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Decentralized Resource Allocation-Based Multiagent Deep Learning in Vehicular Network
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DOI：10.1109/JSYST.2022.3163235.png)
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Abstract
En 中文
Resource allocation (RA) has a significant impact on vehicular network performance. With high mobility, RA is more challenging, as the number of vehicles in close proximity changes dynamically in the nonstationary environment. In this article, we propose a multiagent double deep Q-networks scheme to stabilize the system and maximize the sum-capacity of the vehicle-to-infrastructure (V2I) links, while satisfying the reliability and delay constraints for vehicle-to-vehicle (V2V) links. To avoid interference caused by unstable V2V links, a transmission mode selection is considered in the scheme design. In addition, we introduce a binarized weight algorithm to accelerate the deep neural network learning process and, therefore, improve the computational complexity of our scheme. Through extensive simulations and complexity analysis, we demonstrate that the proposed scheme yields excellent performance in terms of the sum-rate and probability rate of V2I and V2V communication modes. We also compare the proposed scheme with binarized weights with other algorithms in terms of accuracy evaluation.
Keywords:
Quality of service
Resource management
Training
Uplink
Symbols
Reliability
Vehicle-to-infrastructure
Binarized weights
deep reinforcement learning (DRL)
Markovian decision process (MDP)
multiagent scheme
resource allocation (RA)
vehicular communication
Journal
I
IF:
2.4
Papers:
4.5K
Citations:
387