performance evaluation of a class of chaos-based coded modulations

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F.J. Escribano, L. López and M.A.F. Sanjuán Universidad Rey Juan Carlos Spain e-mail: [email protected] Parallel Concatenated Chaos Coded Modulations Split, Croatia, 27 Split, Croatia, 27 th th September 2007 September 2007

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F.J. Escribano, L. López and M.A.F. Sanjuán

Universidad Rey Juan Carlos

Spain

e-mail: [email protected]

Parallel Concatenated Chaos Coded Modulations

Split, Croatia, 27Split, Croatia, 27thth September 2007 September 2007

22

Background

In most cases, chaos based encoders/modulators had so far proved poor performing in terms of bit error rate (BER) and usually not very robust

Previous work hinted towards a potential boost in BER when parallel concatenating bad performing chaos based modulators

Turbo-like Structures for Chaos Coding and Decoding,F. J. Escribano, S. Kozic, L. López, M. A. F. Sanjuán and M. Hasler (submitted)

33

General Setup: concatenated modulator

Similar setup to the parallel concatenation of Trellis Coded Modulations (TCM): Two Chaos Coded Modulation (CCM) blocks + Interleaver

Parallel Concatenated Chaos Coded Modulations (PCCCM)

Differences to Turbo-TCM: Individual CCMs work at a rate of one symbol per bit

Bit interleaver instead of symbol interleaver

Parameters: Kind of CCM (underlying map) + quantization level (Q)

Size of interleaver π (N) + structure of permutation

44

General Setup: CCM block

Map view: one chaotic map (f0(z)=f1(z)), or switched maps

Trellis encoder view: quantized version of the switched map setup driven by small perturbations, with a feedback connection.

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0 )(),(

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+−∈−=⋅+=

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+

+

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1

1

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2

12,,

2

1

1,1 12

2),(),(

Q

Q

Qn

nnn

Qnnnnn

z

xzx

zbgbzfz

[ ] [ ]0,10,1:)(1,0 →zf

55

General Setup: parameters

Maps considered Bernoulli shift map (BSM)

Switched version of the BSM, multi-BSM (mBSM)

Tent map (TM)

Switched version of the TM, multi-TM (mTM)

Quantization level Q>4 is enough to make quantization effects neglibigle in

practice

Interleaver S-random interleaver

The channel consists in additive white Gaussian noise (AWGN channel).

66

General Setup: iterative decoder

The trellis coded characteristics of the chaotic signal allows the use of known decoding frameworks for concatenated coding

Decoder consists on two SISO (soft-input soft-output) decoding blocks working iteratively

The decoders interchange soft information in the form of log likelihood ratios (llr’s).

==

=−

)0(

)1(logllr

1231

1231O1,

Nn

Nn

yyycp

yyycp

77

Error floor analysis: binary error events

Each CCM has minimal binary error event loops with structure 10…01, Hamming weight 2 and length L*=Q+n

n=1,2 depending on the kind of chaos coded modulation

Euclidean distance between CCM sequences xn and xn’

If S>3L*, the dominant error events when Eb/N0->∞ for the PCCCM consist in the concatenation of two of said error events

( )∑−+

=

−=1

2'2

* mL

mnnnE xxd

88

Error floor analysis: Euclidean distance

PCCCM sequences xk and xk’ related through such compound binary error event exhibit four chaos coded subsequences of length L* with non-zero difference

For the BSM CCM, each individual Euclidean distance has the same value dE

2≈4/3, regardless of xk and xk’

For the rest of CCM’s, each individual Euclidean distance depends on the values of xk and xk’ (they do not comply with the uniform error event property)

The evaluation of the corresponding distance spectrum requires evaluating the distance spectrum of the individual error events and of their combinations

( )∑=

+++=−=N

kEEEEkkE ddddxxd

2

1

22222'2

4321

99

Error floor analysis: Euclidean distance spectra

Histograms for the individual error events (Q=5)

mBSM TM mTM

Histogram for the compund error events (mTM)

Distance spectra does not change basically with growing Q

1010

Error floor analysis: error floor bound

The bound for the bit error probability in the error floor region can be given in the general case by numerical integration over the probability density function (pdf) of the Euclidean distance spectrum dE

2 as estimated through the histogram

p(v): pdf of the total dE2

N: size of the interleaver

w4=4: Hamming weight of the related binary error

N4: combinations of two pairs of individual binary errors of Hamming weight 2 and length L* allowed by the interleaver

R=1/2: overall rate of the PCCCM

P≈1/3: power of the chaos coded sequence

⋅≈

2max

2min

0

44

4erfc)(

2

E

E

floor

d

d

bb dv

N

ER

P

vvp

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Simulation results and bounds

N=10000, Q=5, S=23 (left plot), 20 decoding iterations.

mBSM, different N, QSame parameters, different maps

1212

Concluding remarks

BER performance of the PCCCM system can be comparable to the attainable with the turbo-TCM or binary turbocode related systems: Steep waterfal at low Eb/N0

Relatively high error floor for high Eb/N0

The error floor decreases as 1/N By examining the permutation structure of the interleaver, it is

possible to bound the BER at the error floor region The PCCCM system based on a quasi-linear CCM (BSM) complies with

the uniform error property, but the final behaviour is poor The CCM’s not complying with the uniform error property and with

complex distance spectra lead to lower error floors The effect of the quantization level is small and the system

behaviour shows to be rather linked to the underlying dynamics of the map involved

The PCCM system is nonlinear and sends chaotic-like samples to the channel, which exhibit desirable properties. This chaotic-like signal is easy to generate and can be decoded efficiently with known frameworks

1313

Future work

We have shown that chaos based digital communications can attain a similar performance to other successful coding schemes, and that standard analysis techniques can be applied to predict the BER

But there is still a number of questions to be addressed:

Study other possible encoding structures, based upon other chaotic maps, and try to find general properties and design criteria

Verify the exact influence of the design parameters (S, N, Q…)

Consider other kind of channels (e.g. Rayleigh fading channels) and verify the robustness or suitability of the system

Try to find the link between map dynamics and final performance

Try to exploit in the best possible way the chaotic nature of the system in analysis and performance

Thanks for your attention