Design and Optimization for 5G Wireless Communications

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A01=Haesik Kim
Author_Haesik Kim
authors research works
Category=TJKT1
Category=TJKW
communication industry
communication problem
eq_bestseller
eq_isMigrated=1
eq_nobargain
eq_non-fiction
eq_tech-engineering
low latency communication
methods
novel introduction
optimization algorithms
system capacity
system parameters
trade-off solution
use case scenarios

Product details

  • ISBN 9781119494553
  • Weight: 885g
  • Dimensions: 175 x 246mm
  • Publication Date: 16 Apr 2020
  • Publisher: John Wiley & Sons Inc
  • Publication City/Country: US
  • Product Form: Hardback
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This book offers a technical background to the design and optimization of wireless communication systems, covering optimization algorithms for wireless and 5G communication systems design. The book introduces the design and optimization systems which target capacity, latency, and connection density; including Enhanced Mobile Broadband Communication (eMBB), Ultra-Reliable and Low Latency Communication (URLL), and Massive Machine Type Communication (mMTC).

The book is organized into two distinct parts: Part I, mathematical methods and optimization algorithms for wireless communications are introduced, providing the reader with the required mathematical background. In Part II, 5G communication systems are designed and optimized using the mathematical methods and optimization algorithms.

DR. HAESIK KIM (IEEE Senior Member, Series Editor and Associate Technical Editor of IEEE Communications Magazine) is Senior Scientist of 5G and beyond network team in VTT Technical Research Centre of Finland. He is the recipient of the International Conference on Wireless Communications and Signal Processing (WCSP) Best Paper Award in 2010. His current research interests include PHY and MAC layer system design, advanced coding theory, advanced MIMO, multi-carrier system, interference mitigation techniques, resource allocation schemes, machine-type communications, ultra-reliable low latency communications, and machine learning.

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