Channel Estimation for Physical Layer Network Coding Systems Channel Estimation for Physical Layer Network Coding Systems
SpringerBriefs in Computer Science

Channel Estimation for Physical Layer Network Coding Systems

Feifei Gao and Others
    • 42,99 €
    • 42,99 €

Publisher Description

This SpringerBrief presents channel estimation strategies for the physical later network coding (PLNC) systems. Along with a review of PLNC architectures, this brief examines new challenges brought by the special structure of bi-directional two-hop transmissions that are different from the traditional point-to-point systems and unidirectional relay systems. The authors discuss the channel estimation strategies over typical fading scenarios, including frequency flat fading, frequency selective fading and time selective fading, as well as future research directions. Chapters explore the performance of the channel estimation strategy and optimal structure of training sequences for each scenario. Besides the analysis of channel estimation strategies, the book also points out the necessity of revisiting other signal processing issues for the PLNC system. Channel Estimation of Physical Layer Network Coding Systems is a valuable resource for researchers and professionals working in wireless communications and networks. Advanced-level students studying computer science and electrical engineering will also find the content helpful.

GENRE
Computing & Internet
RELEASED
2014
15 October
LANGUAGE
EN
English
LENGTH
89
Pages
PUBLISHER
Springer International Publishing
SIZE
2.2
MB

More Books by Feifei Gao, Chengwen Xing & Gongpu Wang

Other Books in This Series

Practical Backscatter Communication for the Internet of Things Practical Backscatter Communication for the Internet of Things
2024
Efficient Online Incentive Mechanism Designs for Wireless Communications Efficient Online Incentive Mechanism Designs for Wireless Communications
2024
Human Digital Twin Human Digital Twin
2024
From Unimodal to Multimodal Machine Learning From Unimodal to Multimodal Machine Learning
2024
Open-Set Text Recognition Open-Set Text Recognition
2024
Applications of Game Theory in Deep Learning Applications of Game Theory in Deep Learning
2024