Substituting eq. (2.33) into eq. (2.32), eq. (2.34) can be obtained:

E[ | x˜(n) |4 ]=E[ | x(n) |4 ]i| si(n) |4+2E2[ | x(n) |2 ]{ [ i| si(n) |2 ]2i| si(n) |4 }(2.34)+| E[ x2(n) ] |2[ | isi2(n) |2i| si(n) |4 ]

Substituting eqs. (2.29) and (2.31) into eq. (2.34), (2.35) can be obtained:

E[|x~(n)|4]=E[|x(n)|4]i|si(n)|4+2E[|x(n)2|][i|si(n)|2]22E2[|x(n)|2]i|si(n)|4+|E[x2(n)]2||isi2(n)|2|E[x2(n)]|2i|si(n)|4(2.35)=E[|x(n)|4]i|si(n)|4+2E[|x~(n)2|]2E2[|x(n)|2]i|si(n)|4+|E[x~2(n)]|2|E[x2(n)]|2i|si(n)|4

By moving the mathematical expectations of transmitted sequence and recovery sequence to both sides of the equal sign, respectively, eq. (2.36) can be obtained:

E[|x~(n)|4]2E2[|x~(n)|2]|E[x~2(

Get Blind Equalization in Neural Networks now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.