Proposed Resource Allocation Schemes for Rainy Free Space Optical
Network
Abdallah S. Ghazy
1
, Hossam A. I. Selmy
2
and Hossam M. H. Shalaby
1
1
Egypt-Japan University of Science and Technology (E-JUST), Alexandria, Egypt
2
Cairo University, Cairo, Egypt
Keywords:
Free Space Optical, Rainy Weather Influence, Dynamic Networks, Relayed Networks, Resource Allocation
Scheme, Lexicographic and Lex-Max-Min Optimization Theories.
Abstract:
Free space optical (FSO) connections present promising solution for the limited access issue of the last mile
networks. However, several factors contribute to significant FSO link performance degradation. One of Most
serious conditions is the influence of the rain, that frequently appear, thus making the implementation of
strongly connected FSO networks a demanding issue. Dynamic FSO networks is attractive ones over the
robust static ones, i.e, partial and full relayed networks, for this demanding issue. In this paper two new
resource allocation Schemes are proposed for cooperative-dynamic FSO networks, as attractive solution for
both atmospheric variation and high cost of robust static network problems. Each Scheme is formulated
as integer linear multi-objective optimization problem (ILP-MOP), where reliability-fairness, capacity and
bit-error rate functions are targeted. And each scheme is composed of lexicographic, lex-max-min and lex-
min-max criteria. Each ILP-MOP is solved using exhaustive search method to obtain the guaranteed optimal
solution(s). The simulation results is used to reveal that two schemes are more reliable-fairness and cost
efficient than the robust static topology, specially at sever weather conditions. Also, the results show the two
schemes have different behavior, where one prioritize the reliability-fairness over capacity utilization and the
another does the opposite.
1 INTRODUCTION
Free space optics (FSO) is line of site (LOS) wire-
less optical communication used as a promising and
feasible solution for last mile connectivity problem
where remote network nodes have to be connected
to central backbone node. With the significant devel-
opment in the optical technology in the last decade,
more FSO links are deployed in a given service area
to meet the user’s huge demands on internet services
and applications(Kim et al., 2001). Generally, FSO is
used instead of optical fibers when short implemen-
tation time, flexible installation and low implementa-
tion cost are required (Refai et al., 2006). FSO link
could be used to connect different nodes like mobile
base station, telephone office or private networks to
central backbone node as indicated in Fig. 1.
Even though the attractive features of FSO, it suf-
fers from the free space channel impairments in in-
frared (IR) band spectrum, i.e., weather conditions,
background radiations and air turbulence (Kim et al.,
2001), (Bloom et al., 2003), (recommendation ITU-
R P.1814, 2007). The weather conditions include
fog, rain and snow that could absorb and scatter the
transmitted optical signal (Vavoulas et al., 2012). In
addition, eye safety regulation restricts the power of
the transmitted light beam to certain threshold which
consequently limits the communication range of FSO
links (Bloom et al., 2003). Hence, suitable network
topologies have to be investigated to mitigate the
weather impairments and provide the required qual-
ity of service (QoS) for different nodes.
The conventional FSO network implements static
direct links (D-L) between fiber backbone node and
FSO link
Backbon
FSO node
Figure 1: Last-mile FSO connection, the end users could
use wire or wireless connections to FSO node.
76
Ghazy, A., Selmy, H. and Shalaby, H.
Proposed Resource Allocation Schemes for Rainy Free Space Optical Network.
DOI: 10.5220/0005688400740081
In Proceedings of the 4th International Conference on Photonics, Optics and Laser Technology (PHOTOPTICS 2016), pages 76-83
ISBN: 978-989-758-174-8
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
FSO nodes as indicated in Fig. 2(A). Although,
this static topology has simple and low cost imple-
mentation, it has the worst communication perfor-
mance against sever weather conditions. To overcome
this degradation, serial-relayed topology is addressed
(Vavoulas et al., 2012). In this topology, one or more
relays are inserted between far nodes and backbone
node. The relay has two optical transceivers and is
located at equal distances from other nodes (optimal
placement)(Kashani et al., 2013), as indicated in Fig.
2(B) for partial relayed links network (P-L). By in-
creasing number of intermediate relays between re-
mote nodes and backbone node, the best FSO link
performance could be achieved. Obviously, this en-
hancement in the network performance comes at a
significant increase in the network cost. The topol-
ogy where each node is supported with one relay is
called fully relayed links network (F-L) as indicated
in Fig. 2(C).
(A) (B)
(C)
FSO Link
Relay Node
Node with one Transceiver
Fiber Backbone
Figure 2: Different recent static topologies at last-mile, (A)
Direct Link model (D-L), (B) Partial Relayed Link Model
(P-L), (C) Full Relayed Link Model (F-L).
Better performance could be achieved at reason-
able lower cost by implementing dynamic (recon-
figurable) FSO network topologies (Milner et al.,
2002). These dynamic topologies are classified ac-
cording to network resources sharing into coopera-
tive and non-cooperative topologies. In dynamic non-
cooperative FSO network topologies no resources are
shared among different users (Milner et al., 2002).
Users with bad links switch their traffic to users with
relatively better links and the transmission rate of
each user is kept the same. This is achieved by in-
creasing transmission rates of good links to be sum
of switched transmission rates. Clearly, increasing
transmission rate of optical link is not feasible and
has practical limitations (Bloom et al., 2003).
To overcome these limitations, dynamic cooper-
ative topologies are introduced. In these topolo-
gies, users with bad optical links switch their trans-
missions to users with better links and share their
links capacities. Clearly, transmission rate of a
good optical link is divided between node’s traffic
and switched transmissions in order to keep qual-
ity of service for switched ones. In other words,
the networks’ users cooperate and share their re-
sources (optical bandwidths) to keep connectivities
between backbone node and far users which in turn
increases FSO network’s reliability (decrease num-
ber of dropped nodes) during rainy weather condi-
tions. Moreover, at given weather conditions, the
network resources could be fairly allocated (achieves
near the same transmission rate to backbone node)
among different users by implementing proper re-
source allocation scheme. Although, the number of
optical transceivers available at each node plays an
important role in the network performance, it is still
much lower than that required in static topologies to
achieve the same performance.
In this paper, two fair and cooperative resource
allocation schemes are proposed to enhance the per-
formance of dynamic cooperative FSO networks
against atmospheric variation which is caused by rain
droplets. The reset of this paper is organized as the
following. Section 2 presents FSO link model. Sec-
tion 3 illustrates reconfigurable cooperative FSO net-
work parameters. Section 4 introduces the proposed
resource allocation schemes. Section 5 shows the nu-
merical evaluations for proposed resource allocation
schemes. Lastly, section 6 concludes the final net-
work evaluations and the remarkable notes.
2 FSO LINK MODEL
Three main factors affect the FSO link performance
namely, link losses, turbulence (scintillation) and
noises. The link losses include both atmospheric and
geometric losses. These losses cause signal scat-
tering, absorbing and spreading(Gagliardi and Karp,
1995). The atmospheric loss includes fog, rain, snow
(recommendation ITU-R P.1814, 2007). Naturally,
these weather phenomena fog. rain, and snow rarely
occur concurrently, and this allows in studying rainy
influence separately (Vavoulas et al., 2012). At sever
rainy weather conditions, the scintillation has rela-
tively small impact and could be neglected (Vavoulas
et al., 2012), (Khalighi and Uysal, 2014). Therefore,
the total FSO link loss in this case is given by
Proposed Resource Allocation Schemes for Rainy Free Space Optical Network
77
γ = γ
rain
+ γ
geo
. dB (1)
Where γ presents the total link loss. Also γ
rain
and
γ
geo
are rain and geometric losses, respectively.
The rain loss is calculated using Jaban’s empirical
model, as (recommendation ITU-R P.1814, 2007).
γ
rain
= 1.58 × D
0.63
× L . dB (2)
Or by using France’s empirical model, as (recommen-
dation ITU-R P.1814, 2007).
γ
rain
= 1.076 × D
0.67
× L . dB (3)
Where D is the rain fall rate in mm/h and L is the
distance in km.
Even in clear weather conditions, the geometric loss
is presented due to the spreading of the beam when
propagating through the medium of free space. This
loss is calculated by (Bloom et al., 2003):
γ
geo
= 10 × log
d
t
+ L × Θ
d
r
2
. dB (4)
Where d
r
is the receiver diameter, d
t
is the transmit-
ter diameter, both in mm, Θ is divergence angle in
mm.rad/km.
The system noise include both external noise (am-
bient or background noise) and internal noise (dark
current and thermal noises). When the background
radiation level is relatively high, for example in out-
door FSO links, the receiver thermal noise is ignored
and the system noise is modeled using Poisson’s
model (shot-noise limited receiver). Also, the se-
lected modulation formate plays important role in the
FSO link performance (Gagliardi and Karp, 1995).
The prime intensity modulation/direct detection tech-
niques, namely, none return-zero on-off keying (NR-
OOK) are considered in this paper. Hence, at given
transmitted q
t
photons/slot and channel loss, γ, q
s
=
(γ × q
t
) is the average number of received signal pho-
tons per slot and q
b
is the average number of received
ambient photons per slot. The bit-error-rate of OOK,
P
e
, when model the photo detector as shot-noise lim-
ited receiver is given by (Gagliardi and Karp, 1995).
P
e
=
1
2
×
m
t
q=0
(q
b
+ q
s
)
q
×
exp[(q
b
+ q
s
)]
q!
+
1
2
×
q=mt
(q
b
)
q
×
exp[q
b
]
q!
.
(5)
And
m
t
=
q
s
log
1 +
q
s
q
b
. (6)
Where, m
t
is the threshold of bit detection.
In the considered network, the homogeneous
weather is assumed over all the network regions. In
other words, all FSO links are affected by the same
specific atmospheric losses (dB/km) and the same
background radiation level impacts all FSO receivers.
Figure 3: Reconfigurable-Cooperative network, (A) Net-
work topology . (B) Reconfiguration of the links versus the
atmospheric variation using the proposed schemes, where
in (B.1) at clear weather and in (B.2) at rainy weather con-
ditions.
3 RECONFIGURABLE
COOPERATIVE FSO
NETWORK PARAMETERS
Currently, the significant innovation in pointing, ac-
quisition and tracking system (PAT) makes the dy-
namic FSO network more feasible than before (Dat
et al., 2010). In reconfigurable topologies, the number
of FSO transceivers could be significantly reduced by
replacing actual FSO relay nodes by transceivers on
other working nodes (virtual FSO relay nodes) (Mil-
ner et al., 2002).
Generally, the cooperative FSO network consists of
N nodes (v
1
,..., v
N
) with arbitrary geographical dis-
tribution in addition to the backbone node v
0
. The
number of optical transceivers at k
th
node is denoted
by Z
k
where {k} {1,... ,N}. The backbone node is
assumed to be equipped with N optical transceivers.
In the considered FSO network, the inner n
2
nodes
near to the backbone node are assumed to have two
transceivers while the far n
1
= N n
2
nodes have only
one transceiver, i.e. Z
k
{1,2}. An example of re-
configurable cooperative FSO network with one cen-
tral node and nine remote nodes is indicated in Fig.
3(A). In this network N = 9, each node of the inner
four nodes has two transceivers (n
2
=4) and each node
of the outer five nodes has one transceiver (n
1
=5), to-
tal additional transceivers is w =
N
k=1
(Z
k
1) = 4.
At clear weather conditions, all nine nodes are di-
rect connected to the central node as indicated in
Fig. 3(B.1). At high density fall rate of rain, each
PHOTOPTICS 2016 - 4th International Conference on Photonics, Optics and Laser Technology
78
node could switch to its neighbor node to maintain its
connectivity to the central node as indicated in Fig.
3(B.2).
The losses of all FSO links (rain and geomet-
ric attenuations) are summarized in γ matrix, γ =
(γ
00
,. .., γ
0N
;. .., γ
i j
,. .. ; γ
N0
,. .., γ
NN
), where γ
i j
is
the loss coefficient of link between transmitter of i
th
node and receiver of j
th
node. Clearly, 0 γ
i j
1,
γ
ii
= 0 and γ
i j
= γ
ji
for any {i, j} {0, 1,. .., N}.
At a given weather state, the cooperative FSO net-
work could be connected with different feasible con-
figurations that satisfy the required QoS parame-
ters, i.e. grantee minimum bit rates at bit error
rates less than certain threshold. The number of
these configurations is Λ. For l
th
configuration, l
{
1,2, ... ,Λ
}
, the connection status between network
nodes are summarized in connections matrix G
l
=
(g
l00
,. .., g
l0N
;. .., g
li j
,. .. ; g
lN0
,. .., g
lNN
), where g
li j
is the connection status between i
th
and j
th
nodes in
configuration l and g
li j
{0, 1}. The connection be-
tween nodes i and j is established in configuration l if
g
li j
= 1. Also, bidirectional links are assumed so that
g
li j
= g
l ji
and g
lii
= 0.
Moreover, all FSO links are assumed to have the
same average transmitted power, i.e., the power of op-
tical link between nodes i and j in configuration l
is constant, P
li j
= P. However, to increase link ca-
pacity and guarantee an error rate less than a spec-
ified maximum BER
li j
< BER
max
, the link between
nodes i and j in configuration l adapts its transmission
rate, T
li j
, to be one of m + 1 discrete values, where
T
li j
{0,x
1
,x
2
.. .,x
m
} and x
1
< x
2
< ... < x
m
.
The transmission rate of node k in configuration
l is denoted by T
lk
, where T
lk
=
N
j=0
T
lk j
. The
bit rate of node k (its own traffic) through connec-
tion to node j in configuration l is denoted by R
lk j
.
The overall bit rate of node k in configuration l is
R
lk
=
N
j=0
R
lk j
. Obviously, R
lk
T
lk
and {R
lk
,T
lk
}
{0, x
1
,x
2
,. .., x
m
}. The end-to-end bit error rate
of node k in configuration l, BER
lk
, is bounded by
BER
lk
BER
max
.
The bit rates and bit error rates associated with all
nodes in the feasible configurations could be summa-
rized in (Λ × N) matrices R and E, respectively. For a
given configuration l, the bit rates for all nodes are
represented in vector (1 × N) r
l
, r
l
R. Also, the
bit error rates in that configuration are summarized in
vector (1 × N) e
l
, e
l
E. The network capacity asso-
ciated with configuration l is C
l
=
N
k=1
R
lk
, and all
capacities associated with all feasible configuration
are summarized in vector (Λ× 1) C, C
l
C. Also, the
maximum network capacity that could be achieved by
any configuration is that obtained from direct links
configuration l
and is defined by C
max
=
N
k=1
T
l
k0
,
Network Parameters:
v : Nodes vector (1 × (N + 1))
γ : Loss coefficient Matrix ((N + 1) × (N + 1))
G : Connections matrix ((N +1) × (N + 1))
R
k
: Bit rate of k
th
node
r : Bit rate vector (1× N)
R : Bit rate matrix (Λ × N)
BER
k
: Bit error rate of k
th
node
e : Bit error rate vector (1 × N)
E : Bit error rate matrix (Λ × N)
BER
max
: Bit error rate threshold
C
l
: Capacity of the Network for l
th
configuration
C : Capacity vector (Λ × 1)
C
max
: Maximum Capacity
U
l
: Capacity utilization for l
th
configuration
T
k
: Transmission rate of k
th
node
Z
k
: Number of transceivers of k
th
node
w : additional number of transceivers in the network
P
i j
: Power of i, j link
i.e. C
l
C
max
.
The size of the feasible space, Λ, is upper bounded
by the following inequation:
Λ <
"
N+1
ii=1
N + 1
ii
#
×
"
j j=Z
k
=2
j j=0
N + 1
j j
#
(n
2
)
×
"
kk=Z
k
=1
kk=0
N + 1
kk
#
(n
1
)
.
(7)
Clearly, the size of feasible space is defined by num-
ber of FSO nodes, number of nodes equipped by one
transceiver, and number of nodes equipped by two
transceivers.
4 PROPOSED FAIR
COOPERATIVE RESOURCE
ALLOCATION SCHEME
Dynamic cooperative FSO networks deploy resource
allocation schemes in order to increase capacity, reli-
ability and fairness as well as to decrease the bit error
rate under rainy weather conditions. Increasing net-
work capacity is achieved by maintaining the largest
number of direct links to central node. Also, increas-
ing network’s reliability implies decreasing number
of dropped users, while enhancing fairness means
near the same bit rates are assigned to different sup-
ported users. Obviously, at clear weather conditions,
all nodes are direct connected to the central node to
get highest bit rates (maximum network capacity) at
bit error rates less than a predefined threshold as in-
dicated in Fig. 3(B.1). On contrary, at bad weather
conditions, direct links of far nodes are dropped and
switched to other nodes according to the resource al-
location scheme in order to keep to connectivities to
the central node. Resource allocation in dynamic co-
operative FSO network could be optimized for sev-
eral performance metrics. Given number of optical
Proposed Resource Allocation Schemes for Rainy Free Space Optical Network
79
transceivers in each node (one or two transceivers in
our case), loss coefficient matrix of FSO links γ and
transmitted power for FSO link; many feasible config-
urations could enable k
th
node (k
{
1,..., N
}
) to have
bit rate R
k
{0, x
1
,x
2
,..., x
m
} at bit error rate less than
the threshold BER
k
BER
max
. Among these feasible
configurations, one or more could achieve highest net-
work’s reliability, fairness, capacity and/or lowest bit
error rate.
In this section, two resource allocation schemes are
proposed to enhance the performance of dynamic co-
operative FSO networks. However, these schemes
are proposed for FSO networks that use two opti-
cal transceivers for inner nodes and one transceiver
for outer nodes as indicated in Fig.1 (A) as current
case study. The schemes use concept of lex-max-min
fairness which is widely used in computer and wire-
less networks to overcome the congestion and limited
reliability of the network (Ogryczak and Sliwinski,
2007). lex-max-min fairness is a criteria for achieving
near equal resource sharing between N nodes at a rel-
atively high network capacity, i.e avoiding inefficient
fairness (allocate the lowest bit rate, x
1
, for all nodes
to achieve the maximum fairness). lex-max-min fair-
ness is the generalization of ordinary max-min fair-
ness as it searches sequentially for next maximals in
case two or more solutions have the same maximal at
one level in space of feasible solutions (Ogryczak and
Sliwinski, 2007), (Ogryczak and
´
Sliwi
´
nski, 2006).
The first proposed scheme is called lex-max-min con-
strained fairness (LMMCF) which aims to enhance
network’s capacity while keeping the fairness be-
tween different nodes. The second proposed scheme
is called lex-max-min fairness (LMMF) that aims to
enhance both reliability and fairness of the network
regardless the capacity. The LMMCF scheme targets
three objective functions; maximizing network capac-
ity then maximizing bit rate fairness and then mini-
mizing bit-error rate. However, the LMMF scheme
targets two objective functions; maximizing bit rate
fairness then minimizing bit-error rate.
Clearly, the optimized objective functions are con-
flicted, so each scheme is presented by multiple objec-
tive optimization problem (MOP). There are several
methods to treat MOPs such as lexicographic (hier-
archical), weighted summation, product and bounded
objective-functions (Marler and Arora, 2004). Lexi-
cographic is a criteria to optimize the conflicted ob-
jectives hierarchically and it has the ability to achieve
the schemes goals (Isermann, 1982), (Marler and
Arora, 2004). Lexicographic presents LMMCF prob-
lem in three optimization levels and LMMF in two
levels based on the priorities between the objectives.
4.1 LEX-MAX-MIN Constrained
Fairness Scheme
This scheme aims to increase network capacity then
proceed to improve both network reliability and fair-
ness. The network capacity is the summation of all
nodes’ bit rates. Also, the improvement in both reli-
ability and fairness raised by maximizing bit rates of
far nodes. Specifically, at the first optimization level,
LMMCF scheme selects from the feasible Λ configu-
rations the ones that maximize network capacity. Af-
ter that in the second optimization level, the scheme
searches the previously selected configurations for
the ones that maximize the minimum bit rate for all
nodes. If there are more than one configuration that
have the same max-minimum bit rate, the LMMCF
scheme proceeds to select from them the configura-
tions that have next max-minimum bit rate (sequen-
tial max-min optimization) (Ogryczak and Sliwinski,
2007). However, if there are more than one configura-
tion with the same sequential max-minimum values,
the LMMCF selects from them in a third optimization
level the configuration that has sequential minimum
of maximum bit error rate values (sequential min-max
optimization) (Ogryczak and
´
Sliwi
´
nski, 2006).
Lexicographic represents the problem in three lev-
els of optimization based on the priorities between the
objectives as:
Max
(
C
l
=
k=N
k=1
R
lk
: C
l
C
)
Lex-Max-Min
{
r
l
= (R
l1
,R
l2
,..., R
lN
) : r
l
R
}
Lex-Min-Max
{
e
l
= (BER
l1
,..., BER
lN
) : e
l
E
}
Subject to :
BER
lk
BER
max
, R
lk
{0,x
1
,..., x
m
},
P
li j
= P, Z
k
{1,2}, k
{
1,..., N
}
,
l
{
1,..., Λ
}
,
{
i, j
}
{
0,1, ...,N
}
, j 6= i.
(8)
Several constraints are imposed in the stated
multi-objective optimization problem. The bit error
rate of each node must less than a predefined thresh-
old. Also, only specific discrete values for the bit rates
are allowed. Moreover, the same average transmitted
power is used for all nodes. Clearly, in this optimiza-
tion problem, the improvement in the bit rate fairness
between different users is restricted by the network
capacity.
PHOTOPTICS 2016 - 4th International Conference on Photonics, Optics and Laser Technology
80
4.2 LEX-MAX-MIN Fairness Scheme
Toward more increasing in the network reliability and
fairness, the capacity could not be considered and se-
lect among the feasible configuration ones that maxi-
mize the reliable and fair configuration(s), then pro-
ceeding to minimize the bit error rates (maximize
QoS). Lexicographic represents the problem in two
levels of optimization based on the priorities between
the objectives as in:
Lex-Max-Min
{
r
l
= (R
l1
,R
l2
,..., R
lN
) : r
l
R
}
Lex-Min-Max
{
e
l
= (BER
l1
,..., BER
lN
) : e
l
E
}
Subject to :
BER
lk
BER
max
, R
lk
{0,x
1
,..., x
m
},
P
li j
= P, Z
k
{1,2}, k
{
1,..., N
}
,
l
{
1,..., Λ
}
,
{
i, j
}
{
0,1, ...,N
}
, j 6= i.
(9)
This MOP is solved at the same previous con-
straints. Both equations (9) and (8) are classified as
integer linear programming (ILP) optimization prob-
lems (discrete linear MOP). Each equation could be
solved using exhaustive search (ES) method to obtain
the optimal solution(s). ES method generates all pos-
sibles network forms (Λ) then evaluates the objective-
functions and lastly compares between feasible solu-
tion to select the optimal one(s) (global max and min
values) (Paar and Pelzl, 2009). However, the schemes
could be solved by ES method in open time like off-
line schemes (precomputed optimal values) to over-
come the time computing complexity of ES method.
Clearly, two schemes provide different service lev-
els and the SLA (Service Level Agreement) between
the nodes (end users) and backbone (optical service
provider) determines the appropriate resource alloca-
tion technique.
According to the proposed schemes, it is suitable
to add an index to measure the fairness between the
N nodes. Jains index, F, is the most common and
appropriate one (Jain et al., 1998).
F =
k=N
k=1
R
k
2
N ×
k=N
k=1
R
2
k
, 0 F 1 . (10)
5 SIMULATION AND
NUMERICAL RESULTS
In this section LMMCF and LMMF resource alloca-
tion schemes are evaluated and compared to tradi-
Table 1: Simulation Parameters.
Link parameters Values
Signal wavelength (λ) 1550nm
Divergence angle (Θ) 2 mm.rad/m
Diameter of Transmitter (d
t
) 4 cm
Diameter of Receiver (d
r
) 20 cm
average transmitted signal counts/slot (q
t
= q
s
/γ) 250,000
average background counts/slot (q
b
) 50
Average transmitted Power (P) -15 dBm
Average background noise power -52 dBm
Discrete bit-rates ({x
m
,..., x
2
,x
1
}) in Gbps 1, 3/4, 1/3, 1/2, 1/3, 1/4
Modulation formate NR-OOK
BER threshold (BER
max
) 10
4
Area of FSO Network 3 × 3 km
2
Area of FSO-node Cell 1 × 1 km
2
tional robust static ones to indicate the superior per-
formance of the proposed schemes. The evaluations
consider four performance parameters which are re-
liability, capacity, fairness and bit-error rate. Four
topologies which are considered in the evaluations are
direct link (D-L) (Fig. 2(A)), partial relayed (P-L)
(Fig. 2(B)), full relayed (F-L) (Fig. 2(C)) and recon-
figurable cooperative (Fig. 3(A)) models. The num-
ber of the transceivers for these networks are 18, 24,
36, and 22 respectively. As shown in Fig. 4, the as-
sumed service area of the considered FSO networks
is 3 3 km and nine FSO nodes are assumed to be lo-
cated uniformly in this area. Moreover, same homo-
geneous weather is assumed through out the service
area. All FSO links operate with predefined six bit
rates, m = 6, at constant average optical power. Ta-
ble.1 shows the assigned values of the simulated FSO
network parameters which are selected to be in the
practical range, also we use Japan’s rain loss model.
Backbone node
3 km
3 km
1 km
1 km
0.5 km
0.5 km
(1)
(2)
(5)(6)
(7)(8)
(9)
(
4
)
(3)
(0)
FSO node
Figure 4: Dimensions of simulated FSO networks.
Figure 5 indicates the reliability of the topologies
versus rain fall rates. Clearly, at D 3mm/h the 9
nodes for all topologies work properly using their di-
rect FSO links. On contrary, at D 180mm/h all
nodes for all topologies are dropped i.e. can’t achieve
minimum bit rate, 0.25Gbps, at bit error rate less than
threshold 1e 4. Between these two rain fall rate lev-
els, different network topologies have different per-
formances. At D=20mm/h the dropped nodes in the
D-L, P-L, and LMMCF/LMMF are 6, 3 and 2 nodes
Proposed Resource Allocation Schemes for Rainy Free Space Optical Network
81
respectively. Although, LMMCF and LMMF have
identical performance curves, but in general the re-
liability of LMMF is better than the LMMCF due to
its flexibility in link reconfiguration (no capacity con-
straint).
2 32 62 92 122 15218010 205
0
1
2
3
4
5
6
7
8
9
Rain fall rate (D) in mm/h
Dropped nodes
D−L
P−L
F−L
LMMCF
LMMF
Figure 5: Reliability versus the rain fall rate (D) for five
networks; D-L, F-L, P-L, LMMCF and LMMF.
Figure 6 shows the capacity of the networks where
the capacity is 9Gbps for all topologies at D
3mm/h, and zero at D 180mm/h. And the perfor-
mance of three topologies D-L, LMMCF and LMMF
are almost the same, however the D-L and LMMCF
topologies are better than the LMMF at certain D val-
ues as expected. Numerically, at D = 7mm/h the
capacity of D-L and LMMCF are 6Gbps while in
LMMF it is 5.5Gbps, this is due to the maximization
capacity in LMMCF scheme.
10 92 1803210 20 622 5 122 152
0
1
2
3
4
5
6
7
8
9
Rain fall rate(D) in mm/h
Capacity in Gbps
D−L
F−L
P−L
LMMF
LMMCF
Figure 6: Capacity (C) versus the rain fall rate (D) for five
networks; D-L, F-L, P-L, LMMCF and LMMF.
Figure 7 explains the fairness between the nodes
in the capacity of the backbone node. The maximum
fairness is 1 at D 3mm/h for all topologies. The
proposed approaches improve the fairness and out-
perform P-L, specially at channel degradation. At
D = 10mm/h the fairness is 0.9 for both LMMCF and
LMMF, 0.8 for P-L and 0.45 for D-L as shown. Note
that, the fairness performance in LMMF case is better
than that in LMMCF case as indicated from the MOPs
formulations and numerical results. LMMF is better
than LMMFC in fairness performance curve, specifi-
cally, at D = 9mm/h the fairness is 1 and 0.9 for both
LMMF and LMMCF respectively.
2 32 62 92 122 15218010 205
0
0.2
0.4
0.6
0.8
1
Rain fall rate (D) in mm/h
Jain Fairness (F)
D−L
P−L
F−L
LMMCF
LMMF
Figure 7: Fairness (F) versus the rain fall rate (D) for five
networks; D-L, F-L, P-L, LMMCF and LMMF.
Figure 8 indicates to the bit-error rate of the net-
works, and both LMMF and LMMFC have the same
performance around 10
5
bit error rate and dos not
exceed 10
4
. However the F-L and P-L outper-
form the two other topologies, because the proposed
schemes prioritize the reliability-fairness over the bit-
error rate. We show the result for D 25mm/h, be-
cause the networks at D 25mm/h have little number
of survived nodes.
2 4 6 8 10 12 14 16 18 20 22 24
10
−20
10
−18
10
−16
10
−14
10
−12
10
−10
10
−8
10
−6
10
−4
10
−2
10
0
Avergae Bit Error rate
Rain rate (D) In mm/h
D−L
F−L
P−L
LMMF
LMMCF
Figure 8: Average error rate (BER) versus the rain fall rate
(D) for five networks; D-L, F-L, P-L, LMMCF and LMMF.
6 CONCLUSION
Two new resource allocation schemes, namely,
LMMCF and LMMF have been proposed to increase
both reliability and fairness of cooperative reconfig-
urable FSO networks at last-mile during sever rainy
weather conditions. The proposed schemes outper-
form both D-L and P-L traditional schemes. Further-
more, this enhancement comes at much lower imple-
mentation cost as the number of installed transceivers
for P-L and LMMCF/LMMF are 24 and 22 respec-
tively. In addition, LMMCF and LMMF have dif-
ferent optimization criteria, where LMMF gives the
PHOTOPTICS 2016 - 4th International Conference on Photonics, Optics and Laser Technology
82
priority for fairness and reliability over the capacity,
while LMMCF does the opposite. The exhaustive
search method has been used to solve the two ILP-
MOPs. In order to overcome the solution complex-
ity for LMMCF/LMMF the optimal solution(s) could
be computed off line, then it could be registered as
lookup table in FSO tracking controller, which recon-
figures the topology to optimal configuration versus
the rain fall rate in real time environment.
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