Adaptive Radio Unit Selection and Load Balancing in the Downlink of Fog Radio Access Network

Chen, Di; Kuehn, Volker

doi:10.1109/glocom.2016.7841568

Cited by 15 publications

(14 citation statements)

References 12 publications

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“…The author in [17] goes one step further by jointly optimizing RRH selection, data assignment, and multicast beamforming. The data assignment refers to whether the content requested by a user group is delivered to a RRH via backhaul, which is not handled in [7] and [16]. To solve the NP-hard network power consumption minimization problem, which consists of RRH power consumption and backhaul power consumption, a generalized layered group sparse beamforming modeling framework is proposed.…”

Section: A Related Work and Challengesmentioning

confidence: 99%

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Deep Reinforcement Learning-Based Mode Selection and Resource Management for Green Fog Radio Access Networks

Sun

Peng

Mao

2019

IEEE Internet Things J.

199

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Fog radio access networks (F-RANs) are seen as potential architectures to support services of internet of things by leveraging edge caching and edge computing. However, current works studying resource management in F-RANs mainly consider a static system with only one communication mode. Given network dynamics, resource diversity, and the coupling of resource management with mode selection, resource management in F-RANs becomes very challenging. Motivated by the recent development of artificial intelligence, a deep reinforcement learning (DRL) based joint mode selection and resource management approach is proposed. Each user equipment (UE) can operate either in cloud RAN (C-RAN) mode or in device-to-device mode, and the resource managed includes both radio resource and computing resource. The core idea is that the network controller makes intelligent decisions on UE communication modes and processors' on-off states with precoding for UEs in C-RAN mode optimized subsequently, aiming at minimizing long-term system power consumption under the dynamics of edge cache states. By simulations, the impacts of several parameters, such as learning rate and edge caching service capability, on system performance are demonstrated, and meanwhile the proposal is compared with other different schemes to show its effectiveness. Moreover, transfer learning is integrated with DRL to accelerate learning process.Index Terms-Fog radio access networks, communication mode selection, resource management, deep reinforcement learning, artificial intelligence.conducted on F-RANs, in terms of performance analysis [6], radio resource allocation [7], the joint design of cloud and edge processing [8], the impact of cache size [9], and so on.Although significant progress has been achieved, resource management in F-RANs still needs further investigation. Compared with resource management in traditional wireless networks, communication mode selection should be addressed as well due to the coupling with resource management, and meanwhile the dynamics of edge caching complicate the network environment, which both lead to a more challenging problem. Specifically, from the perspective of optimization, communication mode selection problem is usually NPhard [10]. To solve the problem, classical algorithms like branch and bound and particle swarm can be adopted. Nevertheless, considering the network dynamics, communication modes of UEs need to be frequently updated, which makes algorithms with high complexity less applicable.

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Section: A Related Work and Challengesmentioning

confidence: 99%

“…with P f ront m a constant representing the energy consumption for delivering the processed signal of UE m to its associated RRHs via fronthaul [16]. Then, the energy consumption of the whole system is given by…”

Section: Energy Consumption Modelmentioning

confidence: 99%

Deep Reinforcement Learning-Based Mode Selection and Resource Management for Green Fog Radio Access Networks

Sun

Peng

Mao

2019

IEEE Internet Things J.

199

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“…However, in such systems, the energy efficiency aspect has always been a matter of serious concern due to the addition of extra smart components in the system. In [67], the authors have proposed a novel scheme by designing a green network in which an efficient algorithm is incorporated to optimize the selection of RU. Furthermore, the algorithm also jointly optimizes the formation of clustering and beamforming while maintaining the QoS and balancing the load of each of active backhaul as a measure of its capacity.…”

Section: Protocols/algorithmsmentioning

confidence: 99%

“…Different researchers have worked on the F-RAN design considering various aspects. For a green system, as discussed by Chen et al [67,96], minimization of energy consumption is done by considering an F-RAN that is cache-enabled for the selection of Remote Radio Head (RRHs). For balancing front-haul traffic, Park et al [97] have discussed a scheme to deliver data from Base Band Unit (BBU) and RRHs.…”

Section: Challenges With Solutionsmentioning

confidence: 99%

Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges

Basir¹,

Qaisar²,

Ali

et al. 2019

Sensors

110

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Industry is going through a transformation phase, enabling automation and data exchange in manufacturing technologies and processes, and this transformation is called Industry 4.0. Industrial Internet-of-Things (IIoT) applications require real-time processing, near-by storage, ultra-low latency, reliability and high data rate, all of which can be satisfied by fog computing architecture. With smart devices expected to grow exponentially, the need for an optimized fog computing architecture and protocols is crucial. Therein, efficient, intelligent and decentralized solutions are required to ensure real-time connectivity, reliability and green communication. In this paper, we provide a comprehensive review of methods and techniques in fog computing. Our focus is on fog infrastructure and protocols in the context of IIoT applications. This article has two main research areas: In the first half, we discuss the history of industrial revolution, application areas of IIoT followed by key enabling technologies that act as building blocks for industrial transformation. In the second half, we focus on fog computing, providing solutions to critical challenges and as an enabler for IIoT application domains. Finally, open research challenges are discussed to enlighten fog computing aspects in different fields and technologies.

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“…Equipping F-RAN with distributed storing capability can greatly reduce the delay perceived while preserving some benefits of C-RAN. In another F-RAN related work, [17] proposes a green-motivated joint optimization algorithm of RRU selection and load-balancing oriented clustering by taking the operational power and BH capacities into account.…”

Section: State-of-the-art Advances On Joint Ran/backhaul Optimisamentioning

confidence: 99%

On the joint optimisation of radio access and backhaul networks

Jaber

Imran

Tafazolli

et al. 2017

2017 International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT)

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Abstract-The distinction between the radio access network and the backhaul network is disappearing with emerging advanced features. The backhaul is invading the radio assess spectrum to enable wireless in-band connections and the radio network is overlapping with the transport network through cloud processing and distributed functions. As such, it is of pivotal importance to approach the optimisation of the radio and transport networks jointly to avoid suboptimum operation and problem shifting. In this work, we present a critical analysis of the motivations, state-of-the-art advances, and potential benefits and challenges of joint RAN/backhaul optimisation.

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Adaptive Radio Unit Selection and Load Balancing in the Downlink of Fog Radio Access Network

Cited by 15 publications

References 12 publications

Deep Reinforcement Learning-Based Mode Selection and Resource Management for Green Fog Radio Access Networks

Deep Reinforcement Learning-Based Mode Selection and Resource Management for Green Fog Radio Access Networks

Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges

On the joint optimisation of radio access and backhaul networks

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