A New Switched Beam Smart Antenna Model for Extending Inet

Omnet++ Framework

Vincenzo Inzillo

, Floriano De Rango

and Alfonso Ariza Quintana

Dimes, University of Calabria, Via P. Bucci, Rende (CS), Italy

University of Malaga, Av. De Cervantes, Malaga, Spain

Keywords: Smart Antenna Systems, Switched Beam, Phased Array, Omnet++, Inet, IEEE 802.11.

Abstract: Smart Antenna Systems (SAS) are providing a strong increasing impact in digital wireless communication

systems. Due to their great advantages regarding nodes power saving and coverage enhancing, SAS are

largely employed on pervasive network environments such as MANET. Because almost the whole of exist-

ing network simulators use omnidirectional antennas on nodes, we propose a new version of Inet framework

of Omnet++ that extends its operation also for network scenarios in which nodes are equipped with direc-

tional antennas. Furthermore, we created a new directional antenna module that simulates the behaviour of a

Phased Array System and a very simple algorithm for power management in channel. The new proposed

model presents some modifications to key-modules that are involved in a normal wireless communication

scenario, in order to support asymmetrical communications between nodes.

1 INTRODUCTION

Since the early days of wireless communications,

focusing on physical layer of a network node there

are two main kinds of antenna that can be used for

equipping it in order to produce a certain behaviour

on transmission/reception: omnidirectional antennas,

which radiates and receives equally in all directions,

and directional antennas which are capable to radiate

in a fixed particular angular direction. Omnidirec-

tional strategies directly and adversely impact spec-

tral efficiency, limiting frequency reuse (Chu, 1948).

These limitations force system designers and net-

work planners to devise increasingly sophisticated

and costly remedies. In recent years, the limitations

of broadcast antenna technology on the quality, ca-

pacity, and coverage of wireless systems have

prompted an evolution in the fundamental design

and role of the antenna in a wireless system. In low

power design systems, such as Manet or Sensor

Networks, using an omnidirectional approach it is a

difficult and inconvenient way to produce efficient

systems, because the high amount of power con-

sumption of network nodes (Khuzhali, 2014), that

could be translated in overcome phenomena such as

low battery lifetime and interference generations. A

single antenna can also be constructed to have cer-

tain fixed preferential transmission and reception

directions in order to maximize its energy consump-

tion in a particular direction saving power in other

directions (Nasipuri-Sappidi, 2002). Using direc-

tional antenna there could be several advantages, in

terms of reduction of reception packet delay or per-

formance enhancing of a routing protocol (Dimitrou-

Kalis, 2004). In conventional wireless communica-

tions, a single antenna is used at the source, and an-

other single antenna is used at the destination. This

is called SISO (Single Input, Single Output) (Sen-

gar-Rani-Singhal-Sharma-Verma-Singh, 2014). In

later years, it has developed the concept of Smart

Antenna Systems intended as a particular System

that is capable to be “intelligent” basing on physical

and logical implementation features. This kind of

systems can increase the coverage area and the ca-

pacity of a wireless communication system. The

coverage area, is simply the area in which communi-

cation between a mobile and the base station is pos-

sible. The capacity is a measure of the number of

users a system can support in a given area. The em-

ployment of SAS in wireless mobile environments

allows a more efficient medium utilization with re-

spect to the classical Omnidirectional approach. For

example, Spatial Division Multiple Access (SDMA)

seeks to increase the capacity of a system. General-

ly, Smart Antennas fall into three major categories:

SIMO (Single Input, Multiple Output), MISO (Mul-

tiple Input, Single Output), and MIMO (Multiple

Inzillo, V., Rango, F. and Quintana, A.

A New Switched Beam Smart Antenna Model for Extending Inet Omnet++ Framework.

DOI: 10.5220/0006439602630271

In Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2017), pages 263-271

ISBN: 978-989-758-265-3

263

Input, Multiple Output). In SIMO technology, one

antenna is used at the source, and two or more an-

tennas are used at the destination. In MISO technol-

ogy, two or more antennas are used at the source,

and one antenna is used at the destination. In MIMO

technology, multiple antennas are employed at both

the source and the destination. A Smart Antenna

System combines generally an antenna array with a

digital signal-processing capability to transmit and

receive in an adaptive, spatially sensitive manner. In

other words, such a system can automatically change

the directionality of its radiation patterns in response

to its signal environment. This can dramatically in-

crease the performance characteristics (such as ca-

pacity) of a wireless system. One of the most critical

aspect in wireless communications environments is

represented by the fact of using an adequate network

simulator that is able to well emulate and reproduce

an appropriate real scenario. Unfortunately, most of

the existing network simulators do not provide any

support for directional and asymmetrical communi-

cations and thus also for SAS technology. For this

reason, in the present work we propose a model that

extends the functionalities of Omnet++ simulator in

order to provide a useful support for asymmetrical

communications and Smart Antenna Systems. The

paper is structured as follows: section 2 introduces

SAS main principles; section 3 is an overview about

existing network simulator compared to Omnet++.

In sections 4 and 5, the proposed model and the pro-

posed algorithm are presented respectively. Finally,

in section 6 simulation results are discussed.

2 SMART ANTENNA

SYSTEMS (SAS)

As mentioned, SAS are intelligent systems that al-

lows a good SDMA processing (Wei, 2004); exam-

ples of SAS are: digital beamforming systems, adap-

tive antenna systems, phased array and others. Smart

antennas are customarily categorized, however, as

either switched beam or adaptive array systems.

There could be a distinction between the two major

categories of smart antennas in term of the operation

mode (Kulkarni-Bhavani, 2014); (Balanis-Ioannides,

2007):

• switched beam: a finite number of fixed, prede-

fined patterns or combining strategies

• adaptive array: an infinite number of patterns

(scenario-based) that are adjusted in real time

So, these strategies differ between beamforming

building technology. Switched beam antenna sys-

tems form multiple fixed beams with high sensitivity

in particular directions. These antenna systems de-

tect signal strength, choose from one of several pre-

determined, fixed beams, and switch from one beam

to another as the mobile moves throughout an area.

So, they produce a static fixed beam that could be

electronically controlled. Adaptive antenna technol-

ogy, instead, uses adaptive algorithm because of its

ability to effectively locate and track various types

of signals to dynamically minimize interference and

maximize the intended signal reception. In this case,

produced beam is variable and adapt itself depend-

ing on transmission channel conditions and a weight

array that dynamically varies in time. In this context,

the spatial structure is used to estimate the direction

of arrivals (DOAs) or AOA (Angle of Arrive) by

nodes. However, both systems attempt to increase

gain according to the location of the user; We can

synthetize the operating Smart Antennas principle

with the following figure (Wei, 2004):

Figure 1: Smart Antenna general operation schema.

Inputs x

……x

are combined each one with el-

ement values of a weight array that varies according

to an adaptive algorithm (for example LMS or

CMA), so output y(t) is a variable beam in adaptive

array systems. Using a switched beam approach

there is no adaptive algorithm execution and weight

array can be considered missing or constant, beam is

fixed but it is very simple to realize with worse per-

formance than adaptive array systems of course.

3 NETWORK SIMULATORS

OVERVIEW AND OMNET++

There are several network simulators (Chirstu-

Namrata-John-Shibin, 2013) that could be used for

creating a network mobile scenario containing nodes

that are equipped with a particular kind of antenna

system; some of the most used software are: Ns2,

Ns3, Opnet, Omnet++. Ns2 (Charbonneau, 2010)

follows two levels of hierarchy namely C++ Hierar-

chy and the interpreted OTcL, which is one to one

correspondence. Two languages are linked to

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264

achieve a higher efficiency. Ns3 (NS-3, 2014) is a

discreet-event simulator primarily targeted for re-

search and educational purposes. Ns3 is not an ex-

tension of Ns2; the similarity between Ns2 and Ns3

are both written in C++ Codes but Ns3 does not sup-

port Ns2 API. Ns3 simulator is written in C++ and

python. OPNET (Dunaytsev, 2012) is another net-

work simulator, which provides a better User Inter-

face than Ns2 and Ns3. Omnet++ (Omnet++ User

Manual, 2016) is a discrete event simulation envi-

ronment. Omnet++ provides component architecture

for models. Components (modules) are programmed

in C++, and then assembled into larger components

and models using a high-level language (NED). Re-

usability of models comes for free. Logical behavior

of modules is generally written in .cc and .h files

containing all logical functions. Because its simply

features, and because this simulator enhanced and

improved during years with some users contribution,

therefore considering that it is extremely intuitive

from user interface point of view we choose to use

Omnet++ 4.6 for our test simulations. For our pur-

pose, we used Inet framework that provides a very

complete modules and protocols choice especially

for Mobile Networks. Unfortunately, Omnet++ does

not support asymmetrical communication between

nodes (Uribe-Maureira-Dalle, 2010), so we need to

modify some logical modules configuration in order

to achieve our purpose. In this way, we would ex-

tend Inet framework in order to make it able to work

with directional antennas equipped nodes, and so for

smart antenna systems. We used a different ap-

proach with respect to (Uribe-Maureira-Dalle, 2010)

in which a Neighbors Graph Algorithm is created

for power management therefore modifying chan-

nelControl module (no longer available in current

version of Omnet++). In (Uribe-Maureira-Dalle,

2010) For supporting directional communications

between nodes, a DirectionalRadio module is creat-

ed for obtaining a directional antenna pattern; in our

work, instead, we created a new directional antenna

model and next a simple algorithm for power man-

agement that allows a “not static” assignment of

nodes power. Furthermore, the version of Omnet++

used in this work deeply differs from 4.2 version

used in (Uribe-Maureira-Dalle, 2010).

4 PROPOSED MODEL

Our model is based on the simplest SAS technology

that is the Switched Beam approach. Differently

from (Inzillo-De Rango, 2016) in which a MAC

layer analysis is accomplished, this work provides

for modifications on physical layer only. Firstly, we

created a new directional antenna module called

“PhasedArray”. This module, like other models,

extends AntennaBase module. It was created based

on Antenna Array theory, implementing in com-

puteGain function the following formula (Patel,

2007):

G(θ,φ)

TOT

= G(θ,φ)

*G(θ,φ)

(1)

Where the first term is referred to element factor

gain, and the second term is the array factor gain.

Array factor is the main term conditioning System

Array behavior, and its module could be expressed

as follows (Orfanidis), for linear uniform arrays:







sin / 2

Nsin / 2





(2)

Where:

(cos cos )kd







(3)

k is the wavenumber, d is the distance between each

radiating elements and φ

is the steering angle, that

is the angle corresponding to maximum directive

gain of radiation pattern depending on radiating el-

ements number (N) too. We suppose that all ele-

ments produce the same pattern and we choose an

element factor EF = cos θ. Because Omnet++ is not

able to produce radiation pattern figure for antenna

modules, we produce a simple matlab code imple-

menting above expressions. For instance, for N=10,

= 120°, f = 1 MHz, d = 0.5λ we have the follow-

ing situation:

Figure 2: Array Factor plots for N=10, φ

= 120°, d =

0.5λ: (a) polar plot; (b) rectangular plot.

A New Switched Beam Smart Antenna Model for Extending Inet Omnet++ Framework

265

Where φ

varies from -180° to 180°. Fig. 2(a)

represents an example of Array Factor polar gain,

with main lobes pointed to steering angle absolute

value. Fig. 2(b) represents Array Factor rectangular

plot of the whole Phased Array system.

5 MODIFICATIONS ON INET

AND PROPOSED ALGORITHM

We created a new macro network module called

AODVNetwork2 that simulates a scenario for MA-

NET. Of course, no smart antenna submodule exists

in Omnet++ and for this purpose we modified these

existing modules in order to support asymmetrical

communications too. Primarily, we modified the

module related to mobile network nodes, so we used

an array of StandardHost called host having a size

equal to the number of nodes involved in our simula-

tions. On physical layer, we modified the Sca-

larAnalogModelBase Class that implements Free

Space Model Path Loss for propagation and we cre-

ated and inserted an algorithm in this class for node

power management in order to create a dynamic

power quantity assignment based on transmission

direction and angular position of each node. It is

recalled that Omnet++ does not support asymmetric

communication between nodes, so by default, for

each node the power value is the same for all direc-

tions. We modified computeReceptionPower func-

tion in order to implement Free Space PathLoss

Model that satisfies the relation:

Path

(4)

Given a transmitter node A and a receiver B, if A

transmits with a steering angle φ

towards B, B will

receive with an angle φ’ = (φ

0 -

180°) if φ

≥ 0 or φ’ =

(φ

0 +

180°) if φ

< 0; So, receiver node B, in this case,

gets an estimation of AOA based on transmission

direction of A, and adjusts its beam in order to max-

imize its reception power:

Figure 3: AOA estimation principle.

Next, we created an algorithm for dynamic as-

signment power for each host based on the main

beam direction and so on transmission/reception

direction position coordinates of nodes. For a given

node that produces a radiation pattern forming a

beam with main lobe angle φ

it will transmit/receive

with a power value that will be the maximum or a

fraction of this according to radiation pattern form.

Figure 4: Logical block schema 1.

The proposed algorithm takes into account also

of the sidelobes amplitude of the pattern, so it has

implemented a classification level of side lobes

based on transmission/reception direction angle val-

ue. In Fig. 4 it is shown a logical block schema that

illustrates the main steps and modules about modifi-

cations accomplished on implementation level basis:

Rectangle forms contain modules or class names;

rounded rectangles contain class functions names or

variables names; elliptics contain only output varia-

bles names; double rounded elliptics contain only

output variables of a key function, and it can be con-

sidered as a final state of an automata. In Fig.4 we

note that ScalarAnalogModelBase extends and so

implements all functions of AnalogModelBase

Class. In ScalarAnalogModelBase there is a function

computeReceptionPower that assigns the power val-

SIMULTECH 2017 - 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

266

ue to nodes, basing on angle position direction of a

receiver node with respect to a transmitter node; so,

it makes a test comparing main beam angle to

transmissionDirection value that is the return value

of computeTransmissionDirection function invoked

in AnalogModelBase Class. The test is carried out by

a power Algorithm that assigns a power value de-

pending on transmissionDirection value. The fol-

lowing pseudo-code better illustrates the Algo-

rithm’s operations:

Figure 5: Power Algorithm Pseudo-Code.

An important parameter for the algorithm is the

offset variable. This value is function of two parame-

ters: the mainLobeAngle (commonly referred to φ

)

and the spreading factor. The first term, as already

mentioned, represents the angle of maximum radia-

tion, and its value is returned by the getPhizero

function implemented in the PhasedArray module.

The second term varies from -1.5 to 2 according to

the number of radiating elements of the array. In

particular, the larger is the number of elements, the

greater is the spreading factor value. This feature

allows to take into account of the spreading effect

that affects the overall pattern varying the number of

elements. Setting an appropriate offset helps to en-

hance the sidelobes research within the beam and

their classification process. From Fig. 5 it can be

observed that, based on the transmissionDirection

value, the power is fractioned opportunely. If the

transmissionDirection value is not related to any

sidelobe level, the power is reduced to 0. This algo-

rithm is static and approximated, but more flexible,

in fact it also takes into count main beam pattern,

that is a function of elements number antenna array.

Some modifications are accomplished in RadioMe-

dium module, so we show another logical block

schema that synthetizes all functions and modules

that are involved in:

Figue 6: Logical block schema 2.

RadioMedium class, contains a remarkable num-

ber of functions that drives control of traffic flows

passing through the network. We created a get-

NotReachableList function that executes powerAlgo-

rithm and returns the complete list of nodes that

cannot be reached by a transmission due to their

current position with respect to transmitter position.

isPotentialReceiver function uses this list for deter-

mining if a given receiver is reachable or not by a

transmission, and gives a Boolean output according

to the content of list returned by getNotReachable-

List function.

6 MODEL VALIDATION AND

SIMULATION RESULTS

For validation of the designed model we chose to

consider a configuration containing only 10 mobile

nodes, and successively extend our model to config-

urations with an increasing nodes number. Due to

space limitations, we will show and discuss only

results relative to 10 nodes configuration. The fol-

lowing table synthesizes main simulations parame-

ters of this configuration; these parameters are ex-

tracted from .ini file and may be modified for every

need:

A New Switched Beam Smart Antenna Model for Extending Inet Omnet++ Framework

267

Table 1: Setting of parameters used in simulations.

Manet Routing protocol

AODV

Antenna Type

Phased Array

Antenna Freq.

1 MHz

Distance

0.5 λ

Number of Elements

Number of Nodes

Main Beam Angle (Phizero)

7°(rc1),102°(rc2)

ConFig. Mobility Static

Simulation Area Size 500 x 500 m

Simulation Time 300 s

As we can observe, distance between each radiat-

ing element is a good set for avoiding Grating Lobes

in radiation pattern; position of each node is ran-

domly generated by Omnet++; for simplicity, only

host 7 generates UDP traffic and sends it to host

0,1,3,5 that are receiver nodes. Because results de-

pend on angular position and so on transmission

direction of each node with respect to each other,

firstly we created an utility module Mempos, to take

into account the current position of the node and for

generating a matrix that maintains the current posi-

tion angle between all nodes, that is useful for eval-

uating simulations results. As follows, we show the

so called “transmission directions matrix” generated

from Mempos module. It will be very helpful for

results analysis:

Figure 7: Transmission directions matrix.

Fig. 7 shows the current transmission direction

angle of an i-row host row with respect to each other

j-column host of the network. Note that angle value

varies from -180° to 180°. For example, if host 0

sends toward host 1 it transmits with an angle of 68°

because its angular position with respect host 1. In

our simulations, we suppose that host 7 is the only

node that generates any traffic data towards receiv-

ers. For understating if the proposed antenna model

is well designed we have to compare simulations

results with a “default” configuration of AODV

model example of Omnet++ in which mobile nodes

are equipped with omnidirectional antennas. As fol-

lows it is shown the result related to the received

packets when mobile nodes use IsotropicAntenna

model:

Figure 8: Received Packets Omnet++ Omni Configura-

tion.

From Fig. 8 we can note that all nodes (on re-

ceiver side) receive packets. In particular, the overall

received traffic is uniformly distributed among

nodes. Now, we can replace the default antenna

model with our PhasedArray model. We created 2

run configurations. The first one (rc1) has main lobe

angle pointed to 7°, so, referring to our transmission

directions matrix, we can expect that generated traf-

fic will be driven towards nodes lying on main lobe

region. In particular, among all receivers, only host 3

is situated in main lobe region. Radiation pattern for

Phased array with main beam angle of 7° is shown

below:

Figure 9: Array Factor Polar Plot for N=10, φ

= 7°, d =

0.5λ.

In Fig. 9 we can observe that sidelobes amplitude

is very smaller than main lobe. Because the main

lobe degree value is very small, in this case the beam

is flattened close to 0. The following figure shows

received successful packets statistic referring to all

receiver nodes:

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268

Figure 10: Received Packets Omnet++ statistics (rc1).

As we could expect, data packets flow is directed

toward host 3 because its angular position compared

to sender node (host 7). Other receiver nodes, have a

negligible power reception value, due to their

angular position. Furthermore, side lobes amplitude

is very low in this case, consequently they are not

able to receive packets. Second run configuration

presents a main beam angle of 102°, other

parameters are the same of rc1. It shows radiation

polar pattern in this case:

Figure 11: Array Factor Polar Plot for N=10, φ

= 102°, d

= 0.5λ.

Now we have maximum side lobes gain of about

2.5 dB, so side lobe power may be not fully negligi-

ble in this case. Next figure shows received packets

statistic among all receivers:

Figure 12: Received Packets Omnet++ statistics (rc2).

In this case, data packets are mainly directed to

host 1 which is situated within main lobe region, but

due to power contribution of major side lobes, a

fraction of packets will be received by host 0 also

according with our powerAlgorithm. In particular,

from .log analysis we observed that main side lobes

present an amount power of 5 mW that is not negli-

gible with respect to maximum power value in main

lobe region of 10 mW. For a further validation of the

model we also analyzed the results related to the

energy consumption of each receiver node; for this

purpose, we inserted energy simulation modules into

each mobile node setting the initial energy value of

each node to 150 J, and the shutdown energy value

to 0 J. For Analyzing the energy consumption of

nodes, we collected the Residual Capacity statistics

produced by Omnet++:

Figure 13: Residual Capacity Omnidirectional Config..

Figure 14: Residual Capacity rc1 Config..

Figure 15: Residual Capacity rc2 Config..

A New Switched Beam Smart Antenna Model for Extending Inet Omnet++ Framework

269

From Fig. 13 we can observe that the energy

consumption is uniformly distributed between

nodes; in Fig. 14, instead, we can note that the nodes

that are not in the main lobe region preserve its en-

ergy amount. In particular, all energy is conveyed

toward the host that is within the main lobe region.

Furthermore, in Fig. 15 the energy consumption is

preserved, except from host 0 that suffers from the

effect of the sidelobes. Because energy consumption

results clearly reflect values obtained in packet sta-

tistics, we can conclude that our designed module is

functional and could be used for allowing asymmet-

rical and directional communications as extension of

Omnet++ features. Finally, it is shown a summary

Table that compares the main features of the most

used network simulators discussed in sec. 3:

Table 2: Network simulators features comparison.

Network Simulator

Open

Source

Asymmetrical

Comms Sup-

port

SAS Sup-

port

NS-2 yes partial no

NS-3 yes no no

OPNET no partial no

OMNET++

(default Inet)

yes no no

OMNET++

(our Inet)

yes yes yes

Although Opnet and Ns-2 provide some patches

to support asymmetrical communications between

nodes, they do not provide any Smart Antenna Mod-

ule by default; therefore, with respect to Opnet, Om-

net++ is an open source Software that can be en-

hanced by developers. In this regard, our work

demonstrated that, through some appropriate modi-

fications, Omnet++ could be improved in terms of

features, becoming more useful for end users espe-

cially in beamforming and directional antenna con-

texts.

7 CONCLUSIONS

We proposed to extend Inet framework of Omnet++

simulator adapting original source logical structure

also to network systems that supports directional and

asymmetric communications. For this purpose, we

presented a Switched beam smart antenna approach

applied to a real network scenario where mobile

nodes are equipped with directional antennas. After

designing directional antenna module, through sev-

eral modifications accomplished on original mod-

ules, we demonstrated that, applying a very simple

algorithm for managing the power of nodes, de-

pendently on their positions, we can convey data

traffic flows only towards a specific direction; re-

sults showed that, using this approach, instead of the

classical omnidirectional mode, it can be achieved a

considerable energy saving also obtaining a larger

coverage range due to gain value in the main beam

region. Typically, this value is greater than 1 and

depends on geometrical structure of used array. An-

yway, when we drive the main lobe in a certain di-

rection, we have to consider the entity of sidelobes.

Our analysis, also investigated about this situation,

evaluating a case in which sidelobes power value is

not negligible with respect to main lobe power; in

this regard simulation results demonstrated that

some nodes which are not located in main lobe re-

gion could receive sent packets. This effect need to

be accounted in the protocol design at higher layers

such as Mac and Network Layers.

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