Machine Learning for Low-Latency Communications
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Product details:
- Publisher Elsevier Science
- Date of Publication 15 October 2024
- ISBN 9780443220739
- Binding Paperback
- No. of pages216 pages
- Size 234x190 mm
- Weight 470 g
- Language English 604
Categories
Long 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.
Table of Contents:
Part 1: Introduction and Overview
1. Introduction and overview
Part 2: Learning to Estimate for Access Latency Reduction
2. Learning to estimate via group-sparse based algorithm unrolling
3. Learning to estimate via proximal gradient-based algorithm unrolling
4. Learning to detect via multiarmed bandit (MAB)
Part 3: Learning to Compress for Transmission Latency Reduction
5. Learning to compress via information bottleneck
6. Learning to compress via robust information bottleneck with digital modulation
7. Learning to compress for multi-device cooperative edge inference
Part 4: Learning to Optimize for Processing Latency Reduction
8. Learning to optimize via graph neural networks
9. Learning to optimize via knowledge guidance
10. Learning to optimize via decentralized multi-agent reinforcement learning
Part 5: Conclusions
11. Conclusions and Future Research Directions
