Capacity of the Discrete-Time AWGN Channel
Under Output Quantization
Jaspreet Singh, Onkar Dabeer and Upamanyu Madhow∗
Abstract— We investigate the limits of communication over the
discrete-time Additive White Gaussian Noise (AWGN) channel,
when the channel output is quantized using a small number
of bits. We first provide a proof of our recent conjecture on
the optimality of a discrete input distribution in this scenario.
Specifically, we show that for any given output quantizer choice
with K quantization bins (i.e., a precision of log2 K bits), the
input distribution, under an average power constraint, need not
have any more than K + 1 mass points to achieve the channel
capacity. The cutting-plane algorithm is employed to compute
this capacity and to generate optimum input distributions.
Numerical optimization over the choice of the quantizer is then
performed (for 2-bit and 3-bit symmetric quantization), and
the results we obtain show that the loss due to low-precision
output quantization, which is small at low signal-to-noise ratio
(SNR) as expected, can be quite acceptable even for moderate
to high SNR values. For example, at SNRs up to 20 dB, 2-3 bit
quantization achieves 80-90% of the capacity achievable using
infinite-precision quantization.
I. I NTRODUCTION
Analog-to-digital conversion (ADC) is an integral part of
modern communication receiver architectures based on digital
signal processing (DSP). Typically, ADCs with 6-12 bits of
precision are employed at the receiver to convert the received
analog baseband signal into digital form for further processing.
However, as the communication systems scale up in speed and
bandwidth (for e.g., systems operating in the ultrawide band or
the mm-wave band), the cost and power consumption of such
high precision ADC becomes prohibitive [1]. A DSP-centric
architecture nonetheless remains attractive, due to the continuing exponential advances in digital electronics (Moore’s law).
It is of interest, therefore, to understand whether DSP-centric
design is compatible with the use of low-precision ADC.
In this paper, we continue our investigation of the Shannontheoretic communication limits imposed by the use of lowprecision ADC for ideal Nyquist sampled linear modulation
in AWGN. The discrete-time memoryless AWGN-Quantized
Output (AWGN-QO) channel model thus induced is shown
in Fig. 1. In our prior work for this channel model, we
have shown that for the extreme scenario of 1-bit symmetric
quantization, binary antipodal signaling achieves the channel
∗ J. Singh and U. Madhow are with the ECE Department, UC Santa Barbara,
CA 93106, USA. Their research was supported by the National Science Foundation under grant CCF-0729222 and by the Office for Naval Research under
grant N00014-06-1-0066. O. Dabeer is with the Tata Institute of Fundamental
Research, Mumbai 400005, India. His work was supported in part by a grant
from the Dept. of Science and Technology, Govt. of India, and in part by the
Homi Bhabha Fellowship. {jsingh, madhow}@ece.ucsb.edu,
onkar@tcs.tifr.res.in
N
X
Fig. 1.
+
ADC Quantizer
Y
Q
The AWGN-Quantized Ouput Channel : Y = Q(X + N ) .
capacity for any signal-to-noise ratio (SNR) [2]. For multibit quantization [3], we provided a duality-based approach to
bound the capacity from above, and employed the cuttingplane algorithm to generate input distributions that nearly
achieved these upper bounds. Based on our results, we conjectured that a discrete input with cardinality not exceeding
the number of quantization bins achieves the capacity of the
average power constrained AWGN-QO channel. In this work,
we prove that a discrete input is indeed optimal, although our
result only guarantees its cardinality to be at most K + 1,
where K is the number of quantization bins. Our proof
is inspired by Witsenhausen’s result in [4], where Dubins’
theorem [5] was used to show that the capacity of a discretetime memoryless channel with output cardinality K, under
only a peak power constraint is achievable by a discrete input
with at most K points. The key to our proof is to show
that, under output quantization, an average power constraint
automatically induces a peak power constraint, after which we
use Dubins’ theorem as done by Witsenhausen. Although not
applicable to our setting, it is worth noting that for a Discrete
Memoryless Channel, Gallager first showed that the number
of inputs with nonzero probability mass need not exceed the
number of outputs [6, p. 96, Corollary 3].
While the preceding results optimize the input distribution
for a fixed quantizer, comparison with an unquantized system
requires optimization over the choice of the quantizer as
well. We do this numerically for 2-bit and 3-bit symmetric
quantization, and use our numerical results to make the
following encouraging observations: (a) Low-precision ADC
incurs a relatively small loss in spectral efficiency compared
to unquantized observations. While this is expected for low
SNRs, we find that even at moderately high SNRs of up to
20 dB, 2-3 bit ADC still achieves 80-90% of the spectral efficiency attained using unquantized observations. These results
indicate the feasibility of system design using low-precision
ADC for high bandwidth systems. (b) Standard uniform Pulse
Amplitude Modulated (PAM) input with quantizer thresholds
set to implement maximum likelihood (ML) hard decisions
achieves nearly the same performance as that attained by an
optimal input and quantizer pair. This is useful from a system
designer’s point of view, since the ML quantizer thresholds
have a simple analytical dependence on SNR, which is an
easily measurable quantity.
The rest of the paper is organized as follows. The quantized
output AWGN channel model is given in the next section. In
Section III, we show that a discrete input achieves the capacity
of this channel. Quantizer optimization results are presented
in Section IV, followed by the conclusions in Section V.
A. An Implicit peak power Constraint
The following KKT condition can be derived for the
AWGN-QO channel, using convex optimization principles in
a manner similar to that in [8], [9]. The input distribution F
is optimal if and only if there exists a γ ≥ 0 such that
where F is the set of all average power constrained distributions on R.
d(x; F ∗ )+γ ∗ (P −x2 ) < L+γ ∗ P −(L+γ ∗ P −C) = C.
K
X
Wi (x) log
i=1
Wi (x)
+ γ(P − x2 ) ≤ I(F ) ,
R(yi ; F )
(5)
for all x, with equality if x is in the support of F .
The first term on the left hand side of the KKT condition
(5) is the divergence (or the relative entropy) between the
II. C HANNEL M ODEL
transition and the output PMFs. For convenience, let us denote
We consider linear modulation over a real AWGN channel, it by d(x; F ). The following result concerning the behavior of
and assume that the Nyquist criterion for no intersymbol in- d(x; F ) has been proved in [10].
Lemma 1: For the AWGN-QO channel (1) with input disterference is satisfied [7, pp. 50]. Symbol rate sampling of the
tribution
F , the divergence function d(x; F ) satisfies the
receiver’s matched filter output using a finite-precision ADC
following
properties
therefore results in the following discrete-time memoryless
(a)
lim
d(x;
F ) = − log R(yK ; F ).
AWGN-Quantized Output (AWGN-QO) channel (Fig. 1)
x→∞
Y = Q (X + N ) .
(1) (b) There exists a finite constant A0 such that ∀ x > A0 ,
d(x; F ) < − log R(yK ; F ).
Here X ∈ R is the channel input with distribution F (x) and
Proof: See [10].
N is N (0, σ 2 ). The quantizer Q maps the real valued input
We now use Lemma 1 to prove the main result of this
X + N to one of the K bins, producing a discrete channel subsection.
output Y ∈ {y1 , · · · , yK }. We only consider quantizers for
Proposition 1: A capacity-achieving input distribution for
which each bin is an interval of the real line. The quantizer the average power constrained AWGN-QO channel (1) must
Q with K bins can therefore be characterized by the set of have bounded support.
its (K − 1) thresholds q = [q1 , q2 , · · · , qK−1 ] ∈ RK−1 , such Proof: Assume that the input distribution F ∗ achieves1 the
that −∞ := q0 < q1 < q2 < · · · < qK−1 < qK := ∞. The capacity in (4) (i.e., I(F ∗ ) = C), with γ ∗ ≥ 0 being
resulting transition probability functions are given by
a corresponding optimal Lagrange parameter in the KKT
¶
µ
¶
µ
condition. In other words, with γ = γ ∗ , and, F = F ∗ , (5) must
qi − x
qi−1 − x
−Q
, be satisfied with an equality at every point in the support of
Wi (x) = P(Y = yi |X = x) = Q
σ
σ
(2) F ∗ . We exploit this necessary condition next to show that the
where Q(x) denotes
Gaussian distribution support of F ∗ is upper bounded. Specifically, we prove that
R ∞ the complementary
1
2
there exists a finite constant A2 ∗ such that it is not possible
function √2π x exp(−t /2)dt.
∗
The input-output mutual information I(X; Y ), expressed to attain equality in (5) for any x > A2 .
Using Lemma 1, we first let
explicitly as a function of F is
lim
d(x; F ∗ ) = − log(R(yK ; F ∗ )) = L, and also assume
Z ∞X
K
x→∞
Wi (x)
I(F ) =
Wi (x) log
dF (x) ,
(3) that there exists a finite constant A0 such that ∀ x > A0 ,
R(y
i; F )
−∞ i=1
d(x; F ∗ ) < L. We consider two possible cases.
∗
• Case 1: γ > 0.
where {R(yi ; F ) , 1 ≤ i ≤ K} is the Probability Mass
If C > L + γ ∗ P , then pickp
A2 ∗ = A0 .
Function (PMF) of the output when the input is F . Under
∗
Else pick A2 ≥ max{A0 , (L + γ ∗ P − C)/γ ∗ }.
an average power constraint P (i.e., E[X 2 ] ≤ P ), we wish to
In either situation, for x > A2 ∗ , we get d(x; F ∗ ) < L,
compute the capacity of the channel (1), which is given by
and, γ ∗ x2 > L + γ ∗ P − C.
C = sup I(F ),
(4)
This gives
F ∈F
III. D ISCRETE I NPUT ACHIEVES C APACITY
We first use the Karush-Kuhn-Tucker (KKT) optimality
condition to show that an average power constraint for the
AWGN-QO channel automatically induces a constraint on the
peak power, in the sense that an optimal input distribution
must have a bounded support set. This fact is then exploited
to show the optimality of a discrete input.
•
Case 2: γ ∗ = 0.
Putting γ ∗ = 0 in the KKT condition (5), we get
∗
d(x; F ) =
K
X
i=1
1 That
Wi (x) log
Wi (x)
≤ C , ∀x.
R(yi ; F ∗ )
the capacity is achievable can be shown using standard results from
optimization theory. For lack of space here, we refer the reader to [10] for
details.
Thus,
C. Capacity Computation
L = lim d(x; F ∗ ) ≤ C.
x→∞
Picking A2 ∗ = A0 , we therefore have that for x > A2 ∗
d(x; F ∗ ) + γ ∗ (P − x2 ) = d(x; F ∗ ) < L.
=⇒ d(x; F ∗ ) + γ ∗ (P − x2 ) < C.
Combining the two cases, we have shown that the support of
the distribution F ∗ has a finite upper bound A2 ∗ . Using similar
arguments, it can easily be shown that the support of F ∗ has
a finite lower bound A1 ∗ as well, which implies that F ∗ has
a bounded support.
B. Achievability of Capacity by a Discrete Input
To show the optimality of a discrete input for our problem,
we use the following theorem which we have proved in [10].
The theorem holds for channels with a finite output alphabet,
under the condition that the input is constrained in both peak
power and average power.
Theorem 1: Consider a stationary discrete-time memoryless channel with a continuous input X taking values
in the bounded interval [A1 , A2 ], and a discrete output
Y ∈ {y1 , y2 , · · · , yK }. Let the transition probability function
Wi (x) = P(Y = yi |X = x) be continuous in x, for each i
in {1, .., K}. The capacity of this channel, under an average
power constraint on the input, is achievable by a discrete input
with at most K + 1 points.
Proof: See [10].
Our proof in [10] uses Dubins’ theorem [5], and is an
extension of Witsenhausen’s result in [4], wherein he showed
that a distribution with only K points would be sufficient to
achieve the capacity if the average power of the input was not
constrained.
The implicit peak power constraint derived in Section III-A
allows us to use Theorem 1 to get the following result.
Proposition 2: The capacity of the average power constrained AWGN-QO channel (1) is achievable by a discrete
input distribution with at most K + 1 points of support.
Proof: Using notation from the last subsection, let F ∗ be an
optimal distribution for (4), with the support of F ∗ being
contained in the bounded interval [A1 ∗ , A2 ∗ ]. Define F1 to be
the set of all average power constrained distributions whose
support is contained in [A1 ∗ , A2 ∗ ]. Note that F ∗ ∈ F1 ⊂
F, where F is the set of all average power constrained
distributions on R. Consider the maximization of the mutual
information I(X; Y ) over the set F1
C1 = max I(F ).
F ∈F1
(6)
Since the transition probability functions in (2) are continuous
in x, Theorem 1 implies that a discrete distribution with at
most K + 1 mass points achieves the maximum C1 in (6).
Denote such a distribution by F1 . However, since F ∗ achieves
the maximum C in (4) and F ∗ ∈ F1 , it must also achieve the
maximum in (6). This implies that C1 = C, and that F1 is
optimal for (4), thus completing the proof.
We have already addressed the issue of computing the
capacity (4) in our prior work. Specifically, in [2], we have
shown analytically that for the extreme scenario of 1-bit
symmetric quantization, binary antipodal signaling achieves
the capacity (at any SNR). Multi-bit quantization has been
considered in [3], [10], where we show that the cutting-plane
algorithm [11] can be employed for computing the capacity
and obtaining optimal input distributions.
IV. O PTIMIZATION OVER Q UANTIZER
Until now, we have addressed the problem of capacity computation given a fixed quantizer. In this section, we consider
the issue of quantizer optimization, while restricting attention
to symmetric quantizers only. Given the symmetric nature of
the AWGN noise and the power constraint, it seems intuitively
plausible that restriction to symmetric quantizers should not
be sub-optimal from the point of view of optimizing over the
quantizer choice in (1), although a proof of this conjecture has
eluded us.
A Simple Benchmark: While an optimal quantizer (with a
corresponding optimal input) provides the absolute communication limits for our model, from a system designer’s perspective, it would also be useful to evaluate the performance
degradation if we use some standard input constellations and
quantizer choices. We take the following input and quantizer
pair as our benchmark strategy : for K-bin quantization,
consider equispaced uniform K-PAM (Pulse Amplitude Modulated) input distribution, with quantizer thresholds as the
mid-points of the input mass point locations (i.e., ML hard
decisions). With the K-point uniform input, we have the
entropy H(X) = log2 K bits for any SNR. Also, it is easy
to see that as SNR → ∞, H(X|Y ) → 0 for the benchmark
input-quantizer pair. Therefore, our benchmark scheme is nearoptimal if we operate in the high SNR regime. The main issue
to investigate ahead, therefore is: at low to moderate SNRs,
how much gain does an optimal quantizer choice provide over
the benchmark.
In all the results that follow, we take the noise variance
σ 2 = 1. However, the results are scale invariant in the sense
that if both P and σ 2 are scaled by the same factor R (thus
keeping the SNR unchanged), then there is an equivalent
√
quantizer (obtained by scaling the thresholds by R) that
gives an identical performance.
N UMERICAL R ESULTS
A. 2-bit Symmetric Quantization
A 2-bit symmetric quantizer is characterized by a single
parameter q, with {−q, 0, q} being the quantizer thresholds.
Hence we use a brute force search over q to optimize the
quantizer. In Fig. 2, we plot the variation of the channel
capacity (computed using the cutting-plane algorithm) as a
function of the parameter q at various SNRs. We observe that
for any SNR, there is an optimal choice of q that maximizes
the capacity. At high SNRs, the optimal q is seen to increase
monotonically with SNR, which is not surprising since the
Capacity (bits / channel use)
2
SNR(dB)
3-bit optimal
3-bit benchmark
15 dB
10
1.5844
1.5332
20
2.8367
2.8084
AT DIFFERENT
SNRS .
7 dB
1
3 dB
0
0 dB
0
1
2
3
4
5
Quantizer threshold ’q’
6
7
Fig. 2. 2-bit symmetric quantization : channel capacity versus the quantizer
threshold q (noise variance σ 2 = 1).
SNR(dB)
1-bit optimal
2-bit optimal
2-bit benchmark
−10
0.0449
0.0613
0.0527
M UTUAL INFORMATION
0
0.3689
0.4552
0.4401
−5
0.1353
0.1792
0.1658
TABLE I
( IN BITS / CHANNEL USE )
7
0.9020
1.0981
1.0639
15
0.9974
1.9304
1.9211
0 dB
0.4
0.2
0.2
0
0
0.4
0.4
4 dB
0.2
0.2
0
0
7 dB
0.2
0.4
AT DIFFERENT
SNRS .
10 dB
For 3-bit symmetric quantization, we need to optimize over
a space of 3 parameters : {0 < q1 < q2 < q3 }, with the
quantizer thresholds being {±q1 , ±q2 , ±q3 }. Instead of brute
force search, we use an alternate optimization procedure for
joint optimization of the input and the quantizer in this case.
Due to lack of space, we refer the reader to [10] for details,
and proceed directly to the numerical results. (Table II)
Comparison with the benchmark: As for 2-bit quantization
considered earlier, we find that the benchmark scheme performs quite well at low SNRs with 3-bit quantization also. At
−10 dB SNR, for instance, the benchmark scheme achieves
83% of the capacity achievable with an optimal quantizer
choice. Table II gives the comparison for different SNRs.
Optimal Input Distributions: Although not depicted here, we
again observe (as for the 2-bit case) that the optimal inputs
obtained all have at most K points (K = 8 in this case), while
Proposition 2 guarantees the achievability of capacity by at
most K+1 points. Of course, Proposition 2 is applicable to any
quantizer choice (and not just optimal symmetric quantizers
that we consider in this section), it still leaves us with the
question whether it can be tightened to guarantee achievability
of capacity with at most K points.
C. Comparison with Unquantized Observations
15 dB
20 dB
0.2
0
Optimal Input Distributions: The optimal input distributions
(given by the cutting-plane algorithm) corresponding to the
optimal quantizers obtained above are depicted in Fig. 3, for
different SNR values. The locations of the optimal quantizer
thresholds are also shown (by the dashed vertical lines). Binary
signaling is found to be optimal at low SNRs, and the number
of mass points increases (first to 3 and then to 4) with
increasing SNR. Further increase in SNR eventually leads to
the uniform 4-PAM input, thus approaching the capacity bound
of 2 bits. It is worth noting that all the optimal inputs we
obtained have 4 or less mass points, whereas Proposition 2 is
looser as it guarantees the achievability of capacity using at
most 5 points.
B. 3-bit Symmetric Quantization
√
benchmark quantizer’s q scales as SNR and is known to be
near-optimal at high SNRs.
Comparison with the benchmark: In Table I, we compare
the performance of the optimal solution obtained as above with
the benchmark scheme. The capacity with 1-bit quantization
is also shown for reference. While being near-optimal at
moderate to high SNRs, the benchmark scheme is seen to
perform fairly well at low SNRs also. For instance, at −10
dB SNR, it achieves 86% of the capacity achieved with
an optimal 2-bit quantizer and input pair. From a practical
standpoint, these results imply that the benchmark scheme,
which requires negligible computational effort (due to its welldefined dependence on SNR), can be employed even at small
SNRs while incurring an acceptable loss of performance.
PMF
5
0.9753
0.9547
10 dB
−5 dB
0.4
0
0.4817
0.4707
TABLE II
M UTUAL INFORMATION ( IN BITS / CHANNEL USE )
1.5
0.5
0.4
−10
0.0667
0.0557
0
X
Fig. 3. 2-bit symmetric quantization : optimal input distribution and quantizer
at various SNRs (the dashed vertical lines depict the locations of the quantizer
thresholds).
We now compare the capacity results obtained above with
the case when the receiver ADC has infinite precision. Table
III provides these results, and the corresponding plots are
shown in Fig. 4. We observe that at low SNRs, low-precision
quantization is a very feasible option. For instance, at -5 dB
SNR, even 1-bit receiver quantization achieves 68% of the
capacity achievable with infinite-precision. 2-bit quantization
at the same SNR provides as much as 90% of the infiniteprecision capacity. Such high figures are understandable, since
if noise dominates the message signal, increasing the quantizer
SNR(dB)
1-bit ADC
2-bit ADC
3-bit ADC
Unquantized
−5
0.1353
0.1792
0.1926
0.1982
0
0.3689
0.4552
0.4817
0.5000
5
0.7684
0.8889
0.9753
1.0286
10
0.9908
1.4731
1.5844
1.7297
15
0.9999
1.9304
2.2538
2.5138
TABLE III
C APACITY ( IN BITS / CHANNEL USE )
AT VARIOUS
SNRS .
4
Infinite precision ADC
1−bit ADC
2−bit ADC
3−bit ADC
Capacity (Bits/Channel Use)
3.5
3
2.5
2
1.5
1
0.5
0
−5
Fig. 4.
0
5
10
SNR (dB)
15
20
Capacity with 1-bit, 2-bit, 3-bit, and infinite-precision ADC.
V. C ONCLUSIONS
Our Shannon-theoretic investigation indicates the feasibility
of low-precision ADC for designing future high-bandwidth
communication systems such as those operating in UWB and
mm-wave band. The small reduction in spectral efficiency due
to low-precision ADC is acceptable in such systems, given
that the available bandwidth is plentiful. Current research is
therefore focussed on developing ADC-constrained algorithms
to perform receiver tasks such as carrier and timing synchronization, channel estimation and equalization.
An unresolved technical issue concerns the number of mass
points required to achieve capacity. While we have shown
that the capacity for the AWGN channel with K-bin output
quantization is achievable by a discrete input distribution with
at most K +1 points, numerical computation of optimal inputs
reveals that K mass points are sufficient. Can this be proven
analytically, at least for symmetric quantizers? Are symmetric
quantizers optimal? Another problem for future investigation
is whether our result regarding the optimality of a discrete
input can be generalized to other channel models. Under what
conditions is the capacity of an average power constrained
channel with output cardinality K achievable by a discrete
input with at most K + 1 points?
R EFERENCES
precision beyond a point does not help much in distinguishing
between different signal levels. However, we surprisingly find
that even if we consider moderate to high SNRs, the loss due to
low-precision sampling is still very acceptable. At 10 dB SNR,
for example, the corresponding ratio for 2-bit quantization
is still a very high 85%, while at 20 dB, 3-bit quantization
is enough to achieve 85% of the infinite-precision capacity.
Similar encouraging results have been reported earlier in
[12], [13] also. However, the input alphabet in these works
was taken as binary to begin with, in which case the good
performance with low-precision output quantization is perhaps
less surprising.
On the other hand, if we fix the spectral efficiency to that
attained by an unquantized system at 10 dB (which is 1.73
bits/channel use), we find that 2-bit quantization incurs a loss
of 2.30 dB (see Table IV). From a practical viewpoint, this
penalty in power is more significant compared to the 15% loss
in spectral efficiency on using 2-bit quantization at 10 dB SNR.
This suggests, for example, that the impact of low-precision
ADC should be weathered by a moderate reduction in the spectral efficiency, rather than by increasing the transmit power.
1-bit ADC
2-bit ADC
3-bit ADC
Unquantized
Spectral
0.25
−2.04
−3.32
−3.67
−3.83
Efficiency (bits
0.5
1.0
1.79
−
0.59
6.13
0.23
5.19
0.00
4.77
per channel use)
1.73
2.5
−
−
12.30
−
11.04
16.90
10.00
14.91
TABLE IV
SNR ( IN D B)
REQUIRED FOR A GIVEN SPECTRAL EFFICIENCY.
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