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CHAPTER 3

CENTRALIZED SCHEDULING IN

HETEROGENOUS NETWORK

3.1 INTRODUCTION

A basic cooperative network is composed of three nodes which use

amplify-and-forward as a cooperative strategy. This simple setup assumes that

each time, one node is the source, one node is relay, and one node is the

destination. Scheduling is defined as the distribution of the three roles to the

three nodes. In general relay environment, a node cannot always be the

source, the relay, or the destination. A static distribution of these three roles

results in unfairness, poor performance, and high battery consumption for the

relay nodes. Thus, a scheduling algorithm which dynamically decides the role

of each node is required. Multiple-Input Multiple-Output (MIMO) systems

are a natural extension of developments in antenna array communication.

While the advantages of multiple receive antennas, such as gain and spatial

diversity, have been known and exploited for some time, the use of transmit

diversity has been investigated more recently. The 'MIMO co-operative' model

uses the principle of the co-operation between the terminals, in order to

exploit spatial diversity in an Ad hoc network and to optimize thus the use of

the various nodes while drawing aside the MIMO system virtually to have a

good improvement of QoS. Because MIMO communication capacity is

dependent upon channel phenomenology, studying and parameterzing this

phenomenology is of significant interest.

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Azimi-Sadjadi et al (2004) proposed a new cooperative Multi-

Input-Multi-Output (MIMO) diversity scheme for CDMA wireless multihop

networks. None of the nodes have multiple antennas, allowing them to be

small and lightweight. Using cooperative clusters of network nodes to relay

information to its destination, the scheme both increases the effective rates

and extends the nodes service lives.

This thesis makes a number of contributions to this area of study.

First, while most experimental results have focused on indoor

phenomenology, the phenomenology investigated there focuses on outdoor

environments. Second, results for stationary are reported both in time and

frequency. Third, experimental phenomenological results are reported for

both 44 and relatively large 88 MIMO systems, including channel stationary,

both in time and frequency. Fourth, two metrics of channel variation are

introduced. One metric provides a measure of capacity loss assuming that

receiver beam formers are constructed using incorrect channel estimates,

which is useful to determine performance losses due to channel non stationary

(either in time or frequency).The other metric is sensitive to the shape of the

channel Eigen value distribution, which is appropriate for space-time coding

optimization, assuming a uniformed transmitter (UT) (that is transmitters

without channel state information). Finally, a simple channel parameterization

is provided which empirically matches channel Eigen value distributions well

and provides a simple approach to generate representative simulated channels

for space-time coding optimization.

Chase et al (1985) addressed a basic problem in designing a reliable

digital communication system is still the choice of the actual code rate. While

the popular rate-1/2 code rate is a reasonable, but not optimum, choice for

additive Gaussian noise channels, its selection is far from optimum for

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channels where a high percentage of the transmitted bits are destroyed by

interference.

MIMO systems provide a number of advantages over single

antenna communication. Sensitivity to fading is reduced by the spatial

diversity provided by multiple spatial paths. Under certain environmental

conditions, the power requirements associated with high spectral-efficiency

communication can be significantly reduced by avoiding the compressive

region of the information theoretic capacity bound. This is done by

distributing energy amongst multipath modes in the environment. Spectral

efficiency is defined as the total number of bits per second per Hz transmitted

from one array to the other. Because MIMO systems use antenna arrays,

interference can be mitigated naturally. The main idea is to find the outdoor

MIMO channel phenomenology near the PCS frequency allocation,

1.79 GHz, and it is discussed. The channel-probing signal has a bandwidth of

1.3 MHz. This bandwidth is sufficient to resolve some delays, inducing

frequency-selective fading in outdoor environments. Performance of MIMO

communication systems and optimal selection of space-time coding are

dependent upon the complexity of the channel.

3.2 RELAYS

Relays are network element that, down link (base to user) are used

to forwarding data received from base station to the user terminals vice versa.

Relays may have additional transmission power compared to terminals and

much lower in cost compared to base station because of their very limited

functionality. Deploying relays can clearly help to improve the performance

for users near the edge of the cell and has the potential to solve the coverage

problem, because of high data rates in micro cells. It is possible to have

simultaneous transmission by both base station and the relays, capacity gains

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may also be achieved. Relays can be viewed as a special case of Ad-hoc

networks, where any network node can communicate with any other network

node.

Sam Vakil (2008) proposed the technique instead of the link

abstraction used in traditional wireless net-working relay on the much broader

definition of a link used in the context of cooperative communication and also

improves the performance of relay transmission systems operating over the

wireless medium. The performance gain obtained via cooperation is limited

by the inherent increase in the amount of interference that the relay can cause

and also they have proposed evaluation method for trade-off between

exploiting the nodes as relays in a dense wireless network.

3.3 MIMO CHANNEL CAPACITY

MIMO systems provide a number of advantages over single

antenna communication. Sensitivity to fading is reduced by the spatial

diversity provided by multiple spatial paths. Based on the MIMO capacity

result a substantial increase in achievable data rate is possible in wireless

environment with rich multiple scattering. Actual capacity varies according to

different channel conditions it also help as to design space time modulation

schemes that can take full advantage of the MIMO link for various wireless

channels.

3.4 MULTIHOP GAINS IN CELLULAR SYSTEMS

The gain from multihop in a cellular setting arises from the

advantage of spatial reverse (ie) ability to have multiple simultaneous

transmissions within the cell using the same bandwidth resources. Consider a

single cell of a system with the base station in the center and relays deployed

in several rings around the base station. In general as the number of relays

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deployed N increases, the transmission range of each relay should shrink

(since relays are very close to each other) allowing more simultaneous

transmissions at the same rate. For large N, the number of simultaneous

transmission between relays at a given rate R by N/K for some K. Thus, the

number of simultaneous transmissions can increase linearly with the number

of relays deployed. In order to evaluate the performance improvement from

the deployment of relays, it is important to calculate the total time required to

transmit one unit of information from base station to N terminals connected

with the relay locations. For the one hop case the total time to transmit one

unit of information to each of the nodes is given by Equation (3.1)

Ni 1i i avgT 1/ R N / R (3.1)

Ti = Total time to transmit one unit of information to each of

the nodes

Ri - Rate from the base station to the node i

Ravg is defined to be the average rate from the base station to

the terminal nodes in the cell.

On the other hand for the P-hop case an upper bound on the total

time is given by Equation (3.2)

nNKpnN

RNT

bb )1()( (3.2)

Rb is the rate from the base station to the first ring of relays; gain

from multihop is represented as in Equation (3.3)

G C1N/P cp (3.3)

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C some constant as P

p- Number of hops

Rb - determined by the maximum signal to interference and noise

ratio (SINR) achievable between the base station and the receiver. The

maximum SINR is limited by the transmitter and receiver nonlinearities such

as in the power amplifier, analog to digital conversion in the receiver etc.

Thus an upper bound on the gain achievable in multihop cellular systems as

seen in Equation (3.4).

G (1/Ravg)/ (1/Rbmax) (3.4)

Rbmax - Maximum transmission rate from the base station.

BS transmits power for all BSs is increased until the resulting

change in Ravg becomes insignificant so as to an interference – limited results.

Rbmax is obtained from the maximum SINR value using the Shannon formula.

From the Rbmax obtained through simulations and the calculated Rbmax the

above upper bound is obtained. The upper bound is about 3.5 for maximum

SINR limit of 20 dB and 5 for a maximum SINR limit of 30 dB.

3.5 SCHEDULING PROBLEM

3.5.1 Relays and their Impact on the Scheduling Problem

All packets intended for the users first as live at the base station,

then are transmitted either directly to the user or to a relay that then forward

them to the user or to another relay and so on until these packets reach their

intended user. The principal effect of the introduction of relays in a cellular

wireless network on the scheduling problem is that the problem now becomes

one of the scheduling i.e., choosing the user whose packet will be next and

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routing i.e., choosing what sequence of relays these packets will go through

before reaching their destined user. It is assumed that the information at the

start of each time frame on the basis of which the scheduling assignment for

that frame is made, consists of knowledge of the sizes of the queues

corresponding to each user at all the transmitter in the cell.

3.5.2 Scheduling with Multiple Simultaneous Transmission

The link scheduling is used to minimize airtime usage in a new

class of wireless network called multi-transmit–receive wireless networks.

Here a node can simultaneously transmit Number of links used in the system

from I through L, and the users from I through K. An activation vector is a

binary vector c= (cI …cL) where a I (o) indicates that the corresponding link is

active. The relay cannot transmit & receive simultaneously, so not all

activation vectors are feasible and are represented in Equation (3.5).

= arg maxc€s (t).D(t c)] (3.5)

denotes the inner product of the two vectors

(t) links that will be active at the next time frame

3.5.3 Reduced Complexity Implementation

Consider a single cell with N relays and K users, the domain over

which the centralized scheduling is implemented. Denote the base station and

the N relays by indices 1, 2 ……N+1 as seen in Equation (3.6).

Link L = N+K+N(N+K-1) (3.6)

Fortunately it is possible to use the constraints on the network

operation and implement the algorithm more efficiently.

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3.6 SIMULATION RESULTS

To simulate the performance of the throughput-optimal scheme

proposed above for 20 stationery uniformly distributed user in each 120• sector of

a cell. For the “1” relay scenario, the single relay in each sector is located on

the angle bisector of the sector at a distance of half the cell radius from the

base station which is at the centre of the cell, carrier frequency fc=1900 MHz

and is represented in Equations (3.7) and (3.8).

Transmitter height- hT

Receiver height - hR

A = 46.3+33.9 log10 (fc)-13.82 log10 (fc)-[1.1 log10 (fc)-0.7]hR+

[1.56 log10(fc) -0.8] (3.7)

B = 44.9-6.55 log10 (hT) (3.8)

Figure 3.1 Mean Aggregate Loads in Cell Vs Mean Aggregate Throughput in Cell Effect of Number of Relays

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Figure 3.2 Mean Aggregate Loads in Cell Vs Mean Aggregate Throughput in Cell Effect of Relay Power

Figure 3.1 shows the performance of the throughput-optimal

scheme with one, two, three and four relays. The abscissa is the total average

arrival rate in the cell, while the ordinates the total throughput. Initially, all

the configurations show a linear relationship between the two axes, meaning

that the allocation scheme can keep up with increasing load on the system, but

after a certain value of load, the curve starts to become sub linear, which

means the system is overloaded.

Figure 3.2 shows the effect increasing relay power for the three

relays. It is seen that increasing relay power increases the throughput because

of better link rates between the relays to the users. This indicates that the relay

transmissions are not interference limited and the power gain is still a

significant fraction of total gain. This is contrast to a system with a large

number of relays and multiple hops where relays transmit with low power and

the performance gain is obtained primarily because of better reuse of channel.

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In this case, increasing relay power will not increase the data rates since the

system interference is limited.

3.6.1 Impact of Relay

The proposed technique is examined the problem of relay selection

for wireless communications in order to minimize the total transmission time

and also increases the throughput. This indicates that the relay transmissions

are not interference limited and the power gain is still a significant fraction of

total gain Figure 3.2. Performance of the throughput-optimal scheduling

policy for different relay transmits powers for 3 relays.

3.6.2 Constant Total Cell Power

Figure 3.3 depicts the performance gain that can be seen from the

introduction of relays, comparing the care with just the base at 40dBm vs

37dBm and 1, 2, 3, or 4 relays with equal power chosen so as to satisfy the

constraint that the total power in the cell is always 40dBm. It is seen that an

improvement as the number of relays increases, but the relative improvement

reduces as the number of relays increases. Further, the extent of the

improvement in throughput over the case with no of relays is less in the case

of constant total power than in the case where total power is held constant.

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Figure 3.3 Mean Aggregate Load in Cell Vs Mean Aggregate throughput in Cell Constant Total Power

3.7 OPTIMIZATION IN HETEROGENEOUS WIRELESS SENSOR

NETWORKS

Wireless sensor networks involve periodic transmission of data

collected by various sensors to the receiver. It consists of high node density

but, power constraint. Energy and lifetime of two types of wireless sensor

networks namely Homogeneous (identical sensors) and Heterogeneous

(multifarious sensors) are analyzed and the optimal values are obtained, from

which it has been proven that, reduction in energy consumption improves

network lifetime.

3.8 ONE-HOP MODEL

MEMS technology is enabling the development of in expensive,

autonomous wireless sensor nodes with volumes ranging from cubic mm to

several cubic cm. These tiny sensors can form rapidly deployed, massive

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distributed networks to allow unobtrusive, spatially dense, sensing and

communicating in one-hop model. Such deployment is used in the fields of

intrusion detection, image formation of target field and monitoring and also in

unmanned areas. But currently existing wireless sensor networks consist of

homogeneous types of sensors, which comprise identical sensors with equal

capacity in terms of sensing, computation, communication and power. Hence

they become application specific. In the proposed system, heterogeneous

sensor network is utilised, which consists of different compositions of

sensors, for example some sensors collect image data, some collect audio

signal, some have more processing capabilities, some have more power etc.

Thus various operations are performed simultaneously. Based on underlying

application or mission of the network, communication in a wireless sensor

network occurs in three different ways, Clock Driven, Query Driven and

Event Driven. In clock driven communication decisions are made at specific

time intervals and it is applicable to deterministic systems. It takes place

where sensors gather and send data at constant periodic intervals. Combined

data from all sensors generate “snapshots” of the field that is being sensed

overtime. These snapshots produce temporal and spatial information about the

field, for example, acoustic, seismic and meteorological. Query driven and

event driven communication are triggered by certain events or queries.

Collection of sensed data from the field is achieved by means of direct

transmission, Multi-hop routing and Clustering approach.

Direct transmission is alternatively known as “One-hop model”

which is simplest of all. In this, every node in the network transmits to the

sink. It is not only expensive in terms of energy consumption but also

infeasible, because nodes have limited transmission range. Their transmission

fails sometimes. Second approach is Multi-hop routing in which the data

packets are relayed to the collector from the end user after processing through

many neighbouring nodes. This type of routing protocol can be designed to

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achieve different goals, for example minimize the energy consumption.

However, in a network comprising of thousands of sensors, this model will

exhibit high data dissemination latency. Third approach is clustering sensors

form cluster dynamically with neighbouring sensors. One of the sensors in the

cluster is elected as a cluster head based on distance and is responsible or

relaying data from each sensor in the cluster to the remote receiver. However,

percentage of cluster head in a network is pre-assigned. Since cluster head

will inevitably consume more energy and die sooner than other sensors,

method of dynamically changing cluster heads is preferred along with the

allocation of higher energy to overlay sensors compared to normal sensors.

The overlay sensors potentially have more processing capability and

communication capability in addition to higher energy. This approach is

frequently preferred in long distance communication.

3.9 PROTOCOLS USE

The main purpose of MAC is to avoid energy wastage due to

collision, overhearing, control packet overhead, idle listening, over emitting

etc. The version IEEE 802.11 Mac used here, is composed of SMAC and

TDMA. The reasons behind adopting this protocol are, it is energy efficient,

scalable and the nodes can be taken to sleep mode once they are inactive

thereby avoiding energy wastage. AODV- Ad-hoc on demand Distance

Vector is the routing protocol used in dynamic wireless network where nodes

can enter and leave the network at will. In this protocol the source node sends

route request to the neighbouring nodes, if there exists a path from the node to

destination, the node acknowledges to the source node. If not, the node

forwards back the same request to the source. UDP and TCP-User Datagram

Protocol and Transmission Control Protocol are communication protocols

used. In UDP, bit rate is constant and packet size can be varied while

communicating. The protocol of TCP is, every communication is

acknowledged and thus, the communication becomes reliable.

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3.10 NETWORK MODEL AND IMPLEMENTATION

The field assumed here is square sensing field measuring L

(100*100) metres. In Figure 3.4 square represents normal and a circle

represents powerful sensors. The receiver is located at distance D from the

sensing field at (0,-D) and there is a total of n (100) normal sensors in the

field which are uniformly distributed.

Figure 3.4 Heterogeneous Networks

Number of overlay sensors is given by Rq which is also randomly

deployed among which only q overlay sensors are active at a time. These

overlay sensors will take turns being cluster head to overcome energy

depletion and thus the lifetime of network is not affected and the term

involved here is the period in which the sensed data is transferred from

normal to receiver through cluster heads. Powerful sensors have higher

energy and computing capabilities, and can communicate with normal sensors

using same network interface. Communication between powerful sensors may

thus be facilitated by building an overlay on top of the normal sensors. The

topology of the overlay is critical. The overlay must have a low diameter to

efficiently support upper layer application such as resource discovery. It must

also consider the energy consumption of the relaying normal sensors. Overlay

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Number of Clusters

Ener

gyC

onsu

mpt

ion

(mill

iJou

les)

sensor acting as a cluster head in broadcasting the signal and its depends upon

the signal strength and number of normal nodes are ready to combine with

overlay sensor to form the cluster. If the over lay sensor decides not to be the

cluster head, then it goes to the sleep mode. Round ends when the data from

all the sensors are relayed to the collector.

3.11 ANALYSIS AND NUMERICAL RESULTS

3.11.1 Energy Estimation

This energy analysis is done for both homogeneous and

heterogeneous networks and the plots obtained are shown in the figure. From

the graph in Figure 3.5, it is concluded that homogeneous network for any

number of clusters will consume higher energy compared to heterogeneous

network for the same number of clusters, nearly 3% lesser than homogeneous.

Figure 3.5 Comparisons between Homogeneous and Heterogeneous Networks

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Ener

gyC

onsu

mpt

ion

(mill

iJou

les)

3.11.2 Optimal Number of Clusters

In heterogeneous network a detailed calculation of the number of

normal sensors, overlay sensors and round is done. The total number of

sensors (both types) set is 100 in the sensing field apart from a receiver which

is located at a farther instance. The expected number Result from the

calculation is found as of active overlay sensor will be q and p=100-q (R=1)

is the number of normal sensors. The number of active overlay sensors is

varied from 10 to 60. Alternatively, the average number of active sensors

(normal plus overlay) remains fixed, while the average number of cluster

increases. A graph is plotted between number of clusters and the energy

consumed as shown in Figure 3.6. In order to maximize the lifetime of the

network, the energy consumed by the nodes has to be minimum and to be

scalable, the number of clusters must also be minimized. Satisfying both the

condition, the optimal number of clusters determined from the Figure 3.6 and

also it provides better efficiency and increasing level in lifetime of the

wireless sensor networks.

Number of Clusters

Figure3.6 Optimal Cluster in Heterogeneous Network

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3.12 SIMULATION PARAMETERS

The results obtained are simulated using network simulator NS2. It

seems to be a better choice since it is a flexible tool to investigate how various

protocols perform with different configurations and topologies. Optimising

sensor network is also achieved with the help of Ns2.

Configure separate channels for phenomena and data

Create phenomenon nodes

Create sensor nodes

Create non-sensor nodes

Attach sensor agents to sensor nodes

Table 3.1 Simulation parameter for scheduling

Simulation parameters value

Channel bandwidth 10&25 kbps

Number of nodes 120

Priority 20%

3.13 SUMMARY

The chapter has dealt with heterogeneous sensor network and it is designed and compared with is reduced by 3% consumption of power compared to homogeneous for the transmission of least 10 bytes of data. When amount of data transmitted increases, larger quantity of energy can be saved. The optimal number of clusters for heterogeneous network is also calculated. Future research can be carried out for query driven and event driven types of sensor networks. The possibilities of several collectors located at different places can also be considered. The proposed centralized

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scheduling is very helpful for top priority nodes for the usage of energy in efficient manner. In cases where delay and the resolution of the data are just as important, these performance measures should be considered jointly with energy efficiency.

This chapter also dealt with the capacity for Cellular Relay Networks. It is shown that the cell capacity (i.e., aggregate throughput) in CRN is almost M (the number of nearest relays to the central node) times higher than that in CCN. This demonstrates the capacity increase potentials of the CRN can meet the high data rate coverage demand of systems beyond 3G in a cost effective manner. It is also found that the capacity of CRN does not depend on the size of the cell or number of hops. Since the available band width is constant, the only parameter it depends on is the number of relays that directly communicate with the central node. However, if the number of relays is increased then the area that a relay covers decreases. As a result of this distance for the mobile user and relay communication decreases, resulting in reduced propagation losses which can be exploited by using adaptive modulation and coding.