SlideShare une entreprise Scribd logo
1  sur  5
Télécharger pour lire hors ligne
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
e-ISSN: 2278-2834,p- ISSN: 2278-8735. Volume 5, Issue 3 (Mar. - Apr. 2013), PP 15-19
www.iosrjournals.org

        A New Class of Binary Zero Correlation Zone Sequence Sets
                 B. Fassi1, A. Djebbari1, Taleb-Ahmed. A2 And I. Dayoub3
    1
       (Telecommunications and Digital Signal Processing Laboratory, Djillali Liabes University of Sidi Bel
                                               Abbes, Algeria)
    2
      (Laboratoire LAMIH UMR, C.N.R.S, Université de Valenciennes et du Hainaut Cambresis, le Mont Houy,
                                                   France)
3
  (I.E.M.N. Dept. O.A.E, U.M.R, C.N.R.S,Université de Valenciennes et du Hainaut CAMBRESIS, le Mont Houy,
                                                   France)

 Abstract: This paper proposes a new class of binary zero correlation zone (ZCZ) sequence sets, in which the
periodic correlation functions of the proposed sequence set is zero for the phase shifts within the zero-
correlation zone. It is shown that the proposed zero correlation zone sequence set can reach the upper bound on
the ZCZ codes.
Keywords- Sequence design, theoretical upper bound, zero correlation zone (ZCZ) sequences.

                                                      I.       Introduction
Zero correlation zone (ZCZ) sequences can be used in spread spectrum systems and CDMA systems to reduce
the multiple access interference and co-channel interference [1]. There are several intensive studies on the
constructing of ZCZ sequences set [1-8].
          The ZCZ sequences set construction is limited by the correlation property and theory bounds [1-7].
Generally, sets of ZCZ sequences are characterized by the period of sequences L, the family size, namely the
number of sequences M, and the length of the zero-correlation zone ZCZ. A ZCZ (L, M, ZCZ) sequence set that
satisfies the theoretical bound defined by the ratio 𝑀 𝑍 𝑐𝑧 + 1 /𝐿 = 1 is called an optimal zero-correlation zone
sequence set [4]. Compared with earlier works on binary ZCZ sequence sets [1, 6], our proposed zero-
correlation zone sequence set is, in all cases, an optimal or an approach optimal ZCZ sequence set.
           The paper is organized as follows. Section 2 introduces the notations required for the subsequent
sections, the proposed scheme for sequence construction is explained in section 3. Examples of new ZCZ
sequence sets are presented in Section 4. The properties of the proposed sequence sets are shown in Section 5.
Finally, we draw the concluding remarks.

                                              II.           Notations
2.1 Definition 1: Suppose 𝑋𝑗 =(𝑥𝑗 ,0 , 𝑥𝑗 ,1 , … … . 𝑥𝑗 ,𝐿−1 ) and, 𝑋 𝑣 =(𝑥 𝑣,0 , 𝑥 𝑣,1 , … … . 𝑥 𝑣,𝐿−1 ) are two sequences of
period L. Sequence pair (𝑋𝑗 , 𝑋 𝑣 ) is called a binary sequence pair if
 𝑥𝑗 ,𝑖 , 𝑥 𝑣,𝑖 ∈ −1, +1 , 𝑖 = 0,1,2, … … … 𝐿 − 1 [7].
The Periodic Correlation Function (PCF) between 𝑋𝑗 and 𝑋 𝑣 at a shift 𝜏 is defined by [4]:
                             𝐿−1
∀𝜏 ≥ 0, 𝜃 𝑋 𝑗 ,𝑋 𝑣 𝜏 = 𝑖=0 𝑥𝑗 ,𝑖 𝑥 𝑣, 𝑖+𝜏 𝑚𝑜𝑑 (𝐿)                                                                          (1)

2.2 Definition 2: A set of M sequences          𝑋0 , 𝑋1 , 𝑋2 , … … , 𝑋 𝑀−1 is denoted by Xj 𝑗 𝑀−1 .
                                                                                              =0
A set of sequences     Xj 𝑗 𝑀−1
                            =0 is called zero correlation zone sequence set, denoted by Z(L,M,ZCZ) if the periodic
correlation functions satisfy [4] :

∀𝑗, 0 < 𝜏 ≤ 𝑍 𝑐𝑧 , 𝜃    𝑋 𝑗 ,𝑋 𝑗        𝜏 =0                                                                               (2)
∀𝑗, 𝑗 ≠ 𝑣 , 𝜏 ≤ 𝑍 𝑐𝑧 , 𝜃           𝑋 𝑗 ,𝑋 𝑣    𝜏 =0                                                                         (3)

                                        III.          Proposed Sequence Construction
In this section, a new method for constructing sets of binary ZCZ sequences is proposed.
The construction is accomplished through three steps.

3.1 Step 1: The 𝑗 𝑡ℎ row of the Hadamard matrix 𝐻 of order n is denoted by
ℎ 𝑗 = ℎ 𝑗 ,0 , ℎ 𝑗 ,1 , … … … ℎ 𝑗 ,𝑛 −1 . A set of 2𝑛 sequences 𝑑 𝑗 , each of length 2𝑛, is constructed as follows:
For 0 ≤ 𝑗 < 𝑛,

                                                       www.iosrjournals.org                                           15 | Page
A New Class of Binary Zero Correlation Zone Sequence Sets

𝑑 𝑗 +0 = −ℎ 𝑗 , ℎ 𝑗                                                                                                                       (4)
𝑑 𝑗 +1 = ℎ 𝑗 , ℎ 𝑗                                                                                                                        (5)

3.2 Step 2: For a fixed integer value 𝑛, and for the first stage, 𝑝 = 0, we can generate, based on the scheme for
                                                                 2𝑛−1
sequence construction in [6], a series of sets              𝐵𝑗   𝑗 =0
                                                                            of 2𝑛 sequences as follows:
                                2𝑛−1                                                       2𝑛−1
A sequence set             𝐵𝑗   𝑗 =0
                                       is constructed from the sequences set          𝑑𝑗   𝑗 =0
                                                                                                  . A pair of sequences 𝐵𝑗 +0 and 𝐵𝑗 +1 of
                   𝑝+2
length (2        𝑛) are constructed by the process of interleaving a sequence pair 𝑑 𝑗 +0 and 𝑑 𝑗 +1 as follows:
For 0 ≤ 𝑗 < 𝑛,
 𝐵𝑗 +0 = 𝑑 𝑗 +0,0 , 𝑑 𝑗 +1,0 , 𝑑 𝑗 +0,1 , 𝑑 𝑗 +1,1 , … … . . , 𝑑 𝑗 +0,2𝑛−1 , 𝑑 𝑗 +1,2𝑛−1                                                  (6)
and,
 𝐵𝑗 +1 = 𝑑 𝑗 +0,0 , −𝑑 𝑗 +1,0 , 𝑑 𝑗 +0,1 , −𝑑 𝑗 +1,1 , … … . . , 𝑑 𝑗 +0,2𝑛−1 , −𝑑 𝑗 +1,2𝑛−1                                               (7)
The member size of the sequence set 𝐵𝑗 is 2𝑛.
                                                                                                            2𝑛−1
3.3 Step 3: For 𝑝 > 0, we can recursively construct a new series of set,                               𝐵𝑗   𝑗 =0
                                                                                                                   , by interleaving of actual
       2𝑛−1
  𝐵𝑗   𝑗 =0
               .
                   2𝑛−1
The           𝐵𝑗   𝑗 =0
                          is generated as follows:
For 0 ≤ 𝑗 < 𝑛,
 𝐵𝑗 +0 = 𝐵𝑗 +0,0 , 𝐵𝑗 +1,0 , 𝐵 𝑗 +0,1 , 𝐵𝑗 +1,1 , … … . . , 𝐵𝑗 +0,4𝑛−1 , 𝐵 𝑗 +1,4𝑛−1                                                       (8)
and,
 𝐵𝑗 +1 = 𝐵𝑗 +0,0 , −𝐵𝑗 +1,0 , 𝐵 𝑗 +0,1 , −𝐵𝑗 +1,1 , … … . , 𝐵𝑗 +0,4𝑛−1 , −𝐵 𝑗 +1,4𝑛−1                                                     (9)
The length of both sequences 𝐵𝑗 +0 and 𝐵𝑗 +1 is equal to (2 𝑝+2 𝑛).

                              IV.         Example of Construction
4.1 Step 1: Let 𝐻 be a Hadamard matrix of order n=2, given by:
         1          1   ℎ
 𝐻=                   = 0
         1         −1   ℎ1

A set of 2𝑛 = 4 sequences 𝑑 𝑗 , each of length 2𝑛 = 4, is constructed as follows:
For 0 ≤ 𝑗 < 2,
 𝑑0+0 = −ℎ0 , ℎ0 = −1, −1,1,1
 𝑑1+0 = −ℎ1 , ℎ1 = −1,1,1, −1
 𝑑0+1 = ℎ0 , ℎ0 = 1,1,1,1
 𝑑1+1 = ℎ1 , ℎ1 = 1, −1,1, −1

4.2 Step 2: For the first iteration, 𝑝 = 0.
A pair of sequences 𝐵𝑗 +0 and 𝐵𝑗 +1 of length 2 𝑝+2 𝑛 = 8 are constructed by interleaving a sequence pair 𝑑 𝑗 +0
and 𝑑 𝑗 +1 as follows:
For 0 ≤ 𝑗 < 2,
 𝐵𝑗 +0 = 𝑑 𝑗 +0,0 , 𝑑 𝑗 +1,0 , 𝑑 𝑗 +0,1 , 𝑑 𝑗 +1,1 , … … . . , 𝑑 𝑗 +0,3 , 𝑑 𝑗 +1,3
 𝐵0+0 = −1,1, −1,1,1,1,1,1
 𝐵1+0 = −1,1,1, −1,1,1, −1, −1
and,
 𝐵𝑗 +1 = 𝑑 𝑗 +0,0 , −𝑑 𝑗 +1,0 , 𝑑 𝑗 +0,1 , −𝑑 𝑗 +1,1 , … … . . , 𝑑 𝑗 +0,3 , −𝑑 𝑗 +1,3
 𝐵0+1 = −1, −1, −1, −1,1, −1,1, −1
 𝐵1+1 = −1, −1,1,1,1, −1, −1,1
                                                                        3
4.3 Step 3: For the next iteration 𝑝 = 1, The                        𝐵𝑗 𝑗 =0 is generated as follows:
 𝐵𝑗 +0 = 𝐵𝑗 +0,0 , 𝐵𝑗 +1,0 , 𝐵 𝑗 +0,1 , 𝐵𝑗 +1,1 , … … . . , 𝐵𝑗 +0,7 , 𝐵 𝑗 +1,7
 𝐵0+0 = −1, −1, 1, −1, −1, −1, 1, −1,1, 1, 1, −1, 1, 1, 1, −1 ,
 𝐵1+0 = −1, −1, 1, −1, 1, 1, −1, 1,1, 1, 1, −1, −1, −1, −1, 1 ,
and,
  𝐵𝑗 +1 = 𝐵𝑗 +0,0 , −𝐵𝑗 +1,0 , 𝐵 𝑗 +0,1 , −𝐵𝑗 +1,1 , … … . , 𝐵𝑗 +0,7 , −𝐵 𝑗 +1,7
 𝐵0+1 = −1, 1, 1, 1, −1, 1, 1, 1,1, −1, 1, 1, 1, −1, 1, 1 ,
 𝐵1+1 = −1, 1, 1, 1, 1, −1, −1, −1,1, −1, 1, 1, −1, 1, −1, −1 .
                                                         www.iosrjournals.org                                                       16 | Page
A New Class of Binary Zero Correlation Zone Sequence Sets

The length of both sequences 𝐵𝑗 +0 and 𝐵𝑗 +1 is equal to 2 𝑝+2 𝑛 = 16.
The member size of the sequence set 𝐵𝑗 is 2𝑛 = 4.
                                    3
Iteratively for 𝑝 = 2, The     𝐵𝑗   𝑗 =0
                                           is generated as follows:
  𝐵0+0 = −1, −1, −1, 1, 1, 1, −1, 1, −1, −1, −1, 1, 1, 1, −1, 1, 1, 1, 1, −1,1, 1, −1, 1, 1, 1, 1, −1,1, 1, −1, 1
 𝐵1+0 = −1, −1, −1, 1, 1, 1, −1, 1, 1, 1, 1, −1, −1, −1, 1, −1, 1, 1, 1, −1, 1, 1, −1, 1, −1, −1, −1, 1, −1, −1, 1, −1
 𝐵0+1
= −1, 1, −1, −1, 1, −1, −1, −1, −1, 1, −1, −1, 1, −1, −1, −1, 1, −1, 1, 1, 1, −1, −1, −1, 1, −1, 1, 1,1, −1, −1, −1
 𝐵1+1
= −1, 1, −1, −1, 1, −1, −1, −1, 1, −1, 1, 1, −1, 1, 1, 1, 1, −1, 1, 1, 1, −1, −1, −1, −1, 1, −1, −1 − 1, 1, 1, 1
                                                                                      3
   Also, we can find out that the zero correlation zone length of                𝐵𝑗   𝑗 =0
                                                                                             is 4. For example, for 𝜏 =
0,1, … … … … 31, the periodic auto-correlation functions (PACF) (∀𝑗, 𝑗 = 𝑣) given in 1 of 𝐵0+0 , 𝐵1+0 , 𝐵0+1
and 𝐵1+1 are calculated as follows:
       𝜃 𝐵0+0 ,𝐵0+0 𝜏
       = 32, 0, 0, 0, 0, 4, 0, −4,16, −4, 0, 4, 0, 8, 0, −8, 0, −8, 0, 8, 0, 4, 0, −4,16, −4, 0, 4, 0, 0, 0, 0
 𝜃 𝐵1+0 ,𝐵1+0 𝜏 =
 32, 0, 0, 0, 0, −4, 0, 4 , −16, 4, 0, −4, 0, 8, 0, −8, 0, −8, 0, 8, 0, −4, 0, 4, −16, 4, 0, −4, 0, 0, 0, 0
     𝜃 𝐵0+1 ,𝐵0+1 𝜏
     = 32, 0, 0, 0, 0, −4, 0, 4,16, 4, 0, −4, 0, −8, 0, 8, 0, 8, 0, −8, 0, −4, 0, 4,16, 4, 0, −4, 0, 0, 0, 0
      𝜃 𝐵1+1 ,𝐵1+1 𝜏
     = 32, 0, 0, 0, 0, 4, 0, −4, −16, −4, 0, 4, 0, −8, 0, 8, 0, 8, 0, −8, 0, 4, 0, −4, −16, −4, 0, 4, 0, 0, 0, 0

   The periodic cross-correlation functions (PCCF) (∀𝑗, 𝑗 ≠ 𝑣) given in (1) are calculated as follows:
       𝜃 𝐵0+0 ,𝐵1+0 𝜏
      = 0, 0, 0, 0, 0, −4, 0, 4,16, 4, 0, −4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, −4 , −16, −4, 0, 4, 0, 0, 0, 0
     𝜃 𝐵0+0 ,𝐵0+1 𝜏
    = 0, 0, 0, 0, 0, −4, −8, −4, 0, 4, −8, 4, 0, −8, −16, −8, 0, 8, −16, 8, 0, −4, −8, −4, 0, 4, −8, 4, 0, 0, 0, 0
      𝜃 𝐵0+0 ,𝐵1+1 𝜏
    = 0, 0, 0, 0, 0, 4, 8, 4, 0, −4, 8, −4, 0, 0, 0, 0, 0, 0, 0, 0, 0, −4, −8, −4, 0, 4, −8, 4, 0, 0, 0, 0
         𝜃 𝐵1+0 ,𝐵0+1 𝜏
        = 0, 0, 0, 0, 0, −4, −8, −4, 0, 4, −8, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 4, 0, −4, 8, −4, 0, 0, 0, 0
        𝜃 𝐵1+0 ,𝐵1+1 𝜏
        = 0, 0, 0, 0, 0, 4, 8, 4, 0, −4, 8, −4, 0, −8 , −16, −8, 0, 8, −16, 8, 0, 4, 8, 4, 0, −4, 8, −4, 0, 0, 0, 0
𝜃 𝐵0+1 ,𝐵1+1 𝜏 = 0, 0, 0, 0, 0, 4, 0, −4,16, −4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, −4, 0, 4, −16, 4, 0, −4, 0, 0, 0, 0
                                                                  3
We notice that the zero correlation zone length of           𝐵𝑗   𝑗 =0
                                                                         is 4.
The PACF and PCCF confirm that 𝐵𝑗 is a ZCZ (32,4,4) sequence set.

                  V.                            The Properties of the Proposed Sequence
            The proposed zero-correlation zone sequence set can be generated from an arbitrary Hadamard matrix
of order n.
The length of Bj in equations (6) and (7), equal to (2p+2 n), is twice that of Bj in equations (8) and (9).
The proposed ZCZ sequence set can satisfy the ideal autocorrelation and cross-correlation properties in the
zero-correlation zone.
The generated sequence set satisfies the following properties:
∀𝑗, ∀𝜏 ≠ 0, 𝜏 ≤ 2 𝑝
 𝜃 𝐵 𝑗 ,𝐵 𝑗 𝜏 =0
(10)
and,
∀𝑗 ≠ 𝑣, ∀𝜏, 𝜏 ≤ 2 𝑝
 𝜃 𝐵 𝑗 ,𝐵 𝑣 𝜏 =0.
(11)
The 𝐵𝑗 is a ZCZ sequence set having parameters (L,M, ZCZ)=(2 𝑝+2 𝑛, 2𝑛, 2 𝑝 ), its parameters perfectly satisfy
the theory bound of ZCZ sequences set [4, 7]:

                                                    www.iosrjournals.org                                                  17 | Page
A New Class of Binary Zero Correlation Zone Sequence Sets

 𝑀(𝑍 𝐶𝑍 + 1) ≤ 𝐿                                                                                                      (12)
         𝑀(𝑍 𝐶𝑍 +1)
Let 𝜇 =             , if 𝜇 = 1 , it indicates that the ZCZ sequence set is optimal [1, 6].
                 𝐿
                                                                          𝑀(𝑍   +1)   2𝑛(2 𝑝 +1)       (2 𝑝 +1)
For the proposed 𝑍𝐶𝑍(2 𝑝 +2 𝑛, 2𝑛, 2 𝑝 ) sequence set, 𝜇 = 𝐶𝑍
                                                               =                                   =
                                                           𝐿                           2 𝑝 +2 𝑛         2 𝑝 +1
1) For 𝑝 = 0, 𝜇 = 1, the proposed ZCZ sequence set is optimal.
                                    lim
2) For 𝑝 > 0, 1/2 ≤ 𝜇 < 1 and 𝑝→∞ 𝜇 = 1/2.

For a given family size M, we can construct different sets of sequences with different lengths L and zero
correlation zone 𝑍 𝐶𝑍 . As an example, assuming that M=16, we can construct sets of ZCZ sequences with
different lengths L=32, 64, 128, 256, 512, 1024, 2048, 4096…., and different ZCZs, 𝑍 𝐶𝑍 =1, 2, 4, 8, 16, 32, 64,
128,….and different parameters 𝜇=1, 0.75, 0.625, 0.5625, 0.53125, 0.515625, 0.5078125, 0.5039063,…….
In step 1, other set of 2𝑛 sequences 𝑑 𝑗 , each of length 2𝑛, can be constructed as follows:


1-                            For 0 ≤ 𝑗 < 𝑛,
 𝑑 𝑗 +0 = ℎ 𝑗 , −ℎ 𝑗
(13)
 𝑑 𝑗 +1 = ℎ 𝑗 , ℎ 𝑗
(14)

                                        3
In this case, the set of           𝐵𝑗   𝑗 =0
                                               is generated as follows:
 𝐵0+0
= 1,1,1, − 1, 1, 1, −1, 1,1,1,1, − 1, 1, 1, −1, 1, − 1, − 1, − 1,1,1, 1, −1, 1, − 1, − 1, − 1,1,1, 1, −1, 1

𝐵1+0 = 1,1,1, − 1, 1, 1, −1, 1, −1, −1, −1, 1, −1, −1,1, − 1, − 1, − 1, − 1,1,1, 1, −1, 1,1,1,1, −1, −1, −1,1, − 1
 𝐵0+1
= 1, − 1,1,1, 1, −1, −1, −1,1, − 1,1,1, 1, −1, −1, −1, −1,1, − 1, − 1, 1, −1, −1, −1, − 1,1, −1, − 1,1, −1, −1, −1
 𝐵1+1 = 1, − 1,1,1, 1, −1, −1, −1, −1, 1, −1, −1, − 1,1,1,1, −1,1, − 1, − 1, 1, −1, −1, −1, 1, −1, 1,1, −1,1,1,1


2-                            For 0 ≤ 𝑗 < 𝑛,
 𝑑 𝑗 +0 = ℎ 𝑗 , ℎ 𝑗
(15)
 𝑑 𝑗 +1 = −ℎ 𝑗 , ℎ 𝑗
(16)

                       3
The set of       𝐵𝑗    𝑗 =0
                              is generated as follows:
  𝐵0+0 = 1,1,1, − 1, − 1, − 1,1, − 1,1,1,1, −1, −1, − 1,1, − 1, 1, 1, 1, −1,1, 1, −1, 1, 1, 1, 1, −1,1, 1, −1, 1

 𝐵1+0 =
 1,1,1, − 1, − 1, − 1,1, − 1, −1, −1, −1, 1, 1, 1, −1, 1, 1, 1, 1, −1,1, 1, −1, 1, −1, − 1, − 1,1, −1, −1,1, − 1
  𝐵0+1 = 1, − 1,1,1, − 1,1,1,1,1, − 1,1,1, − 1,1,1,1, 1, −1, 1, 1, 1, −1, −1, −1, 1, −1, 1, 11, −1, −1, −1
  𝐵1+1 = 1, − 1,1,1, − 1,1,1,1, −1, 1, −1, −1, 1, −1, −1, −1, 1, −1, 1, 1, 1, −1, −1, −1, − 1,1, −1, −1, −1,1,1,1



3-                            For 0 ≤ 𝑗 < 𝑛,
 𝑑 𝑗 +0 = ℎ 𝑗 , ℎ 𝑗
(17)
 𝑑 𝑗 +1 = ℎ 𝑗 , −ℎ 𝑗
(18)

                                                          www.iosrjournals.org                                    18 | Page
A New Class of Binary Zero Correlation Zone Sequence Sets


It should be noted that in [9], the limits of the correlation function of binary zero-correlation zone sequences
            3
      𝐵𝑗    𝑗 =0
                   obtained from equation (17) and (18) are evaluated.


                                                       VI.             Conclusion
 In this paper, we have proposed a new method for constructing sets of binary ZCZ sequences .The structure of
the proposed ZCZ sequences set is simple and thus it is easy to be generated. The PACF side lobes and PCCF
of the proposed sequence set is zero for the phase shifts within the zero-correlation zone. The proposed ZCZ
sequence set with (2 𝑝+2 𝑛, 2𝑛, 2 𝑝 ) is optimal or asymptotically optimal ZCZ sequence set. This method is
useful for designing spreading sequences for multi-user CDMA system.

                                                                 References
[1]        P. Z. Fan, N. Suehiro, N. Kuroyanagi and X. M. Deng, Class of binary sequences with zero correlation zone, IEE Electronic Letters,
              35(10), 1999, 777-779.
[2]        H. Torii, M. Nakamura, N. Suehiro, A new class of zero-correlation zone sequence, IEEE. Trans. Inf. Theory, 50(3), 2004, 559-565.
[3]        H. Torri, M. Nakamura, N. Suehiro, Enhancement of ZCZ sequence set construction procedure, Proc. IWSDA05, 2005, 67-72.
[4]        T. Hayashi, A class of zero-correlation zone sequence set using a perfect sequence, IEEE Signal ProcessingLetters, 16(4),2009, 331-
              334.
[5]        Kai Liu, ChengqianXu Gang Li, Binary zero correlation zone sequence pair set constructed from difference set pairs, Proceedings of
              International Conference on Networks Security, Wireless Communications and Trusted Computing (NSWCTC’09),2, 2009, 543-
              546.
[6]        T. Maeda, S. Kanemoto, T. Hayashi, A Novel Class of Binary Zero-Correlation Zone Sequence Sets, N°978-1-4244-6890-©2010
              IEEE, TENCON 2010.
[7]        S. Renghui, Z. Xiaoqun, Li. Lizhi, Research on Construction Method of ZCZ Sequence Pairs Set, Journal of Convergence
              Information Technology, 6(1). January 2011.
[8]        A. Rathinakumar and A.K. Chaturvedi, Mutually orthogonal sets of ZCZ sequences, ELECTRONICS LETTERS,40 (18),2004.
[9]        T. Hayashi, Limits of the Correlation Function of a Class of Binary Zero-correlation-zone Sequences, ftp://ftp.u-aizu.ac.jp/u-
              aizu/doc/Tech-Report/2002/2002-1-013.pdf, June 6, 2002.




                                                          www.iosrjournals.org                                                    19 | Page

Contenu connexe

Tendances

Amth250 octave matlab some solutions (2)
Amth250 octave matlab some solutions (2)Amth250 octave matlab some solutions (2)
Amth250 octave matlab some solutions (2)asghar123456
 
Lecture 11 systems of nonlinear equations
Lecture 11 systems of nonlinear equationsLecture 11 systems of nonlinear equations
Lecture 11 systems of nonlinear equationsHazel Joy Chong
 
Some properties of two-fuzzy Nor med spaces
Some properties of two-fuzzy Nor med spacesSome properties of two-fuzzy Nor med spaces
Some properties of two-fuzzy Nor med spacesIOSR Journals
 
51556 0131469657 ism-15
51556 0131469657 ism-1551556 0131469657 ism-15
51556 0131469657 ism-15Carlos Fuentes
 
51554 0131469657 ism-13
51554 0131469657 ism-1351554 0131469657 ism-13
51554 0131469657 ism-13Carlos Fuentes
 
Solve ODE - BVP through the Least Squares Method
Solve ODE - BVP through the Least Squares MethodSolve ODE - BVP through the Least Squares Method
Solve ODE - BVP through the Least Squares MethodSuddhasheel GHOSH, PhD
 
8 queens problem using back tracking
8 queens problem using back tracking8 queens problem using back tracking
8 queens problem using back trackingTech_MX
 
Chandrayan quadratic equation
Chandrayan quadratic equationChandrayan quadratic equation
Chandrayan quadratic equationAnjani
 
Inner product space
Inner product spaceInner product space
Inner product spaceSheharBano31
 
Engg. math 1 question bank by mohammad imran
Engg. math  1 question bank by mohammad imran Engg. math  1 question bank by mohammad imran
Engg. math 1 question bank by mohammad imran Mohammad Imran
 

Tendances (20)

Bca numer
Bca numerBca numer
Bca numer
 
Amth250 octave matlab some solutions (2)
Amth250 octave matlab some solutions (2)Amth250 octave matlab some solutions (2)
Amth250 octave matlab some solutions (2)
 
A05330107
A05330107A05330107
A05330107
 
Calculo integral - Larson
Calculo integral - LarsonCalculo integral - Larson
Calculo integral - Larson
 
Lecture 11 systems of nonlinear equations
Lecture 11 systems of nonlinear equationsLecture 11 systems of nonlinear equations
Lecture 11 systems of nonlinear equations
 
11365.integral 2
11365.integral 211365.integral 2
11365.integral 2
 
Some properties of two-fuzzy Nor med spaces
Some properties of two-fuzzy Nor med spacesSome properties of two-fuzzy Nor med spaces
Some properties of two-fuzzy Nor med spaces
 
51556 0131469657 ism-15
51556 0131469657 ism-1551556 0131469657 ism-15
51556 0131469657 ism-15
 
Alg2 lesson 3-2
Alg2 lesson 3-2Alg2 lesson 3-2
Alg2 lesson 3-2
 
51554 0131469657 ism-13
51554 0131469657 ism-1351554 0131469657 ism-13
51554 0131469657 ism-13
 
Solve ODE - BVP through the Least Squares Method
Solve ODE - BVP through the Least Squares MethodSolve ODE - BVP through the Least Squares Method
Solve ODE - BVP through the Least Squares Method
 
8 queens problem using back tracking
8 queens problem using back tracking8 queens problem using back tracking
8 queens problem using back tracking
 
redes neuronais
redes neuronaisredes neuronais
redes neuronais
 
NUMERICAL METHODS
NUMERICAL METHODSNUMERICAL METHODS
NUMERICAL METHODS
 
Chain Rule
Chain RuleChain Rule
Chain Rule
 
Finite difference &amp; interpolation
Finite difference &amp; interpolationFinite difference &amp; interpolation
Finite difference &amp; interpolation
 
Chandrayan quadratic equation
Chandrayan quadratic equationChandrayan quadratic equation
Chandrayan quadratic equation
 
Sect4 4
Sect4 4Sect4 4
Sect4 4
 
Inner product space
Inner product spaceInner product space
Inner product space
 
Engg. math 1 question bank by mohammad imran
Engg. math  1 question bank by mohammad imran Engg. math  1 question bank by mohammad imran
Engg. math 1 question bank by mohammad imran
 

En vedette

Secure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor Networks
Secure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor  NetworksSecure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor  Networks
Secure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor NetworksIOSR Journals
 
Pretext Knowledge Grids on Unstructured Data for Facilitating Online Education
Pretext Knowledge Grids on Unstructured Data for Facilitating  Online EducationPretext Knowledge Grids on Unstructured Data for Facilitating  Online Education
Pretext Knowledge Grids on Unstructured Data for Facilitating Online EducationIOSR Journals
 
Impact of Distributed Generation on Reliability of Distribution System
Impact of Distributed Generation on Reliability of Distribution SystemImpact of Distributed Generation on Reliability of Distribution System
Impact of Distributed Generation on Reliability of Distribution SystemIOSR Journals
 
A Density Based Clustering Technique For Large Spatial Data Using Polygon App...
A Density Based Clustering Technique For Large Spatial Data Using Polygon App...A Density Based Clustering Technique For Large Spatial Data Using Polygon App...
A Density Based Clustering Technique For Large Spatial Data Using Polygon App...IOSR Journals
 
An Improved Ant-Based Algorithm for Minimum Degree Spanning Tree Problems
An Improved Ant-Based Algorithm for Minimum Degree  Spanning Tree Problems An Improved Ant-Based Algorithm for Minimum Degree  Spanning Tree Problems
An Improved Ant-Based Algorithm for Minimum Degree Spanning Tree Problems IOSR Journals
 
A Comprehensive Overview of Clustering Algorithms in Pattern Recognition
A Comprehensive Overview of Clustering Algorithms in Pattern  RecognitionA Comprehensive Overview of Clustering Algorithms in Pattern  Recognition
A Comprehensive Overview of Clustering Algorithms in Pattern RecognitionIOSR Journals
 
Efficient Design of Reversible Sequential Circuit
Efficient Design of Reversible Sequential CircuitEfficient Design of Reversible Sequential Circuit
Efficient Design of Reversible Sequential CircuitIOSR Journals
 
Technology Integration Pattern For Distributed Scrum of Scrum
Technology Integration Pattern For Distributed Scrum of ScrumTechnology Integration Pattern For Distributed Scrum of Scrum
Technology Integration Pattern For Distributed Scrum of ScrumIOSR Journals
 

En vedette (20)

E0932226
E0932226E0932226
E0932226
 
E0543443
E0543443E0543443
E0543443
 
Secure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor Networks
Secure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor  NetworksSecure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor  Networks
Secure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor Networks
 
Pretext Knowledge Grids on Unstructured Data for Facilitating Online Education
Pretext Knowledge Grids on Unstructured Data for Facilitating  Online EducationPretext Knowledge Grids on Unstructured Data for Facilitating  Online Education
Pretext Knowledge Grids on Unstructured Data for Facilitating Online Education
 
Impact of Distributed Generation on Reliability of Distribution System
Impact of Distributed Generation on Reliability of Distribution SystemImpact of Distributed Generation on Reliability of Distribution System
Impact of Distributed Generation on Reliability of Distribution System
 
K0957079
K0957079K0957079
K0957079
 
G01064046
G01064046G01064046
G01064046
 
J0525460
J0525460J0525460
J0525460
 
H0325660
H0325660H0325660
H0325660
 
A Density Based Clustering Technique For Large Spatial Data Using Polygon App...
A Density Based Clustering Technique For Large Spatial Data Using Polygon App...A Density Based Clustering Technique For Large Spatial Data Using Polygon App...
A Density Based Clustering Technique For Large Spatial Data Using Polygon App...
 
An Improved Ant-Based Algorithm for Minimum Degree Spanning Tree Problems
An Improved Ant-Based Algorithm for Minimum Degree  Spanning Tree Problems An Improved Ant-Based Algorithm for Minimum Degree  Spanning Tree Problems
An Improved Ant-Based Algorithm for Minimum Degree Spanning Tree Problems
 
A Comprehensive Overview of Clustering Algorithms in Pattern Recognition
A Comprehensive Overview of Clustering Algorithms in Pattern  RecognitionA Comprehensive Overview of Clustering Algorithms in Pattern  Recognition
A Comprehensive Overview of Clustering Algorithms in Pattern Recognition
 
C0151216
C0151216C0151216
C0151216
 
B0940509
B0940509B0940509
B0940509
 
Efficient Design of Reversible Sequential Circuit
Efficient Design of Reversible Sequential CircuitEfficient Design of Reversible Sequential Circuit
Efficient Design of Reversible Sequential Circuit
 
G0964146
G0964146G0964146
G0964146
 
G0563337
G0563337G0563337
G0563337
 
J0956064
J0956064J0956064
J0956064
 
Technology Integration Pattern For Distributed Scrum of Scrum
Technology Integration Pattern For Distributed Scrum of ScrumTechnology Integration Pattern For Distributed Scrum of Scrum
Technology Integration Pattern For Distributed Scrum of Scrum
 
I0946770
I0946770I0946770
I0946770
 

Similaire à C0531519

Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mapping
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix MappingDual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mapping
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mappinginventionjournals
 
Sistempertidaksamaanduavariabel2122
Sistempertidaksamaanduavariabel2122Sistempertidaksamaanduavariabel2122
Sistempertidaksamaanduavariabel2122Franxisca Kurniawati
 
DSP_FOEHU - MATLAB 01 - Discrete Time Signals and Systems
DSP_FOEHU - MATLAB 01 - Discrete Time Signals and SystemsDSP_FOEHU - MATLAB 01 - Discrete Time Signals and Systems
DSP_FOEHU - MATLAB 01 - Discrete Time Signals and SystemsAmr E. Mohamed
 
Lecture-1-Mech.pptx . .
Lecture-1-Mech.pptx                   . .Lecture-1-Mech.pptx                   . .
Lecture-1-Mech.pptx . .happycocoman
 
Generalised Statistical Convergence For Double Sequences
Generalised Statistical Convergence For Double SequencesGeneralised Statistical Convergence For Double Sequences
Generalised Statistical Convergence For Double SequencesIOSR Journals
 
Two algorithms to accelerate training of back-propagation neural networks
Two algorithms to accelerate training of back-propagation neural networksTwo algorithms to accelerate training of back-propagation neural networks
Two algorithms to accelerate training of back-propagation neural networksESCOM
 
Comparative analysis of x^3+y^3=z^3 and x^2+y^2=z^2 in the Interconnected Sets
Comparative analysis of x^3+y^3=z^3 and x^2+y^2=z^2 in the Interconnected Sets Comparative analysis of x^3+y^3=z^3 and x^2+y^2=z^2 in the Interconnected Sets
Comparative analysis of x^3+y^3=z^3 and x^2+y^2=z^2 in the Interconnected Sets Vladimir Godovalov
 
HK_Partial Differential Equations_Laplace equation.pdf
HK_Partial Differential Equations_Laplace equation.pdfHK_Partial Differential Equations_Laplace equation.pdf
HK_Partial Differential Equations_Laplace equation.pdfhappycocoman
 
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...mathsjournal
 
Matrix Transformations on Some Difference Sequence Spaces
Matrix Transformations on Some Difference Sequence SpacesMatrix Transformations on Some Difference Sequence Spaces
Matrix Transformations on Some Difference Sequence SpacesIOSR Journals
 
Deep learning study 2
Deep learning study 2Deep learning study 2
Deep learning study 2San Kim
 
ملزمة الرياضيات للصف السادس الاحيائي الفصل الاول
ملزمة الرياضيات للصف السادس الاحيائي الفصل الاولملزمة الرياضيات للصف السادس الاحيائي الفصل الاول
ملزمة الرياضيات للصف السادس الاحيائي الفصل الاولanasKhalaf4
 
ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022
 ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022 ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022
ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022anasKhalaf4
 

Similaire à C0531519 (20)

Calculas
CalculasCalculas
Calculas
 
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mapping
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix MappingDual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mapping
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mapping
 
Sistempertidaksamaanduavariabel2122
Sistempertidaksamaanduavariabel2122Sistempertidaksamaanduavariabel2122
Sistempertidaksamaanduavariabel2122
 
DNN.pptx
DNN.pptxDNN.pptx
DNN.pptx
 
DSP_FOEHU - MATLAB 01 - Discrete Time Signals and Systems
DSP_FOEHU - MATLAB 01 - Discrete Time Signals and SystemsDSP_FOEHU - MATLAB 01 - Discrete Time Signals and Systems
DSP_FOEHU - MATLAB 01 - Discrete Time Signals and Systems
 
Lecture-1-Mech.pptx . .
Lecture-1-Mech.pptx                   . .Lecture-1-Mech.pptx                   . .
Lecture-1-Mech.pptx . .
 
Generalised Statistical Convergence For Double Sequences
Generalised Statistical Convergence For Double SequencesGeneralised Statistical Convergence For Double Sequences
Generalised Statistical Convergence For Double Sequences
 
Two algorithms to accelerate training of back-propagation neural networks
Two algorithms to accelerate training of back-propagation neural networksTwo algorithms to accelerate training of back-propagation neural networks
Two algorithms to accelerate training of back-propagation neural networks
 
Comparative analysis of x^3+y^3=z^3 and x^2+y^2=z^2 in the Interconnected Sets
Comparative analysis of x^3+y^3=z^3 and x^2+y^2=z^2 in the Interconnected Sets Comparative analysis of x^3+y^3=z^3 and x^2+y^2=z^2 in the Interconnected Sets
Comparative analysis of x^3+y^3=z^3 and x^2+y^2=z^2 in the Interconnected Sets
 
A0280106
A0280106A0280106
A0280106
 
1605 power series
1605 power series1605 power series
1605 power series
 
Periodic Solutions for Nonlinear Systems of Integro-Differential Equations of...
Periodic Solutions for Nonlinear Systems of Integro-Differential Equations of...Periodic Solutions for Nonlinear Systems of Integro-Differential Equations of...
Periodic Solutions for Nonlinear Systems of Integro-Differential Equations of...
 
HK_Partial Differential Equations_Laplace equation.pdf
HK_Partial Differential Equations_Laplace equation.pdfHK_Partial Differential Equations_Laplace equation.pdf
HK_Partial Differential Equations_Laplace equation.pdf
 
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...
 
Matrix Transformations on Some Difference Sequence Spaces
Matrix Transformations on Some Difference Sequence SpacesMatrix Transformations on Some Difference Sequence Spaces
Matrix Transformations on Some Difference Sequence Spaces
 
Deep learning study 2
Deep learning study 2Deep learning study 2
Deep learning study 2
 
ملزمة الرياضيات للصف السادس الاحيائي الفصل الاول
ملزمة الرياضيات للصف السادس الاحيائي الفصل الاولملزمة الرياضيات للصف السادس الاحيائي الفصل الاول
ملزمة الرياضيات للصف السادس الاحيائي الفصل الاول
 
ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022
 ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022 ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022
ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022
 
Matrices 2_System of Equations.pdf
Matrices 2_System of Equations.pdfMatrices 2_System of Equations.pdf
Matrices 2_System of Equations.pdf
 
maths ppt.pdf
maths ppt.pdfmaths ppt.pdf
maths ppt.pdf
 

Plus de IOSR Journals (20)

A011140104
A011140104A011140104
A011140104
 
M0111397100
M0111397100M0111397100
M0111397100
 
L011138596
L011138596L011138596
L011138596
 
K011138084
K011138084K011138084
K011138084
 
J011137479
J011137479J011137479
J011137479
 
I011136673
I011136673I011136673
I011136673
 
G011134454
G011134454G011134454
G011134454
 
H011135565
H011135565H011135565
H011135565
 
F011134043
F011134043F011134043
F011134043
 
E011133639
E011133639E011133639
E011133639
 
D011132635
D011132635D011132635
D011132635
 
C011131925
C011131925C011131925
C011131925
 
B011130918
B011130918B011130918
B011130918
 
A011130108
A011130108A011130108
A011130108
 
I011125160
I011125160I011125160
I011125160
 
H011124050
H011124050H011124050
H011124050
 
G011123539
G011123539G011123539
G011123539
 
F011123134
F011123134F011123134
F011123134
 
E011122530
E011122530E011122530
E011122530
 
D011121524
D011121524D011121524
D011121524
 

C0531519

  • 1. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-ISSN: 2278-2834,p- ISSN: 2278-8735. Volume 5, Issue 3 (Mar. - Apr. 2013), PP 15-19 www.iosrjournals.org A New Class of Binary Zero Correlation Zone Sequence Sets B. Fassi1, A. Djebbari1, Taleb-Ahmed. A2 And I. Dayoub3 1 (Telecommunications and Digital Signal Processing Laboratory, Djillali Liabes University of Sidi Bel Abbes, Algeria) 2 (Laboratoire LAMIH UMR, C.N.R.S, Université de Valenciennes et du Hainaut Cambresis, le Mont Houy, France) 3 (I.E.M.N. Dept. O.A.E, U.M.R, C.N.R.S,Université de Valenciennes et du Hainaut CAMBRESIS, le Mont Houy, France) Abstract: This paper proposes a new class of binary zero correlation zone (ZCZ) sequence sets, in which the periodic correlation functions of the proposed sequence set is zero for the phase shifts within the zero- correlation zone. It is shown that the proposed zero correlation zone sequence set can reach the upper bound on the ZCZ codes. Keywords- Sequence design, theoretical upper bound, zero correlation zone (ZCZ) sequences. I. Introduction Zero correlation zone (ZCZ) sequences can be used in spread spectrum systems and CDMA systems to reduce the multiple access interference and co-channel interference [1]. There are several intensive studies on the constructing of ZCZ sequences set [1-8]. The ZCZ sequences set construction is limited by the correlation property and theory bounds [1-7]. Generally, sets of ZCZ sequences are characterized by the period of sequences L, the family size, namely the number of sequences M, and the length of the zero-correlation zone ZCZ. A ZCZ (L, M, ZCZ) sequence set that satisfies the theoretical bound defined by the ratio 𝑀 𝑍 𝑐𝑧 + 1 /𝐿 = 1 is called an optimal zero-correlation zone sequence set [4]. Compared with earlier works on binary ZCZ sequence sets [1, 6], our proposed zero- correlation zone sequence set is, in all cases, an optimal or an approach optimal ZCZ sequence set. The paper is organized as follows. Section 2 introduces the notations required for the subsequent sections, the proposed scheme for sequence construction is explained in section 3. Examples of new ZCZ sequence sets are presented in Section 4. The properties of the proposed sequence sets are shown in Section 5. Finally, we draw the concluding remarks. II. Notations 2.1 Definition 1: Suppose 𝑋𝑗 =(𝑥𝑗 ,0 , 𝑥𝑗 ,1 , … … . 𝑥𝑗 ,𝐿−1 ) and, 𝑋 𝑣 =(𝑥 𝑣,0 , 𝑥 𝑣,1 , … … . 𝑥 𝑣,𝐿−1 ) are two sequences of period L. Sequence pair (𝑋𝑗 , 𝑋 𝑣 ) is called a binary sequence pair if 𝑥𝑗 ,𝑖 , 𝑥 𝑣,𝑖 ∈ −1, +1 , 𝑖 = 0,1,2, … … … 𝐿 − 1 [7]. The Periodic Correlation Function (PCF) between 𝑋𝑗 and 𝑋 𝑣 at a shift 𝜏 is defined by [4]: 𝐿−1 ∀𝜏 ≥ 0, 𝜃 𝑋 𝑗 ,𝑋 𝑣 𝜏 = 𝑖=0 𝑥𝑗 ,𝑖 𝑥 𝑣, 𝑖+𝜏 𝑚𝑜𝑑 (𝐿) (1) 2.2 Definition 2: A set of M sequences 𝑋0 , 𝑋1 , 𝑋2 , … … , 𝑋 𝑀−1 is denoted by Xj 𝑗 𝑀−1 . =0 A set of sequences Xj 𝑗 𝑀−1 =0 is called zero correlation zone sequence set, denoted by Z(L,M,ZCZ) if the periodic correlation functions satisfy [4] : ∀𝑗, 0 < 𝜏 ≤ 𝑍 𝑐𝑧 , 𝜃 𝑋 𝑗 ,𝑋 𝑗 𝜏 =0 (2) ∀𝑗, 𝑗 ≠ 𝑣 , 𝜏 ≤ 𝑍 𝑐𝑧 , 𝜃 𝑋 𝑗 ,𝑋 𝑣 𝜏 =0 (3) III. Proposed Sequence Construction In this section, a new method for constructing sets of binary ZCZ sequences is proposed. The construction is accomplished through three steps. 3.1 Step 1: The 𝑗 𝑡ℎ row of the Hadamard matrix 𝐻 of order n is denoted by ℎ 𝑗 = ℎ 𝑗 ,0 , ℎ 𝑗 ,1 , … … … ℎ 𝑗 ,𝑛 −1 . A set of 2𝑛 sequences 𝑑 𝑗 , each of length 2𝑛, is constructed as follows: For 0 ≤ 𝑗 < 𝑛, www.iosrjournals.org 15 | Page
  • 2. A New Class of Binary Zero Correlation Zone Sequence Sets 𝑑 𝑗 +0 = −ℎ 𝑗 , ℎ 𝑗 (4) 𝑑 𝑗 +1 = ℎ 𝑗 , ℎ 𝑗 (5) 3.2 Step 2: For a fixed integer value 𝑛, and for the first stage, 𝑝 = 0, we can generate, based on the scheme for 2𝑛−1 sequence construction in [6], a series of sets 𝐵𝑗 𝑗 =0 of 2𝑛 sequences as follows: 2𝑛−1 2𝑛−1 A sequence set 𝐵𝑗 𝑗 =0 is constructed from the sequences set 𝑑𝑗 𝑗 =0 . A pair of sequences 𝐵𝑗 +0 and 𝐵𝑗 +1 of 𝑝+2 length (2 𝑛) are constructed by the process of interleaving a sequence pair 𝑑 𝑗 +0 and 𝑑 𝑗 +1 as follows: For 0 ≤ 𝑗 < 𝑛, 𝐵𝑗 +0 = 𝑑 𝑗 +0,0 , 𝑑 𝑗 +1,0 , 𝑑 𝑗 +0,1 , 𝑑 𝑗 +1,1 , … … . . , 𝑑 𝑗 +0,2𝑛−1 , 𝑑 𝑗 +1,2𝑛−1 (6) and, 𝐵𝑗 +1 = 𝑑 𝑗 +0,0 , −𝑑 𝑗 +1,0 , 𝑑 𝑗 +0,1 , −𝑑 𝑗 +1,1 , … … . . , 𝑑 𝑗 +0,2𝑛−1 , −𝑑 𝑗 +1,2𝑛−1 (7) The member size of the sequence set 𝐵𝑗 is 2𝑛. 2𝑛−1 3.3 Step 3: For 𝑝 > 0, we can recursively construct a new series of set, 𝐵𝑗 𝑗 =0 , by interleaving of actual 2𝑛−1 𝐵𝑗 𝑗 =0 . 2𝑛−1 The 𝐵𝑗 𝑗 =0 is generated as follows: For 0 ≤ 𝑗 < 𝑛, 𝐵𝑗 +0 = 𝐵𝑗 +0,0 , 𝐵𝑗 +1,0 , 𝐵 𝑗 +0,1 , 𝐵𝑗 +1,1 , … … . . , 𝐵𝑗 +0,4𝑛−1 , 𝐵 𝑗 +1,4𝑛−1 (8) and, 𝐵𝑗 +1 = 𝐵𝑗 +0,0 , −𝐵𝑗 +1,0 , 𝐵 𝑗 +0,1 , −𝐵𝑗 +1,1 , … … . , 𝐵𝑗 +0,4𝑛−1 , −𝐵 𝑗 +1,4𝑛−1 (9) The length of both sequences 𝐵𝑗 +0 and 𝐵𝑗 +1 is equal to (2 𝑝+2 𝑛). IV. Example of Construction 4.1 Step 1: Let 𝐻 be a Hadamard matrix of order n=2, given by: 1 1 ℎ 𝐻= = 0 1 −1 ℎ1 A set of 2𝑛 = 4 sequences 𝑑 𝑗 , each of length 2𝑛 = 4, is constructed as follows: For 0 ≤ 𝑗 < 2, 𝑑0+0 = −ℎ0 , ℎ0 = −1, −1,1,1 𝑑1+0 = −ℎ1 , ℎ1 = −1,1,1, −1 𝑑0+1 = ℎ0 , ℎ0 = 1,1,1,1 𝑑1+1 = ℎ1 , ℎ1 = 1, −1,1, −1 4.2 Step 2: For the first iteration, 𝑝 = 0. A pair of sequences 𝐵𝑗 +0 and 𝐵𝑗 +1 of length 2 𝑝+2 𝑛 = 8 are constructed by interleaving a sequence pair 𝑑 𝑗 +0 and 𝑑 𝑗 +1 as follows: For 0 ≤ 𝑗 < 2, 𝐵𝑗 +0 = 𝑑 𝑗 +0,0 , 𝑑 𝑗 +1,0 , 𝑑 𝑗 +0,1 , 𝑑 𝑗 +1,1 , … … . . , 𝑑 𝑗 +0,3 , 𝑑 𝑗 +1,3 𝐵0+0 = −1,1, −1,1,1,1,1,1 𝐵1+0 = −1,1,1, −1,1,1, −1, −1 and, 𝐵𝑗 +1 = 𝑑 𝑗 +0,0 , −𝑑 𝑗 +1,0 , 𝑑 𝑗 +0,1 , −𝑑 𝑗 +1,1 , … … . . , 𝑑 𝑗 +0,3 , −𝑑 𝑗 +1,3 𝐵0+1 = −1, −1, −1, −1,1, −1,1, −1 𝐵1+1 = −1, −1,1,1,1, −1, −1,1 3 4.3 Step 3: For the next iteration 𝑝 = 1, The 𝐵𝑗 𝑗 =0 is generated as follows: 𝐵𝑗 +0 = 𝐵𝑗 +0,0 , 𝐵𝑗 +1,0 , 𝐵 𝑗 +0,1 , 𝐵𝑗 +1,1 , … … . . , 𝐵𝑗 +0,7 , 𝐵 𝑗 +1,7 𝐵0+0 = −1, −1, 1, −1, −1, −1, 1, −1,1, 1, 1, −1, 1, 1, 1, −1 , 𝐵1+0 = −1, −1, 1, −1, 1, 1, −1, 1,1, 1, 1, −1, −1, −1, −1, 1 , and, 𝐵𝑗 +1 = 𝐵𝑗 +0,0 , −𝐵𝑗 +1,0 , 𝐵 𝑗 +0,1 , −𝐵𝑗 +1,1 , … … . , 𝐵𝑗 +0,7 , −𝐵 𝑗 +1,7 𝐵0+1 = −1, 1, 1, 1, −1, 1, 1, 1,1, −1, 1, 1, 1, −1, 1, 1 , 𝐵1+1 = −1, 1, 1, 1, 1, −1, −1, −1,1, −1, 1, 1, −1, 1, −1, −1 . www.iosrjournals.org 16 | Page
  • 3. A New Class of Binary Zero Correlation Zone Sequence Sets The length of both sequences 𝐵𝑗 +0 and 𝐵𝑗 +1 is equal to 2 𝑝+2 𝑛 = 16. The member size of the sequence set 𝐵𝑗 is 2𝑛 = 4. 3 Iteratively for 𝑝 = 2, The 𝐵𝑗 𝑗 =0 is generated as follows: 𝐵0+0 = −1, −1, −1, 1, 1, 1, −1, 1, −1, −1, −1, 1, 1, 1, −1, 1, 1, 1, 1, −1,1, 1, −1, 1, 1, 1, 1, −1,1, 1, −1, 1 𝐵1+0 = −1, −1, −1, 1, 1, 1, −1, 1, 1, 1, 1, −1, −1, −1, 1, −1, 1, 1, 1, −1, 1, 1, −1, 1, −1, −1, −1, 1, −1, −1, 1, −1 𝐵0+1 = −1, 1, −1, −1, 1, −1, −1, −1, −1, 1, −1, −1, 1, −1, −1, −1, 1, −1, 1, 1, 1, −1, −1, −1, 1, −1, 1, 1,1, −1, −1, −1 𝐵1+1 = −1, 1, −1, −1, 1, −1, −1, −1, 1, −1, 1, 1, −1, 1, 1, 1, 1, −1, 1, 1, 1, −1, −1, −1, −1, 1, −1, −1 − 1, 1, 1, 1 3 Also, we can find out that the zero correlation zone length of 𝐵𝑗 𝑗 =0 is 4. For example, for 𝜏 = 0,1, … … … … 31, the periodic auto-correlation functions (PACF) (∀𝑗, 𝑗 = 𝑣) given in 1 of 𝐵0+0 , 𝐵1+0 , 𝐵0+1 and 𝐵1+1 are calculated as follows: 𝜃 𝐵0+0 ,𝐵0+0 𝜏 = 32, 0, 0, 0, 0, 4, 0, −4,16, −4, 0, 4, 0, 8, 0, −8, 0, −8, 0, 8, 0, 4, 0, −4,16, −4, 0, 4, 0, 0, 0, 0 𝜃 𝐵1+0 ,𝐵1+0 𝜏 = 32, 0, 0, 0, 0, −4, 0, 4 , −16, 4, 0, −4, 0, 8, 0, −8, 0, −8, 0, 8, 0, −4, 0, 4, −16, 4, 0, −4, 0, 0, 0, 0 𝜃 𝐵0+1 ,𝐵0+1 𝜏 = 32, 0, 0, 0, 0, −4, 0, 4,16, 4, 0, −4, 0, −8, 0, 8, 0, 8, 0, −8, 0, −4, 0, 4,16, 4, 0, −4, 0, 0, 0, 0 𝜃 𝐵1+1 ,𝐵1+1 𝜏 = 32, 0, 0, 0, 0, 4, 0, −4, −16, −4, 0, 4, 0, −8, 0, 8, 0, 8, 0, −8, 0, 4, 0, −4, −16, −4, 0, 4, 0, 0, 0, 0 The periodic cross-correlation functions (PCCF) (∀𝑗, 𝑗 ≠ 𝑣) given in (1) are calculated as follows: 𝜃 𝐵0+0 ,𝐵1+0 𝜏 = 0, 0, 0, 0, 0, −4, 0, 4,16, 4, 0, −4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, −4 , −16, −4, 0, 4, 0, 0, 0, 0 𝜃 𝐵0+0 ,𝐵0+1 𝜏 = 0, 0, 0, 0, 0, −4, −8, −4, 0, 4, −8, 4, 0, −8, −16, −8, 0, 8, −16, 8, 0, −4, −8, −4, 0, 4, −8, 4, 0, 0, 0, 0 𝜃 𝐵0+0 ,𝐵1+1 𝜏 = 0, 0, 0, 0, 0, 4, 8, 4, 0, −4, 8, −4, 0, 0, 0, 0, 0, 0, 0, 0, 0, −4, −8, −4, 0, 4, −8, 4, 0, 0, 0, 0 𝜃 𝐵1+0 ,𝐵0+1 𝜏 = 0, 0, 0, 0, 0, −4, −8, −4, 0, 4, −8, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 4, 0, −4, 8, −4, 0, 0, 0, 0 𝜃 𝐵1+0 ,𝐵1+1 𝜏 = 0, 0, 0, 0, 0, 4, 8, 4, 0, −4, 8, −4, 0, −8 , −16, −8, 0, 8, −16, 8, 0, 4, 8, 4, 0, −4, 8, −4, 0, 0, 0, 0 𝜃 𝐵0+1 ,𝐵1+1 𝜏 = 0, 0, 0, 0, 0, 4, 0, −4,16, −4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, −4, 0, 4, −16, 4, 0, −4, 0, 0, 0, 0 3 We notice that the zero correlation zone length of 𝐵𝑗 𝑗 =0 is 4. The PACF and PCCF confirm that 𝐵𝑗 is a ZCZ (32,4,4) sequence set. V. The Properties of the Proposed Sequence The proposed zero-correlation zone sequence set can be generated from an arbitrary Hadamard matrix of order n. The length of Bj in equations (6) and (7), equal to (2p+2 n), is twice that of Bj in equations (8) and (9). The proposed ZCZ sequence set can satisfy the ideal autocorrelation and cross-correlation properties in the zero-correlation zone. The generated sequence set satisfies the following properties: ∀𝑗, ∀𝜏 ≠ 0, 𝜏 ≤ 2 𝑝 𝜃 𝐵 𝑗 ,𝐵 𝑗 𝜏 =0 (10) and, ∀𝑗 ≠ 𝑣, ∀𝜏, 𝜏 ≤ 2 𝑝 𝜃 𝐵 𝑗 ,𝐵 𝑣 𝜏 =0. (11) The 𝐵𝑗 is a ZCZ sequence set having parameters (L,M, ZCZ)=(2 𝑝+2 𝑛, 2𝑛, 2 𝑝 ), its parameters perfectly satisfy the theory bound of ZCZ sequences set [4, 7]: www.iosrjournals.org 17 | Page
  • 4. A New Class of Binary Zero Correlation Zone Sequence Sets 𝑀(𝑍 𝐶𝑍 + 1) ≤ 𝐿 (12) 𝑀(𝑍 𝐶𝑍 +1) Let 𝜇 = , if 𝜇 = 1 , it indicates that the ZCZ sequence set is optimal [1, 6]. 𝐿 𝑀(𝑍 +1) 2𝑛(2 𝑝 +1) (2 𝑝 +1) For the proposed 𝑍𝐶𝑍(2 𝑝 +2 𝑛, 2𝑛, 2 𝑝 ) sequence set, 𝜇 = 𝐶𝑍 = = 𝐿 2 𝑝 +2 𝑛 2 𝑝 +1 1) For 𝑝 = 0, 𝜇 = 1, the proposed ZCZ sequence set is optimal. lim 2) For 𝑝 > 0, 1/2 ≤ 𝜇 < 1 and 𝑝→∞ 𝜇 = 1/2. For a given family size M, we can construct different sets of sequences with different lengths L and zero correlation zone 𝑍 𝐶𝑍 . As an example, assuming that M=16, we can construct sets of ZCZ sequences with different lengths L=32, 64, 128, 256, 512, 1024, 2048, 4096…., and different ZCZs, 𝑍 𝐶𝑍 =1, 2, 4, 8, 16, 32, 64, 128,….and different parameters 𝜇=1, 0.75, 0.625, 0.5625, 0.53125, 0.515625, 0.5078125, 0.5039063,……. In step 1, other set of 2𝑛 sequences 𝑑 𝑗 , each of length 2𝑛, can be constructed as follows: 1- For 0 ≤ 𝑗 < 𝑛, 𝑑 𝑗 +0 = ℎ 𝑗 , −ℎ 𝑗 (13) 𝑑 𝑗 +1 = ℎ 𝑗 , ℎ 𝑗 (14) 3 In this case, the set of 𝐵𝑗 𝑗 =0 is generated as follows: 𝐵0+0 = 1,1,1, − 1, 1, 1, −1, 1,1,1,1, − 1, 1, 1, −1, 1, − 1, − 1, − 1,1,1, 1, −1, 1, − 1, − 1, − 1,1,1, 1, −1, 1 𝐵1+0 = 1,1,1, − 1, 1, 1, −1, 1, −1, −1, −1, 1, −1, −1,1, − 1, − 1, − 1, − 1,1,1, 1, −1, 1,1,1,1, −1, −1, −1,1, − 1 𝐵0+1 = 1, − 1,1,1, 1, −1, −1, −1,1, − 1,1,1, 1, −1, −1, −1, −1,1, − 1, − 1, 1, −1, −1, −1, − 1,1, −1, − 1,1, −1, −1, −1 𝐵1+1 = 1, − 1,1,1, 1, −1, −1, −1, −1, 1, −1, −1, − 1,1,1,1, −1,1, − 1, − 1, 1, −1, −1, −1, 1, −1, 1,1, −1,1,1,1 2- For 0 ≤ 𝑗 < 𝑛, 𝑑 𝑗 +0 = ℎ 𝑗 , ℎ 𝑗 (15) 𝑑 𝑗 +1 = −ℎ 𝑗 , ℎ 𝑗 (16) 3 The set of 𝐵𝑗 𝑗 =0 is generated as follows: 𝐵0+0 = 1,1,1, − 1, − 1, − 1,1, − 1,1,1,1, −1, −1, − 1,1, − 1, 1, 1, 1, −1,1, 1, −1, 1, 1, 1, 1, −1,1, 1, −1, 1 𝐵1+0 = 1,1,1, − 1, − 1, − 1,1, − 1, −1, −1, −1, 1, 1, 1, −1, 1, 1, 1, 1, −1,1, 1, −1, 1, −1, − 1, − 1,1, −1, −1,1, − 1 𝐵0+1 = 1, − 1,1,1, − 1,1,1,1,1, − 1,1,1, − 1,1,1,1, 1, −1, 1, 1, 1, −1, −1, −1, 1, −1, 1, 11, −1, −1, −1 𝐵1+1 = 1, − 1,1,1, − 1,1,1,1, −1, 1, −1, −1, 1, −1, −1, −1, 1, −1, 1, 1, 1, −1, −1, −1, − 1,1, −1, −1, −1,1,1,1 3- For 0 ≤ 𝑗 < 𝑛, 𝑑 𝑗 +0 = ℎ 𝑗 , ℎ 𝑗 (17) 𝑑 𝑗 +1 = ℎ 𝑗 , −ℎ 𝑗 (18) www.iosrjournals.org 18 | Page
  • 5. A New Class of Binary Zero Correlation Zone Sequence Sets It should be noted that in [9], the limits of the correlation function of binary zero-correlation zone sequences 3 𝐵𝑗 𝑗 =0 obtained from equation (17) and (18) are evaluated. VI. Conclusion In this paper, we have proposed a new method for constructing sets of binary ZCZ sequences .The structure of the proposed ZCZ sequences set is simple and thus it is easy to be generated. The PACF side lobes and PCCF of the proposed sequence set is zero for the phase shifts within the zero-correlation zone. The proposed ZCZ sequence set with (2 𝑝+2 𝑛, 2𝑛, 2 𝑝 ) is optimal or asymptotically optimal ZCZ sequence set. This method is useful for designing spreading sequences for multi-user CDMA system. References [1] P. Z. Fan, N. Suehiro, N. Kuroyanagi and X. M. Deng, Class of binary sequences with zero correlation zone, IEE Electronic Letters, 35(10), 1999, 777-779. [2] H. Torii, M. Nakamura, N. Suehiro, A new class of zero-correlation zone sequence, IEEE. Trans. Inf. Theory, 50(3), 2004, 559-565. [3] H. Torri, M. Nakamura, N. Suehiro, Enhancement of ZCZ sequence set construction procedure, Proc. IWSDA05, 2005, 67-72. [4] T. Hayashi, A class of zero-correlation zone sequence set using a perfect sequence, IEEE Signal ProcessingLetters, 16(4),2009, 331- 334. [5] Kai Liu, ChengqianXu Gang Li, Binary zero correlation zone sequence pair set constructed from difference set pairs, Proceedings of International Conference on Networks Security, Wireless Communications and Trusted Computing (NSWCTC’09),2, 2009, 543- 546. [6] T. Maeda, S. Kanemoto, T. Hayashi, A Novel Class of Binary Zero-Correlation Zone Sequence Sets, N°978-1-4244-6890-©2010 IEEE, TENCON 2010. [7] S. Renghui, Z. Xiaoqun, Li. Lizhi, Research on Construction Method of ZCZ Sequence Pairs Set, Journal of Convergence Information Technology, 6(1). January 2011. [8] A. Rathinakumar and A.K. Chaturvedi, Mutually orthogonal sets of ZCZ sequences, ELECTRONICS LETTERS,40 (18),2004. [9] T. Hayashi, Limits of the Correlation Function of a Class of Binary Zero-correlation-zone Sequences, ftp://ftp.u-aizu.ac.jp/u- aizu/doc/Tech-Report/2002/2002-1-013.pdf, June 6, 2002. www.iosrjournals.org 19 | Page