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A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author – a noted expert on the topic – covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.
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H. Schwartz M. Multi-Agent Machine Learning. A Reinforcement Approach albalate amparo semi supervised and unsupervised machine learning novel strategies
Parag Kulkarni Reinforcement and Systemic Machine Learning for Decision Making albalate amparo semi supervised and unsupervised machine learning novel strategies
Ilya Narsky Statistical Analysis Techniques in Particle Physics. Fits, Density Estimation and Supervised Learning albalate amparo semi supervised and unsupervised machine learning novel strategies
Dehmer Matthias Statistical and Machine Learning Approaches for Network Analysis albalate amparo semi supervised and unsupervised machine learning novel strategies
Ling Guan Unsupervised Learning. A Dynamic Approach albalate amparo semi supervised and unsupervised machine learning novel strategies
Uwe Gottschalk Process Scale Purification of Antibodies albalate amparo semi supervised and unsupervised machine learning novel strategies
Chad Udell Mastering Mobile Learning albalate amparo semi supervised and unsupervised machine learning novel strategies
Colin McFarlane Learning the City. Knowledge and Translocal Assemblage albalate amparo semi supervised and unsupervised machine learning novel strategies
Fa-Long Luo Machine Learning for Future Wireless Communications albalate amparo semi supervised and unsupervised machine learning novel strategies
Pooja Agarwal K. Powerful Teaching albalate amparo semi supervised and unsupervised machine learning novel strategies
Rebecca Jackson Williams The Complete Learning Disabilities Handbook. Ready-to-Use Strategies and Activities for Teaching Students with Learning Disabilities albalate amparo semi supervised and unsupervised machine learning novel strategies
Tony Guida Big Data and Machine Learning in Quantitative Investment albalate amparo semi supervised and unsupervised machine learning novel strategies
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