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Machine Learning for Low-Latency Communications

2026-06-25 分类：宗教与灵性 评论(0)

Machine Learning for Low-Latency Communications book cover

Machine Learning for Low-Latency Communications

Author(s): Zhou (Author)

  • Publisher: Academic Press
  • Publication Date: 1 Nov. 2024
  • Language: English
  • Print length: 216 pages
  • ISBN-10: 0443220735
  • ISBN-13: 9780443220739

Book Description

Machine Learning for Low-Latency Communications presents the principles and practice of various deep learning methodologies for mitigating three critical latency components: access latency, transmission latency, and processing latency. In particular, the book develops learning to estimate methods via algorithm unrolling and multiarmed bandit for reducing access latency by enlarging the number of concurrent transmissions with the same pilot length. Task-oriented learning to compress methods based on information bottleneck are given to reduce the transmission latency via avoiding unnecessary data transmission.

Lastly, three learning to optimize methods for processing latency reduction are given which leverage graph neural networks, multi-agent reinforcement learning, and domain knowledge. Low-latency communications attracts considerable attention from both academia and industry, given its potential to support various emerging applications such as industry automation, autonomous vehicles, augmented reality and telesurgery. Despite the great promise, achieving low-latency communications is critically challenging. Supporting massive connectivity incurs long access latency, while transmitting high-volume data leads to substantial transmission latency.

  • Presents the challenges and opportunities of leveraging data and model-driven machine learning methodologies for achieving low-latency communications
  • Explains the principles and practices of modern machine learning algorithms (e.g., algorithm unrolling, multiarmed bandit, graph neural network, and multi-agent reinforcement learning) for achieving low-latency communications
  • Gives design, modeling, and optimization methods for low-latency communications that apply appropriate learning methods to solve longstanding problems
  • Provides full details of the simulation setup and benchmarking algorithms, with downloadable code
  • Outlines future research challenges and directions

Editorial Reviews

Review

Covers master design, modeling, and optimizing methods for low-latency communications that apply appropriate deep learning methods to solve longstanding problems

From the Back Cover

Low-latency communications attracts considerable attention from both academia and industry, given its potential to support various emerging applications such as industry automation, autonomous vehicles, augmented reality and telesurgery. Despite the great promise, achieving low-latency communications is critically challenging. Supporting massive connectivity incurs long access latency, while transmitting high-volume data leads to substantial transmission latency. In addition, applying advanced signal processing techniques demands high processing latency. As these challenges cannot be effectively tackled by traditional design methods, there is a need for the wide adoption of powerful deep learning techniques that have the potential to achieve automatic structure extraction, thereby effectively supporting low-latency communications.

Machine Learning for Low-Latency Communications presents the principles and practice of various deep learning methodologies for mitigating three critical latency components: access latency, transmission latency, and processing latency. In particular, the book develops learning to estimate methods, via algorithm unrolling and multiarmed bandit, for reducing access latency by enlarging the number of concurrent transmissions with the same pilot length. Task-oriented learning to compress methods based on information bottleneck are given to reduce the transmission latency via avoiding unnecessary data transmission. Lastly, three learning to optimize methods for processing latency reduction are given which leverage graph neural networks, multi-agent reinforcement learning, and domain knowledge.

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