Information Theory

Channel Estimation for Physical Layer Network Coding Systems by Feifei Gao, Chengwen Xing, Gongpu Wang PDF

By Feifei Gao, Chengwen Xing, Gongpu Wang

ISBN-10: 3319116673

ISBN-13: 9783319116679

ISBN-10: 3319116681

ISBN-13: 9783319116686

This SpringerBrief provides channel estimation recommendations for the actual later community coding (PLNC) platforms. besides a evaluate of PLNC architectures, this short examines new demanding situations introduced through the precise constitution of bi-directional two-hop transmissions which are diverse from the conventional point-to-point platforms and unidirectional relay platforms. The authors talk about the channel estimation concepts over standard fading eventualities, together with frequency flat fading, frequency selective fading and time selective fading, in addition to destiny study instructions. Chapters discover the functionality of the channel estimation procedure and optimum constitution of educating sequences for every state of affairs. in addition to the research of channel estimation options, the ebook additionally issues out the need of revisiting different sign processing concerns for the PLNC approach. Channel Estimation of actual Layer community Coding platforms is a useful source for researchers and pros operating in instant communications and networks. Advanced-level scholars learning computing device technology and electric engineering also will locate the content material helpful.

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Additional resources for Channel Estimation for Physical Layer Network Coding Systems

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1, each frame contains multiple OFDM blocks, while each OFDM block contains N information symbols and a cyclic prefix (CP) of length Lcp . To avoid the inter-block interference, Lcp should be greater than or equal to maxfL1 1; L2 1g. Denote the information vector of one OFDM block from Tj as dQ j D ŒdQj;0 ; dQj;1 ; : : : ; dQj;N 1 T , j D 1; 2. q 1/=N . After N the CP insertion, Tj ’s send out the resultant signal simultaneously during Phase I. 2) where nr D Œnr;0 ; nr;1 ; : : : ; nr;N 1 T denotes the zero-mean additive-whiteGaussian-noise (AWGN), each element with variance n2 .

27(5),pp. 699–712 , June 2009. 4. S. Ohno and G. B. Giannakis. Optimal training and redundant precoding for block transmissions with application to wireless OFDM. IEEE Trans. , 50(12), pp. 2113–2123, Dec. 2002. 5. I. Barhumi, G. Leus, and M. Moonen. Optimal trianing design for MIMO OFDM systems in mobile wireless channels. IEEE Trans. Signal Processing, 51(5), pp. 1615–1624, June 2003. 6. H. Minn and N. Al-Dhahir. Optimal training signals for MIMO OFDM channel estimation. IEEE Trans. ,5(6), pp.

1. 30) vH Rz v Ä C: Similarly, the LMMSE estimator of v is v D ˛ simulations. 3 Linear Maximum Signal-to-Noise Ratio Based Estimation 27 Proof. 31) must be smaller than zero. This is seen by q C t , which results in a negative value of the cost function. choosing v D tH R t 2 2 z 2 Hence, we can use the new constraint vH Rz v Ä C instead, because at the optimal point the equality always holds; otherwise, we can scale the value of v by a positive factor to keep the equality while getting an even smaller value of cost function.

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Channel Estimation for Physical Layer Network Coding Systems by Feifei Gao, Chengwen Xing, Gongpu Wang


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