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The symbolic dynamics method of communication using chaotic systems has previously been shown to offer performance better than conventional binary phase shift keying (BPSK), while also providing similar spectrum efficiency. This is achieved by exploiting diversity in the waveform, through a mechanism similar to partial response signalling. In order to achieve this performance, a correlation method for detection has been proposed, but complexity was high and only performance in additive white Gaussian noise (AWGN) channels has been considered. It is demonstrated that the complexity can be reduced from requiring 1024 correlations per symbol down to only 32, while degrading performance only by 0.6 dB. The ability to further reduce the occupied bandwidth is investigated. Spectrum occupancy equivalent to a root-raised cosine-filtered BPSK signal is demonstrated, and performance is maintained when the reference waveforms are similarly filtered to maintain the signal match. Finally, a novel equalisation technique that incorporates a decision feedback structure into the correlation detector is proposed. Performance in multipath channels is investigated and shown to be effective.
References
-
-
1)
-
Kolumban, G., Kennedy, M.P., Kis, G., Jako, Z.: `FM-DCSK: a novel method for chaotic communications', Proc. ISCAS'98, May 1998, IV, p. 477–480.
-
2)
-
H. Leung
.
System identification using chaos with application to equalisation of a chaotic modulation system.
IEEE Trans. Circuits Syst. I
,
3 ,
314 -
320
-
3)
-
N. Sharma ,
E. Ott
.
Combating channel distortions in communication with chaotic systems.
Phys. Letts. A
,
347 -
352
-
4)
-
L. Zhu ,
Y.C. Lai ,
F.C. Hoppensteadt ,
E.M. Bollt
.
Numerical and experimental investigation of the effect of filtering on chaotic symbolic dynamics.
Chaos
,
1 ,
410 -
419
-
5)
-
C. Williams
.
Chaotic communication over radio channels.
IEEE Trans. Circuits Syst.
,
12 ,
1394 -
1404
-
6)
-
G.J. Pottie ,
D.P. Taylor
.
A comparison of reduced complexity decoding algorithms for trellis codes.
IEEE J. Sel. Areas Commun.
,
1369 -
1380
-
7)
-
Carroll, M., Willliams, C.: `Symbolic dynamics method for chaotic communication systems', MILCOM 2002. Proc., October 2002, 1, p. 232–236.
-
8)
-
P.R. Chevillat ,
E. Eleftheriou
.
Decoding of trellis-encoded signals in the presence of intersymbol interference and noise.
IEEE Trans. Commun.
,
669 -
676
-
9)
-
R. Raheli ,
A. Polydoros ,
C.-K. Tzou
.
Per-survivor processing: a general approach to MLSE in uncertain environments.
IEEE Trans. Commun.
,
354 -
364.
-
10)
-
M.V. Eyuboglu ,
S.U.H. Qureshi
.
Reduced state sequence estimation with set partitioning and decision feedback.
IEEE Trans. Commun.
,
13 -
20
-
11)
-
Kuomo, K.M., Openheim, A.V., Barron, R.J.: `Channel equalisation for self-synchronising chaotic systems', Proc. ICASSP'96, 1996, p. 1605–1608.
-
12)
-
S. Hayes ,
C. Grebogi ,
E. Ott ,
A. Mark
.
Experimental control of chaos for communication.
Phys. Rev. Lett.
,
13 ,
1781 -
1784
-
13)
-
S.H. Qureshi
.
Adaptive equalisation.
Proc. IEEE
,
9 ,
1349 -
1387
-
14)
-
J.G. Proakis
.
(1995)
Digital communications.
-
15)
-
C. Williams
.
Robust chaotic communications exploiting waveform diversity – Part 1: correlation detection and implicit coding.
IET Commun.
,
10
-
16)
-
Ciftci, M., Williams, D.B.: `Iterative equalisation for chaotic communication systems', ICASSP 2005, 2005, IV, p. 165–168.
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