- Research Article
- Open Access
- Published:
End-to-End Joint Antenna Selection Strategy and Distributed Compress and Forward Strategy for Relay Channels
EURASIP Journal on Wireless Communications and Networking volume 2009, Article number: 295418 (2009)
Abstract
Multihop relay channels use multiple relay stages, each with multiple relay nodes, to facilitate communication between a source and destination. Previously, distributed space-time codes were proposed to maximize the achievable diversity-multiplexing tradeoff; however, they fail to achieve all the points of the optimal diversity-multiplexing tradeoff. In the presence of a low-rate feedback link from the destination to each relay stage and the source, this paper proposes an end-to-end antenna selection (EEAS) strategy as an alternative to distributed space-time codes. The EEAS strategy uses a subset of antennas of each relay stage for transmission of the source signal to the destination with amplifying and forwarding at each relay stage. The subsets are chosen such that they maximize the end-to-end mutual information at the destination. The EEAS strategy achieves the corner points of the optimal diversity-multiplexing tradeoff (corresponding to maximum diversity gain and maximum multiplexing gain) and achieves better diversity gain at intermediate values of multiplexing gain, versus the best-known distributed space-time coding strategies. A distributed compress and forward (CF) strategy is also proposed to achieve all points of the optimal diversity-multiplexing tradeoff for a two-hop relay channel with multiple relay nodes.
1. Introduction
Finding optimal transmission strategies for wireless ad-hoc networks in terms of capacity, reliability, diversity-multiplexing (DM) tradeoff [1], or delay has been a long standing open problem. The multi-hop relay channel is an important building block of wireless ad-hoc networks. In a multi-hop relay channel, the source uses multiple relay nodes to communicate with a single destination. An important first step in finding optimal transmission strategies for the wireless ad-hoc networks is to find optimal transmission strategies for the multi-hop relay channel.
In this paper, we focus on the design of transmission strategies to achieve the optimal DM-tradeoff of the multi-hop relay channel. The DM-tradeoff [1] characterizes the maximum achievable reliability (diversity gain) for a given rate of increase of transmission rate (multiplexing gain), with increasing signal-to-noise ratio (SNR). The DM-tradeoff curve is characterized by a set of points, where each point is a two-tuple whose first coordinate is the multiplexing gain and the second coordinate is the maximum diversity gain achievable at that multiplexing gain. We consider a multi-hop relay channel, where a source uses 
relay stages to communicate with its destination, and each relay stage is assumed to have one or more relay nodes. Relay nodes are assumed to be full-duplex. Under these assumptions we find and characterize multi-hop relay strategies that achieve the DM-tradeoff curve (in the two hop case) or come close to the optimum DM-tradeoff curve while outperforming prior work (with more than two hops).
In prior work there have been many different transmit strategies proposed to achieve the optimal DM-tradeoff of the multi-hop relay channel, such as distributed space time block codes (DSTBCs) [2–17], or relay selection [2, 3, 18–23]. The best known DSTBCs [14, 15] achieve the corner points of the optimal DM-tradeoff of the multi-hop relay channel, corresponding to the maximum diversity gain and maximum multiplexing gain, however, fail to achieve the optimal DM-tradeoff for intermediate values of multiplexing gain. Moreover, with DSTBCs [14, 15] the encoding and decoding complexity can be quite large. Antenna selection (AS) or relay selection (RS) strategies have been designed to achieve only the maximum diversity gain point of the optimal DM-tradeoff when a small amount of feedback is available from the destination for a two-hop relay channel in [2, 3, 18–23], and for a multi-hop relay channel in [24]. RS is also used for routing in multi-hop networks [25–27] to leverage path diversity gain. The primary advantages of AS and RS strategies over DSTBCs are that they require a minimal number of active antennas and reduce the encoding and decoding complexity compared to DSTBCs. The only strategy that is known to achieve all points of the optimal DM-tradeoff is the compress and forward (CF) strategy [28], but that is limited to a 
-hop relay channel with a single relay node.
In this paper we design an end-to-end antenna selection (EEAS) strategy to maximize the achievable diversity gain for a given multiplexing gain in a multi-hop relay channel. The EEAS strategy chooses a subset of antennas from each relay stage that maximize the mutual information at the destination. The proposed EEAS strategy is an extension of the EEAS strategy proposed in [24], where only a single antenna of each relay stage was used for transmission. The proposed EEAS strategy is shown to achieve the corner points of the optimal DM-tradeoff corresponding to maximum diversity gain and maximum multiplexing gain. For intermediate values of multiplexing gains, the achievable DM-tradeoff of the EEAS strategy does not meet with an upper bound on the DM-tradeoff, but outperforms the achievable DM-tradeoff of the best known DSTBCs [15]. Other advantages of the proposed EEAS strategy over DSTBCs [14, 15] include lower bit error rates due to less noise accumulation at the destination, reduced decoding complexity, and lesser latency. We assume that the destination has the channel state information (CSI) for all the channels in the receive mode. Using the CSI, the destination performs subset selection, and using a low rate feedback link feedbacks the index of the antennas to be used by the source and each relay stage.
Even though our EEAS strategy performs better than the best known DSTBCs [14, 15], it fails to achieve all points of the optimal DM-tradeoff. To overcome this limitation, we propose a distributed CF strategy to achieve all points of the optimal DM-tradeoff of a 
-hop relay channel with multiple relay nodes. Previously, the CF strategy of [29] was shown to achieve all points of the optimal DM-tradeoff of the 
-hop relay channel with a single relay node in [28]. The result of [28], however, does not extend for more than one relay node. With our distributed CF strategy, each relay transmits a compressed version of the received signal using Wyner-Ziv coding [30] without decoding any other relay's message. The destination first decodes the relay signals and then uses the decoded relay messages to decode the source message.
Our distributed strategy is a special case of the distributed CF strategy proposed in [31], where relays perform partial decoding of other relay messages and then use distributed compression to send their signals to the destination. With partial decoding, the achievable rate expression is quite complicated [31], and it is hard to compute the SNR exponent of the outage probability. To simplify the achievable rate expression, we consider a special case of the CF strategy [31] where no relay decodes any other relay's message. Consequently, the derivation for the SNR exponent of the outage probability is simplified, and we show that the special case of CF strategy [31] is sufficient to achieve the optimal DM-tradeoff for a 
-hop relay channel with multiple relays.
Organization
The rest of the paper is organized as follows. In Section 2, we describe the system model for the multi-hop relay channel and summarize the key assumptions. We review the diversity multiplexing (DM-) tradeoff for multiple antenna channels in Section 3 and obtain an upper bound on the DM-tradeoff of multi-hop relay channel. In Section 4 our EEAS strategy for the multi-hop relay channel is described, and its DM-tradeoff is computed. In Section 5 we describe our distributed CF strategy and show that it can achieve the optimal DM-tradeoff of 
-hop relay channel with any number of relay nodes. Final conclusions are made in Section 6.
Notation 1.
We denote by 
a matrix, 
a vector, and 
the 
element of 
. 
denotes the transpose conjugate of matrix 
. The maximum and minimum eigenvalue of 
is denoted by 
and 
, respectively. The determinant and trace of matrix 
is denoted by 
and 
. The field of real and complex numbers is denoted by 
and 
, respectively. The set of natural numbers is denoted by 
. The set 
is denoted by 
. The set 
denotes the set 
, 
. 
denotes 
. The space of 
matrices with complex entries is denoted by 
. The Euclidean norm of a vector 
is denoted by 
. The 
, 
represent the transpose and the transpose conjugate. The cardinality of a set 
is denoted by 
. The expectation of function 
with respect to 
is denoted by 
. A circularly symmetric complex Gaussian random variable 
with zero mean and variance 
is denoted as 
. We use the symbol 
to represent exponential equality, that is, let 
be a function of 
, then 
if 
, and similarly 
and 
denote the exponential less than or equal to and greater than or equal to relation, respectively. To define a variable we use the symbol 
.
2. System Model
We consider a multi-hop relay channel where a source terminal with 
antennas wants to communicate with a destination terminal with 
antennas via 
stages of relays as shown in Figure 1. The 
th relay stage has 
relays and, the 
th relay of 
th stage has 
antennas 
. The total number of antennas in the 
th relay stage is 
. In Section 5 we consider a 
-hop relay channel with 
relay nodes, where the 
relay has 
antennas and 
. We assume that the relays do not generate their own data, and each relay stage has an average power constraint of 
. We assume that the relay nodes are synchronized at the frame level. To keep the relay functionality and relaying strategy simple we do not allow relay nodes to cooperate among themselves. For Section 4 we assume that there is no direct path between the source and the destination, but we relax this assumption in Section 5 for the 
-hop relay channel. The absence of the direct path is a reasonable assumption for the case when relay stages are used for coverage improvement, and the signal strength on the direct path is very weak. We also assume that relay stages are chosen in such a way that all the relay nodes of any two adjacent relay stages are connected to each other, and there is no direct path between relay stage 
and 
. This assumption is reasonable for the case when successive relay stages appear in increasing order of distance from the source toward the destination, and any two relay nodes are chosen to lie in adjacent relay stages if they have sufficiently good SNR between them. In any practical setting there will be interference received at any relay node of stage 
because of the signals transmitted from relay nodes of relay stage 
and 
. Due to relatively large distances between nonadjacent relay stages, however, this interference is quite small and we account for that in the additive noise term. The system model is similar to the fully connected layered network with intralayer links [15] and more general than the directed multi-hop relay channel model of [14]. We consider the full-duplex multi-hop relay channel, where each relay node can transmit and receive at the same time.
System block diagram of a multi-hop relay channel with
stages.
As shown in Figure 1, the channel matrix between the subset 
of antennas of stage 
and the subset 
of antennas of stage 
is denoted by 
, 
, where 
. Stage 
represents the source and stage 
the destination.
In Section 5, we only consider a 
-hop relay channel and denote the channel matrix between the source and 
th relay by 
and between the 
th relay and destination by 
. The channel between the source and destination is denoted by 
and the channel matrix between relay 
and relay 
by 
.
We assume that the CSI is known only at the destination, and none of the relays have any CSI, that is, the destination knows 
. For Section 5, we assume that the destination knows 
, and the 
th relay node knows 
and 
. We assume that 
, and 
have independent and identically distributed (i.i.d.) 
entries for all 
to model the channel as Rayleigh fading with uncorrelated transmit and receive antennas. We assume that all these channels are frequency flat, block fading channels, where the channel coefficients remain constant in a block of time duration 
and change independently from block to block.
3. Problem Formulation
We consider the design of transmission strategies to achieve the DM-tradeoff of the multi-hop relay channel. In the next subsection we briefly review the DM-tradeoff [1] for point-to-point channels and obtain an upper bound on the DM-tradeoff of the multi-hop relay channel.
Review of the DM-Tradeoff: following [1], let 
be a family of codes, one for each 
. The multiplexing gain of 
is 
if the data rate 
of 
scales is 
with respect to 
, that is,
Then the diversity gain 
is defined as the rate of fall of probability of error 
of 
with respect to 
The exponent 
is called the diversity gain at rate 
, and the curve joining 
for different values of 
characterizes the DM-tradeoff. The DM-tradeoff for a point-to-point multi antenna channel with 
transmit and 
antennas has been computed in [1] by first showing that 
and then computing the exponent 
, where
where 
, for 
.
Next, we present an upper bound on the DM-tradeoff of the multi-hop relay channel obtained in [14].
Lemma 1 (see [14]).
The DM-tradeoff curve of the multi-hop relay channel 
is upper bounded by the piecewise linear function connecting the points 
where
for each 
.
The upper bound on the DM-tradeoff of multi-hop relay channel is obtained by using the cut-set bound [32] and allowing all relays in each relay stage to cooperate. Using the cut-set bound it follows that the mutual information between the source and the destination cannot be more than the mutual information between the source and any relay stage or between any two relay stages. Moreover, by noting the fact that mutual information between any two relays stages is upper bounded by the maximum mutual information of a point-to-point MIMO channel with 
transmit and 
receive antennas, 
, then the result follows from (3).
In the next section we propose an EEAS strategy for the multi-hop relay channel and compute its DM-tradeoff. We will show that the achievable DM-tradeoff of the EEAS strategy meets the upper bound at 
and 
.
4. Joint End-to-End Multiple Antenna Selection Strategy
In this section we propose a joint end-to-end multiple antenna selection strategy (JEEMAS) for the multi-hop relay channel and compute its DM-tradeoff. In the JEEMAS strategy, a fixed number 
of antennas are chosen from each relay stage to forward the signal towards the destination using amplify and forward (AF). Before introducing our JEEMAS strategy and analyzing its DM-tradeoff, we need the following definitions and Lemma 2.
Definition 1.
Let 
be a subset of antennas of stage 
, that is, 
. Let 
be the edge joining the set of antennas 
of stage 
to the set of antennas 
of stage 
, where 
. Then a path in a multi-hop relay channel is defined as the sequence of edges 
.
Definition 2.
Two paths 
and 
are called independent if 
.
In the next lemma we compute the maximum number of independent paths in a multi-hop relay channel.
Lemma 2.
The maximum number of independent paths in a multi-hop relay channel is
Proof.
Follows directly from [24, Theorem 3] by replacing 
by 
.
Now we are ready to describe our JEEMAS strategy for the full-duplex multi-hop relay channel. To transmit the signal from the source to the destination, a single path in a multi-hop relay channel is used for communication. How to choose that path is described in the following. Let the chosen path for the transmission be 
. Then the signal is transmitted from the 
subset of antennas of the source and is relayed through 
subset of antennas of relay stage 
and decoded by the 
subset of antennas of the destination. Each antenna on the chosen path uses an AF strategy to forward the signal to the next relay stage, that is, each antenna of stage 
on the chosen path transmits the received signal after multiplying by 
, where 
is chosen to satisfy an average power constraint 
across 
antennas of stage 
.
Therefore with AF by each antenna subset on the chosen path, the received signal at the 
subset of antennas of the destination at time 
of a multi-hop relay channel is
where 
and 
are functions of channel coefficients 
, 
ensures that the power constraint at each stage is met, 
is a function of 
's, 
is the complex Gaussian noise with zero mean and unit variance added at stage 
, and 
. Since the destination has the CSI, accumulated noise 
is white and Gaussian distributed. From hereon in this paper we assume that the accumulated noise at the destination for all the multi-hop relay channels is white Gaussian distributed without explicitly mentioning it. Let 
be the covariance matrix of 
, then by multiplying 
to the received signal we have
where 
is a matrix with 
entries. Note that 
is a function of channel coefficients 
.
We propose to use successive decoding at the destination with the JEEMAS strategy, similar to [24]. With successive decoding, the destination tries to decode only 
at time 
assuming that all the symbols 
have been decoded correctly. Assuming that at time 
all the symbols 
have been decoded correctly, the received signal (7) can be written as
since the channel coefficients 
are known at the destination. Let the probability of error in decoding 
from (8) be 
, then the probability of error 
in decoding 
from (7) with successive decoding 
is
where the last equality follows from [24].
From (8) it is clear that 
is the same for any 
, since the channel coefficients 
do not change for 
time instants. Therefore without loss of generality we compute an upper bound on 
to upper bound 
. Next, we describe our JEEMAS strategy and compute an upper bound on 
of the JEEMAS strategy to evaluate its DM-tradeoff. Let 
. Let 
, then the mutual information of path 
is
Then the JEEMAS strategy chooses the path that maximizes the mutual information at the destination, that is, it chooses path 
, if
Thus defining 
, the mutual information of the chosen path is
Since we assumed that the destination of the multi-hop relay channel has CSI for all the channels in the receive mode, this optimization can be done at the destination, and using a feedback link, the source and each relay stage can be informed about the index of antennas to use for transmission. Next, we evaluate the DM-tradeoff of the JEEMAS strategy by finding the exponent of the outage probability (8).
From [1] we know that 
, where 
is the outage probability of (8). Therefore it is sufficient to compute an upper bound on the outage probability of (8) to upper bound 
. With the proposed EEAS strategy, the outage probability of (8) can be written as
From [14, 15] 
can be dropped from the DM-tradeoff analysis without changing the outage exponent, since 
[14], that is, the maximum or the minimum eigenvalue of 
does not scale with 
. Thus,
We first compute the DM-tradeoff of the JEEMAS strategy for the case when there exists 
such that 
, and then for the general case.
If 
, then by Lemma 2, the total number of independent paths in a multi-hop relay channel is 
. Thus,
since from (14) 
for any 
.
From [14]
where
where 
, and 
. Thus, 
, and the DM-tradeoff of the JEEMAS strategy is given by
For the general case when 
, let 
, for some 
and 
. Then partition the multi-hop relay channel into two parts, the first partition 
containing 
antennas of each stage, such that the chosen set of antennas by the JEEMAS strategy 
, and the second partition 
containing the rest 
antennas of each stage. By reordering the index of antennas, without loss of generality, let 
contain antennas 
to 
of each relay stage, and let 
contain antennas 
to 
of stage 
. Recall that the JEEMAS strategy chooses those 
antennas of each stage that have the maximum mutual information at the destination. Thus,
where 
, and 
is the 
channel matrix between 
to 
antennas of stage 
and 
to 
antennas of stage 
. Note that the channel coefficients in 
are not independent of the channel coefficients in 
, and therefore we cannot write 
as the product of
To circumvent this problem, let 
, where 
is the channel matrix between the last 
antennas of stage 
and the last 
antennas of stage 
of partition 
, and 
is the channel matrix between the last 
antennas of stage 
and the last 
antennas of stage 
of partition 
. Basically we pick 
and 
antennas alternatively, note that use of more antennas increases the mutual information of the channel, and consequently reduces the outage probability. Since 
uses a subset of antennas of 
, therefore from (19),
Since the channel coefficients in 
are independent of the channel coefficients of 
,
Therefore,
since the number of independent paths in partition 
is 
.
From [14], 
where
, where 
and 
is the nondecreasing ordered version of 
, 
. Thus,
Therefore, using (16), the DM-tradeoff of the JEEMAS strategy is
.
Recall that in the JEEMAS strategy the design parameter is 
, the number of antennas to use from each stage. To obtain the best lower bound on the DM-tradeoff of JEEMAS strategy one needs to find out the optimal value of 
. From (26), it follows that using a single antenna 
, maximum diversity gain point can be achieved. Similarly, choosing 
, the maximum multiplexing gain point can also be achieved. For intermediate values of 
, however, it is not apriori clear what value of 
maximizes the diversity gain. After tedious computations it turns out that choosing 
provides with the best achievable DM-tradeoff for 
. Thus, we propose a hybrid JEEMAS strategy, where for 
use 
, and for 
use 
. Our approach is similar to [15], where for each 
an optimal partition of the multi-hop relay channel is found by solving an optimization problem. We compare the achievable DM-tradeoff of our hybrid JEEMAS strategy and the strategy of [15] for 
and 
in Figures 2 and 3.
DM-tradeoff comparison of hybrid JEEMAS with the strategy of [ 15 ].
DM-tradeoff comparison of hybrid JEEMAS with the strategy of [ 15 ].
For the case when 
, the achievable DM-tradeoff of our hybrid JEEMAS strategy matches with that of the partitioning strategy of [15]. For the case when 
, however, it is difficult to compare the hybrid JEEMAS strategy with the strategy of [15] in terms of achievable DM-tradeoff, since an optimization problem has to be solved for the strategy of [15]. For a particular example of 
the hybrid JEEMAS strategy outperforms the strategy of [15] as illustrated in Figure 3. Moreover, in [15] a new partition is required for each 
, in contrast to our strategy, which has only two modes of operation, one for 
and the other for 
.
The following remarks are in order.
Remark 1.
Recall that we assumed that 
, that is, equal number of antennas are selected at each relay stage. The justification of this assumption is as follows. Let us assume that 
antennas are used from each relay stage. Now assume that all relay stages are using the same number of antennas 
, except 
, which is using 
antennas, 
, and 
. Using (26), it can be shown that the achievable DM-tradeoff with 
, and 
is a subset of the union of the achievable DM-tradeoff with using 
(all relay stages using 
antennas), and 
(all relay stages using 
antennas). Thus, it is sufficient to consider same number of antennas from each relay stage. It turns out, however, that different values of 
provide with different achievable DM-tradeoff's because of the different number of independent paths in the multi-hop relay channel. To optimize over all possible values of 
we keep 
as a variable and choose 
to obtain the best achievable DM-tradeoff.
Remark 2.
Using the DM-tradeoff analysis of the JEEMAS strategy, we can obtain the DM-tradeoff of an antenna selection strategy for the point-to-point MIMO channel by considering a multi-hop relay channel with 
, 
transmit, and 
receive antennas such that (
). Surprisingly we could not find this result in literature and provide it here for completeness sake. Let 
, and the transmitter uses 
antennas out of 
antennas that have maximum mutual information at the destination, then the DM-tradeoff is given by
. The proof follows directly from (26).
Remark 3 (CSI Requirement).
With the proposed hybrid JEEMAS strategy, the destination needs to feedback the index of the path with the maximum mutual information to the source and each stage. Recall from the derivation of the achievable DM-tradeoff of the JEEMAS strategy that only 
paths in a multi-hop relay channel are independent, and control the achievable DM-tradeoff for 
. Thus, the destination only needs to feedback the index of the best path among 
independent paths with the maximum mutual information. Consequently the destination only needs to know CSI for 
paths. For the case when 
, we need to consider one more path from partition 
corresponding to 
and 
antennas of alternate relay stages. Thus, the CSI overhead is moderate for the proposed EEAS strategy.
Remark 4 (Feedback Overhead).
As explained in Remark 3, to obtain the achievable DM-tradeoff of the hybrid JEEMAS strategy it is sufficient to consider any one set of 
or 
independent paths. Let the destination choose a particular set 
of 
independent paths. Then each relay node knows on which of the paths of 
it lies, and depending on the index of the element of 
from the destination, it knows whether to transmit or remain silent. Thus, only 
bits of feedback is required from the destination to the source and each stage. Therefore the feedback overhead with the proposed EEAS strategy is quite small and can be realized with a very low-rate feedback link.
Discussion
In this section we proposed a hybrid JEEMAS strategy that has two modes of operation, one for 
, where it uses a single antenna of each stage, and the other for 
, that uses 
antennas of each stage. The proposed strategy is shown to achieve both the corner points of the optimal DM-tradeoff curve, corresponding to the maximum diversity gain and the maximum multiplexing gain. For intermediate values of multiplexing gain, the diversity gain of our strategy is quite close to that of the upper bound. Even though our strategy does not meet the upper bound, we show that it outperforms the best known DSTBC strategy [15] with smaller complexity and possess several advantages over DSTBCs as described in [24]. In the next section we propose a distributed CF strategy to achieve the optimal DM-tradeoff of the 
-hop relay channel.
5. Distributed CF Strategy for 2-hop Relay Channel
In this section we consider a 
-hop relay channel with multiple relay nodes in the presence of a direct path between the source and the destination. For this 
-hop relay channel we propose a distributed compress and forward (CF) strategy to achieve the optimal DM-tradeoff. The signal model for this section is as follows. We consider a 
-hop relay channel with 
relay nodes, where the 
relay has 
antennas, and 
. The source and destination are assumed to have 
and 
antennas, respectively. We assume that the source and each relay have an average power constraint of 
. Different transmit power constraints do not change the DM-tradeoff. Let the signal transmitted from the source be 
, and from the relay node 
let it be 
, respectively. Then,
where 
is the received signal at the destination, and 
is the signal received at relay 
.
Previously in [28], the CF strategy of [29] has been shown to achieve the optimal DM-tradeoff of a 
-hop relay channel with a single relay node (
) in the presence of direct path between the source and the destination. The result of [28], however, does not generalize to the case of 
-hop relay channel with multiple relay nodes. The problem with multiple relay nodes is unsolved, since how multiple relay nodes should cooperate among themselves to help the destination to decode the source message is hard to characterize. A compress and forward (CF) strategy for a 
-hop relay channel with multiple relay nodes has been proposed in [31], which involves partial decoding of other relays messages at each relay and transmission of correlated information from different relay nodes to the destination using distributed source coding. The achievable rate expression obtained in [31], however, is quite complicated and cannot be computed easily in closed form.
The achievable rate expression of the CF strategy [31] is complicated because each relay node partially decodes all other relay messages. Partial decoding introduces auxillary random variables which are hard to optimize over. To allow analytical tractability, we simplify the strategy of [31] as follows. In our strategy each relay compresses the received signal from the source using Wyner-Ziv coding similar to [31], but without any partial decoding of any other relay's message. The compressed message is then transmitted to the destination using the strategy of transmitting correlated messages over a multiple access channel [33]. Our strategy is a special case of CF strategy [31], since in our case the relays perform no partial decoding. Consequently our strategy leads to a smaller achievable rate compared to [31]. The biggest advantage of our strategy, however, is its easily computable achievable rate expression and its sufficiency in achieving the optimal DM-tradeoff as shown in the sequel. We refer to our strategy as distributed CF from hereon in the paper. Even though the relays do not perform any partial decoding in the distributed CF strategy, in the sequel we show that they still provide the destination with enough information about the source message to achieve the optimal DM-tradeoff. Before describing our distributed CF strategy and showing its optimality in achieving the optimal DM-tradeoff, we present an upper bound on the DM-tradeoff of the 
-hop relay channel.
Lemma 3 (see [14]).
The DM-tradeoff of a two-way relay channel is upper bounded by
.
Proof.
Let us assume that all the relay nodes and the destination are colocated and can cooperate perfectly. This assumption can only improve 
. In this case, the communication model from the source to destination is a point to point MIMO channel with 
transmit antennas and 
receive antennas. The DM-tradeoff of this MIMO channel is 
, and since this point to point MIMO channel is better than our original 
-hop relay channel, 
. Next, we assume that the source is co-located with all the relay nodes and can cooperate perfectly for transmission to the destination. This setting is equivalent to a MIMO channel with 
transmit and 
receive antenna with DM-tradeoff 
. Again, this point to point MIMO channel is better than our original 
-hop relay channel and hence 
, which completes the proof.
To achieve this upper bound we propose the following distributed CF strategy. Let the rate of transmission from source to destination be 
. Then the source generates 
independent and identically distributed 
according to distribution 
. Label them 
. The codebook generation, the relay compression, and transmission remain the same as in [31], expect that no relay node decodes any other relay's codewords, that is, no partial decoding at any relay node. Relay node 
generates 
independent and identically distributed 
according to distribution 
and labels them 
, and for each 
generates 
's, each with probability 
. Label these 
and 
and randomly partition the set 
into 
cells 
.
Encoding
A Block Markov encoding [29] together with Wyner-Ziv coding [30] is used by each relay. Let in block 
the message sent from the source be 
, then the source sends 
. Let the signal received by relay 
in block 
be 
. Then 
is compressed to 
using Wyner-Ziv coding [30] where correlation among 
is exploited. Then relay 
determines the cell index 
in which 
lies and transmits 
in block 
. We consider transmission of 
blocks of 
symbols each from the source in which 
messages will be sent. Each message is chosen from 
. Thus, as 
, for fixed 
, rate 
is arbitrarily close to 
[29]. In the first block, the relay has no information about 
necessary for compression. In this case, however, any good sequence allows each relay to start block Markov encoding [29]. In the last block, the source is silent, and only the relays transmit to destination.
Decoding
Backward decoding is employed at the destination. At the end of block 
, the codeword sent by source in block 
is decoded. At the end of block 
, the destination first decodes 
for each 
by looking for a jointly typical 
and 
. If 
, 
can be decoding reliably. Next, given that 
's have been decoded correctly for each 
, the destination tries to find a set 
of 
such that 
is jointly typical. The destination declares that 
were the correctly sent codewords if 
. After decoding 
and 
the destination decodes 
if 
is jointly typical. With this distributed CF strategy,
is achievable with the joint probability distribution
subject to
where 
, 
are vectors with elements 
, respectively, 
is the vector containing 
, and 
is the complement of 
, where 
. For more detailed error probability analyses we refer the reader to [31]. In the next theorem we compute the outage exponents for (30) and show that they match with the exponents of the upper bound.
Theorem 1.
CF strategy achieves the DM-tradeoff upper bound (Lemma 3).
Proof.
To prove the theorem we will compute the achievable DM-tradeoff of the CF strategy (30) and show that it matches with the upper bound.
To compute the achievable rates subject to the compression rate constraints for the signal model (28), we fix 
, where 
is 
vector with covariance matrix 
. Also, we choose 
and 
to be complex Gaussian with covariance matrices 
, and 
, and independent of each other, respectively. Next, we compute the various mutual information expressions to derive the achievable DM-tradeoff of the CF strategy. By the definition of the mutual information,
From (28),
where 
is defined as
and 
. From (28),
which implies
Next, we compute the values of 
's that satisfy the compression rate constraints (32). Note that in (32), we need to satisfy the constraints for each subset 
. Towards that end, first we consider the subsets 
of the form 
and obtain the lower bound on the quantization noise 
needed to satisfy (32), that is not proportional to 
for each 
. It is important to note that 
should not be proportional to 
; otherwise, from (37) it can be concluded that our distributed CF strategy cannot achieve the optimal DM-tradeoff. In the sequel we will point out how to obtain 
satisfying (32) for all subsets of 
.
For 
, from (32), for each relay 
, we need to satisfy
By definition
Similarly,
where 
is defined as
Similarly,
where 
is defined as
From (39), (40), and(42), to satisfy the compression rate constraints (38), we need
Note that both sides of (44) are functions of 
; however, the resulting 
is not a function of 
or SNR similar to [28]. Recall that we have only considered the subsets of 
of the form 
. For the rest of the subsets also, we can show that the quantization noise 
required to satisfy (32) is not proportional to 
. The analysis follows similarly and is deleted for the sake of brevity. Thus, to satisfy (32), we can take the maximum of the 
required for each subset 
and use that to analyze the DM-tradeoff. Let the maximum 
required to satisfy (32) be 
. Since 
for each subset 
is not proportional to 
, and 
is also not proportional to 
.
Then, using (30) and (37), we can compute the outage probability of the distributed CF as follows. From [1], to compute 
, it is sufficient to find the negative of the exponent of the 
of outage probability at the destination, where outage probability 
is defined as
From (30) and (37),
Let 
. Then choose 
such that
It is possible to choose 
's that satisfy (47), since 
is not proportional to 
.
Then
where the last equality follows since multiplying 
by constant does not change the DM-tradeoff.
From here on we follow [28] to compute the exponent of the 
.
Let
Then, from (34), 
; therefore, using [28, Lemma 2], it follows that
Therefore, to lower bound the DM-tradeoff we need to find out the outage exponents 
and 
of 
and 
. Notice that, however, 
is the mutual information between the source and the destination by choosing the covariance matrix to be 
and allowing all the relays and the destination to cooperate perfectly. From [1], choice of 
as the covariance matrix does not change the optimal DM-tradeoff; therefore, 
. Similar argument holds for 
, by noting that 
is the mutual information between the source and the destination if all the relays and the source were co-located and could cooperate perfectly, while using covariance matrix 
, where
Thus, 
. Thus, the achievable DM-tradeoff with CF strategy meets the upper bound (Lemma 3).
Discussion
In this section we proposed a simplified version of the distributed CF strategy of [31] and showed that it can achieve the optimal DM-tradeoff for the 
-hop relay channel for any number of relays. In our distributed CF strategy, each relay uses Wyner-Ziv coding to compress the received signal without any partial decoding of other relay messages. After compression, each relay transmits the message to the destination using the strategy for multiple access channel with correlated messages [33], since the relay compressed messages are correlated with each other. Even though the achievable rate with our strategy is smaller than the one obtained in [31] (because of no partial decoding at any relay), we show that it is sufficient to achieve the optimal DM-tradeoff. We prove the result by showing that the exponent of the outage probability of our strategy matches with the upper bound on the optimal DM-tradeoff, without requiring the compression noise constraints to be proportional to the 
.
Generalizing our distributed CF strategy is possible for more than 2-hop relay channel; however, computing the exponents of the outage probability of achievable rate and compression rate constraints is a nontrivial problem.
6. Conclusions
In this paper we considered the problem of achieving the optimal DM-tradeoff of the multi-hop relay channel. First, we proposed an antenna selection strategy called JEEMAS, where a subset of antennas of each relay stage is chosen for transmission that has the maximum mutual information at the destination. We showed that the JEEMAS strategy can achieve the maximum diversity gain and the maximum multiplexing gain in a multi-hop relay channel. Then we compared the DM-tradeoff performance of the JEEMAS strategy with the best known DSTBC strategy [15]. We observed that the DM-tradeoff of the JEEMAS is better than the DSTBCs [15], except for the case when the number of antennas at each stage are divisible by the minimum of the antennas across all relay stages, in which case the DM-tradeoffs of JEEMAS and DSTBCs [15] match.
Next, we proposed a distributed CF strategy for the 
-hop relay channel with multiple relay nodes and showed that it achieves the optimal DM-tradeoff. Our distributed CF strategy is a special case of the strategy proposed in [31], where the specializations are done to allow analytical tractability. We showed that if each relay transmits a compressed version of the received signal using Wyner-Ziv coding, it is sufficient to achieve the optimal DM-tradeoff. Our distributed CF strategy can be extended to more than 
-hop relay channels; however, computing the outage probability exponents is a non-trivial problem.
References
- 1.
Zheng L, Tse DNC: Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels. IEEE Transactions on Information Theory 2003, 49(5):1073-1096. 10.1109/TIT.2003.810646
- 2.
Laneman JN, Wornell GW: Distributed space-time-coded protocols for exploiting cooperative diversity in wireless networks. IEEE Transactions on Information Theory 2003, 49(10):2415-2425. 10.1109/TIT.2003.817829
- 3.
Laneman JN, Tse DNC, Wornell GW: Cooperative diversity in wireless networks: efficient protocols and outage behavior. IEEE Transactions on Information Theory 2004, 50(12):3062-3080. 10.1109/TIT.2004.838089
- 4.
Nabar RU, Bolcskei H, Kneubuhler FW: Fading relay channels: performance limits and space-time signal design. IEEE Journal on Selected Areas in Communications 2004, 22(6):1099-1109. 10.1109/JSAC.2004.830922
- 5.
Jing Y, Hassibi B: Diversity analysis of distributed space-time codes in relay networks with multiple transmit/receive antennas. EURASIP Journal on Advances in Signal Processing 2008., 2008:
- 6.
Jing Y, Hassibi B: Distributed space-time coding in wireless relay networks. IEEE Transactions on Wireless Communications 2006, 5(12):3524-3536.
- 7.
Yang S, Belfiore J-C: Optimal space-time codes for the MIMO amplify-and-forward cooperative channel. IEEE Transactions on Information Theory 2007, 53(2):647-663.
- 8.
Yiu S, Schober R, Lampe L: Distributed space-time block coding for cooperative networks with multiple-antenna nodes. Proceedings of the 1st International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP '05), December 2005 52-55.
- 9.
Barbarossa S, Scutari G: Distributed space-time coding strategies for wideband multihop networks: regenerative vs. non-regenerative relays. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '04), May 2004, Montreal, Canada 4: 501-504.
- 10.
Damen MO, Hammons R Jr.: Distributed space-time codes: relays delays and code word overlays. Proceedings of the International Wireless Communications and Mobile Computing Conference (IWCMC '07), August 2007, Honolulu, Hawaii, USA 354-357.
- 11.
Oggier F, Hassibi B: An algebraic family of distributed space-time codes for wireless relay networks. Proceedings of IEEE International Symposium on Information Theory, July 2006 538-541.
- 12.
Yindi J, Hassibi B: Using orthogonal and quasi-orthogonal designs in wireless relay networks. IEEE Transactions on Information Theory 2007, 53(11):4106-4118.
- 13.
Jing Y, Jafarkhani H: Network beamforming with channel means and covariances at relays. Proceedings of the IEEE International Conference on Communications (ICC '08), May 2008, Beijing, China 3743-3747.
- 14.
Yang S, Belfiore J: Diversity of MIMO multihop relay channels. http://arxiv.org/PS_cache/arxiv/pdf/0708/0708.0386v1.pdf
- 15.
Sreeram K, Birenjith S, Vijay Kumar P: DMT of multi-hop cooperative networks—part II: half-duplex networks with full-duplex performance. submitted to IEEE Transactions on Information Theory
- 16.
Vaze R, Heath RW Jr.: Maximizing reliability in multi-hop wireless networks. Proceedings of IEEE International Symposium on Information Theory (ISIT '08), July 2008, Toronto, Canada 11-15.
- 17.
Oggier F, Hassibi B: Code design for multihop wireless relay networks. EURASIP Journal on Advances in Signal Processing 2008, 2008:-12.
- 18.
Peters SW, Heath RW Jr.: Nonregenerative MIMO Relaying with Optimal Transmit Antenna Selection. IEEE Signal Processing Letters 2008, 15: 421-424.
- 19.
Bletsas A, Khisti A, Reed DP, Lippman A: A simple cooperative diversity method based on network path selection. IEEE Journal on Selected Areas in Communications 2006, 24(3):659-672.
- 20.
Lin Z, Erkip E: Relay search algorithms for coded cooperative systems. Proceedings of IEEE Global Telecommunications Conference (GLOBECOM '05), November-December 2005, St. Louis, Mo, USA 3: 1314-1319.
- 21.
Ibrahim AS, Sadek AK, Su W, Liu KJR: Cooperative communications with relay-selection: when to cooperate and whom to cooperate with? IEEE Transactions on Wireless Communications 2008, 7(7):2814-2827.
- 22.
Lo CK, Vishwanath S, Heath RW Jr.: Relay subset selection in wireless networks using partial decode-and-forward transmission. submitted to IEEE Transactions on Vehicular Technology
- 23.
Tannious R, Nosratinia A: Spectrally-efficient relay selection with limited feedback. IEEE Journal on Selected Areas in Communications 2008, 26(8):1419-1428.
- 24.
Vaze R, Heath RW Jr.: To Code in Space and Time or Not in a Multi-Hop Relay Channels. IEEE Transactions on Signal Processing 2009, 57: 3736-2747.
- 25.
Park M, Andrews JG, Nettles SM: Wireless channel-aware ad hoc cross-layer protocol with multi-route path selection diversity. Proceedings of the 58th IEEE Vehicular Technology Conference (VTC '03), October 2003, Orlando, Fla, USA 4: 2197-2201.
- 26.
Gui B, Dai L, Cimini LJ Jr.: Routing strategies in broadband multihop cooperative networks. Proceedings of the 41st Annual Conference on Information Sciences and Systems (CISS '07), March 2007 661-666.
- 27.
Bohacek S: Performance improvements provided by route diversity in multihop wireless networks. IEEE Transactions on Mobile Computing 2008, 7(3):372-384.
- 28.
Yuksel M, Erkip E: Multiple-antenna cooperative wireless systems: a diversity-multiplexing tradeoff perspective. IEEE Transactions on Information Theory 2007, 53(10):3371-3393.
- 29.
Cover TM, El Gamal AA: Capacity theorems for relay channels. IEEE Transactions on Information Theory 1979, 25(5):572-584. 10.1109/TIT.1979.1056084
- 30.
Wyner AD, Ziv J: The rate-distortion function for source coding with side information at the decoder. IEEE Transactions on Information Theory 1976, 22(1):1-10. 10.1109/TIT.1976.1055508
- 31.
Kramer G, Gastpar M, Gupta P: Cooperative strategies and capacity theorems for relay networks. IEEE Transactions on Information Theory 2005, 51(9):3037-3063. 10.1109/TIT.2005.853304
- 32.
Cover T, Thomas J: Elements of Information Theory. John Wiley & Sons, New York, NY, USA; 2004.
- 33.
Cover TM, El Gamal AA, Salehi M: Multiple access channels with arbitrarily correlated source. IEEE Transactions on Information Theory 1980, 26(6):648-657. 10.1109/TIT.1980.1056273
Acknowledgment
This work was funded by DARPA through IT-MANET Grant no. W911NF-07-1-0028.
Author information
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About this article
Received
Revised
Accepted
Published
DOI
Keywords
- Mutual Information
- Channel State Information
- Outage Probability
- Relay Node
- Channel Coefficient
