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Cooperative Coverage Extension in Vehicular Land Mobile Satellite Networks

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We study the application of random linear network coding (RLNC) for cooperative coverage extension in land mobile satellite (LMS) vehicular networks. We perform an analytical assessment of the limits of cooperation in the presence of both a satellite and a terrestrial repeater (gap-filler). Using the Max-flow Min-cut theorem we derive exact expressions as well as closed form lower bounds on the coverage in a setup of practical interest. Furthermore, we propose a practical implementation of the RLNC cooperative approach for the Digital Video Broadcasting - Satellite services to Handheld (DVB-SH) standard. We evaluate numerically the performance of the proposed protocol using a simulator based on physical layer abstraction and widely adopted propagation channel models for both the satellite and the terrestrial segments. Our simulation results show that the proposed scheme extends the coverage with respect to a system in which simple cooperative relaying is used, making a more efficient use of the terrestrial channel resources. Additionally, the proposed solution can help keeping a relatively low outage probability while limiting the number of required gap-fillers.
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Cooperative Coverage Extension in Vehicular Land
Mobile Satellite Networks
Giuseppe Cocco, Nader Alaghaand Christian Ibars
German Aerospace Center (DLR), Weßling, Germany
European Space Agency, Noordwijk, The Netherlands
Intel Corporation, Santa Clara, CA, USA
giuseppe.cocco@dlr.de, nader.alagha@esa.int, christian.ibars.casas@intel.com
Abstract—We study the application of random linear network
coding (RLNC) forcooperative coverage extension in land mobile
satellite (LMS) vehicular networks. We perform an analytical
assessment of the limits of cooperation in the presence of both a
satellite and a terrestrial repeater (gap-filler). Using the Max-flow
Min-cut theorem we derive exact expressions as well as closed-
form lower bounds on the coverage in a setup of practical interest.
Furthermore, we propose a practical implementation of the
RLNC cooperative approach for the Digital Video Broadcasting
- Satellite services to Handheld (DVB-SH) standard. We evaluate
numerically the performance of the proposed protocol using
a simulator based on physical layer abstraction and widely
adopted propagation channel models for both the satellite and
the terrestrial segments. Our simulation results show that the
proposed scheme extends the coverage with respect to a system
in which simple cooperative relaying is used, making a more
efficient use of the terrestrial channel resources. Additionally,
the proposed solution can help keeping a relatively low outage
probability while limiting the number of required gap-fillers.
I. INTRODUCTION
In the last decade several proprietary solutions as well as
open standards have been developed to enable data broad-
casting via satellite to mobile users. An example of a recent
open standard is the Digital Video Broadcasting - Satellite
services to Handheld (DVB-SH) [1]. Satellite broadcast and
relaying capabilities give rise to the possibility of creating
mobile broadcast systems over wide geographical areas, which
opens large market opportunities for both handheld and ve-
hicular user terminals. Mobile broadcasting is of paramount
importance for services such as digital TV or Machine to Ma-
chine (M2M) communication, a new communication paradigm
which will bring about a tremendous increase in the number
of deployed wireless terminals [2].
The possibility for all nodes to correctly receive the data
transmitted by a central node (e.g., a satellite or a base
station), denoted here as system coverage, is a main issue for
networks with a large number of terminals. As an example,
such system could be used for navigation maps update in
vehicle-mounted positioning systems or for terminal software
and firmware update in M2M networks, where reliable broad-
cast transmission is of primary importance. Protocols such as
Copyright (c) 2015 IEEE. Personal use of this material is permitted.
However, permission to use this material for any other purposes must be
obtained from the IEEE by sending a request to pubs-permissions@ieee.org.
Giuseppe Cocco was partially supported by the European Space Agency under
the Networking/Partnering Initiative.
Automatic Repeat reQuest (ARQ), although very effective in
point-to-point communication (see, e.g., [3, section 7.1.5]),
may not be applicable in multicast contexts; there may be
many retransmission requests by the terminals in case of
packets losses, which would saturate the return channel and
overwhelm the source [4]. A cooperative approach may be
applied in heterogeneous networks [2], in which terminals are
equipped with both a long range (i.e., satellite reception) and
a short range communication air interfaces.
A lot of work has been done on the use of cooperation [5]
in multicast and broadcast communications in both terrestrial
[6][7] and satellite networks [8][9][10]. Many of the proposed
solutions [4][11][12], particularly in terrestrial networks, are
based on network coding (NC) [13], that can achieve the Max-
flow Min-cut capacity in ad-hoc networks. Cooperative content
dissemination from road side units to vehicular networks
based on rateless codes has also been studied in [14] and
[15]. Physical layer strategies for cooperative relaying in land
mobile satellite (LMS) networks have been studied in [16],
[17] and [18].
In the present paper we study the application of network
coding for cooperative satellite coverage extension in LMS
systems. Our solution does not require any modification to
the the physical and the data link layers of existing standards
for LMS and vehicle-to-vehicle (V2V) communications. This
makes our approach different from those in [16], [17] and
[18]. In the solution we present, the LMS and the V2V
terminals, co-located in the vehicles, exchange information at
an intermediate level between the data link and the network
layer. We study the advantages of the propose approach at
system level.
The present work extends and consolidates the preliminary
results we presented in [19] and [20] by including the presence
of terrestrial repeaters, also called gap-fillers, in both the
analytical and the numerical analyses. In particular, we extend
the results of [19] by building up an end-to-end system model
which is inspired by the Digital Video Broadcasting - Satellite
services to Handheld (DVB-SH) [1] standard. In this model
the same information is transmitted by the satellite and the
terrestrial repeaters, and combined at the physical layer by
the user terminal. The network of gap-fillers constitutes the
complementary ground component (CGC) and is meant to
provide coverage to areas where the signal received from
the satellite suffers from shadowing caused, for example, by
tall buildings. It should be noted that the combination of the
2
signals from satellite and CGC is already foreseen in DVB-
SH [1]. We model the network as a directed graph. The joint
effect of the satellite and the gap-filler are taken into account
through the statistics of the link between the source and each
terminal. By applying the Max-flow Min-cut theorem [4] and
assuming Gaussian signalling under the hypothesis of block
fading channels, we derive an exact expression of the outage
probability as well as a closed-form lower bound expression
for the case in which either the link from the satellite or from
the gap-filler suffers strong fading.
We support and complement our theoretical study with a
numerical analysis based on a simulator we developed. The
enhancements with respect to [20] are:
the inclusion of a gap-filler,
the implementation of the physical layer of IEEE 802.11p
standard [21] 1,
the use of widely adopted channel models to generate the
time series for the terrestrial channels [22] 2,
the implementation of the physical layer combining of the
signals received from satellite and CGC at the terminal
nodes and the Raptor encoder/decoder as described in the
DVB-SH-B standard [23].
The simulator interface between the physical channel and
the protocol stack at higher layers leverages on physical
layer abstraction (PLA) [24] [25], which has been adopted
in the standardization process of the IEEE 802.16 standard
[26]. Several works [27] [28] showed that PLA allows to
evaluate the link level performance of a communication
system in a computationally affordable manner and with a
good accuracy. Our simulation results show that the proposed
scheme extends the coverage with respect to a system
where cooperation among vehicular terminals is implemented
through simple relaying. The results also suggest that the
cooperative approach can help to decrease the system outage
probability while limiting the number of required gap-fillers
in areas with challenging propagation conditions such as
urban environments.
The rest of the paper is organized as follows. In Section
II the system model is described. In Section III we study the
non cooperative and the cooperative cases from a theoretical
perspective deriving analytical expressions for the coverage
in both setups. In Section IV we introduce the DVB-SH
standard. This is used as the reference baseline in Section V,
where a practical implementation of the cooperative approach
is described. The proposed scheme implements the cooperative
approach described in Section III-B within the limits of
existing standards. In Section VI we describe the adopted
simulation approach and the simulation setup, while in Section
VII we present the numerical results. Finally, Section VIII
contains the conclusions.
II. SYSTEM MODEL
A network is considered in which a source (S) has a set of
Ksource messages w1,...,wKof kbits each, to broadcast
1Modulation, channel code and a simplified version of the channel access
mechanism.
2Both vehicle-to-vehicle and gap-filler-to-vehicle.
to a population of Mterminal nodes (or user terminals). The
set of Ksource messages is represents a generation. Each
user terminal is assumed to have both satellite reception (one-
way communication) as well as short range communication
(two-way, half duplex) capabilities. The source Sdelivers the
data to the terminals through two relay nodes S1and S2and
there is no direct link between Sand any of the terminals.
The links between Sand Si,i= 1,2, are assumed to be
lossless 3.S1and S2relay the data to the terminal nodes
through orthogonal lossy wireless channels. With reference to
DVB-SH-B, S1models a transparent satellite while S2models
a gap-filler. The orthogonality is achieved in the frequency
domain by allocating two disjoint frequency bands for the
satellite and the gap-fillers. No feedback from the terminals or
channel state information at the transmitter (CSIT) is assumed
at S,S1and S2, which implies a non-zero probability of
packet loss. S1and S2protect each message using the same
channel code (i.e., they transmit exactly the same signal) in
order to decrease the probability of packet loss on the channel.
A second level of protection is applied by Sat packet level
in order to compensate for packet losses. The encoding at
packet level takes place before the channel code is applied.
NKcoded packets are created by Sapplying a random
linear network code (RLNC) to the Ksource messages. We
define R=K/N as the rate of the NC encoder at S. Network
coding operates in a finite field of size q(GF (q)), so that each
message is treated as a vector of k/ log2(q)symbols. Source
messages are linearly combined to produce encoded packets.
An encoded packet xis generated as follows:
x=
K
X
i=1
̺iwi,
where ̺i,i= 1,...,K are random coefficients drawn ac-
cording to a uniform distribution in GF (q). The coefficients
̺i,i= 1,...,K, are appended to each message xbefore its
transmission. The set of appended coefficients represents the
coordinates of the encoded message xin GF (q)with respect
to the basis {wi},i= 1,...,K, and is called global encoding
vector.
The encoding at the physical layer is applied by S1and
S2to network-encoded packets, each consisting of of kbits.
S1and S2encode each packet using the same Gaussian
codebook of size 2nr, with r=k
nbits per channel use
(bpcu), associating a codeword cmof ni.i.d. symbols drawn
according to a Gaussian distribution to each xm,m= 1 ...,N
[3]. The time needed for S1and S2to transmit a packet is
called transmission slot (TS). Transmissions are synchronized
in time such that symbols from the two orthogonal channels
3In DVB-SH data is transmitted to the satellite through a high-throughput
and highly reliable feeder link, while the distribution to the CGC network
can take place either through a highly reliable satellite link or a terrestrial
distribution network. Due to their high reliability such links are assumed as
ideal, i.e., no packet loss occurs in the distribution network.
3
are aligned at the receiver 4.
S
1
N
2
N
1
N
2
N
S
Fig. 1. Example of the physical network under study (left) and the corre-
sponding graph representation (right).
The signals from S1and S2are combined by each terminal
at the physical layer using maximal-ratio combining (MRC).
In MRC received signals are weighted using coefficients that
depend on the signal’s phase and amplitude and such that the
SNR of the combined signal is the sum of the SNRs of the
two received signals. Further details on the implementations
of the MRC in the DVB-SH standard can be found in [1].
Terminals cooperate in order to recover the packets that are
lost in the link from the transmitters. We assume terminals
with high mobility 5, so that nodes have little time to set
up connections with each other. For this, and in order to
exploit the broadcast nature of the wireless medium, nodes
act in promiscuous mode, broadcasting packets to all terminals
within reach. Similarly as in the broadcast mode of IEEE
802.11p standard, no request to send (RTS)/clear to send
(CTS) mechanism is assumed [29]. No CSIT is assumed at
the transmitting terminal in the terrestrial communication, so
that there is a non-zero probability of packet loss. Unlike in
802.11p, each terminal uses two levels of encoding, one at
the packet level and one at the physical level. The encoding
procedure is detailed in the following.
Let Lbe the number of packets correctly decoded at the
physical level by a terminal, received either through the satel-
lite or the short range communication interface. The terminal
selects the LLpackets which constitute the largest set
of linearly independent packets with respect to the basis wi,
i= 1,...,K. Without loss of generality we assume that
such set be x1,...,xL. Linear independence can be verified
through the global encoding vectors of the packets. The L
selected packets are re-encoded together using RLNC, and
then re-encoded at the physical layer. RLNC encoding at the
terminals works as follows. Given the set of received packets
x1,...,xL, the message y=PL
m=1 σmxmis generated, σm,
m= 1,...,L, being coefficients drawn at random according
to a uniform distribution in GF (q). Each time a new encoded
message is created, it is appended its global encoding vector.
4This is actually the case in DVB-SH-B, where synchronization is kept
between the satellite and the CGC such that symbol-level alignment can be
achieved at the user terminals in case the same code rates and puncturing
patterns are used in the two channels [1]. The different propagation delays
from the CGC to terminals at different distances are compensated by the guard
interval (GI) of the OFDM modulation used in the terrestrial segment.
5For instance, this is the case of vehicular networks.
The overhead this generates is negligible if messages are
sufficiently long [30]. The new global encoding vector ηcan
be easily calculated by the transmitting node as follows:
η=σenΨ,
where σen = [σ1··· σL]is the local encoding vector, i.e.,
the vector of random coefficients chosen by the transmitting
node, while Ψis an L×Kmatrix that has the global encoding
vector of xm,m= 1,...,L, as row m. We assume that the
transmission of a message by a terminal is completed within
one TS. The physical layer encoding at a mobile node takes
place in the same way as at the source, and using the same
average transmission rate r. The scenario just described is
depicted in Fig. 1.
A. Source-to-Node Channel Model
An urban environment is considered. We assume that chan-
nels from S1and S2to a given terminal Ni(SjNchannel,
j= 1,2) are affected by both Rayleigh fading and log-normal
shadowing. The two channels are assumed to be statistically
independent. The power of the signal received at the terminal
from Sj,j= 1,2, is modeled as the product of a unit-mean
exponential random variable (r.v.) γjand a log-normal r.v.
ΓSj. This model has been largely used to model propagation
in urban scenarios [31] and, with some modifications, in LMS
systems [32]. The fading coefficient γjtakes into account
the fast channel variations due to the terminal motion. We
define a channel block as the time interval during which the
channel variation can be considered to be negligible. The
channel is assumed to remain constant within a channel block,
while changing in an i.i.d. fashion at the end of it. For
mathematical tractability we assume that the channel block
and the transmission slot have the same duration, i.e., the
time needed for Sto transmit a message coincides with the
coherence time of the terrestrial channel. Such simplifications
will be later removed when addressing the simulation part. The
shadowing coefficient ΓSjincludes the transmitted power at Sj
and accounts for path loss and obstruction caused by buildings
in the line of sight and changes much slowly with respect to γj.
We assume that ΓSjremains constant for Ntransmission slots,
i.e., until all Npackets relative to a given generation have
been transmitted by S. We call the time needed to transmit N
messages a generation period (GP). The fading and shadowing
processes of two different nodes and between the two different
channels are assumed to be independent. We study the case of
groups of terminals close enough to each other so that short-
range communication can be used. We also assume that nodes
are relatively far away from the gap-filler 6. The case of nodes
close to the gap-filler is not explicitly considered, since such
nodes have good coverage with high probability and thus do
not constitute an issue. However, the mathematical results we
derive implicitly take this case into account.
Due to the lack of CSIT there is a non-zero probability that a
message is not correctly decoded at each of the terminal nodes.
This happens if the instantaneous channel capacity, assuming
6Under these assumptions the hypothesis of equal channel statistics across
nodes can be regarded as a good approximation.
4
maximal-ratio combining, is lower than the transmission rate
at the physical layer r. For ease of exposition we will refer to
such equivalent channel as the SN channel. Assuming the same
channel statistics for all nodes, the packet loss probability in
the SN channel for any given node is:
PSN =P r {log2(1 + γ1ΓS1+γ2ΓS2)< r},(1)
where γjexp(1) while ΓSj=eXj
10 , for j= 1,2, with Xj
N(µj, σ2
j).ΓSjis constant within a GP, while γjchanges
independently at the end of each channel block. Fixing the
values of ΓS1and ΓS2, the two random variables γ
1=γ1ΓS1
and γ
2=γ2ΓS2are exponentially distributed with parameters
1/ΓS1and 1/ΓS2, respectively. Using the fact that the sum
γ
1+γ
2in Eqn. (1) has a hypoexponential distribution with
parameters 1/ΓS1and 1/ΓS2, we find the expression for the
packet loss probability PS N in the SN channel:
PSN = 1 ΓS1
ΓS1ΓS2
e2r1
ΓS1+ΓS2
ΓS1ΓS2
e2r1
ΓS2.(2)
With reference to Fig. 1, the effect of the links from S1
and S2in the physical network is taken into account in the
SN link of the graph model by means of the packet loss
ratio given by Eqn. (2). In the rest of the paper we will use
the expressions “packet loss ratio” and “probability of packet
loss” interchangeably. Due to shadowing, ΓS1and ΓS2change
randomly and independently at each generation period and,
within a generation, from one node to the other. Thus the
packet loss ratio PSN is also a r.v. that remains constant within
a generation and changes in an i.i.d. fashion across generations
and terminals.
B. Node-to-Node Channel Model
We model the channels between the transmitting terminal
and each of the receiving terminals (NN channel) as indepen-
dent block fading channels, i.e., the fading coefficient of each
channel changes in an i.i.d. fashion at the end of each channel
block. The probability of packet loss in the NN channel PN N
is:
PNN =P r {log2(1 + γΓN)< r}= 1 e12r
ΓN,(3)
where ΓNaccounts for path loss and transmitted power, and is
assumed to remain constant for a whole generation period and
across terminals. In order not to saturate the terrestrial channel,
we assume that a node can transmit at most one packet within
one TS.
Note that expression (2) has been derived assuming that the
same information is transmitted over both the satellite (S1)
and the gap-filler (S2) links and the two signals are combined
by the terminal nodes at the physical level. Since (3) grasps
the effect of both links it allows to simplify the equivalent
network topology by removing the intermediate nodes S1and
S2as shown in the example of Fig. 1.
III. COVERAGE ANALYSIS
A. Non-Cooperative Coverage
Let us consider a network with a source Sand Mter-
minals. We define the coverage () as the probability that
all Mterminals correctly decode the whole set of Ksource
messages. Assuming that Kis large enough, and using the
results in [4], the probability that a given node Nican decode
all the Ksource messages of a given generation in case of no
cooperation is:
P r {PS Ni<1R},(4)
R=K/N being the rate of the NC encoder at S. We
recall that, due to shadowing, the packet loss ratio PSNiis
a r.v. which changes in an i.i.d. fashion across generations
and terminals. The coverage is the probability that each of the
nodes decodes all source messages, that is:
Ω = P r {PS N1<1R,...,PSNM<1R},(5)
where PSNiis the packet loss ratio in the SN link of node
Ni,i= 1,...,M. Under the assumption of i.i.d. channels we
have FPSNi=FPSN ,i∈ {1,...,M}. Thus, using Eqn. (2),
we can rewrite Eqn. (5) as:
Ω = (P r {PS N <1R})M
=
P r
1ΓS1e2r1
ΓS1
ΓS1ΓS2
+ΓS2e2r1
ΓS2
ΓS1ΓS2
<1R
M
=
P r
ΓS1
ΓS1ΓS2
e2r1
ΓS1ΓS2e2r1
ΓS2
ΓS1ΓS2
> R
M
.
(6)
Finding a closed form expression for Eqn. (6) for the general
case is a challenging task since ΓS1and ΓS2are lognormal
random variables with parameters (µ1, σ1)and (µ2, σ2),
respectively. However, in Theorem 1 we derive a closed form
expression in case either µ1→ ∞ or µ2→ ∞. Note that
these are cases of practical interest since they correspond to
a situation in which either the signal from the satellite or
that from the gap-filler are faintly weak, which is the case of
dense urban areas and rural areas, respectively.
Theorem 1: Theorem 1: For any non-zero ǫµ,ǫσiand ǫσj
if 0< ǫµ< µi<,0< ǫσi< σi<,0< ǫσj< σj<,
i6=j, then
lim
µj→−∞ P r ΓS1
ΓS1ΓS2
e2r1
ΓS1ΓS2
ΓS1ΓS2
e2r1
ΓS2> R
=1
21
2erf
10 ln h12r
ln(R)iµi
2σ2
i
(7)
Proof See the Appendix.
Note that, fixing Rand M, the expression in Eqn. (7)
goes to 0as the rate at physical level rgoes to infinity.
This confirms the intuition according to which in the non-
cooperative case the coverage decreases as the transmission
rate increases. As said previously, this result holds for any
value of qas long as Kis large enough. Thus, Eqn. (7) can
also be interpreted as the coverage in a network of Mnodes
5
in presence of fading and shadowing that can be achieved for
a rate couple (r, R)by a fountain code such as, e.g., a Raptor
code.
B. Cooperative Coverage
The wireless network is modeled as a directed hypergraph
H= (N,A),Nbeing a set of nodes and Aa set of hyperarcs.
A hyperarc is a pair (i, J ), where iis the head node of the
hyperarc while Jis the subset of Nconnected to the head
through the hyperarc. Jis also called tail. A hyperarc (i, J )
can be used to model a broadcast transmission from node i
to nodes in J. We want to study the relationship between the
coverage and the rate at which the information is transferred to
mobile terminals, which depends on both the rate at physical
level rand the rate at which new messages are injected in the
network, which is the rate at packet level R. In [4] (Theorem
2) it is shown that, if Kis large, random linear network
coding achieves the network capacity in wireless multicast
connections, even in case of lossy links, if the number of
innovative packets transmitted by the source per unit of time
is lower than or equal to the flow across the minimum flow
cut between the source and each of the sink nodes, i.e.:
Rmin
Q∈Q(S,t)
X
(i,J)Γ+(Q)X
T*Q
ziJT
,(8)
where ziJT is the average injection rate of packets in the arcs
departing from ito the tail subset TJ,Q(S, t)is the set
of all cuts between Sand t, and Γ+(Q)denotes the set of
forward hyperarcs of the cut Q, i.e.:
Γ+(Q) = {(i, J )∈ A|iQ, J \Q6= 0}.(9)
In other words, Γ+(Q)denotes the set of arcs of Qfor which
the head node is on the same side as the source, while at least
one of the tail nodes of the relative hyperarc belongs to the
other side of the cut. The rate ziJT is defined as:
ziJT = lim
τ→∞
AiJT (τ)
τ,(10)
where AiJT (τ)is the counting process of the packets sent by
ithat arrive in TJin the temporal interval [0, τ ). The
existence of an average rate is a necessary condition for the
applicability of the results in [4]. In the following we derive
ziJT for the considered network setup as a function of both
physical layer and MAC layer parameters such as transmission
rate, transmission power and medium access probability.
C. Medium Access in the Terrestrial Channel
Let us consider a network with Mterminal nodes. We
assume that all nodes have independent SN and NN channels.
We further assume that channel statistics are the same for all
terminals, which is the case if the distances from node Nito
node Njchange little i, j ∈ {1, . . . , M },i6=jand with
respect to each node’s distance to the source.
In our setup the terminals are set in promiscuous mode so
that each node can receive the broadcast transmissions of any
other node within communication range [29]. The terminals
share the wireless medium, i.e., they transmit in the same
frequency band. We assume that an ideal CSMA/CA protocol
is adopted by the nodes and that all nodes hear each other,
so that the medium is shared among the terminals willing
to transmit but no collision happens. The communication
rate ziJT has been derived in [19] and has the following
expression:
ziJT =1(1 pa)M
Mh1(PNN )|T|i,(11)
where |T|is the cardinality of T, and the term 1(PN N )|T|
is the probability that at least one of the |T|nodes whose S-
link belongs to the cut receives correctly a transmission from
a node that is in the other side of the cut. We do not report
here the derivation for a matter of space. Note that expression
(11) represents the rate at which packet are received by the
subset of terminals Tconsidered as a single node, that is, the
counting process AiJ T (τ)increases of one unit when at least
one of the terminals in Treceives one packet, independently
from how many terminals receive it.
D. Coverage Derivation
In the following we derive an expression for the maximum
coverage as a function of relevant network parameters by
applying the Max-flow Min-cut theorem. We recall that such
maximum coverage can be attained by using the random
coding scheme described in Section II.
Let us consider Eqn. (8). For each of the Mnodes we must
consider all the possible cuts of the network such that the
considered node and the source are on different sides of the
cut. We recall that a cut is a set of edges that, if removed
from a graph, separates the source from the destination. Fig. 2
gives an example of a network with four nodes where the cut
QSN4(i.e., the cut such that N4and Sare on the same side)
is put into evidence. In the example we consider Nt=N1
as the destination node. The dashed black lines represent the
edges which are to be removed to get the cut. Note that the
set of nodes that receive from S(only node N4in the figure)
are isolated by the cut from the nodes with satellite cut (nodes
N1,N2and N3in Fig. 2). We define an S-edge as an edge
of the kind (S, Nj), j 6=t. We further define a T-edge as one
of the kind: (Nj, Nt), j 6=t. Unlike in [19], here each S-edge
incorporates the effect of the availability of two different paths
for the signal transmitted by Sthrough S1and S2and the fact
that the signals are combined at the physical level. This is
reflected by the lower packet loss ratio within each generation
which is given by Eqn. (2). As mentioned in Section II this
does not change the equivalent graph representation of the
network. Hence the derivation of the coverage carried out in
[19] is still formally valid. Thus the expression of the coverage
is given by Eqn. (12) [19], where Yj=PSN j is a r.v.
representing the packet loss ratio within a generation for node
Nj,Qnsis one of the cuts with nssatellite links relative to
the node Nt,
α(ns) = 1 R+ (Mns)1(1 pa)M
M[1 (PNN )ns],
(13)
6
Fig. 2. Graph model for a network with four terminals. There are 2M1= 8
possible cuts for each of the Mnodes. The set of nodes that receive from
S(only node N4in the figure) are isolated by the cut from the nodes with
satellite cut.
while S(ns,Nt)is the set of subsets of N \ Ntwith ns
elements, N \Ntbeing a set including all nodes except Nt.
Although formally equivalent, Eqn. (12) differs from that in
[19] in the Yvariables, that in this case take the form given
in Eqn. (2).
E. Lower Bound on Achievable Coverage
Finding a simple closed form expression for Eqn. (12) is
a challenging task. Thus in the following we derive a lower
bound LB on .can be lower bounded as follows:
Ω = P r
\
Nt∈N \
ns∈{1,...,M}
\
Qns∈S(ns,Nt)
Y
jQns
Yj< α(ns)
P r
\
Nt∈N \
ns∈{1,...,M}
ns
Y
j=1
Y(j)< α(ns)
(14)
P r
\
Nt∈N \
ns∈{1,...,M}hYns
(1) < α(ns)i
(15)
=P r
\
Nt∈N \
ns∈{1,...,M}hY(1) <ns
pα(ns)i
=P r Y(1) <min
ns∈{1,...,M}
ns
pα(ns)
=
P r
ΓS1e2r1
ΓS1
ΓS1ΓS2
ΓS2e2r1
ΓS2
ΓS1ΓS2
> β
M
,(16)
where Y(i)is the i-th largest packet loss ratio across all S-
edges of the network, i.e., Y(i)Y(j)if i < j, i, j N , and
we defined
β= min
ns∈{1,...,M}
ns
pα(ns).(17)
Inequality (14) derives from the fact that:
Y
jS
Yj
ns
Y
j=1
Y(j),for S∈ S(ns,t),ns, t, (18)
i.e., we substitute the product of nsrandom variables, chosen
within a set of Mvariables, with the product of the nslargest
variables of the same set. Inequality (15) follows from the fact
that
ns
Y
j=1
Y(j)Yns
(1) ,ns, t. (19)
Expression (14) can be further simplified in case one between
the satellite link and the terrestrial link is strongly degraded.
Using the result of Theorem 1 we find:
lim
µj→−∞ LB =1
2M
1erf
10 ln h12r
ln(1β)iµi
2σ2
i
M
,
(20)
where j= 1 or j= 2 if the degraded link is the one from the
satellite or from the gap-filler, respectively.
Ω = P r
\
Nt∈N \
ns∈{1,...,M1}\
Qns∈S(ns,Nt)
Y
jQns
Yj<1R+ (Mns)1(1 pa)M
M[1 (PNN )ns]
.(12)
7
IV. COOPERATIVE COVERAGE EXTENSION IN DVB-SH
In the following we propose a practical scheme that im-
plements the cooperative approach described in the previous
section in heterogeneous satellite vehicular networks.
A. Space Segment
1) Satellite Channel: The considered setup is an LMS
system with a GEO satellite broadcasting a DVB-SH-B (time-
division multiplexing (TDM) waveform from satellite and
OFDM from the gap-fillers) signal to a population of mobile
terminals. Propagation conditions change due mainly to the
shadowing effect of building and trees and are classified in
urban, suburban and rural. The main cause of channel impair-
ment in urban and suburban environments is the long-lasting
shadowing of the buildings that causes an intermittent satellite
connectivity, while in the rural propagation scenarios the main
source of impairment is tree shadowing. Signal reception in
LMS systems is limited by three phenomena, namely path loss
at large scale, shadowing at mid-scale and multipath fading at
small scale. We adopt the Perez-Fontan (LMS) channel model
[33], based on a three-state Markov chain in which the possible
states represent line of sight reception, moderate shadowing
reception and deep shadowing reception.
2) Channel Impairment Countermeasures in DVB-SH: In
the following we recall the channel impairment countermea-
sures foreseen in the DVB-SH standard.
a) Physical Layer: The physical layer error protection
scheme of the DVB-SH standard consists of a turbo code with
different rates/word lengths, a bit interleaver, which works
at bit level within a turbo codeword and a time interleaver,
the depth of which spans more than one codeword and uses
interleaving blocks of 126 bits each. This last element is partic-
ularly important to counteract long blockage periods, as it can
span time intervals of up to about 10 seconds. The drawbacks
in using a long time interleaver are the large decoding delay
and the memory requirements at mobile terminals, which can
be met only by high class user terminals.
b) MPE-IFEC in DVB-SH: The MPE-IFEC is a process
section between the IP and the transport layers introduced in
DVB-SH in order to counteract long lasting shadowing which
is typical of LMS channels. The encoding is made over several
datagram bursts, i.e., groups of datagrams. Two different kinds
of code are envisaged in the standard, namely Raptor codes
and Reed-Solomon codes [34]. The Raptor code adopted for
the DVB-SH is the same as in the 3GPP standard, which has
also been adopted in the DVB-Handheld (DVB-H) standard
[1]. In this paper we only consider the solution based on the
Raptor code, as described in the following. We remind the
interested reader to reference [34] for details related to the
Reed-Solomon encoding process.
Let us consider a datagram burst entering the MPE-IFEC
process. The burst is reshaped in a matrix of Tby Kbytes
called Application Data Table (ADT). The Raptor code, always
systematic, is applied on the ADT producing a Tby Nrparity
matrix, called IFEC Data Table (iFDT). In Fig. 3 the ADT
matrix is shown (on the left) together with the iFDT (parity)
matrix (on the right). The reshaped datagrams composing the
ADT are also shown for sake of clarity. After the iFDT is
calculated, an IFEC burst is generated by taking groups of
columns from the iFDT. Each columns of the parity matrix is
a parity symbol called repair symbol. With reference to the
notation introduced in Section III, the rate of the Raptor code
is R=K
K+Nr(i.e., N=K+Nr).
Systematic and repair symbols are jointly referred to as
encoding symbols. Each symbol is identified by an Encoding
Symbol Identifier (ESI). The encoding procedure consists in
the sequential application of a high rate low-density parity
check (LDPC) encoder, a binary reflected Gray encoder and a
Luby Transform (LT) encoder. Each of the encoding symbols
is transmitted together with its ESI and a triple (d, a, b)where
dis the symbol degree and aand bare integer numbers
related to the encoding procedure. The encoding symbol triple
together with the ESI and the value Kallows the decoder
to determine which source symbols were (linearly) combined
together to form a given encoding symbols. Further details on
the encoding procedure can be found in [35] 7. An IFEC burst
is made up of several IFEC sections. Each section is comprised
of a header, a payload containing gcolumns from the same
iFDT and a cyclic redundancy check (CRC). The k-th IFEC
burst is merged with the (kE P )-th datagram burst (and
eventual MPE-FEC redundancy) to form a time-slice burst.
The time slice burst is then multiplexed on Moving Picture
Expert Group - Transport Stream 2(MPEG2-TS) frames and
passed down to lower layers.
Datagram 1
Datagram 1 (cont.) Datagram 2
Datagram 2 (cont.)
Datagram 2 (cont.) Datagram 3
Last datagram
Last datagram (cont.) Padding bytes
Padding bytes
K systematic symbols (columns)
T bytes
Repair symbol 1
Repair symbol 2
Repair symbol
repair symbols
Fig. 3. Reshaping of datagram bursts in an ADT (left) and parity matrix
(right) in case a Raptor code is used.
B. Ground Segment
1) Terminal Types: We consider high class terminals as
defined in [23]. High class terminals are not energy constrained
and have relatively good computation capabilities and memory
[23]. This is the case of vehicular terminals, which are powered
by rechargeable batteries and can host computation units of
high speed, thanks to the relatively low impact in terms of
cost, space and weight. We assume that each terminal has both
satellite and short-range communication capabilities, which
7Note that a source block in [35] corresponds to an ADT and a source
symbol is a column of the ADT.
8
give rise to the possibility of implementing a vehicular ad-
hoc network.
2) Terrestrial Channel: In [36] a measurements campaign
made on the 5.9 GHz frequency is presented. The measure-
ments presented in [36] have been made using a Dedicated
Short Range Communication (DSRC)/IEEE 802.11p prototype
radio. In the paper a dual slope model for the path loss in
urban V2V scenarios is derived based on real measurements.
We adopt the model of [36] for the path loss together with
the TU6 multi-tap channel model [37]. An OFDM signal with
52 carriers (48 information carriers) and a rate 1/2convolu-
tional encoder are assumed. All physical layer parameters are
taken from the 802.11p standard. As usual practice in V2V
simulations, we assume a finite communication range which
is fixed for all vehicles. Collisions are taken into account, as
they constitute an important throughput-limiting and delay-
increasing factors in ad-hoc wireless networks [38].
V. NETWORK-CODED COOPERATION FOR DVB-SH
The analysis carried out in Section III-B gives hints about
the advantages and the limits of cooperation in providing
missing coverage in mobile satellite networks. Although useful
to understand the effect of relevant system parameters such as
the number of nodes, the probability of accessing the terres-
trial channel and the physical layer channel statistics, many
other interacting factors are present in a real system. These
can not be accurately taken into account in a mathematical
way without incurring in a model which is overwhelmingly
complex. Among these factors are the specific communication
standards considered, that may lead to a gap with respect to
the theoretical performance derived in subsections III-A and
III-B. In particular, the interaction between the physical layer
of the considered standards and the propagation channel can
be quite complex. Such interaction gives rise to specific packet
loss patterns at the higher layers, where the throughput of the
system is measured. Apart from this, the changing terrestrial
network topology and connectivity, together with the imper-
fections in the medium access mechanism, further complicate
the picture. A possible implementation of the cooperative
approach described in the previous sections has been presented
in [20]. Such scheme, called Network-Coded Cooperative
Coverage Extension (NCCCE), has been designed such that
existing PHY layer communication standards do not need to
be modified. The main novelty with respect to [20] is the
enhancement in the simulator used to evaluate the performance
of the NCCCE protocol. Specifically, we included a gap-filler,
implemented the physical layer of 802.11p standard 8, we used
widely adopted channel models to generate the time series
in the terrestrial channels (both vehicle-to-vehicle and gap-
filler-to-vehicle) and included the physical layer combining of
the signals received from the satellite and the CGC at the
terminal nodes and the Raptor encoder according to DVB-
SH standard. All these features were not taken into account
in the preliminary simulation results presented in [20]. In the
following we describe the NCCCE protocol.
8Modulation, channel code and a simplified version of the channel access
mechanism.
Let us consider a satellite broadcasting a DVB-SH-B sig-
nal with MPE-IFEC protection to a population of vehicular
terminals with both DVB-SH-B and IEEE 802.11p radio
interfaces. During a time window (0, t)the satellite transmits
N=K+NrIFEC symbols generated starting from an
ADT. Terrestrial and satellite communications take place in
orthogonal frequency bands. Due to long-lasting shadowing
caused by urban propagation conditions, it can happen that a
user decodes less than Klinearly independent symbols during
the interval (0, t). In this case the user cannot decode the entire
source data block. In order to enhance satellite coverage each
node re-encodes the received packets (either received directly
from the satellite or from other terminals) and broadcasts
them to nodes within its transmission range. In Fig. 4 a block
diagram of the proposed cooperative method is shown. In the
MPE-IFEC
MPEG TS
DVB-SH
Transparent payload
MPE-IFEC
MPEG TS
DVB-SH
Network Coding
802.11p
MPE-IFEC
MPEG TS
DVB-SH
Network Coding
802.11p
Terminal #1 Terminal #2
Gateway
Satellite
Fig. 4. Block diagram of the proposed cooperative scheme for two cooperating
nodes. Red lines represent IFEC blocks flowing from the satellite to the
terminal nodes while blue lines represent network coded packets exchanged
between nodes on the short range communication channel.
following sections we give further details on the proposed
NCCCE.
A. Encoding at Land Mobile Nodes
Let us assume that a node is able to decode some of the
encoding symbols directly from the satellite. Each symbol
carries an ESI and a triple (d, a, b). As described in subsection
IV-A2b the node can use this information to find out which of
the source symbols were combined together to form a received
encoding symbol. We propose to apply a network encoding
scheme at land mobile nodes using the source symbols of
iFEC as source symbols of the network code. In other words,
nodes exchange linear combinations of encoding symbols in
some finite field, with the aim of recovering all the source
symbols.
Each received encoding symbol is interpreted by a node as
a linear combination of source symbols with coefficients 0or
1in GF (q). The node, then, applies the network encoding
procedure described in Section II. The encoding vector of the
received encoding symbol can be derived from symbol’s ESI
and triple (d, a, b).
9
The probability to access the channel in each slot is deter-
mined by the parameter cooperation level, fixed for all nodes,
which we indicate with ζ,0ζ2. If ζ1, in each slot,
if a node stored a number of linearly independent packets
which is larger than the number of transmitted packets in the
current generation, it creates a linear combination of all the
stored packets as described in Section III-B and tries to access
the channel with probability ζ. If ζ > 1two cases must be
considered. In case the number of transmissions made by the
node is lower than the number of linearly independent packets
received, the node tries to access the channel with probability
pa= 1. If the node has a number of stored packets which is
lower than or equal to the number of those transmitted, instead,
it tries to access the channel with probability pa=ζ1.
When a node receives a packet from another node, it checks
whether such packet and those previously stored are linearly
independent and, if this is the case, the new packet is stored.
Otherwise, it is discarded.
Another possible relaying choice is to have the nodes simply
forwarding the received symbols without combining them.
We call this scheme simple relaying (SR) and use it as a
benchmark. SR is described more in detail in Section VI.
B. Implementation Aspects
According to the DVB-SH standard we consider a source
symbol size of 1024 bytes each. Each source symbol is divided
into nss subsymbols of 1024
nss bytes. Each of these subsymbols
is multiplied by a randomly chosen coefficient in a field with
q= 2 1024
nss ×8elements. The coefficient is the same for all
subsymbols within a symbol. In this way the complexity of
the network encoder/decoder can be kept at a reasonable level
[12]. A field size of 28(one byte) may constitute a valid
choice. The NC is applied as in [12], appending the encoding
vector at the end of each packet. Thus, for a Ksymbols
generation, a field with K×qbits is appended to each symbol.
The loss in spectral efficiency is then (Kq)/8192. Assuming
coefficients of 1byte are used, the loss becomes K/1024.
In order to keep the loss at a reasonable value we should
limit the size of the generation. For instance, if generations
of K= 100 symbols are used, the loss is below 10%. The
adoption of small generation sizes has the drawback that the
code efficiency is reduced. For example, it is known that the
efficiency of the Raptor code increases with the source block.
There is, however, an advantage in terms of delay in using
small blocks. In Section VII we show the gap between the
asymptotic results obtained in Section III-B and the simulation
results obtained in the same setup but with the 3GPP Raptor
code, having finite block-length.
An important aspect in the implementation of the proposed
cooperative scheme is the complexity,mostly due to the decod-
ing at LMS nodes. The complexity of a Raptor based on belief
propagation is O(K). However, in practical implementation
of the code, decoding blockage can occur due to the lack of
degree-1 nodes. Thus more complex methods are used such
as inactivation decoding [39]. Inactivation decoding can be
described roughly as an efficient way of carrying out Gaussian
elimination. In the system we propose, RLNC is plugged to
a Raptor code at LMS terminals. The use of RLNC modifies
the degree distribution (and the field size) originally used in
the Raptor code. This leads to a higher probability of early
blockage in the iterative decoding process, which implies that
the decoder has to turn more often to Gaussian elimination,
which has a complexity that grows with K3. Depending on
the specific packet loss pattern in the satellite segment, the
final coefficient matrix may be still partially decodable using
belief propagation, which would lower the size of the matrix
on which the Gaussian elimination is applied, thus reducing
complexity. Although we consider vehicular terminals, which
can potentially host decoders with high computational power,
keeping complexity low may still be required. In order to limit
complexity, the block size Kcan be kept at relatively low
values. In general, it would be interesting to study the impact
of reducing the block size Kon the system performance in
terms of coverage. The vastness of the subject and the space
limitations do not allow for an in depth analysis within the
present paper and open up possibilities for future studies.
VI. SIMULATION SETUP
A. Interaction of Physical Layer and Upper Layers
In order to evaluate numerically the performance of the
proposed methods at the system level, the simulator must be
capable of taking into account the channel impairments of the
physical layer. Physical layer simulations should be run for
each of the nodes, taking into account the channel characteris-
tics and the error correction capabilities of the considered PHY
layer standard as done in [40]. Such approach is, however,
extremely time consuming, which makes it unfit for a system
level simulation. A valid alternative is given by the PLA [24]
[25]. The use of PLA allows to take into account the effects
of physical layer elements such as coding, modulation and the
presence of an interleaver in a computationally affordable way.
This is particularly useful in case of time-selective channels,
in which the channel gain changes within the duration of a
codeword. The PLA has been widely studied in the last decade
achieving a growing accuracy for a wide range of transmission
setups.
In recently proposed types of PLA the instantaneous sym-
bol signal to interference plus noise ratio (SINR) vector
transformed in a single SINR value, the effective SINR
(SI N Ref f ). Such approach is called effective SINR mapping
(ESM). Several ESM PHY abstraction methods have been pro-
posed in the literature based on mean instantaneous capacity,
exponential-effective SINR mapping and Mutual Information
Effective SINR Mapping (MIESM). A more detailed descrip-
tion as well as more references on the topic can be found
in [26]. The SIN Ref f in the ESM methods is obtained as
follows:
SI N Ref f = Φ1 1
n
n
X
i=1
Φ(SI N Ri)!,(21)
where Φ(x)is an invertible function that depends on the
specific ESM method and nis the codeword length. In MIESM
such function can be related to the mutual information per
received coded bit. This approach is referred to as Received
10
Bit Information Rate (RBIR). The function Φ(x)is a function
obtained by normalizing the modulation-constrained symbol
mutual information (SI) vs SNR function. Once S I N Reff is
obtained, it is used to determine the FER using curves for the
considered channel code in AWGN. Note that SIN Ref f is
referred to the coded symbol, which means that modulation
order and coding rate must be taken into account before using
it in the FER curves. In our simulator we implemented the
RBIR approach and validated it by comparing the obtained
FER curve with that resulting from the simulation of the whole
transmission chain. The results of the RBIR validation are not
reported here for a matter of space but can be found in [20].
B. Simulated Scenario
We evaluated the performance of the proposed scheme
through a simulator that models a satellite to land mobile
broadcast transmission over DVB-SH-B. 150 nodes were
randomly placed on a Manhattan grid of one square kilometer
with 10 intersecting roads. The distance between two parallel
roads is 110 m. Each node moves at a speed of 50 km/h along
one of the roads 9, keeping a constant direction of motion
during the whole simulation. The verse of motion is chosen
at random for each node. When a node reaches the border
of the map it enters back into the map from the opposite
side, as is common practice in this kind of simulations. Nodes
can communicate with each other and have network coding
capabilities. Communication can take place between two nodes
only if they are within a radius of 300 m. A combination of
the path loss model derived in [36] and the TU6 multi-tap
propagation model [37] is used. The coding and modulation
considered are the ones of 802.11p, namely OFDM modulation
and rate 1/2convolutional code at 5.9GHz. The correctness
of the reception is evaluated through PLA. One IFEC block
of K= 150 IFEC symbols, corresponding to a generation,
is transmitted at each trial. Each block contains Ksource
symbols of 1024 bytes each. The total number of coded
symbols transmitted for a single generation is K/R, where
Ris the rate of the Raptor encoder and xis the smallest
integer larger than or equal to x. The 3GPP Raptor encoder
described in [35] has been implemented. Each IFEC symbol
is encapsulated within an MPEG2-TS packet and sent to the
channel encoder. The channel encoder is the turbo encoder
specified in [41]. Each source message of the channel encoder
has a fixed length of 12288 bits (about one and a half IFEC
symbols per Turbo codeword). Once encoded at PHY layer
with a rate r, the IFEC symbols are first interleaved with
the bit interleaver and successively with the time interleaver,
which provides time diversity to the signal. In the simulator
we implemented two of the time interleavers described in [23],
namely the short uniform interleaver and the long uniform
9Note that in a real scenario vehicles can move at different speeds. Although
the different speeds impact the correlation in both LMS and terrestrial
channels, we verified through simulations that the effect in terms of coverage
is negligible if lower speeds (in the range 10 50 kmph) are used. This is
mainly due to the fact that the differences in terms of packet loss ratio in both
channels (satellite and terrestrial) appear to be negligible. Furthermore, since
the time needed for the transmission of a generation (using the parameters
described in the following) is around 1.5 seconds, the difference in the distance
traveled by different vehicles during a generation period is also negligible.
interleaver. The former has a depth on the order of 200
milliseconds while the latter has a depth on the order of 10
seconds. After time interleaving, the bits are QPSK modulated
and transmitted with roll-off factor 0.35. For each of the
mobile nodes we generate a channel time series according the
three state Perez-Fontan LMS channel model. The correctness
of the reception of each turbo codeword is evaluated using
PLA as described in Section VI-A, taking into account data
rate, channel interleaver, channel code rate, and other relevant
parameters. In the setup in which the gap-filler is present, an
OFDM modulation with 6048 carriers and a guard interval GI
of 224 microseconds is used by the gap-filler. All parameters
conform to the DVB-SH-B standard. The propagation model
from the gap-filler to each of the nodes is a combination of the
modified COST 231 Hata path loss model with the classical
TU6 channel model, as suggested in [1]. The signals from
the satellite and the gap-filler are combined (after the time
de-interleaver has been applied) at the physical level by each
terminal using maximal-ratio combining. As recommended in
[1], a weighted sum of the two received signals (at the physical
level) is generated and sent to the demodulator/decoder. The
successful or unsuccessful decoding of a given codeword is
evaluated using PHY abstraction and considering the same
channel code as in DVB-SH standard. The same channel code
and interleaver are used at both the satellite and the gap-filler.
The gap-filler is located at a distance dgap f ill < GI ·cfrom
the center of the map, where c= 3 ·108m/sec is the speed
of light. The link budget adopted for the satellite network
is the one in [1], Table 11.28. Table I below summarizes
the main simulation parameters. The sequence of decoded
TABLE I
SIMUL ATION PA RAME TERS .
Environment Urban
Satellite carrier frequency 2.2 GHz
Satellite SNR (LOS) 12 dB
Time interleaver depth 200 ms - 10 s
Modulation QPSK
Roll-off factor 0.35
Bandwidth 5MHz
LL-FEC symbol size 1024 bytes
Size of LL-FEC blok (K) 150 (150 kB)
Rate Turbo Code (r) 1/2
Rate Raptor Code (R) 1/4
Gap-filler distance (dgap fill )3 km
Gap-filler carrier frequency 2.12 GHz
EIRP gap-filler 25 dBW
Number of gap-filler OFDM carriers 6048
Subcarrier spacing gap-filler 0.69754 kHz
Scenario surface 1sq. km
Number of terminals 150
Terminal type Vehicular
Terminal speed 50 km/h
V2V carrier frequency 5.9 GHz
V2V transmission power 20 dBm
Number of IEEE 802.11p OFDM carriers 52
Subcarrier spacing IEEE 802.11p 0.15625 MHz
Conv. code rate IEEE 802.11p 1/2
IFEC symbols are determined based on on the codewords that
are correctly decoded at the physical layer. Nodes exchange
IFEC messages using DSRC/IEEE 802.11p interfaces. The
11
transmission rate in the ground segment is set high enough
so that an IFEC symbol can be transmitted before the next
one is received on the satellite channel. The MAC mechanism
in the terrestrial segment is a simplified version of the CSMA
used in 802.11p. Nodes are set in promiscuous mode so that
each node can receive the transmissions of any other node.
We compare two different relay methods. One is the NC-
CCE scheme described in Section V, which is based on
network coding. The other relay scheme is the simple relaying
(SR) scheme, also introduced in Section V. Unlike in the
NCCCE scheme, in the SR scheme nodes do not combine
IFEC symbols, they just transmit the oldest non transmitted
packet. In SR, if all the received packets have already been
transmitted, then, if ζ > 1, a node tries to access the channel
(with probability 1ζ) and transmit a randomly chosen packet.
The amount of received data is measured at the interface
between the IFEC and the upper layers, as indicated in Fig.
5, considering the IFEC block as a fundamental data unit.
The reason for this choice is that data coming from the upper
layers are reshaped in the ADST’s. Thus, receiving one or
more IFEC symbols, even if systematic, may not be useful,
as they are part of a larger bunch of data, or may be parts
of incomplete IP datagrams. Thus when we refer to decoded
data we mean decoded IFEC blocks. Linear independence of
packets is evaluated through their global encoding vectors.
SRTP/UDP
IP
MPE IFEC
MPEG2 TS
DVB-SH physical layer
Fig. 5. The amount of received data is measured at the interface between the
IFEC and the upper layers.
VII. NUMERICAL RESULTS
Before moving to the simulation results relative to the
scenario described in Section VI-B, we evaluate the theoretical
bounds obtained in Section III. Although simplified assump-
tions are taken, such results give an indication of the extent
of the gains that can be obtained with a cooperative approach
and the way relevant system parameters impact such gains,
independently of the specific types of channel code or packet
level code considered.
Fig. 6 shows the coverage , obtained by evaluating nu-
merically Eqn. (12), plotted against the rate at physical level
rfor a fixed message rate Rand different network sizes. The
relative lower bounds and the coverage curve in case of no
cooperation are also shown. In the simulation we assumed that
one between the satellite and the gap-filler links is affected by
severe fading and set R= 2/3,pa= 0.2,ΓN= 10 dB in
the NN channel, µ= 3 and σ= 1 in the SN channel for the
available link. We recall that pais the probability of channel
access, assumed to be the same for all nodes. It is interesting
to note how increasing the number of nodes also increases the
achievable rate rfor a given . This is because the higher
is the number of nodes, the higher is the probability that all
the information broadcasted by Sreaches at least one node of
the network, i.e., it has not been lost. Once the information
has reached the network, it is efficiently distributed among the
terminals using RLNC. An important gain in the transmission
rate can be observed, with an increase of about 0.4bpcu
when passing from no cooperation to cooperation in a network
with 2nodes, and about 1bpcu in case of a network with
4nodes. The lower bound is fairly tight for M= 2 and
M= 4. We point out that this result is achieved without any
feedback to the source or any packet request among nodes,
as the decision on whether to encode and transmit or not
is taken autonomously by each terminal depending on the
probability of medium contention pa. As mentioned in the
previous sections, such performance curves are achievable,
which implies asymptotically long code lengths should be
used. In order to evaluate the loss in performance due to
finite code lengths and real coding schemes implementation,
we also show in Fig. 6 the curves obtained for the same setup
but with a finite block-length Raptor code (NC Raptor). The
Raptor encoder is the one used in DVB-SH and introduced in
Section VI 10. A block length of K= 150 source symbols was
chosen. Although an important gain in terms of physical layer
rate is achieved thanks to cooperation and such gain increases
with the number of terminals as in the asymptotic case, a gap
between theoretical and numerical results is present. This is
due to the finite and relatively small block length. Such gap can
be reduced by applying NC directly in the space segment [42].
However, such approach has the drawback that the decoder
complexity is higher also in case no cooperation is used, which
is not the case when a Raptor code is adopted. Moreover, it
would imply a modification in the satellite segment, which, in
our proposed scheme, remains unaltered. In Fig. 7 the coverage
0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4
0
0.2
0.4
0.6
0.8
1
r (bpcu)
NC theory M= 2
NC theory M= 4
NC theory M= 6
NC Raptor M= 2
NC Raptor M= 4
NC Raptor M= 6
NC LB M= 2
NC LB M= 4
NC LB M= 6
No coop. M= 2
No coop. M= 4
No coop. M= 6
No coop. Raptor M= 2
No coop. Raptor M= 4
No coop. Raptor M= 6
Fig. 6. Coverage plotted against rate at physical layer rin the cooperative
case for different values of M. The lower bound and the non cooperative case
are also shown. In the simulation we set R= 2/3messages/slot, pa= 0.2,
ΓN= 10 dB in the NN channels, µ= 3 and σ= 1 in the SN channel.
is plotted against the per-node probability of transmission
attempt pafor M= 4,ΓN= 10 dB,r= 1 bpcu and R= 2/3.
10Decoding has been implemented taking inactivation decoding into ac-
count.
12
It is interesting to note that relatively small values of pa(lower
than 0.15 for the asymptotic case) are sufficient to achieve full
coverage for values of rand Rwhich are of practical interest.
We further observe that the lower bound tightly approximates
the simulated theoretical curve. In the figure we also plotted
the curve for the case of a practical cooperative scheme using
the 3GPP Raptor code with source block length K= 150 (NC
Raptor). As in Fig. 6, the loss with respect to the theoretical
curve is due to the finite block length. The coverage for the
non cooperative case in the setup considered in Fig. 7 is 0,
coherently with Fig. 6.
0.06 0.08 0.1 0.12 0.14 0.16 0.18
0
0.2
0.4
0.6
0.8
1
pa
NC theory
NC lower bound
NC Raptor
Fig. 7. Coverage plotted against the probability of medium contention
pain the cooperative case for a network with M= 4 and ΓN= 10 dB.
The lower bound LB curve and the curve of a practical scheme with finite
block length Raptor code are also shown. In the simulation we set R= 2/3
messages/slot, r= 1 bpcu, µ= 3 and σ= 1 in the SN channel.
In the rest of the section we compare the performance of the
three practical schemes described in previous sections, namely
the proposed NCCCE scheme described in Section V, the SR
system described in Section VI and a non cooperative system
in which the nodes can receive only from the satellite (or
from the satellite + gap-filler, when present). We consider
as performance metric the average percentage of nodes that
receive all the transmitted data. The metric is evaluated for
different values of the cooperation level ζin the range [0,2].
Note that the system with satellite-only reception corresponds
to a cooperative system with ζ= 0. Considering different
values of ζwe can evaluate the performance gain of the
cooperative methods with respect to the non cooperative
system as a function of the terrestrial channel utilization. Fig.
8 shows the average percentage of nodes that receive all data
plotted against ζ. In the simulations we set the rate at physical
level to 1/2while the rate of the Raptor encoder has been set
to R= 1/4. The short interleaver has been used. We also
evaluated the case of long interleaver with and without gap-
filler and with no IFEC protection (which corresponds to a
Raptor rate of R= 1). We did not consider the case of long
interleaver with forward error correction because, according
in the DVB-SH-B standard, the IFEC protection is meant to
be applied only in combination with the short interleaver. In
case the long interleaver is used together with a gap-filler
(not shown in the figure) 100% of the nodes are covered.
The NCCCE scheme achieves the best performance among
all others setups, with a gain of about 25% with respect to the
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
0
10
20
30
40
50
60
70
80
90
100
ζ
covered nodes (%)
NCCCE w.o. gf, R=1/4
SR w.o. gf, R=1/4
No cooperation w.o. gf, short int., R=1/4
No cooperation w.o. gf, long int., R=1
NCCCE w. gf, R=1/4
SR w. gf, R=1/4
No cooperation w. gf, short int., R=1/4
No cooperation w.o. gf, short int., R=1
Fig. 8. Average percentage of nodes that decode all data plotted against the
cooperation level ζfor NCCCE, SR and the non cooperative scheme. The rate
couple (r, R) = (1/2,1/4) has been set in the simulation and the DVB-SH
short interleaver has been considered. The non cooperative case with long
interleaver and R= 1 is also shown for comparison.
non cooperative scheme and a gain of about 29% with respect
to the SR scheme in case no gap-fillers are user (w.o. gf). It
is worth noting that full coverage is achieved by the NCCCE
scheme for ζ= 0.05, i.e., with little use of the terrestrial
channel, either in case a gap-filler is used or not. We further
notice that this is similar to what shown in Fig. 7, in that
the maximum advantage of the network-coded cooperative
scheme is achieved for small values (smaller than 0.15) of
the channel access probability pa. The performance of the
scheme worsens as ζapproaches 2. This is due to the fact that
the terrestrial channel load increases with ζ, determining an
increase in the number of collisions due to hidden nodes and
thus decreasing the spectral efficiency of the vehicular ad-hoc
network. From Fig. 8 we also notice that the NCCCE scheme
with short interleaver achieves a higher percentage of covered
nodes with respect to the non cooperative configuration with
long interleaver. On the one hand this result suggests that a
short interleaver can be used instead of a long one, with a
huge memory saving in the physical layer architecture of the
receiver. Of course this comes at the expense of larger memory
resources at higher levels (IFEC), which are likely to have,
however, an overall cost which is lower than the memory at
lower levels. On the other hand, for a fair comparison we
must take into account that the long interleaver scheme does
not use IFEC protection, which implies a gain in terms of
spectral efficiency of 1/R = 4, i.e., there is a tradeoff between
complexity and transmission rate.
The gain of the cooperative schemes with respect to the non
cooperative case derives from the use of the terrestrial channel
bandwidth. In order to evaluate which between the NCCCE
scheme and SR scheme uses such resources more efficiently,
we plot the average number of decoded messages (per node
and per channel access) against the average number of channel
accesses (per node and per generation) for the NCCCE and
the SR schemes. Although the efficiency in the use of the
terrestrial channel decreases with the number of transmissions,
the NCCCE scheme makes a much more efficient use of ter-
restrial channel resources with respect to the SR scheme. The
13
30 40 50 60 70 80 90 100 110
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Number of channel accesses p er node per generation
Number of decoded messa ges per channel access
NCCCE w.o. gf
SR w.o. gf
NCCCE w. gf
SR w. gf
Fig. 9. Average number of decoded source symbols per node normalized to
the average number of channel accesses plotted against the average number
of times the channel is accessed per node within a generation for the NCCCE
and the SR schemes. For each of the schemes both the curve for the case with
and without gap-fillers are shown. Although the efficiency in the use of the
terrestrial channel decreases with the number of transmissions, the NCCCE
scheme makes a much more efficient use of terrestrial channel resources with
respect to the SR scheme.
figure indicates how, fixing the number of channel accesses per
node within a generation period (horizontal axis) the NCCCE
decodes on average more messages (IFEC source symbols)
compared to the SR scheme. Fig. 8 and Fig. 9 show that the
NCCCE scheme achieves a larger gain in terms of percentage
of covered nodes with respect to the SR scheme by using the
terrestrial channel resources in a more efficient way.
Note that, although latency in GEO satellite communica-
tions is large with respect to delays typically found in wireless
terrestrial networks, the system we propose is completely
transparent to the satellite transmission. The Raptor code ap-
plied at the satellite is already foreseen by the ETSI DVB-SH
standard and no feedback channel is assumed from terrestrial
nodes to the satellite, which means that no further delay is
added on the satellite segment. Moreover, each LMS terminal
can begin to exchange messages relative to a given generation
before the whole generation is received from the satellite (i.e.,
the correspondent signals reach the ground). The additional
delay that may be introduced by the NCCCE is the delay
due to the V2V communication, which is much smaller than
the GEO round trip time. If on the one hand the absence of
feedback suits well networks with large delays, on the other
hand it implies a non-zero probability that a node can not
decode some of the messages. In practical applications such
probability can be usually kept reasonably low by tuning some
of the system parameters such as, but not limited to, r,Rand
N.
VIII. CONCLUSIONS
We investigated the performance of a cooperative approach
to provide missing coverage in heterogeneous LMS networks
in the presence of both a direct satellite link and a link
from a gap-filler. We carried out an analytical study consider-
ing a mathematically tractable and yet practically interesting
network model assuming a combination of the signals from
the two links if performed by the receivers at the physical
level. Fading and shadowing effects in both links, as well
as the medium access mechanism in the V2V network, have
been taken into account. By applying the Max-flow Min-cut
theorem we derived an exact expression for the coverage as a
function of both the information rate at physical layer and the
rate of innovative packets injected in the network per unit-time
as well as a closed form expression of a lower bound in case
one of the two physical links is severely degraded. Our results
show a tradeoff between the coverage and the rate at which
the information can be injected in the network and at the same
time quantify the gain derived from node cooperation through
the short range interface. We showed that the diversity gain
grows with the number of terminals, opening up the possibility
of increasing the transmission rate at the source while still
guaranteeing a good coverage.
Based on the considered theoretical model we proposed
a practical cooperative scheme which leverages on network
coding for enhancing coverage in heterogeneous satellite ve-
hicular LMS systems over DVB-SH. The proposed scheme
does not require any modification at the lower layers. Our
numerical results, based on physical layer abstraction, show
that a cooperative relaying system based on random linear
network coding can bring important benefits in terms of
both coverage and terminal complexity (since shorter time
interleaver could be used) with respect to a system in which
nodes receive from satellite only, as well as with respect to
a relaying scheme in which network coding is not used. As
a final remark we point out that the delay induced by the
NC decoding as well as the relative memory requirements are
essentially the same in the proposed scheme an in DVB-SH
LL-FEC, since the NC encoding is done only within an LL-
FEC block. This is thanks to the fact that the terminals need
to buffer only network coded packets relative to the same LL-
FEC block, as is also the case in LL-FEC without NC.
ACKNOWLEDGEMENTS
The authors would like to thank Roberto Prieto-Cerdeira of
the European Space Agency for providing the LMS channel
time series simulator.
The view expressed herein can in no way be taken to reflect
the official opinion of the European Space Agency.
APPENDIX
Proof of Theorem 1
P r
ΓSje2r1
ΓSj
ΓSjΓSi
ΓSie2r1
ΓSi
ΓSjΓSi
> R
=P r Ξ + e2r1
ΓSi> R
=P r Ξ + e2r1
ΓSi> R |e2r1
ΓSi> RP r e2r1
ΓSi> R
+P r Ξ + e2r1
ΓSi> R |e2r1
ΓSiRP r e2r1
ΓSiR
(22)
where we defined
Ξ,
ΓSje2r1
ΓSje2r1
ΓSi
ΓSjΓSi
.
14
The first term of the sum in Eqn. (22) can be written as:
P r Ξ> R e2r1
ΓSi|Re2r1
ΓSi<0P r e2r1
ΓSi> R
=P r e2r1
ΓSi> R,
(23)
where the equality follows from the fact that ΓSi>0and
ΓSj>0, hence the left side term of the inequality in the
expression within brackets is always non negative. Let us now
consider the second term of the sum in Eqn. (22). We show
that it goes to zero in the limit of µjgoing to infinity. Let us
rewrite Eqn. (22) as follows:
P r Ξ> R e2r1
ΓSi|Re2r1
ΓSi0P r Re2r1
ΓSi0
=P r {Ξ> δi|δi0}P r {δi0}
=P r Ξ> δi|δi0,ΓSj>ΓSiP r δi0|ΓSj>ΓSi
·P r ΓSj>ΓSi
+P r Ξ> δi|δi0,ΓSj<ΓSiP r δi0|ΓSj<ΓSi
·P r ΓSj<ΓSi,
(24)
where we defined the r.v. δi=Re2r1
ΓSi. The first term
of the sum in Eqn. (24) is the product of three probabilities.
Recalling that γjexp(1) while ΓSj=eXj
10 , for j= 1,2,
with Xj N (µj, σ2
j), we have:
P r ΓSj>ΓSi=P r ne
Xj
10 > e Xi
10 o=P r {Xj> Xi}
=Z+
−∞
1
p2σ2
i
e(xiµi)2
2σ2
iZ+
xi
1
q2σ2
j
e(xjµj)2
2σ2
jdxjdxi
=Z+
−∞
1
p2σ2
i
e(xiµi)2
2σ2
i
1
21
2erf
xiµj
q2σ2
j
dxi,
(25)
where erf(x)is the error function, defined as 2
πRx
0et2dt.
Eqn. (25), under the hypotheses in the theorem statement, goes
to 0as µj→ −∞. Let us now consider the second term of
the sum in Eqn. (24). It can be easily shown that the first of
the three probabilities that multiplied together compose such
term goes to zero as µj→ −∞. Let us call such term P1.
Then we have:
P1=P r Ξ> δi|δi0,ΓSj<ΓSi
=P r
ΓSje2r1
ΓSje2r1
ΓSi
ΓSjΓSi
> δi|δi0,ΓSj<ΓSi
P r ΓSj
ΓSiΓSj
·R > δi|δi0,ΓSj<ΓSi
=P r ΓSj>ΓSi
δi
R(1 + δi)|δi0,ΓSj<ΓSi.(26)
It can be shown in a similar way as in Eqn. (25) that (26) goes
to zero asymptotically as µjgoes to −∞. Using equations
(23)-(26) in Eqn. (7) we finally have:
lim
µj→−∞ P r ΓS1
ΓS1ΓS2
e2r1
ΓS1ΓS2
ΓS1ΓS2
e2r1
ΓS2> R
=P r e2r1
ΓSi> R
=1
21
2erf
10 ln h12r
ln(R)iµi
2σ2
i
.
(27)
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... So far, there are plenty of researches presented to design and study cooperative satellite-terrestrial communications (CSTC) involving ground relays in terms of bit/symbol error performance [2]- [10], outage performance [11]- [13], and physical layer security (PLS) [14]- [18]. ...
... Closed-form expressions were derived in [12] for the OP of both primary and secondary networks in a hybrid satellite-terrestrial spectrum sharing system. The application of random linear network coding was studied for cooperative coverage extension in land mobile satellite vehicular networks [13]. ...
... Using (13) into (11), the CP for non-interference scenario can be derived as ...
Preprint
Full-text available
Aerial relays have been regarded as an alternative and promising solution to extend and improve satellite-terrestrial communications, as the probability of line-of-sight transmissions increases compared with adopting terrestrial relays. In this paper, a cooperative satellite-aerial-terrestrial system including a satellite transmitter (S), a group of terrestrial receivers (D), and an aerial relay (R) is considered. Specifically, considering the randomness of S and D and employing stochastic geometry, the coverage probability of R-D links in non-interference and interference scenarios is studied, and the outage performance of S-R link is investigated by deriving an approximated expression for the outage probability. Moreover, an optimization problem in terms of the transmit power and the transmission time over S-R and R-D links is formulated and solved to obtain the optimal end-to-end energy efficiency for the considered system. Finally, some numerical results are provided to validate our proposed analysis models, as well as to study the optimal energy efficiency performance of the considered system.
... So far, there are plenty of researches presented to design and study cooperative satellite-terrestrial communications (CSTC) involving ground relays in terms of bit/symbol error performance [2]- [10], outage performance [11]- [13], and physical layer security (PLS) [14]- [18]. ...
... Closed-form expressions were derived in [12] for the OP of both primary and secondary networks in a hybrid satellite-terrestrial spectrum sharing system. The application of random linear network coding was studied for cooperative coverage extension in land mobile satellite vehicular networks [13]. ...
... Using (13) into (11), the CP for non-interference scenario can be derived as ...
Article
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... navigation and rescue, owing to its high data rate transmission and large area of coverage [1]- [4]. As security threats such as eavesdropping, hijacking and data corruption emerge due to the inherent property of wireless transmission, demanding requirements are further imposed on satellite communication. ...
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In this paper, a hybrid satellite-terrestrial relay network (HSTRN) interconnecting a satellite and multiple terrestrial nodes is considered, where communication is achieved by the satellite transmitting information to a destination through multiple relays at the appearance of an eavesdropper attempting to intercept the transmissions from both the satellite and relays. We present the single-relay selection and multi-relay selection as well as round-robin scheduling schemes to investigate the physical-layer security of this considered HSTRN by adopting the decode-and-forward (DF) relay strategy. Specifically, in singlerelay selection scheme, a relay is chosen as the “best” relay who has the maximum instantaneous capacity of relay-destination channel out of the decoding relay set, which is composed of all the relays capable to decode the received signals from satellite successfully. By contrast, in multi-relay selection scheme, all relays of the decoding relay set are invoked simultaneously to aid the satellite communicating with the destination. Moreover, suppose that only the main channels’ state information is known while the wiretap channels’ is unavailable due to the passive eavesdropper, we analyze the secrecy performance in accordance with secrecy outage probability (SOP) of the HSTRN by driving out the closed-form expressions for the single-relay selection and baseline round-robin scheduling schemes, as well as by computer simulations for multi-relay selection scheme. Numerical results show that the two relay selection schemes generally outperform the round-robin scheduling baseline scheme in the light of improving the secrecy performance of HSTRN even when the legitimate links are inferior to the wiretap links.
... They have shown that the Bit Error Rate (BER) can be improved significantly using hybrid satellite/terrestrial cooperative relaying strategies. Further, cooperative terrestrial satellite communication can be helpful for improving outage probability and coverage extension [20], [21]. In this work, we focus on the advancement of satellite communications to deliver HD/UHD TV content to households at a lower cost while maintaining high QoE. ...
... Authors in [10] examined a use case for the realization of end-to-end traffic engineering in a combined terrestrialsatellite network used for mobile backhauling. Literature [18] proposed an analytical assessment of the cooperation limits in the presence of both a satellite and a terrestrial repeater (gap filler) and derived exact expressions and closed-form lower bounds on coverage in a setup of practical interest by using the max-flow min-cut theorem. Furthermore, they studied a practical implementation of the Random Linear Network Coding (RLNC) cooperative approach for the Digital Video Broadcasting-Satellite services to Handheld (DVB-SH) standard. ...
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... They have shown that the Bit Error Rate (BER) can be improved significantly using hybrid satellite/terrestrial cooperative relaying strategies. Further, cooperative terrestrial satellite communication can be helpful for improving outage probability and coverage extension [20], [21]. In this work, we focus on the advancement of satellite communications to deliver HD/UHD TV content to households at a lower cost while maintaining high QoE. ...
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