Learning to Construct Nested Polar Codes: An Attention-Based Set-to-Element Model

Li, Yang; Chen, Zhitang; Liu, Guochen; Wu, Yik-Chung; Wong, Kai-Kit

doi:10.1109/lcomm.2021.3114118

Cited by 11 publications

(6 citation statements)

References 15 publications

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“…Recent studies have shown that incorporating a permutation equivariance property into the neural network architecture can reduce the parameter space, avoid a large number of unnecessary permuted training samples, and most importantly make the neural network generalizable to different problem scales [60][61][62][63]. In particular, graph neural networks (GNNs) [60,61] and attention-based transformers [62,63] have been shown to possess the permutation equivariance property and have demonstrated superior performance, scalability, and generalization ability in wireless resource allocation problems. For instance, in the beamforming problem, a GNN trained with data generated in a setting of 50 users was shown to achieve near optimal testing performance under a much larger setting of 1000 users [60].…”

Section: Scalability Of Ai-based Methodsmentioning

confidence: 99%

Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods

Wang

Lin

et al. 2022

Network

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Reconfigurable intelligent surfaces (RISs) offer the potential to customize the radio propagation environment for wireless networks. To fully exploit the advantages of RISs in wireless systems, the phases of the reflecting elements must be jointly designed with conventional communication resources, such as beamformers, the transmit power, and computation time. However, due to the unique constraints on the phase shifts and the massive numbers of reflecting units and users in large-scale networks, the resulting optimization problems are challenging to solve. This paper provides a review of the current optimization methods and artificial-intelligence-based methods for handling the constraints imposed by RISs and compares them in terms of the solution quality and computational complexity. Future challenges in phase-shift optimization involving RISs are also described, and potential solutions are discussed.

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Section: Scalability Of Ai-based Methodsmentioning

confidence: 99%

Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods

Wang

Lin

et al. 2022

Network

Self Cite

View full text Add to dashboard Cite

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“…Recent studies have shown that incorporating permutation equivariance property into the neural network architecture can reduce the parameter space, avoid a large number of unnecessary permuted training samples, and most importantly make the neural network generalizable to different problem scales [54]- [57]. In particular, graph neural networks (GNNs) [54], [55] and attention-based transformers [56], [57] have been shown to possess the permutation equivariance property and have demonstrated superior performance, scalability, and generalization ability in a few wireless resource allocation problems. For instance, in the beamforming problem, a GNN trained with data generated in a setting of 50 users was shown to achieve near optimal testing performance under a much larger setting of 1000 users [54].…”

Section: Scalability Of Ai-based Methodsmentioning

confidence: 99%

Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods

Li¹,

Wang²,

Li³

et al. 2022

Preprint

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Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks. To fully exploit the advantages of RISs in wireless systems, the phases of the reflecting elements must be jointly designed with conventional communication resources, such as beamformers, transmit power, and computation time. However, due to the unique constraints on the phase shift, and massive numbers of reflecting units and users in large-scale networks, the resulting optimization problems are challenging to solve. This paper provides a review of current optimization methods and artificial intelligence-based methods for handling the constraints imposed by RIS and compares them in terms of solution quality and computational complexity. Future challenges in phase shift optimization involving RISs are also described and potential solutions are discussed. I. INTRODUCTIONIt is well-known that line-of-sight (LoS) propagation is a desirable but rarely occurring scenario for wireless communications. Traditional techniques for addressing this issue is to deploy more

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“…Huang et al [185] Construction of nested polar codes via advantage actor-critic algorithms. Li et al [189] Stochastic policy optimization by a customized network. Ankireddy et al [190] Nested polar code construction based on sequence modeling and Transformer.…”

Section: Nested Polar Codesmentioning

confidence: 99%

“…Meanwhile, the authors in [189] first transformed the problem of nested polar code construction into a stochastic policy optimization problem for sequential decision, and then represented the policy by a customized neural network. Furthermore, the authors proposed a gradient-based algorithm to minimize the average loss of the policy.…”

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confidence: 99%

Security Gap Improvement of BICM Systems Through Bit-Labeling Optimization for the Gaussian Wiretap Channel

2022

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This paper provides a comprehensive survey on recent advances in deep learning (DL) techniques for the channel coding problems. Inspired by the recent successes of DL in a variety of research domains, its applications to the physical layer technologies have been extensively studied in recent years, and are expected to be a potential breakthrough in supporting the emerging use cases of the next generation wireless communication systems such as 6G. In this paper, we focus exclusively on the channel coding problems and review existing approaches that incorporate advanced DL techniques into code design and channel decoding. After briefly introducing the background of recent DL techniques, we categorize and summarize a variety of approaches, including model-free and mode-based DL, for the design and decoding of modern error-correcting codes, such as low-density parity check (LDPC) codes and polar codes, to highlight their potential advantages and challenges. Finally, the paper concludes with a discussion of open issues and future research directions in channel coding.

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Learning to Construct Nested Polar Codes: An Attention-Based Set-to-Element Model

Cited by 11 publications

References 15 publications

Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods

Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods

Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods

Security Gap Improvement of BICM Systems Through Bit-Labeling Optimization for the Gaussian Wiretap Channel

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