinforcement Learning (RL)

Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach

2019-11-27 · Mohamed K. Abdel-Aziz, Sumudu Samarakoon, Mehdi Bennis, Walid Saad

In this letter, an age of information (AoI)-aware transmission power and resource block (RB) allocation technique for vehicular communication networks is proposed. Due to the highly dynamic nature of vehicular networks, …

Active Learning GPR

Multi-Agent Reinforcement Learning from Delayed Marketplace Feedback for Objective-Weight Adaptation in Three-Sided Dispatch

2026-06-11 · Haochen Wu, Yi Hou, Shiguang Xie arxiv

Dispatch in three-sided marketplaces provides a natural setting for reinforcement learning from world feedback: decisions are evaluated by delayed operational outcomes such as delivery speed, courier utilization, and mer…

Multi-agent Reinforcement Learning

Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks

2020-11-17 · Yueyue Dai, Du Xu, Ke Zhang, Sabita Maharjan 외

Vehicular Edge Computing (VEC) is a promising paradigm to enable huge amount of data and multimedia content to be cached in proximity to vehicles. However, high mobility of vehicles and dynamic wireless channel condition…

Deep Reinforcement Learning Edge-computing Reinforcement Learning (RL)

Federated Learning for Ultra-Reliable Low-Latency V2V Communications

2018-05-11 · Sumudu Samarakoon, Mehdi Bennis, Walid Saad, Merouane Debbah

In this paper, a novel joint transmit power and resource allocation approach for enabling ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The objective is to minimize the network-wide …

Federated Learning