Communications and Network, 2013, 5, 260-265
http://dx.doi.org/10.4236/cn.2013.53B2047 Published Online September 2013 (http://www.scirp.org/journal/cn)
Performance Analysis of Optical W ir eless Communication
System Employing Neuro-Fuzzy Based Spot-Diffusing
Techniques
Shamim Al Mamun1, M. Shamim Kaiser1, Muhammad R Ahmed2, Md. Shafiqul Islam3, Md. Imdadul
Islam4
1Institute of Information Technology, Jahangirnagar University, Savar, Bangladesh
2School of Information Technology & Engineering, University of Canberra, Australia
3Department of Physics, Jahangirnagar University, Savar, Bangladesh
4Department of Computer Science of Engineering, Jahangirnagar University, Savar, Bangladesh
[e-mail], [e-mail]
Received June, 2013
ABSTRACT
The spot-diffusing technique provides better performance compared to conventional diffuse system for indoor opti-
cal-wireless communication (OWC) system. In this paper, the performance of an OW spot-diffusing communication
system using Neuro-Fuzzy (NF) adaptive multi-beam transmitter configuration has been proposed. The multi-beam
transmitter generates multiple spots pointed in different directions, hence, forming a matr ix of diffusing spots based on
position of the receiver and receiver mobility. Regardless of the position of the transmitter and receiver, NF controller
target the spots adaptively at the best locations and allocates optimal power to the spots and beam angle are adapted in
order to achieve better signal-to-noise plus interference ratio (SNIR). Maximum ratio combining (MRC) is used in the
imaging receiver. The proposed OW spot-diffusing communication system is compared with other spot-beam diffusion
methods proposed in literature. Performance evaluation revels that the proposed NF based OW spot-diffusing commu-
nication system outperforms other spot-beam diffusion methods.
Keywords: Optical-wireless Communication; Spot Diffusion Techni que; Neuro-fuzzy; Imaging Receiver; Adaptive
Power Alloca t ion
1. Introduction
With the increasing development of ultra-broadband
wireless application the radio frequency (RF) spectrum
became congested and scare resources. Thus optical
wireless communication (OWC) has drawn considerable
attention to the researchers. OWC for indoor application
was first proposed by Gfeller and Baps t [1]. In number of
applications where higher data throughputs is more of
requirement than the mobility, transmission lin k based on
optical wireless would be one of the best options as out-
lined in [2-5]. The performance of OW systems depends
on the propagation and type of system used. The basic
system types fall into diffuse or line of sight (LOS) sys-
tems. In LOS systems, high data rates in the order of
Gbit/s can be achieved [6], but the system is vulnerable
to blockage/shadowing because of its directionality. In a
diffuse OW system, several paths from source to receiver
exist, which makes the system robust to blockage/
shadowing. However, the pa th losses are high and multi-
paths create inter-symbol interference (ISI) which limits
the achievable data rate [7,8]. There are several advan-
tages of OWC over traditional RF systems, these are: an
abundant free spectrum, extremely high communication
speed is possible by all network, does not interfere with
the over congested RF spectrum. But limitations are: a
beam is short ranged, may be harmful for eye. The first
limitation can be overcome by wavelength reuse tech-
nique, whereas eye safety can be ensured by maximum
transmit power. It can be classified as line of sight (LOS)
and diffuse system. Many researchers have considered
diffuse systems for indoor applications it offers robust
link and overcomes the problem of shadowing [7], does
not require transmitter-receiver alignment and uses the
wall or ceiling for multi path reflection [8]. The multi-
path reflections increases delay spread or inter-symbol
interference. Ambient light such as florescent, incandes-
cent light and Compact Florescent Lamp (CFLs) pro-
duces channel noise which reduces signal-to-noise plus
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S. Al MAMUN ET AL. 261
interference rat o (SNIR). In order to improve the system
performance several spot diffusion configuration using
multi beam transmitter have been proposed [9]. Multi
beam transmitter is place in center of the room and
pointed upward. A multi-spot pattern have been gener-
ated, illuminated multiple small areas in the ceiling and
then reflected multiple spot have been received by re-
ceivers. In [10], to improve the bandwidth, reduce the
effect of inter-symbol interference, and increase the sig-
nal to noise ratio (SNR) when the transmitter operates at
a higher data rate under the impact of multipath disper-
sion, background noise, and mobility in conjunction with
an imaging receiver. It proposed different line streaming
multi-beam spot diffusion (LSMS) model to gain about
32.3 dB SNR at worst communication path. But the multi
path dispersion reduces the performances due to trans-
mitter power and can be improve using power adaptive
system by [9]. User mobility is very important aspect of
wireless communication especially with today’s hand
hold devices. As the user device can mobilize with the
room then power adaptation will be a great solution to
get higher SNR. In this aspect [12] propose a genetic
algorithm for multi spot diffuse system in indoor wireless
communication. But it is noted from different research
that if the diffuse system has a predefined spot for a room
and use an adaptive power allocation for beam using
calculation of delay spread then it can improve the per-
formance of the OWC. Neural network and Adaptive
Linear Equalizers can be a solution in this case for adap-
tive power distribution. [12] Presents a comparative
study of two equalizers, the adaptive linear and the neu-
ral equalizer for indoor optical wireless (OW) links using
OOK modulation technique to reduce ISI effect. This
paper introduce adaptive neuro-fuzzy interference system
(ANFIS) for selecting spot beam matrix for a room and
also distribute power allocation by calculating delay
spread in considering Doppler shift effect of the mobile
devices. The paper is organized as follows: the system
model is presented in section 2; power allocation algo-
rithm is explained in section 3; Section 4 presents dis-
cussion and results. The concluding remarks and future
work is included in section 5.
2. Proposed System Model
Consider an empty room with floor dimensions of 8×4
m2 and ceiling height of 3m as shown in Figure 1. The
reflection coefficient of the ceiling is considered to be
0.8. There are eight spot lights on the ceiling. In the Fig-
ure, is the elevation angle,

is the azimuth angle, d =
8, w = 4 and h = 3, x0 and x are the position of the imag-
ing receiver and v is the velocity. Neuro-Fuzzy (NF)
adaptive multi-beam transmitter is located at the center
of the room whereas an imaging receiver is placed at
x0 = (1, 1, 0.5). The transmitter generates multi spot
beam matrix on the ceiling where beam power and beam
angle ( ,


x
R
) are adapted and the reflected beams are
received by the imaging receiver. The transmitter learns
receiver position, mobility through the low rate diffuse
channel. At low data rate, the beam maintains the fixed
power.
2.1. Signal to Noise Plus Interference Ratio
In indoor optical-wireless communication, the ambient
light affects signal-to-noise-plus interference (SNIR) at
the receiver. Many researchers have considered intensity
modulation with direct detection (IM/DD) as most viable
approximation. The received signal, denoted by y(t), can
be expressed as
()()(, ,)(, ,)I(, ,)ytt htntt

  



(1)
where, R is the receiver responsively , x(t) is the instan-
taneous optical transmitted power, (,, )ht


(,, )nt is the im- pulse response of the OW channel,

 is the am-
bient light noise, (,, )It

 is the instantaneous inter-
ference pow er.
The SNIR, denoted by

, of the received signal can
be calculated by [9]
10
22
2
10
()
()
ii
s
si
ii
RPPh


 (2)
where, Ps1 and Ps0 are the optical power associated with
the binary 1 and binary 0 respectively,

s1 are

s0 are
the shot noise variation component with Ps1 and Ps0 re-
spectively.
2.2. Adaptive Power Allocation
The achievable data transmission rate, denoted by b, of
the OWC system is given by
10
22
22
110
()
1log 1()
ii
Mssi
iii
RPPh
M 



 




b (3)
The optimization problem and constraint of the power
allocation can be written as,


d
h
w
v
x
0
x
Figure 1. System Model for OWC based on spot-diffusing
technique.

S. Al MAMUN ET AL.
262
max b (4)
1
.. J
j
j
s
tP


P (5)
where P is the average power. We can use the La-
grange multiplier method to analyze the above optimiza-
tion problem and the Lagrangian function is defined as
1
J
jj
j
LbP P 


 



(6)
where, µj is the Lagrange multiplier. After solving the
Eqn. (6), we can write
1
1
1
J
ji
ji
Ph
PCh











(7)
1
= max(C),0
i
h






(8)
2.3. Delay Spread
The delay spread of an impulse is expressed as rms value
by,
22
2
(t )
i
r
P
DP
 
r
(9)
where,

and is the delay time and is the re-
ceived power i
tr P
2.4. Doppler Shift
Light waves require no medium and being able to travel
even through vacuum. Let v is the relative velocity
between transmitter and receiver, the proper frequency of
the transmitted information signal from the optical
transmitter is 0
f
. Let f is the frequency of the received
signal accepted by the moving receiver with a velocity
v, then
01
1
ff



 (10)
where, /, vcc

 is the speed of light. For low speed,
i.e. 1

 , the above eqn. (10) is reduced to
1
2
0
2
0
(1 )
1
= (1)
2
ff
f






(11)
2.5. ANFIS Model
NF inference system is considering if learning capabili-
ties are required. In this paper, we consider the adaptive
neuro-fuzzy inference system (ANFIS) for the imple-
mentation of the spot beam matrix selection as shown in
Figure 2 Based on the signal to noise ratio , i.e.,

, and
link delay, i.e.,


, ANFIS decides a spot is eligible for
selection or not. The ANFIS is trained iteratively to
achieve the desired output for the input parameters and
their membership functions. This can be done by back
propagation gradient descendent which evaluates the
error signals recursively from the output layer backward
to the input nodes. In this way, ANFIS learns the sys-
tem’s behavior. Sugeno ANFIS model contains if and
then rules, e.g., If x is Ai and y is Bi then,

, w h ere ,,
ii iiiii
f
pxqyrp q r ,
is the consequent parameter set. ANFIS model for spot
beam matrix selection consists of five layers: input layer,
output layer and three hidden layers. Each adaptive node
in the input layer generates membership grades. If bell
shape membership functions are considered, output of
this node, denoted by , can be written as
1
i
O
1
2
1
()
1
ii
ib
i
i
Ox xc
a

 






(12)
where,


,
iii
A
B   is the in put vector, are
the premise parameters.

,b ,c
iii
a
Nodes in the first hidden layer calculate the firing
strength of a rule via multiplication. The output of the
each node, denoted by , can be written as
2
i
O
2.()
ii
iiAB
O   x (13)

1
x
2
x









N
N
N
N
N
N
N
N
N
f
i
w
i
w
Figure 2. System Model for OWC based on spot-diffusing
technique.

S. Al MAMUN ET AL.
263
where, . ANFIS performs AND operation in this
layer. Nodes in the second hidden layer compute the
normalized value of the firing strength. The output of the
each node, denoted by , can be written as
=1,2,3i
3
i
O
3/
iii
Oi

   (14)
Nodes in the third hidden layer compute the contribu-
tion of i-th rule towards the overall output. The output of
the each node, denoted by , can be written as
3
i
O
4(
iiiiiii
Of pxqy   )r (15)
Signal node in the output layer computes the overall
output, denoted by as follows:
5
i
O
5
i
i
O
i i
f

 (16)
Spot hologram matrix has been generated using Eqn.
(16).
3. Adaptive Spot-Beam Selection Algorithm
Figure 3 shows the block diagram of the adaptive spot-
beam selection algorithm. In the first step the beam
hologram or matrix generates 40×20 equal powered
spot-beams in the ceiling. The SNIR and delay spread for
each beam have been calculated by the image receiver.
The receiver periodically evaluates the SNIR after 1
second interval whereas the delay spread for each beam
is same if the receive is not moving. In the second step,
the receiver sends the spot-beam information which con-
tains SNIR and delay spread to the transmitter. Based on
the minimum SNIR and maximum delay spread, trans-
mitter select the spot-beam matrix by NF based algo-
rithm in the third step. The transmitter allocates the
power for each selected beam adaptively using eqn. (8) in
the fourth step. Finally based on the velocity of movement
of the receiver, transmitter moves spot-beam matrix for
the receiver. The algorithm is summarized as follows:
The following algorithm will find the spot beam with
an equal power allocation over 40 × 20 beam hologram
or matrix, H.
Step 1 A spot beam scans the ceiling, SNIR,

and
delay


spread, for each beam have been calculated
by the image receiver using Eqn (2) and (9).
Step 2 Based on the required minimum SNIR, i.e.,
min

and maximum delay spread, i.e., max

, transmit-
ter selects the spot-beam matrix (H) by NF controll er .
Step 3 The transmitter allocates the power for each se-
lected beam adaptively using Eqn (7)
Step 4 Based on Doppler shift, the transmitter adapts
the beam angles and

 .
Step 5 Multi-spot optical transmitter further reduce the


by scheduling.
Step 6 Finally, Multi-spot optical transmitter transmits
the spot beam matrix to receiver via ceiling.
Step 7 Go to Step 1 if transmitter gets receiver’s posi-
tion update.
4. Numerical Analysis
In this section, Neuro-Fuzzy based multi-beam system
(NFMS) is investigated with diversity receiver configu-
ration. It is compared with other spot-beam diffusion
method. The ANFIS model, adaptive power allocation
and multi-spot diffuse pattern formation are implemented
in MATLAB/SIMULINK. ANFIS consider two inputs
such as SNR and delay.
Simulation parameters considered for the analysis are:
length, width and height of the room are 8m, 4m and 3 m;
the reflection coefficient of the ceiling is 0.8

; there
is one transmitter which is located at (2, 4, 1) location;
there is also one receiver; the area, acceptance semi-an-
gle of the each photo-diode are 2 cm2 and 650 respec-
tively. The number of pixel at the receiver is 200 (with
area of 0.01 cm2) Pedestrians move typically at the speed
of 1 m/s. If the SNIR is computed after 10
s

; there are
8 spots lamp in the room which are located at (1,1,1),
(1,3,1), (1,5,1),(1,7,1),(3,1,1), (3,3,1), (3,5,1), and (3,7,1);
and the wavelength of the light is 850 nm.
Neuro-
Fuzzy
Controller
11 1112 12112
21 21222222
112 2
,, ,
,, ,
,,... ,
n
nn
mmm mmnmn

 

 

 
 


 



 




T

est
f
Spot Scanning the ceiling, SNR and time
delay w.r.t. maximum tolerable delay are
recorded at the receiver
The spot information send
to the transmit te r
Based on required target SNR and
Doppler Shift Information Trasmitter
generates t he spot hologra m (H)
Adaptive
Power
Allocation
Spot
hologram
Delay information of the
selected spot
Multi-spot
Optical
Transmitter
Schedule the
spot beam
transmission to
reduce del ay
tends to zero
SNR information of the selected spot
Angle
Adaptation
(,)


Figure 3. System Mo de l for OWC based on spot-diffusing technique.

S. Al MAMUN ET AL.
264
The 80 ms adaptation time will give overhead of 8%.
Adaptation time depends on environment. Receiver
computes the SNIR and delay spread and sends this in-
formation via a low rate channel to the transmitter.
ANFIS consider two inputs. Iterative training of the
ANFIS has been done to achieve the desired output. Af-
ter a predefined simulation time to obtain the simulation
result and use them to train. Based on the training data
set, ANFIS.
Figure 4 shows the effect of receiver position on the
SNIR for proposed model, line strip multi-spot diffuse
system (LSMS) and conventional diffuse system. The
SNR calculations were performed for the receiver is
moving towards the transmitter (i.e., the values of x is
increasing) while neglecting the movement along y-axis.
Significant SNIR improvement of almost 3 dB is ob-
served as the NFC moves the spot beam, selects the best
positioned spot only, and allocate the power adaptively
based on the channel condition of the selected slots. It is
also found that the SNIR performances have been de-
graded as the receiver is moving. This degradation in
SNIR increases as the velocity of the receiver increases.
Figure 5 shows the SNIR for proposed model has
been improved further (almost 1 dB), if we change the
slot beam angle of the selected slot.
00.5 11.5 22.5 33.5 4
20
21
22
23
24
25
26
27
28
Receiver Position
SNIR [dB]
Proposed method with v=0m/s
Proposed method with v=1m/s
Proposed method with v=2m/s
LSMS with v=0m/s
Figure 4. Effect of receiver position on SNIR distribution
for proposed model and LSMS.
00.5 11.5 22.5 33.5 4
18
20
22
24
26
28
Rec eiver posit ion in meter
SNIR [dB]
Proposed method V=0 m/s
Proposed method with beam angle correction (V=1 m/s)
Proposed method with beam angle correction (V=3 m/s)
LSMS with V=0 m/s
Figure 5. Effect of receiver’s position on SNIR for the
proposed model and LSMS. Here the beam pattern is
shifted with Doppler shift.
0.5 11.5 22.5 33.5 44.5
0
0.5
1
1.5x 10
-9
Receiver position in meter
Delay spread [ns]
ANFI S ba s ed p r oposed method
LSMS
Uniformly dis t rib ut ed be am
Figure 6. Effect of receiver’s position on delay spread.
Figure 6 shows the effect of relay position on the de-
lay spread for proposed model, line strip multi-spot dif-
fuse system (LSMS) and conventional diffuse system. In
contrast to LSMS, the delay spread variation for the pro-
posed model is small for the receiver position greater
than 1 m from the corner wall. It is found that the de-
lay-spread performance is about 2.3 ns for the proposed
model compared to LSMS.
5. Conclusions
In this paper, we have proposed a new method of real-
time beam and angle adaptation technique for optical
wireless communication system using ANFIS. This NF
controller has five layers and is trained with back-
propagation gradient decent algorithm. The controller is
trained with data obtained by simulations. Simulation
results show that the proposed NF based OW spot dif-
fusing communication system outperforms other spot-
beam diffusion methods in terms of SNIR and delay
spread.
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