Coding Theory
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		    201 Jacobus H. van Lint California Institute of Technology, Pasadena, CA/USA
 
 Coding Theory
 
 Springer-Verlag Berlin Heidelberg GmbH 1971
 
 AMS Subject Classifications (1970): 91j A 10
 
 ISBN 978-3-540-05476-4
 
 ISBN 978-3-662-20712-3 (eBook)
 
 DOI 10.1007/978-3-662-20712-3 This work is ~uhjecr to (oryrighr. All rights arc reserved, wht>~hn the whoic or parr of rh(' mareria.i i~ concerned, spe(ific~llr rhosr of translaTion , r(,printing. :or-usc of illustrations, broadcasting, rl'produ(tion by photocopying machine Of c;i~ : !ar ::1(':;:05,
 
 an2
 
 s~oragc
 
 in
 
 ,~ "f.t b~~ks.
 
 I Jnner ~ ~ 4 of the German COJlyright Law where cnpic:-s ,He' made for other than priv.ne use, a rhe .m{lI:nr {)f rhe fcc to n., ~crer",inrd hy agreement with the publ isher. :i;;
 
 fpC'
 
 is p:4yabJe to thl' ;'IlIhli 0 and R < C
 
 < E.
 
 Before giving the main idea of the proof we isolate
 
 a number of the more technical details to be used in the main
 
 If a code 'liard is transmitted over
 
 t~e
 
 part
 
 of the proof.
 
 channel then the prObability of a spec-
 
 ific error pattern (at the receiver) vith 'II errors is p'llqn-w, i.e. this probability depends only on the number of errors.
 
 Remark that p(!lr)
 
 = p(~I!) .
 
 errors in received words is a random variable 'IIith expected value np np(l~p).
 
 If b:c
 
 ( ~1-=1'l)1/2 ~
 
 Let p :
 
 ..
 
 (np + bJ.
 
 and variance
 
 then by the Bienaym~-Chebyshev inequality Prob ('II > np + b) ::
 
 (1.4.4)
 
 The number of
 
 1
 
 2
 
 €.
 
 By (1.2.4) the number of points in a sphere S (x) is P n n (n) < ~ n(n) < ~ n Is p (!) I c 'II 2 P - 2 pP (n-p )n- p
 
 L
 
 ( 1.4·5)
 
 '0J
 
 The last inequality follows frem nn • We have
 
 ( 1.4.6)
 
 ~ log n~ c (p + n£) log (p
 
 r.
 
 c
 
 and analogously (1 -
 
 ~) n
 
 +
 
 ~) n
 
 ~){log p + log (1 + ~)} c n np
 
 P log P + 0(n- 1/ 2 ),
 
 log (1 -
 
 ~)
 
 n
 
 c q log q + 0(n- 1/ 2 ).
 
 We introduce two auxiliary f~~ctionG. (1.4.7)
 
 • (p +
 
 f(~,~) '. . { 0 1
 
 If ~ E (e ,l}n, if
 
 ~(~,:::) > p ,
 
 if
 
 '1r(:!.':::)::
 
 If ~i is any vector in the code and y
 
 E (O,l)n then
 
 (1.4.8)
 
 - f(;t'!i) c
 
 gi(r):c
 
 v
 
 E {O,l}n we define:
 
 p
 
 L f(;i:'!j)' j~i
 
 12
 
 Rem.aIit that gi (r) '"
 
 °if 1St E Sp
 
 (~) and no other code word is in this sphere and
 
 otherwise Si (~) ~ I. Nov ve come to the main part of our proof.
 
 We choose the code words !I'
 
 "', ~ at random from {O,l}n and use these to transmit M possible messages. decoding rule for the receiver 1s as follows.
 
 r
 
 If
 
 ~I
 
 The
 
 is received and there 1s one
 
 code word !i vi th distance :: p to "L then "L is decoded into !i' is declared (or one could always take
 
 ~,
 
 in this case).
 
 probability of incorrect decoding when !i is transmitted.
 
 Otherwise an error
 
 Again, let Pi denote the We have, by the remark
 
 following (1.4.8), Pi
 
 ~
 
 •
 
 ~he
 
 L
 
 P{rl!i )Si (X) "
 
 ;LE(O, 1}n
 
 L L f(;i'!J)p{xl!i)'
 
 LP{"LI1St)(1 - f{XJ!t)} + l l
 
 first sum on the right-hand side is just the probability that l 1s not 1n Sp{!i)'
 
 This number does not depend on!1 but only on p.
 
 ap
 
 j!i
 
 :::
 
 1
 
 2"
 
 Call it a p '
 
 By (1.4.4) we
 
 I~ve
 
 t;.
 
 Now by {1.4.1} M
 
 L LP{:z:I1St)f(Z,~j)'
 
 P error - n. s1des of (2.2.8) by z1-1 and		
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