Distributed Scheduling Using Graph Neural Networks

Zhao, Zhongyuan; Verma, Gunjan; Rao, Chirag; Swami, Ananthram; Segarra, Santiago

doi:10.1109/icassp39728.2021.9414098

Cited by 47 publications

(16 citation statements)

References 46 publications

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“…Similarly, [27] makes use of location information and tackles link scheduling problem using a deep learning based graph embedding process. In [28], the link scheduling problem is solved using a graph convolutional network based solution with user interference relationship as the primary input. However, these existing works on scheduling are for device-to-device networks without the RIS.…”

Section: A Related Workmentioning

confidence: 99%

Learning Based User Scheduling in Reconfigurable Intelligent Surface Assisted Multiuser Downlink

Zhang,

Jiang,

2022

Preprint

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Reconfigurable intelligent surface (RIS) is capable of intelligently manipulating the phases of the incident electromagnetic wave to improve the wireless propagation environment between the base-station (BS) and the users. This paper addresses the joint user scheduling, RIS configuration, and BS beamforming problem in an RIS-assisted downlink network with limited pilot overhead. We show that graph neural networks (GNN) with permutation invariant and equivariant properties can be used to appropriately schedule users and to design RIS configurations to achieve high overall throughput while accounting for fairness among the users. As compared to the conventional methodology of first estimating the channels then optimizing the user schedule, RIS configuration and the beamformers, this paper shows that an optimized user schedule can be obtained directly from a very short set of pilots using a GNN, then the RIS configuration can be optimized using a second GNN, and finally the BS beamformers can be designed based on the overall effective channel. Numerical results show that the proposed approach can utilize the received pilots more efficiently than the conventional channel estimation based approach, and can generalize to systems with an arbitrary number of users.

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Section: A Related Workmentioning

confidence: 99%

Learning Based User Scheduling in Reconfigurable Intelligent Surface Assisted Multiuser Downlink

Zhang,

Jiang,

2022

Preprint

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“…Naderializadeh [21] investigates the challenge of bilateral link scheduling, where the power control policy is learnt using GNN as the basis to transform channel matrix to graph embeddings series by utilizing interference graph. A GNN-based approach was demonstrated in [22] to enhance the efficiency of greedy schedulers such as Longest-Queue-First. This result is compared, however, on interference model known as the conflict graph model, that only records binary correlations between edges.…”

Section: A Related Workmentioning

confidence: 99%

Graph Based Deep Learning for Spatial Reuse Optimization in Dense WLAN Deployments

Azeez¹,

Henna²

2022

Preprint

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Dataset description: This is the dataset generated for to access each APs signal strength. In particular, the to output a prediction function of possible interference between two interacting APs. The features included in the dataset are: Id - Identifier of the APs X – position on the AP in each office in the X- direction (from the bottom left) Y - position on the AP in each office in the Y- direction (from the bottom left) Distance – distance between the transmitting AP and receiver Obstacles – Number of possible walls to the signal propagation Path Loss - Path loss between the transmitting APs RSSI – signal strength obtained from the AP AP1,AP2 – Interaction between Access points

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“…A distributed schedule transmission scheme for wireless networks is proposed to overcome the difficulty encountered in solving the maximum weighted independent set (MWIS) problem [61]. The authors first proposed a GCN-based distributed MWIS solver for link scheduling by combining the learning capabilities of GCNs and the efficiency of greedy MWIS solvers.…”

Section: B Link Schedulingmentioning

confidence: 99%

An Overview on the Application of Graph Neural Networks in Wireless Networks

He¹,

Xiong²,

Ou³

et al. 2021

Preprint

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With the rapid enhancement of computer computing power, deep learning methods, e.g., convolution neural networks, recurrent neural networks, etc., have been applied in wireless network widely and achieved impressive performance. In recent years, in order to mine the topology information of graphstructured data in wireless network as well as contextual information, graph neural networks have been introduced and have achieved the state-of-the-art performance of a series of wireless network problems. In this review, we first simply introduce the progress of several classical paradigms, such as graph convolutional neural networks, graph attention networks, graph autoencoder, graph recurrent networks, graph reinforcement learning and spatial-temporal graph neural networks, of graph neural networks comprehensively. Then, several applications of graph neural networks in wireless networks such as power control, link scheduling, channel control, wireless traffic prediction, vehicular communication, point cloud, etc., are discussed in detail. Finally, some research trends about the applications of graph neural networks in wireless networks are discussed.

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Distributed Scheduling Using Graph Neural Networks

Cited by 47 publications

References 46 publications

Learning Based User Scheduling in Reconfigurable Intelligent Surface Assisted Multiuser Downlink

Learning Based User Scheduling in Reconfigurable Intelligent Surface Assisted Multiuser Downlink

Graph Based Deep Learning for Spatial Reuse Optimization in Dense WLAN Deployments

An Overview on the Application of Graph Neural Networks in Wireless Networks

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