A Constrained Channel Coding Approach to Joint Communication and Channel Estimation

0.11 MB·English

by  Wenyi Zhang

A Constrained Channel Coding Approach to Joint
Communication and Channel Estimation
Wenyi Zhang, Satish Vedantam, and Urbashi Mitra
Ming Hsieh Department of Electrical Engineering
University of Southern California
{wenyizha, vedantam, ubli}@usc.edu
Abstract—Ajointcommunicationandchannelstateestimation Increasingrandomnessinchannelinputsincreasesinformation
8 problem is investigated, in which reliable information transmis- transfer while reducing the receiver’s ability to estimate the
0 sion over a noisy channel, and high-fidelity estimation of the channel.Incontrast,deterministicsignalingfacilitateschannel
0 channel state, are simultaneously sought. The tradeoff between
estimation at the expense of zero information transfer. In this
2 the achievable information rate and the estimation distortion is
quantified by formulating the problem as a constrained channel paper,we showthatthe optimaltradeoffcan beformulatedas
n coding problem, and the resulting capacity-distortion function a channelcoding problem, with the channelinput distribution
a characterizes the fundamental limit of the joint communication constrained by an average “estimation cost” constraint.
J
and channel estimation problem. The analytical results are
The rest of this paper is organized as follows. Section
7 illustrated through case studies, and further issues such as
II introduces the channel model and the capacity-distortion
multiple cost constraints, channel uncertainty, and capacity per

] unit distortion are also briefly discussed. function,andSectionIIIformulatestheequivalentconstrained
T
channelcodingproblem.Section IV illustrates the application
I I. INTRODUCTION of the capacity-distortion function through several simple
.
s
In this paper, we consider the problem of joint commu- examples. Section V briefly discusses some related issues
c
[ nication and channel estimation over a channel with a time- including multiple cost constraints, channel uncertainty, and

varying channel state. We consider a noisy channel with a capacity per unit distortion. Finally, Section VI concludesthe
1
randomchannelstate that evolveswith time, in a memoryless paper.
v
6 fashion, and is neither available to the transmitter nor the
II. CHANNEL MODEL
3 receiver.Theobjectiveis tohavethe receiverrecoverboththe
1 information transmitted from the transmitter as well the state We consider the channelmodel in Figure 1. For a length-n
1 blockof channelinputs,a message M is equallyprobablyse-
of the channel over which the information was transmitted.
1. The problemsetting may proverelevantfor situations such as lected among{1,..., enR }, and is encodedby the encoder,
0 environment monitoring in sensor networks [1], underwater generating the corresponding channel inputs {X1,...,Xn}.
(cid:6) (cid:7)
8 We provide the following definition.
acoustic applications [2], and cognitive radio [3]. A distinct
0 Definition 1: (Encoder) An encoder is defined by a func-
featureofourproblemformulationisthatbothcommunication
v: tion, f :M={1,..., enR }→Xn, for each n∈N.
and channel estimation are required. n
i
X Theinterplaybetweeninformationmeasuresandestimation (cid:6) (cid:7)
r (minimummean-squarederror(MMSE)inparticular)haslong
a been investigated; see, e.g., [4] and references therein. Previ- Mˆ
ously, however, estimation was only to facilitate information M encoder Xi cPh(ayn|nxe,ls) Yi jaonindt edsetcimodaetorr
transmission, rather than a separate goal. For example, a Sˆi
commonstrategyinblockinterferencechannels[5]ischannel
estimation via training[6]. The purposeof channeltraining is Si
only to increase the information rate for communication, and
thus the quality of channel estimate is not traded off with the
Fig.1. Channelmodelforjointcommunication andchannel estimation.
information rate, as we consider in this paper.
The problem formulation in [7], [8] bears some similarity Thechannelisdescribedbya transitionfunctionP(y|x,s),
to the one we consider in that the receiver is interested in which is the probability distribution of the channel output Y,
both communication and channel estimation. It differs from conditioned on the channel input X and the channel state S.
our work in a critical way: the channel state is assumed Upon receiving the length-n block of channel outputs, the
non-causally known at the transmitter. In contrast, neither the joint decoder and estimator (defined below) declares Mˆ ∈
transmitter nor the receiver knows the channel state in our {1,..., enR }asthedecodedmessage,andalength-nblock
problem formulation. of estimates of the channel state.
(cid:6) (cid:7)
Intuitively,thereexistsatradeoffbetweenachannel’scapa- For technical purposes, in this paper, we assume that the
bilitytotransferinformationanditscapabilitytoexhibitstate. random channel state evolves with time in a memoryless
fashion. We note that this model encompasses the block where the expectation is with respect to the channel state S
interference channel model, because we can treat a block as and the channel output Y conditioned upon the channel input
a super-symbol and thus converta block interference channel X = x, and h : X×Y → S denotes an arbitrary one-shot
0
into a memoryless channel. estimator of S given the channel input and output.
Definition 2: (Jointdecoderand estimator)A jointdecoder The following theorem establishes the constrained channel
and estimator is defined by a pair of functions, g :Yn →M coding formulation.
n
and h :Yn →Sn, for each n∈N. Theorem 1: The capacity-distortion function for the chan-
n
Thisdefinitiondiffersfromthatoftheconventionalchannel nel model in Figure 1 is given by
decoder (e.g., [9]) in that it explicitly requires estimation of
the channel state S at the receiver. The quality of estimation C(D)= sup I(X;Y), (5)
is measured by the distortion function d:S×S→R+∪{0}. PX∈PD
That is, if Sˆi is the ith element of hn(Yn), then d(Si,Sˆi) where
denotes the distortion at time i, i = 1,...,n. For technical
convenience,we assume thatd(·,·) is boundedfromaboveso
that there exists a finite T > 0 with d(s,s′) ≤ T < ∞ for PD = PX : PX(x)d∗(x)≤D . (6)
( )
any s,s′ ∈ S. Note that for length-n block coding schemes, Remark: Theorem 1xX∈aXpplies to general input/output/state
the average distortion is given by alphabets.IfXisacontinuousrandomvariable,thesummation
in (6) should be understood as an integral over X.
n
1
d¯(Sn,Sˆn)= d(S ,Sˆ ). (1) InordertoproveTheorem1,weshallemploythefollowing
i i
n
i=1 lemmas.
X
Lemma 1: For any (f ,g ,h )-sequence that achieves
Finally, we have the following definitions. n n n
C(D), as n → ∞, the achieved average distortion (2) is (in
Definition 3: (Achievable rate) A nonnegative number
probability) equal to the average distortion with Sˆn replaced
R(D) is an achievable rate if there exist a sequence of
by
encodersandcorrespondingjointdecodersandestimatorssuch
that (a) the average probability of decoding error Pe(n) = Sˆn =h∗(Xn,Yn), (7)
(1/ enR(D) )· ⌈enR(D)⌉Pr[Mˆ 6=m|M=m] tends to zero n
m=1
as n → ∞; and (b) the average distortion in channel state whereh∗(Xn,Yn)denotestheblock-nestimatorthatachieves
n
estim(cid:6)ation, (cid:7) P the minimum average distortion conditioned upon both the
block-n channel inputs and outputs.
limsupEd¯(Sn,Sˆn)≤D. (2) Proof: For each n, let us replace the estimator h by h∗
Definition 4: (nC→ap∞acity-distortion function) The capacity- in (7), with its first argument being the channel innputs Xˆnn
distortion function is defined as correspondingtothedecodedmessageMˆ.WhenMˆ =M,the
minimumaveragedistortionisachievedbyh∗;whenMˆ 6=M,
n
C(D)= sup R(D). (3) theincrementin theaveragedistortiondueto replacingh by
n
fn,gn,hn h∗ is bounded from above because d(·,·) ≤ T < ∞. By
Remark: The reader may want to distinguish between the n
Definitions 3 and 4, as n → ∞, the average probability of
capacity-distortionfunction and the rate-distortionfunction in
decoding error P(n) → 0. Hence as n → ∞, the minimum
lossy source coding [9]. The capacity-distortion function is e
averagedistortionisachievedbyh∗(Xˆn,Yn), whichisfurther
defined with respect to a state-dependent channel, seeking n
equal to (7), in probability. Q.E.D.
to characterize the fundamental tradeoff between the rate of
Lemma 1 shows that the joint decoder and estimator can
informationtransmissionandthedistortionofstateestimation.
utilize the reliably decoded channel inputs for channel state
In contrast, the rate-distortionfunctionis definedwith respect
estimation. The nextlemma,Lemma 2, furthershows thatthe
toasourcedistribution,seekingtocharacterizethefundamen-
tal tradeoff between the rate of its lossy description and the length-n block estimator can be decomposed into n one-shot
estimators, each for one channel use.
achievable distortion due to the description.
Lemma 2: For any (f ,g ,h )-sequence that achieves
n n n
III. A CONSTRAINED CHANNEL CODING FORMULATION C(D), as n → ∞, the achieved average distortion (2) is (in
probability) equal to that achieved by
In this section, we show that the joint communication and
channelestimationproblemcan be equivalentlyformulatedas Sˆ =h∗(X ,Y ), i=1,...,n, (8)
i 0 i i
a constrained channel coding problem. For this purpose, the
following minimum conditional distortion will be important. where h∗(X ,Y ) denotes the one-shotestimator that achieves
0 i i
The minimum conditional distortion function is defined for theminimumexpecteddistortionforS conditioneduponboth
i
each possible realization of the channel input X, as the channel input X and output Y .
i i
Proof: From Lemma 1, as n → ∞, h (Yn) is in probability
d∗(x)= inf E[d(S,h0(x,Y))], (4) equivalenttoh∗(Xn,Yn).Thedecompnosition(8)thenfollows
h0:X×Y→S n
becausethechannelismemoryless.Foreachfixedn,wehave A. Uniform Estimation Costs
P(Sn|Xn,Yn)= P(Xn,Yn,Sn) A special case is that d∗(x) = d0 for all x ∈ X. For
P(Xn,Yn) such type of channels, the average cost constraint in (6)
n P(S ,X ,Y ) exhibits a singular behavior. If D < d0, then the joint
= i=1 i i i communication and channel estimation problem is infeasible;
n P(S ,X ,Y )
SQn i=1 i i i otherwise, P consists of all possible input distributions, and
n P(S ,X ,Y ) D
= P Q i=1 i i i thus the capacity-distortion function C(D) is equal to the
ni=1 SQiP(Yi|Xi,Si)P(Si) PX(Xi) unconstrained capacity of the channel. One of the simplest
= iQ=n1PP((cid:2)YP(iS|Xi,iX)Pi,XY(iX)i) =i=n1P(S(cid:3)i|Xi,Yi). (9) icYthica=annnXesliusb+wtriSathci,tufonofirffoXwrmhifcrehosmtiamsYatht.ieonreccoesitvseirsrtehleiaabdlydidtievceocdheasnMne,l
Y Y i i
As we take n→∞, the lemma is established. Q.E.D.
B. A Scalar Multiplicative Channel
ProofofTheorem1:FromLemmas1and2,we canrewrite
the average distortion constraint (2) as Consider the following scalar multiplicative channel
limsup 1 n Ed(S ,Sˆ )≤D Yi =SiXi, (12)
i i
n
n→∞
Xi=1 where all the alphabets are binary, X = Y = S = {0,1},
n
1 and the multiplication is in the conventional sense for real
⇒ limsup Ed(S ,h∗(X ,Y ))≤D. (10)
n i 0 i i numbers. The reader may interpret S as the status of an
n→∞
i=1
X informed jamming source, a fading level, or the status of
Utilizing (4) and the fact that the channel is memoryless, we anothertransmitter.ActivatingStoits“effectivestatus”S=0
can further deduce from (10) that shuts down the link between X and Y; otherwise, the link
Ed∗(X)≤D. (11) X→Y isessentiallynoiseless.We takethedistortionmeasure
astheHammingdistance:d(s,sˆ)=1ifandonlyifsˆ6=sand
So now the constraints in Definition 3 reduce to having zero otherwise.
P(n) → 0 as n → ∞, subject to the constraint (11). This is The tradeoff between communication and channel estima-
e
exactly the problem of channel coding with a cost constraint tion is straightforward to observe from the nature of the
ontheinputdistribution,andTheorem1directlyfollowsfrom channel:for good estimation of S, we want X=1 as often as
standard proofs; see, e.g., [10]. Q.E.D. possible, whereas this would reduce the achieved information
Discussion: rate. In this example, we assume that P(S = 1) = r ≤ 1/2.
(1) The proof of Theorem 1 suggests the joint decoder We shall optimize P(X = 1), denoted by p ∈ [0,1]. The
and estimator first decode the transmitted message in a “non- channel mutual information is I(X;Y)=H (pr)−p·H (r),
2 2
coherent”fashion,thenutilizethereconstructedchannelinputs where H (·) denotes the binary entropy function H (t) =
2 2
along with the channel outputs to estimate the channel states. −tlogt−(1−t)log(1−t). For x=0, the optimal one-shot
As the coding block length grows large, such a two-stage estimator is Sˆ = 0 (note that P(S = 1) = r ≤ 1/2), and the
procedure becomes asymptotically optimal. resulting minimum conditional distortion is d∗(0) = r. For
(2) For each x ∈ X, d∗(x) quantifies its associated min- x = 1, the optimal one-shot estimator is Sˆ = Y = S, leading
imum distortion. Alternatively, d∗(x) can be viewed as the to d∗(1) = 0. Therefore the input distribution should satisfy
“estimation cost” due to signaling with x. Hence the average (1−p)r ≤D.
distortionconstraintin(6)regulatestheinputdistributionsuch After manipulations, we find that the optimal solution is
that the signaling is estimation-efficient. We emphasize that, given by
d∗(x) is dependenton the channel through the distribution of
−1 1 −1
the channel state S, and thus differs from other usual costs If D ≥r− 1+eH2(r)/r , p∗ = 1+eH2(r)/r ,
r
such as symbol energies or time durations.
and C(Dh)=H (p∗r)i−p∗·H (r);h i
2 2
(3) A key condition that leads to the constrained channel
D
coding formulation is that the channel is memoryless. Due to else p∗ =1− ,
r
thememorylessproperty,wecandecomposeablockestimator
D
into multiple one-shot estimators, without loss of optimality and C(D)=H (r−D)− 1− H (r).
2 2
r
asymptotically. If the channel state evolves with time in a (cid:18) (cid:19)
correlated fashion, then such a decomposition is generally From the solution, we observe the following. For relatively
suboptimal. large D, the average distortion constraint is not active, and
thus the optimal input distribution coincides with that for the
IV. ILLUSTRATIVEEXAMPLES unconstrained channel capacity. As the estimation distortion
In this section, we discuss several simple examples to constraint D falls below a threshold, the average distortion
illustrate the application of Theorem 1. constraintbecomesactive,andthecapacity-distortionfunction
C(D) deviates from the unconstrained channel capacity. We thesameasthatinthescalarmultiplicativechannelcase.After
can show from the expression of C(D) that, as D →0, somemanipulations,wefindthattheresultingoptimalsolution
for general K ≥1 is
log(1−r)
C(D)= D+o(D). (13)
−r Case 1 2K >1+(1−r)−1/r :
rlog(2K −1)
Figure 2 depicts C(D) versus D for different values of r. p∗ =1, C(D)= .
We notice that the tradeoff between communication rates and K
estimation distortions is evidently visible. Case 2 2K ≤1+(1−r)−1/r :
−1
1
if D ≥r− 1+ eH2(r)/r ≥0,
2K −1
0.25 (cid:20) (cid:21)
−1
1 1
p∗ = 1+ eH2(r)/r ;
r 2K −1
0.2 (cid:20) (cid:21)
D
else p∗ =1− .
r
)0.15
D 1
( C(D)= H (p∗r)+p∗ rlog(2K −1)−H (r) .
C 2 2
K
0.1
Case 1 arises becaus(cid:8)e if the channe(cid:2)l block length K is (cid:3)(cid:9)
sufficiently large such that 2K > 1+(1−r)−1/r, then the
0.05
resulting p∗ as given by Case 2 would be greater than one,
r = 0.1, 0.2, 0.3, 0.4, 0.5 whichisimpossibleforavalidprobability.InCase1,wehave
00 0.2 0.4 D 0.6 0.8 1 PX(0) = 0, and all the nonzero symbols selected with equal
probability 1/(2K −1).
Infact, Case 1 kicksin forrathersmallvaluesof K.Inour
Fig.2. Capacity-distortion functionforthescalarmultiplicative channel.
channel model we have assumed r ∈ [0,1/2]. For r smaller
than 0.175, Case 1 arises for K ≥ 2; and for r larger than
0.175, Case 1 arises for K ≥3.
C. A Block Multiplicative Channel
In the scalar multiplicative channel (K = 1), we have
A generalization of the scalar multiplicative channel is the
noticed that C(D) linearly scales to zero as D →0; see (13).
following block multiplicative channel
For K >1, however, we have
Y =S X , (14) rlog(2K −1)
i i i C(0)= >0. (17)
K
whereXandY are length-K blocksso thatthe super-symbols
For comparison, let us consider a suboptimal approach based
in the blockmemorylesschannelhave alphabetsXK =YK =
upon training that transmits X=1 in the first channel use in
{0,1}K. The channel state S ∈ S = {0,1} remains fixed
each channel block. The receiver can thus perfectly estimate
for each block, and changes in a memoryless fashion across
the channelstate S and achieve D =0. The encoder then can
blocks.WeagainadopttheHammingdistanceasthedistortion
usetheremaining(K−1)channelusesineachchannelblock
measure.
to encode information, and the resulting achievable rate is
For such a channel, there are 2K possible vectors for an
inputsuper-symbol.However,wenotethat,allofthemexcept rlog(2K−1)
R(0)= . (18)
the all-zero x = 0 are symmetric. This is because they all K
lead to the same conditional distribution for Y as well as the
Comparing C(0) and R(0), we notice that their ratio ap-
same minimum conditional distortion d∗(x)= 0, ∀x6=0. So proaches one as K → ∞, consistent with the intuition that
from the concavity propertyof channelmutualinformationin
training usually leads to negligiblerate loss for channelswith
input distributions, the optimal input distribution should take
long coherence blocks.
the following form:
V. FURTHER ISSUES
PX(0)=1−p, andPX(x)=p/(2K −1), ∀x6=0. In this section, we briefly discuss a few issues that are
related to the capacity-distortion function formulation.
We can find that the channel mutual information per channel
use is A. Multiple Estimators and Other Cost Constraints
I(X;Y) 1 In certain applications, multiple cost constraints may be
= H (pr)+p· rlog(2K −1)−H (r) , (15)
K K 2 2 present. For example, the receiver may be simultaneously
and that the av(cid:8)erage distortion(cid:2) constraint is (cid:3)(cid:9) interested in two or more different distortion measures, or
the transmitter may have an average energy constraint for the
(1−p)r ≤D, (16) channel input, besides the average distortion constraint. The
multiplecostconstraintsshouldbesimultaneouslysatisfiedby VI. CONCLUSIONS
augmenting the feasible set of input distributions, P (6), to
D In this paper, we introduce a joint communication and
the intersection of multiple feasible sets, each for one cost
channelestimation problem for state-dependentchannels, and
constraint.
characterize its fundamental tradeoff by formulating it as a
For either single or multiple cost constraints, the capacity-
channel coding problem with input distribution constrained
distortion function can be defined following Section II, for-
by an average “estimation cost” constraint. The resulting
mulated as a constrained channel coding problem following
capacity-distortion function permits a systematic investiga-
Section III, and computed following efficient algorithms like
tion of the channel property for communication and state
theBlahut-Arimotoalgorithm[11],[12]fordiscretealphabets.
estimation. Future research topics include specializing the
B. Uncertainty in Channel State Statistics general framework to particular channel models in realistic
applications,andgeneralizingthe resultsto multiusersystems
The constrained channel coding formulation in Section III
and channels of generally correlated state processes.
can also be extended to the case in which the distribution
of the channel state S is uncertain. For such a compound ACKNOWLEDGMENT
channel setting, we assume that the joint channel distribu-
ThisworkhasbeensupportedinpartbyNSFOCE0520324,
tion Pθ(x,s,y) = P(y|x,s)PX(x)PS,θ(s) is parametrized by the Annenberg Foundation, and the University of Southern
an unknown parameter θ ∈ Θ, which is induced by the
California.
parametrized distribution of S, PS,θ(s). If all the alphabets
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d
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d