Learning Based User Scheduling in Reconfigurable Intelligent Surface Assisted Multiuser Downlink

Zhang, Zhongze; Jiang, Tao; Yu, Wei

doi:10.1109/jstsp.2022.3178213

Cited by 19 publications

(12 citation statements)

References 35 publications

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“…14 includes K +1 nodes, in which one node represents the RIS and the rest are K UEs. Given this scheme, GNN is applied to user scheduling and RIS configurations in [167] and [168]. In particular, the GNN is trained in an unsupervised manner, and the inputs are user weights and pilot sub-frames of the scheduled users, and the outputs are RIS configurations and beamformers.…”

Section: E Graph Learningmentioning

confidence: 99%

“…In [167]- [169], a useful feature of GNN is used to reduce the interference between users. Specifically, when updating one node in the GNN, all the neighbour nodes will be included in the updating function, which means GNN can better capture the mutual interference between users.…”

Section: E Graph Learningmentioning

confidence: 99%

“…GNN is used for user schedule and RIS configurations in [167]- [169] Hierarchical learning decouples the task into multiple sub-tasks and goals, increasing the task exploration efficiency.…”

Section: ) Dynamic and Generative Changing Graph; 2)interpretability ...mentioning

confidence: 99%

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Knowledge Transfer and Reuse: A Case Study of Ai-Enabled Resource Management in RAN Slicing

Zhang¹,

Erol‐Kantarci²,

Poor³

2023

IEEE Wireless Commun.

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Reconfigurable intelligent surfaces (RISs) have received considerable attention as a key enabler for envisioned 6G networks, for the purpose of improving the network capacity, coverage, efficiency, and security with low energy consumption and low hardware cost. However, integrating RISs into the existing infrastructure greatly increases the network management complexity, especially for controlling a significant number of RIS elements. To unleash the full potential of RISs, efficient optimization approaches are of great importance. This work provides a comprehensive survey on optimization techniques for RISaided wireless communications, including model-based, heuristic, and machine learning (ML) algorithms. In particular, we first summarize the problem formulations in the literature with diverse objectives and constraints, e.g., sum-rate maximization, power minimization, and imperfect channel state information constraints. Then, we introduce model-based algorithms that have been used in the literature, such as alternating optimization, the majorization-minimization method, and successive convex approximation. Next, heuristic optimization is discussed, which applies heuristic rules for obtaining low-complexity solutions. Moreover, we present state-of-the-art ML algorithms and applications towards RISs, i.e., supervised and unsupervised learning, reinforcement learning, federated learning, graph learning, transfer learning, and hierarchical learning-based approaches. Modelbased, heuristic, and ML approaches are compared in terms of stability, robustness, optimality and so on, providing a systematic understanding of these techniques. Finally, we highlight RISaided applications towards 6G networks and identify future challenges.

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Section: E Graph Learningmentioning

confidence: 99%

Section: E Graph Learningmentioning

confidence: 99%

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Knowledge Transfer and Reuse: A Case Study of Ai-Enabled Resource Management in RAN Slicing

Zhang¹,

Erol‐Kantarci²,

Poor³

2023

IEEE Wireless Commun.

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“…Deep learning has been used for RIS channel estimation [97,98] widely. The learning ability of neural networks can be used to estimate the full channel from the partial pilot channel information [99,100], it can also be combined with image processing techniques to imagine the RIS channel information as two images. One corresponding to the real part channel information and one corresponding to the imaginary part channel information.…”

Section: Compressed Sensing-based Algorithms For Ris Channel Estimationmentioning

confidence: 99%

Review on Channel Estimation of Reconfigurable Intelligent Surface Assisted Wireless Communication System

Yun¹,

Wang²,

Zhou³

et al. 2023

Preprint

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With the dramatic increase in the number of mobile users and wireless devices accessing the network, the performance of the fifth generation (5G) wireless communication systems has been severely challenged. Reconfigurable intelligent surfaces (RIS), one of the potential technologies for the sixth generation (6G), has received a lot of attention. Since it is easier to deploy, consumes less power, and is inexpensive. RIS is an electromagnetic metamaterial that serves to reconfigure the wireless environment by adjusting the phase, amplitude and frequency of the wireless signal. To maximize channel transmission efficiency and improve the reliability of communication systems, the acquisition of channel state information (CSI) is essential. Therefore, an effective channel estimation method guarantees the achievement of excellent RIS performance. This paper conducts a comprehensive investigation of the existing channel estimation methods of RIS, analysis and comparison of channel model building and CSI acquisition schemes in different frequency bands, in addition to a comprehensive description of generic channel estimation methods, with a focus on the application of deep learning. Finally, we conclude the paper and provide an outlook in the future development of RIS channel estimation.

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“…The field of machine learning for communication system design has exploded in recent years [2]- [5]. We mention some of the references here, e.g., in source and channel coding [6]- [8], waveform design [9], signal detection [10]- [12], resource allocation [13]- [18] and channel estimation [19], [20], etc. This article does not attempt to do justice in surveying the entire literature and the recent progress on this topic.…”

Section: Introductionmentioning

confidence: 99%

Role of Deep Learning in Wireless Communications

Sohrabi

Jiang

2022

IEEE BITS Inform. Theory Mag.

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Traditional communication system design has always been based on the paradigm of first establishing a mathematical model of the communication channel, then designing and optimizing the system according to the model. The advent of modern machine learning techniques, specifically deep neural networks, has opened up opportunities for data-driven system design and optimization. This article draws examples from the optimization of reconfigurable intelligent surface, distributed channel estimation and feedback for multiuser beamforming, and active sensing for millimeter wave (mmWave) initial alignment to illustrate that a data-driven design that bypasses explicit channel modelling can often discover excellent solutions to communication system design and optimization problems that are otherwise computationally difficult to solve. We show that by performing an end-to-end training of a deep neural network using a large number of channel samples, a machine learning based approach can potentially provide significant system-level improvements as compared to the traditional model-based approach for solving optimization problems. The key to the successful applications of machine learning techniques is in choosing the appropriate neural network architecture to match the underlying problem structure.

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Learning Based User Scheduling in Reconfigurable Intelligent Surface Assisted Multiuser Downlink

Cited by 19 publications

References 35 publications

Knowledge Transfer and Reuse: A Case Study of Ai-Enabled Resource Management in RAN Slicing

Knowledge Transfer and Reuse: A Case Study of Ai-Enabled Resource Management in RAN Slicing

Review on Channel Estimation of Reconfigurable Intelligent Surface Assisted Wireless Communication System

Role of Deep Learning in Wireless Communications

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