Machine Learning for Low-Latency Communications 1st Edition

Machine Learning for Low-Latency Communications (1st Edition) by Yong Zhou, Yinan Zou, Youlong Wu, Yuanming Shi, and Jun Zhang delivers a clear, authoritative roadmap for engineers and researchers pushing the boundaries of real-time wireless systems. This practical yet rigorous guide explains how modern machine learning techniques can be harnessed to meet stringent latency requirements in 5G/6G, edge computing, and Internet-of-Things (IoT) deployments.

Begin with concise explanations of latency fundamentals, then move into advanced topics: latency-aware learning algorithms, resource allocation, scheduling, model compression, and distributed inference for ultra-reliable low-latency communications (URLLC). The authors blend theoretical insight with applied strategies—offering frameworks for designing low-latency networks, real-world performance analysis, and scalable solutions for heterogeneous environments.

Designed for network architects, ML engineers, graduate students, and R&D teams, this book emphasizes actionable takeaways. Learn how to reduce end-to-end delay using adaptive learning, optimize edge-cloud collaboration, and implement robust models for autonomous vehicles, augmented reality, industrial automation, and smart-city infrastructures. Regional practitioners—from North America and Europe to Asia-Pacific—will find case studies and scenarios relevant to diverse regulatory and deployment landscapes.

Readable, technically rich, and forward-looking, this 1st Edition prepares readers for the evolving demands of latency-critical applications. Whether you’re prototyping low-latency services or building production networks, this title is an essential reference that bridges machine learning and communications engineering. Add it to your professional library to accelerate development of dependable, real-time wireless systems worldwide.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.