Deep reinforcement learning-based scheduling for roadside communication networks

Atallah, Ribal; Assi, Chadi; Khabbaz, Maurice

doi:10.23919/wiopt.2017.7959912

Cited by 62 publications

(42 citation statements)

References 13 publications

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“…Here, the throughput in the reward function is the received instantaneous throughput, hence the UE with the best channel condition tends to be chosen. The f airness is calculated through Equation (5). α and β are weighting factors for throughput and fairness, respectively.…”

Section: Learning Methodologymentioning

confidence: 99%

Deep Reinforcement Learning for Scheduling in Cellular Networks

Wang

Huangfu

et al. 2019

2019 11th International Conference on Wireless Communications and Signal Processing (WCSP)

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Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in both industry and academia. A common solution is to replace partial or even all modules in the conventional systems, which is often lack of efficiency and robustness due to their ignoring of expert knowledge. In this paper, we take deep reinforcement learning (DRL) based scheduling as an example to investigate how expert knowledge can help with AI module in cellular networks. A simulation platform, which has considered link adaption, feedback and other practical mechanisms, is developed to facilitate the investigation. Besides the traditional way, which is learning directly from the environment, for training DRL agent, we propose two novel methods, i.e., learning from a dual AI module and learning from the expert solution. The results show that, for the considering scheduling problem, DRL training procedure can be improved on both performance and convergence speed by involving the expert knowledge. Hence, instead of replacing conventional scheduling module in the system, adding a newly introduced AI module, which is capable to interact with the conventional module and provide more flexibility, is a more feasible solution.

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Section: Learning Methodologymentioning

confidence: 99%

Deep Reinforcement Learning for Scheduling in Cellular Networks

Wang

Huangfu

et al. 2019

2019 11th International Conference on Wireless Communications and Signal Processing (WCSP)

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“…Analysis [17], [112], [235]- [266] [73], [97], [187], [267]- [291] Mobility Analysis [227], [292]- [310] User Localization [272], [273], [311]- [315] [111], [316]- [334] Wireless Sensor Networks [335]- [346], [346]- [356] Network Control [186], [293], [357]- [368] [234], [368]- [403] Network Security [185], [345], [404]- [419] [223], [420]- [429], [429]- [436] Signal Processing [378], [380], [437]- [444] [322], [445]- [458] Emerging Applications For each domain, we summarize work broadly in tabular form, providing readers with a general picture of individual topics. Most important works in each domain are discussed in more details in text.…”

Section: App-level Mobile Datamentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,328

838

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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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“…In [25], the downlink scheduling has been optimized for battery-charged roadside units in vehicular networks using RL methods to maximize the number of fulfilled service requests during a discharge period, where Q learning is employed to obtain the highest long-term returns. The framework has been further extended in [26], where a deep RL based scheme has been proposed to learn a scheduling policy with high dimensional continuous inputs using end-to-end learning. A distributed user association approach based on RL has been developed in [27] for vehicular networks with heterogeneous BSs.…”

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.

371

205

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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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Deep reinforcement learning-based scheduling for roadside communication networks

Cited by 62 publications

References 13 publications

Deep Reinforcement Learning for Scheduling in Cellular Networks

Deep Reinforcement Learning for Scheduling in Cellular Networks

Deep Learning in Mobile and Wireless Networking: A Survey

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

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