Capacity of Finite-State Channels:
Lyapunov Exponents and Shannon Entropy
Tim Holliday
Peter Glynn
Andrea Goldsmith
Stanford University
Introduction

We show the entropies H(X), H(Y), H(X,Y), H(Y|X) for finite
state Markov channels are Lyapunov exponents.

This result provides an explicit connection between
dynamic systems theory and information theory

It also clarifies Information Theoretic connections to
Hidden Markov Models

This allows novel proof techniques from other fields to be
applied to Information Theory problems
Finite-State Channels

Channel state Zn  {c0, c1, … cd} is a Markov Chain with
transition matrix R(cj, ck)

States correspond to distributions on the input/output
symbols P(Xn=x, Yn=y)=q(x ,y|zn, zn+1)

Commonly used to model ISI channels, magnetic recording
channels, etc.
c0
c1
R(c1, c3)
R(c0, c2)
c2
c3
Time-varying Channels
with Memory

We consider finite state Markov channels with no
channel state information

Time-varying channels with finite memory induce
infinite memory in the channel output.

Capacity for time-varying infinite memory channels is
defined in terms of a limit
1
C  maxn lim I X n ; Y n
p ( X ) n  n


Previous Research

Mutual information for the Gilbert-Elliot channel
 [Mushkin Bar-David, 1989]

Finite-state Markov channels with i.i.d. inputs
 [Goldsmith/Varaiya, 1996]

Recent research on simulation based computation of
mutual information for finite-state channels
 [Arnold, Vontobel, Loeliger, Kavčić, 2001, 2002, 2003]
 [Pfister, Siegel, 2001, 2003]
Symbol Matrices

For each symbol pair (x,y)  X x Y define a
|Z|x|Z| matrix G(x,y)
G(x,y)(c0,c1) = R(c0,c1) q(x0 ,y0|c0,c1),  (c0,c1)  Z

Where (c0,c1) are channel states at times (n,n+1)

Each element corresponds to the joint
probability of the symbols and channel
transition
Probabilities as Matrix Products


Let m be the stationary distribution of the channel


P X 0n  x0n , Y0n  y0n 

n
n
n
n
n
n
n
n
P
X

x
,
Y

y
|
Z

c
P
Z

c
 0 0 0 0 0 0 0 0
c0 ,c1 ,,cn


c0 ,c1 ,,cn
n
m (c0 ) R(c j , c j 1 )q( x j , y j | c j , c j 1 )
j 0
 m G( x1 , y1 )G( x2 , y2 ) G( xn , yn ) e
 G( x1 , y1 )G( x2 , y2 ) G( xn , yn )
The matrices G are deterministic
functions of the random pair (x,y)

Entropy as a Lyapunov Exponent

The Shannon entropy is equivalent to the Lyapunov
exponent for G(X,Y)
1
H(X, Y)   lim Elog P( X 1 ,, X n , Y1 ,, Yn ) 
n  n
1
  lim log P( X 1 ,, X n , Y1 ,, Yn )
n  n
1
  lim log G( X1 ,Y1 ) G( X n ,Yn )
n  n
1
  lim E log G( X1 ,Y1 ) G( X n ,Yn )  λ(Y | X)
n  n



Similar expressions exist for H(X), H(Y), H(X,Y)
Growth Rate Interpretation

The typical set An is the set of sequences
x1,…,xn satisfying
2  nH(X)   P X 1  x1 ,, X n  xn   2  nH(X) 

By the AEP P(An)>1- for sufficiently large n

The Lyapunov exponent is the average rate of
growth of the probability of a typical sequence

In order to compute l(X) we need information
about the “direction” of the system
Lyapunov Direction Vector

The vector pn is the “direction” associated with l(X)
for any m.

Also defines the conditional channel state probability
m GX GX ...GX
n


pn
P( Zn 1 | X )
|| m GX GX ...GX ||1
1
1

2
2
n
n
Vector has a number of interesting properties

It is the standard prediction filter in hidden Markov
models

pn is a Markov chain if m is the stationary distribution for
the channel)
Random Perron-Frobenius Theory

The vector p is the random Perron-Frobenius
eigenvector associated with the random matrix GX
For all n we have
For the stationary
version of p we have
pn 
pn 1G X n
pn 1G X n
1
D
pGX  L p
The Lyapunov exponent l ( X )  E , X log L 
we wish to compute is
 E , X log pG X
1
Technical Difficulties

The Markov chain pn is not irreducible if the
input/output symbols are discrete!

Standard existence and uniqueness results cannot be
applied in this setting

We have shown that pn possesses a unique
stationary distribution if the matrices GX are
irreducible and aperiodic

Proof exploits the contraction property of
positive matrices
Computing Mutual Information


Compute the Lyapunov exponents l(X), l(Y), and l(X,Y)
as expectations (deterministic computation)
Then mutual information can be expressed as
I ( X ; Y )  l ( X )  l (Y )  l ( X , Y )

We also prove continuity of the Lyapunov exponents on
the domain q, R, hence
C  max [l ( X )  l (Y )  l ( X , Y )]
( q, R)
Simulation-Based Computation
(Previous Work)


Step 1: Simulate a long sequence of input/output
symbols
Step 2: Estimate entropy using
1 n1
 H n ( X )  ln ( X )   log p j GX j
n j 0
1

Step 3: For sufficiently large n, assume that the
sample-based entropy has converged.

Problems with this approach:
Need to characterize initialization bias and confidence
intervals
 Standard theory doesn’t apply for discrete symbols

Simulation Traces for Computation of
H(X,Y)
Rigorous Simulation Methodology

We prove a new functional central limit theorem
for sample entropy with discrete symbols

A new confidence interval methodology for
simulated estimates of entropy


A method for bounding the initialization bias in
sample entropy simulations


How good is our estimate?
How long do we have to run the simulation?
Proofs involve techniques from stochastic
processes and random matrix theory
Computational Complexity of
Lyapunov Exponents

Lyapunov exponents are notoriously difficult to
compute regardless of computation method

NP-complete problem [Tsitsiklis 1998]

Dynamic systems driven by random matrices
typically posses poor convergence properties

Initial transients in simulations can linger for
extremely long periods of time.
Conclusions

Lyapunov exponents are a powerful new tool for
computing the mutual information of finite-state channels

Results permit rigorous computation, even in the case of
discrete inputs and outputs

Computational complexity is high, multiple computation
methods are available

New connection between Information Theory and
Dynamic Systems provides information theorists with a
new set of tools to apply to challenging problems