Abstract:
5G New Radio (NR) is envisioned to efficiently support ultra-reliable low-latency communication (URLLC) for new services and applications with high reliability, availabil...Show MoreMetadata
Abstract:
5G New Radio (NR) is envisioned to efficiently support ultra-reliable low-latency communication (URLLC) for new services and applications with high reliability, availability and low latency such as factory automation and autonomous vehicles. Multi-hop Device-to-device (D2D) communication is one such means that expands D2D coverage and achieves lower latency in the mobile edge and NR sidelink. In this paper, we first analyze the URLLC requirements in 5G and the multihop D2D communication problem with perfect knowledge of the network. Subsequently, we investigate the deep reinforcement learning (DRL) algorithm for the scheduling and resource allocation problem with only local information for each node. A simulation is employed to evaluate the performance of the related algorithms. Numerical results show that the proposed DRL algorithm outperforms the greedy algorithm in terms of different relay nodes between the source and destination, and is robust to the coming or leaving of relay nodes.
Date of Conference: 27-30 September 2021
Date Added to IEEE Xplore: 10 December 2021
ISBN Information:
ISSN Information:
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- IEEE Keywords
- Index Terms
- Resource Allocation ,
- Ultra-reliable Low-latency Communications ,
- Deep Learning ,
- Learning Algorithms ,
- Local Information ,
- Performance Of Algorithm ,
- Autonomous Vehicles ,
- Deep Reinforcement Learning ,
- Reinforcement Learning Algorithm ,
- Scheduling Algorithm ,
- Resource Allocation Problem ,
- Mobile Edge ,
- Deep Reinforcement Learning Algorithm ,
- Relay Nodes ,
- Scheduling Allocation ,
- D2D Communication ,
- Multi-hop Communication ,
- Deep Neural Network ,
- Random Selection ,
- Resource Block ,
- Block Error Rate ,
- User Equipment ,
- Multi-hop Networks ,
- Latency Requirements ,
- Transmission Time Interval ,
- Latency Constraints ,
- Markov Decision Process ,
- Total Delay ,
- Average Latency
- Author Keywords
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Resource Allocation ,
- Ultra-reliable Low-latency Communications ,
- Deep Learning ,
- Learning Algorithms ,
- Local Information ,
- Performance Of Algorithm ,
- Autonomous Vehicles ,
- Deep Reinforcement Learning ,
- Reinforcement Learning Algorithm ,
- Scheduling Algorithm ,
- Resource Allocation Problem ,
- Mobile Edge ,
- Deep Reinforcement Learning Algorithm ,
- Relay Nodes ,
- Scheduling Allocation ,
- D2D Communication ,
- Multi-hop Communication ,
- Deep Neural Network ,
- Random Selection ,
- Resource Block ,
- Block Error Rate ,
- User Equipment ,
- Multi-hop Networks ,
- Latency Requirements ,
- Transmission Time Interval ,
- Latency Constraints ,
- Markov Decision Process ,
- Total Delay ,
- Average Latency
- Author Keywords
