Graph-Based Resource Sharing in Vehicular Communication

Liang, Le; Xie, Shijie; Li, Geoffrey Ye; Ding, Zhi; Yu, Xingxing

doi:10.1109/twc.2018.2827958

Cited by 164 publications

(173 citation statements)

References 30 publications

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“…Sum ergodic capacity of V2I links has been maximized with V2V reliability guarantee using large-scale fading channel information in [17] or CSI from periodic feedback in [18]. A novel graph-based approach has been further developed in [19] to deal with a generic V2X resource allocation problem.…”

Section: B Related Workmentioning

confidence: 99%

Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning

Liang

Hao

2019

IEEE J. Select. Areas Commun.

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401

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This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. Fast channel variations in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In response, we model the resource sharing as a multi-agent reinforcement learning problem, which is then solved using a fingerprint-based deep Q-network method that is amenable to a distributed implementation. The V2V links, each acting as an agent, collectively interact with the communication environment, receive distinctive observations yet a common reward, and learn to improve spectrum and power allocation through updating Q-networks using the gained experiences. We demonstrate that with a proper reward design and training mechanism, the multiple V2V agents successfully learn to cooperate in a distributed way to simultaneously improve the sum capacity of V2I links and payload delivery rate of V2V links.Index Terms-Vehicular networks, distributed spectrum access, spectrum and power allocation, multi-agent reinforcement learning.

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Section: B Related Workmentioning

confidence: 99%

Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning

Liang

Hao

2019

IEEE J. Select. Areas Commun.

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401

205

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“…Sun et al [8] propose a cluster-based resource allocation algorithm for V2X communications, where the latency and reliability requirements are transformed into outage constraints that can be tractable with slowly varying large-scale channel information. In [9], the ergodic capacity of V2I communications and the reliability of V2V communications are derived based on statistics of fast fading components, and then centralized resource allocation and power control algorithms are proposed to ensure diverse QoS requirements. Besides, impacts of delayed channel state information (CSI) and queue latency are investigated for cellular V2X communications in [10], [11], respectively.…”

Section: A Related Work and Chanllengesmentioning

confidence: 99%

“…denotes achievable data rate of V2V pair k. On the other hand, similar to [9], we denote outage probability as reliability metric. With outage threshold γ o and tolerable outage probability p o , the reliability requirement of V2V pair k ∈ K is expressed as…”

Section: B Qos Requirements Of I-vues and V2v Pairsmentioning

confidence: 99%

Deep-Reinforcement-Learning-Based Mode Selection and Resource Allocation for Cellular V2X Communications

Zhang

Peng

Yan

et al. 2020

IEEE Internet Things J.

206

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Cellular vehicle-to-everything (V2X) communication is crucial to support future diverse vehicular applications. However, for safety-critical applications, unstable vehicle-to-vehicle (V2V) links and high signalling overhead of centralized resource allocation approaches become bottlenecks. In this paper, we investigate a joint optimization problem of transmission mode selection and resource allocation for cellular V2X communications. In particular, the problem is formulated as a Markov decision process, and a deep reinforcement learning (DRL) based decentralized algorithm is proposed to maximize the sum capacity of vehicle-to-infrastructure users while meeting the latency and reliability requirements of V2V pairs. Moreover, considering training limitation of local DRL models, a two-timescale federated DRL algorithm is developed to help obtain robust model. Wherein, the graph theory based vehicle clustering algorithm is executed on a large timescale and in turn the federated learning algorithm is conducted on a small timescale. Simulation results show that the proposed DRL-based algorithm outperforms other decentralized baselines, and validate the superiority of the twotimescale federated DRL algorithm for newly activated V2V pairs.

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“…For the centralized schemes, decisions are usually made centrally at a given node, such as the head in a cluster or the base station (BS) in a given coverage area. Novel graph-based resource allocation schemes have been proposed in [8] and [9] to maximize the vehicle-to-infrastructure (V2I) capacity, exploiting the slow fading statistics of channel state information (CSI). In [10], an interference hyper-graph based resource allocation scheme has been developed in the non-orthogonal multiple access (NOMA)-integrated V2X scenario with the distance, channel gain, and interference known in each vehicle-to-vehicle (V2V) and V2I group.…”

mentioning

confidence: 99%

Learn to Compress CSI and Allocate Resources in Vehicular Networks

Wang

Hao

Liang

et al. 2020

IEEE Trans. Commun.

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Resource allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. In this paper, we develop a hybrid architecture consisting of centralized decision making and distributed resource sharing (the C-Decision scheme) to maximize the long-term sum rate of all vehicles. To reduce the network signaling overhead, each vehicle uses a deep neural network to compress its observed information that is thereafter fed back to the centralized decision making unit. The centralized decision unit employs a deep Q-network to allocate resources and then sends the decision results to all vehicles. We further adopt a quantization layer for each vehicle that learns to quantize the continuous feedback. In addition, we devise a mechanism to balance the transmission of vehicle-to-vehicle (V2V) links and vehicle-to-infrastructure (V2I) links. To further facilitate distributed spectrum sharing, we also propose a distributed decision making and spectrum sharing architecture (the D-Decision scheme) for each V2V link. Through extensive simulation results, we demonstrate that the proposed C-Decision and D-Decision schemes can both achieve near-optimal performance and are robust to feedback interval variations, input noise, and feedback noise. Index TermsVehicular networks, deep reinforcement learning, spectrum sharing, binary feedback.

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Graph-Based Resource Sharing in Vehicular Communication

Cited by 164 publications

References 30 publications

Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning

Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning

Deep-Reinforcement-Learning-Based Mode Selection and Resource Allocation for Cellular V2X Communications

Learn to Compress CSI and Allocate Resources in Vehicular Networks

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