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

By Feifei Gao, Chengwen Xing, Gongpu Wang

This SpringerBrief provides channel estimation innovations for the actual later community coding (PLNC) structures. besides a overview of PLNC architectures, this short examines new demanding situations introduced by means of the certain constitution of bi-directional two-hop transmissions which are diversified from the normal point-to-point platforms and unidirectional relay platforms. The authors talk about the channel estimation suggestions over regular fading situations, together with frequency flat fading, frequency selective fading and time selective fading, in addition to destiny learn instructions. Chapters discover the functionality of the channel estimation method and optimum constitution of teaching sequences for every situation. along with the research of channel estimation thoughts, the publication 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 helpful source for researchers and execs operating in instant communications and networks. Advanced-level scholars learning desktop technological know-how and electric engineering also will locate the content material helpful.

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28) Finally, we need to deal with the unknown jhQ ik j2 in the denominator. ConsidPL1 1 j 2 ik l=N ering hQ ik D , jhQ ik j2 can be represented by its expectation lD0 hl e 2 Q Efjhik j g D ˇ1 if L1 is relatively large. 29) jdQ1;ik j2 Ä P1t : kD0 The problem is standard convex optimization in terms of jdQ1;ik j2 and j˛Q ik j2 , and can be solved from the Karush–Kuhn–Tucker (KKT) conditions [10]. The optimal solution can be obtained as jdQ1;ik j2 D P1t =K1 and j˛Q ik j2 D Prt1 =K1 . 2. hQ qj gQ qj / ˇhg D 2 Q 2 n jhqk j jdQ2;qk j2 C 2 n jaQ qk dQ2;qk j2 !

8) where nQ 1 D ŒnQ 1;0 ; nQ 1;1 ; : : : ; nQ 1;N 1 T is the corresponding AWGN in the frequency domain. i /th element to the i th position. 0/ ; g and ˇ denotes the Hadamard product between two vectors. 10) Note that the permutation function . /, or equivalently the matrix J must be known as a prior to T1 in order to decode the data. 11) where C2 is the signal constellation of T2 . Remark. Note that, only the cascaded channels hQ i hQ data detection. 4 Channel Estimation Strategy Although the knowledge of the individual channels h and g do not contribute to the ML data detection, the task of the channel estimation in PLNC should still focus on estimating h and g, as mentioned in Chap.

15). 15). 2 The N -point DFT of b can be expressed as bQ D ŒbQ0 ; bQ1 ; : : : ; bQN 1 T , where 1=2 bQi D hQ 2i . Then, only bQi D Qi hQ i can be obtained by the rooting operation, where Qi D ˙1 contains the channel uncertainty in each carrier. Define Q D 1=2 1=2 1=2 Q Q . Construct an auxiliary Œ Q0 ; Q1 ; : : : ; QN 1 T and Qt D ŒbQ0 ; bQ1 ; : : : ; bQN 1 T D hˇ T vector ÄQ D ŒÄQ 0 ; ÄQ 1 ; : : : ; ÄQ N 1  , where ÄQ i 2 fC1; 1g, and define Q D Q ˇ ÄQ D Œ Q0 ; Q1 ; : : : ; QN 1 T , where Qi D Qi ÄQ i belongs to fC1; 1g.

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