Optimal Throughput-Diversity-Delay Tradeoff in MIMO ARQ Block-Fading Channels

0.41 MB·English

by  Allen Chuang

1
Optimal Throughput-Diversity-Delay Tradeoff
in MIMO ARQ Block-Fading Channels
Allen Chuang, Albert Guille´n i Fa`bregas, Lars K. Rasmussen,
and Iain B. Collings
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0 Abstract
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In this paper, we consider an automatic-repeat-request (ARQ) retransmission protocol signaling
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over a block-fading multiple-input, multiple-output (MIMO) channel. Unlike previous work, we allow

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2 for multiple fading blocks within each transmission (ARQ round), and we constrain the transmitter to

] fixed rate codes constructed over complex signal constellations. In particular, we examine the general
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case of averageinput-power-constrainedconstellationsaswellas thepracticallyimportantcase offinite
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.
s discrete constellations. This scenario is a suitable modelfor practicalwireless communicationssystems
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employing orthogonal frequency division multiplexing techniques over a MIMO ARQ channel. Two

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cases of fading dynamics are considered, namely short-term static fading where channel fading gains
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change randomly for each ARQ round, and long-term static fading where channel fading gains remain
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1 constant over all ARQ rounds pertaining to a given message. As our main result, we prove that for
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0 the block-fading MIMO ARQ channel with discrete input signal constellation satisfying a short-term
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0 power constraint, the optimal signal-to-noise ratio (SNR) exponent is given by a modified Singleton
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s bound, relating all the system parameters. To demonstrate the practical significance of the theoretical
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: analysis, we present numerical results showing that practical Singleton-bound-achieving maximum
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i
X distance separable codes achieve the optimal SNR exponent.
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A. Chuang is with the School of Electrical and Information Engineering, University of Sydney (e-mail:
[email protected]).
A.Guille´niFa`bregaswaswiththeInstituteforTelecommunicationsResearch,UniversityofSouthAustralia.Heisnowwith
the Department of Engineering, University of Cambridge, UK, (e-mail: [email protected]).
L. K. Rasmussen is with the Institute for Telecommunications Research, University of South Australia (e-mail:
[email protected]).
I. B. Collings is with the ICT Centre, CSIRO (e-mail: [email protected]).
This work was supported by the Australian Research Council under ARC grants DP0558861 and RN0459498. The material
of this paper has been presented in part at the 2006 IEEE Inf. Theory Workshop, Chengdu, China, Oct. 2006.
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I. INTRODUCTION
In 1957 multi-carrier transmission was first proposed by Doelz et al. [1] as a way to increase
data rate by transmittingmultiplebits streams in parallel over multiplecarriers. Originally,multi-
carrier transmission was implemented using banks of sinusoidal generators. The use of discrete
Fourier transforms for modulation and demodulation was first suggested by Weinstein and Ebert
in 1971 [2], significantly reducing implementation complexity, and leading to what we now
know as orthogonal frequency division multiplexing (OFDM). A review of the development of
multi-carrier and OFDM systems can be found in [3].
Almost fifty years after the invention of multi-carrier transmission [1,2], the use of OFDM
has been adopted for broadband wireless communications systems as a means to significantly
increase transmission rates [4]. Standards such as IEEE 802.11 (WiFi) [5,6] and IEEE 802.16
(WiMax) [7,8] have now been extended to include OFDM techniques. Further improvements of
data rate and reliability are promised through the use of multiple transmit and receive antennas
[9,10]. Multiple-input, multiple-output (MIMO) antenna systems are now being introduced into
the IEEE 802 standards [6,8], as well as being integral parts of fourth-generation mobile
cellular communication systems proposals [11,12]. In addition, adaptive coding and modulation,
combined with automatic-repeat-request (ARQ) retransmission protocols, are becoming integral
parts of data transmissionservices in the Universal Mobile TelecommunicationsSystem (UMTS)
[13], and in WiMax [8].
Practical wireless communication systems will therefore soon feature MIMO OFDM modu-
lation with overlaying ARQ protocols. It is thus important to obtain a thorough understanding
of the fundamental characteristics of such systems. In this paper, we model a practical point-
to-point MIMO OFDM ARQ wireless communication system as a system transmitting signals
from a complex signal constellation over a block-fading MIMO ARQ channel. In the following
subsections, we first review prior art and technical concepts relevant to our work. We then for-
mulate our problem and summarize contributions, before outlining the organization and defining
notation of the paper.
A. Prior Art
1) Fundamental Tradeoff: The work of Teletar [14], and Foschini and Gans [15], has inspired
a flurry of research activitiesin MIMO antennasystemsfor wireless communications.Previously,
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multiple-antenna systems were primarily used for providing receiver diversity, thus combatting
random amplitude fluctuations due to fading [16]. In contrast, the prevailing thesis for MIMO
systems is that fading can increase channel capacity by providing a set of well-behaved parallel
channels [14,15]. In fact, in the high signal-to-noise (SNR) regime it has been shown that the
capacity of a channel with N transmit antennas, N receive antennas, and independent, identical
t r
distributed (i.i.d.) complex Gaussian channel gains between each antenna pair is given by
C(SNR) = min N ,N logSNR+O(1),
t r
{ }
suggesting that capacity increases linearly with the minimum number of transmit and receive
antennas. Therefore, the use of multiple-antenna systems can improve both reliability and data
rate, when transmitting over a quasi-static MIMO channel where channel gains are i.i.d. complex
Gaussian and fixed during the transmission.
Zheng and Tse described the fundamental tradeoff between diversity gain and multiplexing
gain1 for quasi-static MIMO channels in the high SNR regime in [17], assuming Gaussian dis-
tributed input signals.The fundamental tradeoff developedin [17] has since becomea benchmark
for the performance evaluation of space-time coding schemes, and the corresponding framework
has become a preferred approach for characterizing classes of MIMO channels. For example, in
[18] the fundamental diversity-multiplexing-delay tradeoff is characterized for the MIMO ARQ
channel,and thefundamentaldiversity-multiplexingtradeoffforMIMOchannelswithresolution-
constrained feedback is determined in [19], both under the assumption that Gaussian distributed
input signals are used.
2) OFDM and the Block-Fading Channel: The block-fading channel model was introduced in
[20],withthepurposeofmodellingslowlyvaryingfadingchannelswherethedurationofablock-
fading period is determined by the channel coherence time. Within a block-fading period, the
1The diversity gain (or SNR exponent) is defined as
logP (SNR)
d,− lim e ,
SNR→∞ logSNR
where P (SNR) denotes the probability that the transmitted message is decoded incorrectly. The multiplexing gain is defined
e
as
R(SNR)
r , lim ,
m SNR→∞ logSNR
where R(SNR) is the code rate. The multiplexing gain essentially quantifies how close the code rate is to the capacity of a
single-input single-output link at high SNR [17].
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channel fading gain remains constant, while between periods the channel gains change randomly
according to a fading distribution. In this setting, transmission typically extends over multiple
block-fading periods. A thorough treatment of fading channels is found in [21].
Theblock-fadingchannelmodelisareasonablemodelforOFDMtransmissionoverfrequency-
selective wireless channels, as an OFDM system is typically designed such that each sub-carrier
experiences flat fading. Despite its simplicity, the model captures important aspects of OFDM
modulation over frequency-selective fading channels and proves useful for developing coding
design criteria.
Thedefinitionofmultiplexinggain,fundamentalin theformulationpresented in [17,18], relies
on coding schemes with transmission rates that increase linearly with the logarithm of the SNR.
Non-zeromultiplexinggainscan onlybeachievedwithcontinuousinputconstellationsordiscrete
constellationswith cardinalitiesscalingwith theSNR. From a practical perspective,it isdesirable
to operate at a fixed code rate and deal with small alphabet sizes. We are therefore interested
in the performance of such practical schemes, which effectively operates at zero multiplexing
gain. Under this scenario, the general diversity-multiplexing tradeoff can only provide a coarse
characterization of the rate-diversitytradeoff. The rate-diversity tradeoff for fixed-rate space-time
codes constructed over discrete signal constellations, and transmitted across a quasi-static MIMO
channel, was presented in [22].
Union-bound arguments [23] and error exponent calculations [24] were used to show that the
diversity gain of a block-fading channel with an arbitrary, but fixed number of fading blocks,
fixed code rate, and a discrete input signal constellation, is described by a modified version of
the Singleton bound [25]. The same problem is considered in [26], where outage probability
arguments are used to formally prove that the optimal rate-diversity tradeoff is indeed the
modified form of the Singleton bound presented in [24,25], which is achieved using maximum
distance separable (MDS) codes.
The block-fading ARQ channel model has recently been considered in [27,28] for discrete
input signal constellations. In [27] the Singleton bound is presented as an upper bound to the
SNR exponent, while the optimality of the Singleton bound is formally proven for the ARQ case
in [28]. In [28] it is also demonstrated that asymptotically optimal throughput can be achieved
by MDS codes.
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B. Problem Formulation and Contributions
In this paper, we consider an ARQ system signaling over a block-fading MIMO channel with
L maximum number of allowable ARQ rounds and B fading blocks per ARQ round. In contrast
to the work in [17,18], we allow for multiple fading blocks within each transmission (ARQ
round), and we constrain the transmitter to fixed rate codes constructed over complex signal
constellations. In particular, we examine the general case of average input-power-constrained
constellations as well as the practically important case of discrete constellations of finite car-
dinality. The receiver is able to generate a finite number of one-bit repeat-requests, subject to
a latency constraint, whenever an error is detected in the decoded message. A maximum of L
transmissions pertaining to each information message is allowed.
As in [18], we consider two cases of fading statistics; for the short-term static fading case,
the channel fading gains change randomly for each ARQ round, while for the long-term static
fading case, the channel fading gains remain constant over all ARQ rounds pertaining to a given
message, but change randomly for each message and corresponding suite of ARQ rounds. This
scenario is a suitable model for practical wireless communications systems employing OFDM
modulation over a MIMO ARQ channel.
The main focus of our work is to derive the optimal tradeoff between throughput, diversity
gain and delay of ARQ schemes signaling over block-fading MIMO channels. In particular, we
show that the tradeoff highlights the roles of the complex-plane signal constellation, the rate of
the first ARQ round R , the maximum number of ARQ rounds L, and the number of fading
1
blocks per ARQ round B.
As a first result, we prove that for the block-fading MIMO ARQ channel with the input
constellation satisfying a short-term power constraint, the optimal SNR exponent is given by
N N LB for short-term static fading and N N B for long-term static fading, which is achieved
t r t r
by Gaussian codes of any positive rate. This is, however, not the case with discrete signal
constellations. In order to attain full diversity the signal constellations must feature certain
properties. In general, due to the discrete nature of these signal sets, a tradeoff between rate,
diversity and delay arises.
As our main result, we prove that for the block-fading MIMO ARQ channel with discrete
input signalconstellationof cardinality 2QNt satisfyinga short-term powerconstraint, the optimal
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SNR exponent is given by a modified Singleton bound, relating all the system parameters. Note,
however, that modulating across all fading blocks increases the dimensionality of the decoding
problem by a factor of B [29]. For further flexibility in terms of decoding complexity, we
consider the case where modulation is performed over a number 1 M B of fading blocks,
≤ ≤
such that B = MD. The resulting optimal SNR exponent is then expressed as
LB R
MN N 1+ 1 1 for short-term static fading
t r
M − LQN
 (cid:18) (cid:22) (cid:18) t(cid:19)(cid:23)(cid:19)
d⋆ (R ) = (1)
D 1 
 B R
MN N 1+ 1 1 for long-term static fading
t r
M − LQN
(cid:18) (cid:22) (cid:18) t(cid:19)(cid:23)(cid:19)


The expression in (1) implies that as the target rate R increases, the achievable optimal diversity
 1
order d⋆ (R ) decreases in steps. Our main result generalizes the result of [22] for the quasi-static
D 1
MIMO channel to the ARQ block-fading case with encoding across M fading blocks.
Directly following from the results, we demonstrate that while the optimal SNR exponent of
the system is an increasing function of the maximum number of allowed ARQ rounds L, the
throughput of the system becomes independent of L for sufficiently high SNR, and is determined
bytherateofthefirstARQ round.Wethereforedenoteourmainresultastheoptimalthroughput-
diversity-delay tradeoff. This result provides strong incentive to use ARQ as a way to increase
reliability without suffering code rate penalties.
To demonstrate the practical coding aspects of our results, some examples are presented with
corresponding error rate and throughput performances. The diversity tradeoff function can be
viewed as a modified version of the Singleton bound [25], which naturally leads us to investigate
the role of Singleton-bound-achieving MDS codes. Our examples illustrate that the optimal SNR
exponent can be achieved with practical MDS coding schemes.
C. Organization and Notation
The paper is organized as follows. In Section II we define the system model, and in Section
III we review relevant ARQ performance measures, namely, error probability, throughput and
average latency. In Section IV we review the concepts of information accumulation and out-
age probability, while the main theorems of the paper, detailing the throughput-diversity-delay
tradeoff, are presented in Section V. A thorough discussion is included in Section V, where
the results are interpreted and related to existing results in the literature. To demonstrate the
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practical relevance of the results, numerical examples are included in Section VI, showing that
MDS codes achieve the tradeoff. Concluding remarks are summarized in Section VII, while the
details of the proofs have been collected in the appendices.
The following notation is used in the paper. Sets are denoted by calligraphic fonts with
.
the complement denoted by superscript c. The exponential equality f(z) = zd indicates that
. .
lim logf(z) = d. The exponential inequality , are similarly defined. and denote
z→∞ logz ≤ ≥ ≻ ≺
component-wise inequality of > and <, respectively. I denotes the identity matrix, vector/matrix
transpose is denoted by ′ (e.g. v′) and is the Frobenius norm. 1 is the indicator function,
F
k·k {·}
and x ( x ) denotes the smallest (largest) integer greater (smaller) than x.
⌈ ⌉ ⌊ ⌋
II. SYSTEM MODEL
In thissectionwedescribetheblock-fadingMIMO ARQ channel modeland coded modulation
schemes under consideration.
A. Channel Model
Consider a block-fading MIMO ARQ system with N transmit antennas and N receive
t r
antennas. We investigate the use of a simple stop-and-wait ARQ protocol where the maximum
number of ARQ rounds is denoted by L. Each ARQ round consists of B independent block-
fading periods, each of length T (coherence time/bandwidth) in channel uses. Hence each ARQ
round spans BT channel uses. Figure 1 shows the overall system model. We write the received
signal at the bth block and ℓth ARQ round as
ρ
Y = H X +W , (2)
ℓ,b ℓ,b ℓ,b ℓ,b
N
t
r
where X CNt×T,Y ,W CNr×T and H CNr×Nt denote the transmitted signal
ℓ,b ℓ,b ℓ,b ℓ,b
∈ ∈ ∈
matrix, received signal matrix, the noise matrix and the channel fading gain matrix, respectively.
We define x CNt as the vectors containing the transmitted symbols of each antenna at
ℓ,b,t
∈
ARQ round ℓ, block b and time t, which are such that X = [x ,...,x ].
ℓ,b ℓ,b,1 ℓ,b,T
Both the elements of the channel fading gain matrix H and the elements of the noise
ℓ,b
matrix W are assumed i.i.d. zero mean complex circularly symmetric complex Gaussian with
ℓ,b
variance σ2 = 0.5 per dimension. We assume perfect receiver-side channel state information
(CSI), namely, the channel coefficients are assumed to be perfectly known to the receiver.
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We obtain the long-term static model of [18] by letting H = H for all ℓ = ℓ′ in (2),
ℓ,b ℓ′,b
6
namely, all ARQ rounds undergo the same MIMO block-fading channel. This models well
a slowly varying MIMO OFDM ARQ system with B subcarriers or B groups of correlated
subcarriers. On the other hand, when the matrices H are i.i.d. from block to block and from
ℓ,b
ARQ round to ARQ round, (2) corresponds to the short-term static model of [18]. In order to
keep the presentation general, and since (2) encompasses both models, we will index the channel
matrices according to ARQ round and block as in the short-term static model. We will outline
the changes for the long-term static model whenever necessary.
Therefore, the channel model corresponding to ARQ round ℓ becomes
ρ
Y = H X +W , (3)
ℓ ℓ ℓ ℓ
N
t
r
where
Y = Y′ ,...,Y′ ′ CBNr×T
ℓ ℓ,1 ℓ,B ∈
X = (cid:2)X′ ,...,X′ (cid:3)′ CBNt×T
ℓ ℓ,1 ℓ,B ∈
W = (cid:2)W′ ,...,W′(cid:3) ′ CBNt×T
ℓ ℓ,1 ℓ,B ∈
H = (cid:2)diag(H ,...,H(cid:3) ) CBNr×BNt.
ℓ ℓ,1 ℓ,B
∈
One channel use of the equivalent model (3) corresponds to BT channel uses of the real channel
(2). In a similar way to the previous model, we define the vectors x CBNt for t = 1,...,T
ℓ,t
∈
as
X = [x ,...,x ] CBNt×T.
ℓ ℓ,1 ℓ,T
∈
The receiver attempts to decode following the reception of an ARQ round. If the received
codeword can be decoded, the receiver sends back a one-bit acknowledgement signal to the
transmitter via a zero-delay and error-free feedback link. The transmission of the current code-
word ends immediately following the acknowledgment signal and the transmission of the next
message in the queue starts. If an error is detected in the received codeword before the Lth
ARQ round, then the receiver requests another ARQ round by sending back a one-bit negative
acknowledgment along the perfect feedback path. However, a decision must be made at the end
of the Lth ARQ round regardless of whether errors are detected.
In general, the optimal ARQ decoder makes use of all available coded blocks and correspond-
ing channel state information up to the current ARQ round in the decoding process. This leads
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to the concept of information accumulation, where individual ARQ rounds are combined, along
with any other side information. We hence introduce the ARQ channel model up to the ℓth ARQ
round, completely analagous to (2), but allowing for a more concise notation. In particular, we
have that
ρ
Y = H X +W , (4)
ℓ ℓ ℓ ℓ
N
t
r
where e e e f
Y = [Y′,...,Y′]′ CℓBNr×T,
ℓ 1 ℓ ∈
X = [X′,...,X′]′ CℓBNt×T,
eℓ 1 ℓ ∈
W = [W′,...,W′]′ CℓBNr×T,
eℓ 1 ℓ ∈
H = diag(H ,...,H ) CℓBNr×ℓBNt.
fℓ 1 ℓ
∈
That is, Y ,X and W areesimply collections of the received, code and noise matrices, re-
ℓ ℓ ℓ
spectively, available at the end of the ℓth ARQ round, concatenated into block column matrices.
e e f
The new channel matrix H CℓBNr×ℓBNt is a block diagonal matrix with the diagonal blocks
ℓ
∈
composed of the respective channel state during each block-fading period up to ARQ round ℓ.
e
In the case of long-term static model, H = diag(H,...,H). Note that a channel use of the
ℓ
ℓtimes
equivalent model (4) corresponds to ℓBTe channel uses of the real channel (2).
| {z }
B. Encoding
In this section we discuss the specific construction of the space-time ARQ codewords. The
information message m to be transmitted is passed through a space-time coded modulation
encoder with codebook CLBNt×T and code rate R , where R , R1 and
C ⊂ 0 0 L
1
R , log
1 BT 2|C|
is the code rate of the first ARQ round. Therefore, = 2R0LBT and m , where =∆
|C| ∈ M M
1,2,...,2R0LBT is the set of possible information messages. We denote the codeword corre-
{ }
sponding to information message m by X(m). The rate R codeword can be partitioned into a
0
sequence of LB space-time coded matrices, denoted X CNt×T. According to the previously
ℓ,b
∈
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described model, we have that
X(m) = X′(m),...,X′ (m) ′
1 L
= (cid:2)X′ (m),...,X′ (m(cid:3) ),...,X′ (m),...,X′ (m) ′ CLBNt×T
1,1 1,B L,1 L,B ∈
(cid:2) (cid:3)
We consider a short term average power constraint, namely, the transmitted codewords are
normalized in energy such that X , 1 E[ X 2] = N . Therefore, together with the
∀ ∈ C LBT k kF t
model assumptions in the previous section, ρ in (2), (3) and (4) represents the average SNR per
receive antenna.
In this paper we analyze space-time coded modulation schemes constructed over discrete
signal sets. In particular, we consider that is obtained as the concatenation of a classical
C
coded modulation scheme LBTNt constructed over a complex-plane signal set =
Q
C ⊆ Q Q
q ,...,q C [30]with a unit rate linear dispersionspace-time modulator[31]. Let c
1 |Q| Q Q
{ } ⊂ ∈ C
denote a codeword of of length LBTN and Q = log the number of bits conveyed in
CQ t 2|Q|
one symbol of , namely, = 2Q. Since the linear dispersion space-time modulator has unit
Q |Q|
rate we have that 0 R N QL.
1 t
≤ ≤
To allow for a general case, we consider that the linear dispersion space-time modulator
spreads the symbols of c over the N transmit antennas and the B fading blocks. In particular,
Q t
we consider that the codewords c of , of length LBTN are partitioned into L vectors
Q Q t
C
of length BTN each, denoted by c BTNt such that c = c′ ,...,c′ ′. For every
t Q,ℓ ∈ Q Q Q,1 Q,L
ℓ = 1,...,L, the vectors cQ,ℓ are multiplied by the unit rate gen(cid:2)erator matrix (cid:3)of the linear
dispersion space-time modulator R CBTNt×BTNt to form
∈
x = Rc (5)
ℓ Q,ℓ
where x = vec(X ) CBNtT is the vector representation of the portion of codeword of
ℓ ℓ Q
∈ C ∈ C
transmitted at ARQ round ℓ. Without any loss in generality we consider that R is a rotation
matrix [32–35], i.e., R is unitary [36]. Note that introduction of the linear dispersion space-time
modulator rotation matrix R increases the decoding complexity compared to the unrotated case
where R = I. This is due to the fact that now the components of x depend on each other, since
ℓ
R induces a change of the reference axis for detection [32–35]. This implies that the detection
problem is of dimension BTN .
t
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