Spatial Deep Learning for Wireless Scheduling

Cui, Wei; Shen, Kaiming; Yu, Wei

doi:10.1109/jsac.2019.2904352

Cited by 226 publications

(179 citation statements)

References 22 publications

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“…In "Spatial deep learning for wireless scheduling", Cui et al focus on scheduling interfering links in a wireless communications network using deep learning-based framework and techniques [20]. The proposed spatial deep learning network gives the near-optimal performance for sum-rate maximization and is capable of generalizing to larger deployment areas and to deployments of different link densities.…”

Section: Prescriptive Ai In Communications Networkmentioning

confidence: 99%

Special Issue on Artificial Intelligence and Machine Learning for Networking and Communications

Chemouil

Hui

Kellerer

et al. 2019

IEEE J. Select. Areas Commun.

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I. INTRODUCTION R ESEARCH in large-scale networking systems has been shaped and will continue to be guided by specific characteristics of applications and the underlying platforms and infrastructures. On the one hand, applications are growing at an accelerated pace, which is fundamentally unpredictable in both breadth and depth. On the other hand, the underlying networking has been the focus of a huge transformation enabled by new models resulting from virtualization and cloud computing. This has led to a number of novel architectures supported by emerging technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and more recently, edge cloud and fog networking, or network slicing [1], [2]. This evolution towards enhanced design opportunities along with increasing complexity in networking and its applications has fueled the need for improved network automation in agile infrastructures. At the same time, their complexity has dramatically increased. The networking dynamics have had the effect of making it even more important and challenging to design scalable network measurement and analysis techniques and associated tools. Critical applications such as resource allocation, network monitoring, security enforcement, or dynamic network management require real-time mechanisms for online analysis as well as efficient techniques for offline deep analysis of massive historical data.

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Section: Prescriptive Ai In Communications Networkmentioning

confidence: 99%

Special Issue on Artificial Intelligence and Machine Learning for Networking and Communications

Chemouil

Hui

Kellerer

et al. 2019

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

show abstract

“…The target of their algorithm was to maximize the throughput of the system by selecting the proper level of power for the resource blocks of the cellular user and D2D pairs. In [34], the authors adopted deep learning to solve the objective function of maximizing the weighed sum rate over N D2D users and demonstrated that link scheduling does not necessarily require the exact channel estimates. In [35], the author adopted a method based on reinforcement learning to solve the resource scheduling for vehicle-to-vehicle (V2V) communications based on D2D.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for Maximum Activity Links in D2D Communications

Zhang

Palmieri

et al. 2019

Sensors

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Mobile cellular communications are experiencing an exponential growth in traffic load on Long Term Evolution (LTE) eNode B (eNB) components. Such load can be significantly contained by directly sharing content among nearby users through device-to-device (D2D) communications, so that repeated downloads of the same data can be avoided as much as possible. Accordingly, for the purpose of improving the efficiency of content sharing and decreasing the load on the eNB, it is important to maximize the number of simultaneous D2D transmissions. Specially, maximizing the number of D2D links can not only improve spectrum and energy efficiency but can also reduce transmission delay. However, enabling maximum D2D links in a cellular network poses two major challenges. First, the interference between the D2D and cellular communications could critically affect their performance. Second, the minimum quality of service (QoS) requirement of cellular and D2D communication must be guaranteed. Therefore, a selection of active links is critical to gain the maximum number of D2D links. This can be formulated as a classical integer linear programming problem (link scheduling) that is known to be NP-hard. This paper proposes to obtain a set of network features via deep learning for solving this challenging problem. The idea is to optimize the D2D link schedule problem with a deep neural network (DNN). This makes a significant time reduction for delay-sensitive operations, since the computational overhead is mainly spent in the training process of the model. The simulation performed on a randomly generated link schedule problem showed that our algorithm is capable of finding satisfactory D2D link scheduling solutions by reducing computation time up to 90% without significantly affecting their accuracy.

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“…To make full use of topology information for effective learning, related work has been studied in depth. In [ 24 ], the authors explore the use of spatial convolution for scheduling under the sum-rate maximization criterion, while utilizing only location information. The work in [ 25 ] proposes a novel graph embedding-based method for link scheduling in D2D networks and develops a

-nearest neighbor graph representation method to reduce the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

A Graph Convolutional Network-Based Deep Reinforcement Learning Approach for Resource Allocation in a Cognitive Radio Network

Zhao

Qin

Song

et al. 2020

Sensors

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Cognitive radio (CR) is a critical technique to solve the conflict between the explosive growth of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can effectively achieve spectrum sharing and co-channel interference (CCI) mitigation. In this paper, we propose a joint channel selection and power adaptation scheme for the underlay cognitive radio network (CRN), maximizing the data rate of all secondary users (SUs) while guaranteeing the quality of service (QoS) of primary users (PUs). To exploit the underlying topology of CRNs, we model the communication network as dynamic graphs, and the random walk is used to imitate the users’ movements. Considering the lack of accurate channel state information (CSI), we use the user distance distribution contained in the graph to estimate CSI. Moreover, the graph convolutional network (GCN) is employed to extract the crucial interference features. Further, an end-to-end learning model is designed to implement the following resource allocation task to avoid the split with mismatched features and tasks. Finally, the deep reinforcement learning (DRL) framework is adopted for model learning, to explore the optimal resource allocation strategy. The simulation results verify the feasibility and convergence of the proposed scheme, and prove that its performance is significantly improved.

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Spatial Deep Learning for Wireless Scheduling

Cited by 226 publications

References 22 publications

Special Issue on Artificial Intelligence and Machine Learning for Networking and Communications

Special Issue on Artificial Intelligence and Machine Learning for Networking and Communications

A Deep Learning Approach for Maximum Activity Links in D2D Communications

A Graph Convolutional Network-Based Deep Reinforcement Learning Approach for Resource Allocation in a Cognitive Radio Network

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