AN EFFICIENT DISTRIBUTED CHANNEL …AN EFFICIENT DISTRIBUTED CHANNEL ALLOCATION ALGORITHM FOR SPECTRUM SENSING TO AVOID INTERFERENCE IN COGNITIVE RADIO NETWORKS Mr.M.Kannan 1 Dr.B.Rosiline

AN EFFICIENT DISTRIBUTED CHANNEL ALLOCATION ALGORITHM FOR

SPECTRUM SENSING TO AVOID INTERFERENCE IN COGNITIVE RADIO

NETWORKS

Mr.M.Kannan1 Dr.B.Rosiline Jeetha

2

1Research Scholar, Dept of PG and Research Computer Science,

Dr.N.G.P.Arts and Science College(Autonomous),Coimbatore, Tamil Nadu, India

2Professor & Head, Dept of PG and Research Computer Science,

Dr.N.G.P.Arts and Science College(Autonomous),Coimbatore, Tamil Nadu, India

ABSTRACT

CR technology enables the reprocess of the available spectrum resources. The basic limiting

factor for spectrum reprocess is interference, which is caused by the environment (noise) or

by other radio transmissions. The scope of this work is to give an overview of the problem of

spectrum assignment in cognitive radio networks, presenting the state-of-the-art proposals

that have appeared in the literature, analyzing the criteria for selecting the most suitable

portion of the spectrum and showing the most common approaches and techniques used to

solve the spectrum assignment problem. Lastly, an analysis of the techniques and approaches

is presented, discussing also the open issues for future research in this area.

Keywords: Spectrum inference, Relay selection, Channel Allocation, Cognitive Radio

Network (CRN), Priority Queue Scheduling, Primary User (PU), Secondary User (SU)

Introduction

A Cognitive Radio Network(CRN) is a structure of wireless communication in which a node

has the intellect to determine which of the contact channels in a wireless spectrum are being

used and which are not. Based on this, it performs transmission or reception, thereby enabling

greater concurrent wireless communication and also reducing the delay caused, if nodes did

not have the intellect to determine if any channels were free and waited for channels that are

currently being used to become free. A CRN manages the spectrum dynamically. In CRN,

there are 2 types of users. Primary Users (PU) which are licensed users and Secondary Users

(SU) which are Unlicensed Users. Both users use the RF spectrum for transmission of data.

International Journal of Pure and Applied MathematicsVolume 119 No. 18 2018, 1639-1655ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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However, it‟s the primary users who have the priority over the secondary users. Only when

the primaries are not using a channel in the spectrum, a secondary is allotted the channel.

This allocation is done by the network.The network manages this type of allocation by having

centralized Base Stations for both primary and secondary users. The PUs and SUs

communicate through the base station. If a SU wants to make use of a channel in the

spectrum, it posts a request to its base station. The base station of the SUs will have a list of

requests from all the SUs under it. When a channel in the spectrum becomes available, the

Base Station of the PUs intimates the Base Station of the SUs about the availability. The SU

Base station then checks its list for the requests posted by its SUs for the channel. Based on

the priority, the request with the highest priority is selected. The corresponding SU is

intimated about the channel availability. The SU then uses the channel for packet

transmission.

Figure 1: Topology of a CRN co-located with primary networks.

2. COGNITIVE RADIO SYSTEM

A cognitive radio system allows the unlicensed users to dynamically and opportunistically

access the “under-utilized” licensed bands. It was first termed by Joseph Mitola in 1991. It is

a radio that includes a transmitter in which operating parameter such as frequency range,

modulation type or maximum output power can be altered by software and also known as

“dynamic spectrum allocation”. CR technology has the potential to exploit the inefficiently

utilized licensed bands without causing interference to incumbent users.

2.1 Principles of Cognitive Radio

Recent advances in wireless telecommunication technologies have sparked the ever growing

wireless applications and services. This condition resulted in a burden which takes form of

International Journal of Pure and Applied Mathematics Special Issue

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the spectrum scarcity. The electromagnetic radio spectrum is a natural resource whose usage

in transmitting and receiving is controlled by the government. It is concluded that the scarcity

of electromagnetic spectrum is more due to inadequate access techniques rather than

nonavailability. This has resulted in major re-thinking in the regulation of electromagnetic

spectrum usage by the government as well as the technology of spectrum access itself. Many

research works have been carried out to overcome this problem, and one of the initiatives is

the idea of Cognitive Radio (CR). The conditions of the under-utilized spectrum are:

a. Some frequency bands in the spectrum are largely un-occupied most of the time.

b. Some of the frequency bands are partially occupied.

c. The remaining frequency bands are heavily occupied. So we can say that there are

frequency bands which are assigned to primary users, but at a particular time and specific

geographical location, the bands are not used by the licensed users. This condition is referred

as the spectrum holes. The basic idea of CR is to improve the spectrum utilization by

enabling the secondary users (unlicensed) to utilize the spectrum holes which are unoccupied

by the primary users in a particular time and location. This is done in a way that the

secondary users are invisible to the licensed users. In this scenario the licensed users are the

mobile terminals and their associated base stations, which do not possess such intelligence.

On the other hand, the secondary users should possess the capability of sensing the spectrum

and use whatever available resources when they need them. At the same time, the secondary

users must give up the utilized spectrum whenever a licensed user begins transmission.

Haykin described the CR as an intelligent wireless communication system that is aware of its

surrounding environment (i.e., outside world), and uses the methodology of understanding-

by-building to learn from the environment and adapt its internal states in the incoming RF

(radio frequency) stimuli by corresponding changes in certain operating parameters in real-

time with two primary objective in mind:

• Highly reliable communications whenever and wherever needed.

• Efficient utilization of the radio spectrum. Modulation scheme, transmit power, channel

coding and carrier frequency are examples of the parameters that can be exploited in CR.

2.2 Cognitive Tasks

The basic fundamental tasks of the system are:


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(i) Spectrum estimation: is to gauge the radio spectrum scenario and perform radio scene

analysis.

(ii) Channel state estimation and predictive modeling: is an accurate and timely channel

state information (CSI) at the transmitter, and is important for accurate power control,

prediction of channel capacity and scheduling.

(iii) System reconfiguration: based on the radio spectrum scenario and the channel state

information, the system adapts the parameter.

2.3 Channel Allocation Schemes

In radio resource management for wireless and cellular network, channel allocation schemes

are required to allocate bandwidth and communication channels to base stations, access

points and terminal equipment. The objective is to achieve maximum system spectral

efficiency in bit/s/Hz/site by means of frequency reuse. There are two types of strategies that

are followed.

Fixed:

FCA, fixed channel allocation.

The fixed channel allocation is manually assigned by the network operator.

Dynamic:

DCA, dynamic channel allocation

DFS, dynamic frequency selection

Spread spectrum

2.4 Dynamic Spectrum Allocation (DSA)

Most networks are subject to time and regional variations (traffics may vary with time and

location) in the degree to which spectrum is utilized. The waste ofspectrum happens when

traffic in one place is low while in another place is high. Objective of Dynamic Spectrum

Allocation: Manage spectrum in a converged radio system and share it among all

participating radio networks over space and time, to increase overall the spectrum efficiency.

2.5 Spectrum Holes


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Spectrum Holes represent the potential opportunities for non-interfering (safe) use of

spectrum and can be considered as multidimensional regions within frequency, time, and

space. The region of space-time frequency in which a particular secondary use is possible is

called a „spectrum hole.‟

2.6 Functions of CR

The main functions of cognitive radio are:

Spectrum sensing: Detecting unused spectrum and sharing it, without harmful interference

to other users; an important requirement of the cognitive-radio network to sense empty

spectrum. Detecting primary users is the most efficient way to detect empty spectrum.

Spectrum-sensing techniques may be grouped into three categories:

Transmitter detection: Cognitive radios must have the capability to determine if a signal

from a primary transmitter is locally present in a certain spectrum.

Cooperative detection: Refers to spectrum-sensing methods where information from

multiple cognitive radio users is incorporated for primary user detection.

Interference-based detection

Power Control: Power control is used for both opportunistic spectrum access and spectrum

sharing CR systems for finding the cut-off level in SNR supporting the channel allocation and

imposing interference power constraints for the primary user's protection respectively. In a

joint power control and spectrum sensing is proposed for capacity maximization.

Spectrum management: Capturing the best available spectrum to meet user communication

requirements, while not creating undue interference to other (primary) users. Cognitive radios

should decide on the best spectrum band (of all bands available) to meet quality of service

requirements; therefore, spectrum-management functions are required for cognitive radios.

Spectrum management functions are classified as:

Spectrum analysis

Spectrum decision

2.7 Advantages of CR

CR can sense its environment and, without the intervention of the user, can adapt to

the user's communications needs.


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A CR can intelligently detect whether any portion of the spectrum is in use, and can

temporarily use it without interfering with the transmissions of other users.

Fully utilize multi-channel.

Cognitive abilities include determining its location, sensing spectrum use by

neighboring devices, changing frequency, adjusting output power or even altering

transmission parameters and characteristics.

CR has the ability to adapt to real-time spectrum conditions, offering regulators,

licenses and the general public flexible, efficient and comprehensive use of the

spectrum".

3.Related work

The channel assignment problem has been largely investigated in the literature related to

multi-radio wireless ad hoc and mesh networks [19-22]. For instance, the solutions proposed

in [20,21] are centralized approaches, and aim to limit interference while preserving

connectivity [21] or while considering nodes traffic [20]. More complete approaches study

the channel assignment problem in conjunction with the routing problem [19,22].

Although presenting interesting solutions, they disregard the challenges imposed by CR

networks. In fact, the challenges in performing channel assignment in CRNs arose from

different issues compared with ad hoc and meshed networks, even though some channel

assignment solutions can be found in the literature for these latter type of networks. In

particular, when multi-channels are considered in mesh or ad hoc networks, they are usually

well-known channels and are a priori defined. Instead, nodes in CRNs make use of the

underutilized portions (i.e., white holes) of the licensed frequency spectrum as new

communication opportunities. Such communication opportunities are, however, highly time-

variable and are usually possible only for short periods of time. In addition, no well-known or

a priori-defined set of channels can be considered. Finally, primary nodes have higher

priorities in the communication process, requiring a constant verification of their presence

apart from secondary nodes. All these factors add a lot of dynamism and, consequently, new

challenges to the channel assignment problem in CRNs.

The channel assignment problem for CRNs has been previously addressed by centralized [6]

and distributed [7,8] approaches. Li and Zekavat [7] present different methods to channel

assignment using the CR and cluster techniques. The proposed methods–five in total–


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mitigate the need of a central controller and reduce the overhead of the CRNs. This is

achieved by clustering the CR nodes and electing a cluster-head to drive the channel

assignment on each cluster forming a hierarchical structure.

One of these methods (the fourth one) is comparable to ZAP as it proposes a channel

assignment based on the interference level. Nevertheless, this method proposes a random

choice of the cluster-head, and the channel assignment is done using an ascendant order of

the interference level. Huang et al. [15] analyze throughput performance bounds in CRNs.

Their aim is to mainly maintain the protection of the PUs without any degradation of the

throughput performance of the CR nodes. The impact of the interference is not considered in

the throughput performance of both the PUs and the CR nodes.

Shiang and van der Schaar [8] investigate the management problem of multiuser resources in

CRNs for delaysensitive applications. They propose a distributed algorithm based on local

information through the adoption of a multiagent learning concept (i.e., adaptive fictitious

play) that utilizes the available interference information. In fact, the proposed channel

distribution is based on the learning of the behavior of PUs and should be repeated for each

changing in such behavior.

Hence, there exist a mandatory information exchange and a special cost to the learning phase.

Moreover, the performance of their proposed study is dependent on the low variability of

applications and network conditions. ZAP analyzes the interference models to multihop

wireless networks and proposes a distributed proposal for the channel assignment problem in

a CRNs.

4.System Model

A CRN is made up of two types of nodes, namely, Primary users and Secondary Users. Out

of these, it‟s the PUs who have more authority towards the access of radio spectrum. The SUs

have low spectrum access authority. This means the SU cannot access the channels of a radio

spectrum whenever they want and carry out data transmission. They will have to wait for a

channel to be made available by one of the PUs. This means that the SUs must continuously

keep track of the spectrum to see if any channels have become free. Once a channel is made

free, the SU must make sure that it is taking over that channel and performing transmission

without causing any troubles to other primaries that are using the spectrum. Also when the

SU senses that a PU is approaching to access a channel, it must make way for the PU by

releasing the channel that it has been utilizing for transmission


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5.Design of Joint Channel And Power Allocation Schemes

When multiple spectrum bands are available at the CR nodes then the system model in Fig. 1

is able to support three different types of diversity schemes, source to relay diversity, relay to

destination diversity and source to destination diversity along with the source to source to

relay diversity and relay to destination diversity scheme. The system throughput for the

different diversity schemes are discussed in this section. Let BSj and BR be the set that

contains all the available spectrum bands at source Sj and relay R respectively. If more than

one spectrum band is available at the CR node, then the total transmit power at the source and

relay are limited by both sum power constraint and per band power constraint. The sum

power constraint is expressed as

𝑃 𝑗 ≤ 𝑃𝑗𝑠𝑢𝑚

𝑖𝑠

𝑖∈𝐵s 𝑎𝑛𝑑 𝑃𝑖𝑅

𝑖∈𝐵𝑅 ≤ 𝑃𝑅𝑠𝑢𝑚 𝑗 = 1,2 ….(1)

where sum 𝑃𝑗𝑠𝑢𝑚 and sum 𝑃𝑅

𝑠𝑢𝑚 are the maximum sum power at the jth

source and the relay

respectively.

a) Source to relay (SR)

diversity scheme Consider a SR diversity model shown in Fig. 2. In this model, four spectrum

bands namely, BD1, BD2, BD3 and BD4 are available at the secondary users. It is assumed

that BD3 is not available at 1 S & 2 S nodes and BD1 is not available at the R node, and BD2

& BD4 are not available at D1 & D2 nodes. Now, an extra link can be introduced between

the sources and relay to enhance the existing SR link by SR diversity scheme. Since there is

no direct path between the source 2 S to D1 and 1 S to D2 , communication between them

can be established via relay by dual hop channel. The overall throughput of the system can be

maximized by allocating optimal power at the two sources, and relay with both sum power

constraint and per band power constraints.


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Figure 2 : Source to Relay Diversity Scheme

b) Relay to Destination

(RD) diversity scheme Consider a RD diversity model shown in Fig. 3. In this model, it is

assumed that BD3 and BD4 are not available at 1 S and 2 S nodes, BD1 is not available at the

R node and BD2 is not available at D1 and D2 nodes. Now, an extra link can be introduced

between relay and destination to improve the throughput by RD diversity scheme.

Figure 3: Relay to Destination Diversity Scheme

Combined diversity schemes

Consider the system model shown in Fig. 4. This system provides SR, RD and SD

diversities. In this model, it is assumed that BD3 is not available at 1 S & 2 S , BD1 is not


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available at R and BD2 is not available at D1 & D2 . Now an extra link can be introduced in

all the paths, namely SR, SD and RD to improve the throughput.

Figure 4. Combined diversity scheme

6.Spectrum Assignment In CRNs

A. Problem definition

To maximize the performance of a CRN, one major challenge is to reduce interference that is

caused to PUs, as well as interference among SUs. Interference results in additional noise at

the receiver and lowers the Signal to Interference plus Noise Ratio (SINR), which in turn

results in:

(i) Reduced transmission rate of the wireless interfaces,

(ii) Reduced utilization of the wireless resources,

(iii) Higher frame loss ratio,

(iv) Higher packet delay and

(v) Lower received throughput.

In the absence of interference, a link should provide its maximum capacity, which depends on

the available transmission rates and corresponding delivery ratios. Interference affects both

the sender and the receiver of a link; the sender transmits at a rate less than its maximum,

while there is a higher probability of unsuccessful packet reception at the receiver [98].


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The interference that the CR transmissions create plays a key role in the operation of not only

the CRNs, but also of the PNs that are operating in the same geographical area. A SU has the

ability to operate in any frequency band, because that user is equipped with a reconfigurable

device, capable of transcribing in any frequency (in practice the device will be capable of

transmitting at a specific frequency range and not at the whole spectrum). Since SUs are

unlicensed users, this capability may cause problems to licensed transmissions if the SU

selects a licensed band.

Figure 5: Overview of the Spectrum Assignment Issues

Channel Allocation Algorithm

Algorithm 1:

Step1 : Local assignment–State 2

Input: LinkList, ConflictGraph

Output: AssignedList L R links of LinkList yet to be assigned;

AssignedList R links already assigned from LinkList;

while L ≠ ∅ do

select a link according to the criteria and store it in link;


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remove link from L;

if no channel in C is available for link then

insert link in Interferent List;

continue;

end

foreach l € L do

if l is neighbor of link in ConflictGraph then

remove ch from C in the position that corresponds to l;

end

end

end

foreach link € InterferentList do c R channel of ChannelList available for link with the

lowest occurrence among the neighbors of link in ConflictGraph;

assign c to link in AssignedList;

end

ch R best channel in C available for link;

assign ch to link in Assigned List;

end

Algorithm : 2

Inputs: „V‟ Number of nodes, „C‟ total channels in the network, „E‟ number of edges,

„k‟ is the size of maximum independent set

Step 1:S= MIS (V, k) //Maximum Independent Set

Step 2:P ← Cluster formation according to the maximum independent set S

Step 3:for∀pv∈ P do

Step 4:for∀vu ∈ pv, ∀m ∈ Cvu, do

Step 5:calculate weight wvu(m)


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Step 6:calculate maximal matching between channels and adjacent links by the

auction algorithm.

Step 7:for∀vu ∈ pvdo

Step 8: update Xvu

Step 9:for∀uv ∈ E‟ do

Step 10: if uv and any link in Nuv have conflicts then

Step 11: remove the channel from the link with the lower priority.

Step 12: for∀ pv ∈ P do

Step 13:for∀vu ∈ pv do

Step 14: if Xuv> 0 then

Step 15: pv ← pv−{vu}

Step 16: E‟ ← E‟ − {vu}, Cvu ←Cvu− Xvu

Step 17:If ∀pv satisfies |C‟v| > 0, and Xvu = 0

for ∀vu ∈ pvthen

Step 18: go to step 2

7.Simulation And Results

Parameters taken for Simulation: The outer radius of the primary cell (R) is taken as 1000

meters. The Interference protection radius of primary cell (R_p) is taken as 600 meter sand

the Interference protection radius of secondary Transmitter (alpha) is taken as 300 meters.

The proposed Channel selection algorithm is implemented using the NS-2 simulator. The

simulation is run for a time period of 100 seconds.

The size of the data being transmitted is 256 B. The standard used at the MAC sub layer is

IEEE 802.11. The area size considered for the simulation is 500m X 500m. The X-axis

represents the number of nodes while the Y- axis represents the throughput. The simulation

was run for different number of nodes ranging from 20, 40, 60, 80 and 100. Out of the all

nodes 5 nodes we considered to be the source. The data recorded as a result of this simulation

is graphically presented next.


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Figure 6:Comparison of Throughput vs Number of Nodes

8.Conclusion

The proposed algorithm predicts the channel weight and it gives an approach for efficient

and localized channel assignment that can adjust well with cognitive radio network,

maximize the node connectivity, and based on minimum interference between CR nodes.

Calculating the conflict probability and then weight provides the secondary user regarding

the channel availability to determine whether to use the channel or not. This prediction

enhances the channel enhances the communication of nodes.

References


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Documents

AN EFFICIENT DISTRIBUTED CHANNEL …AN EFFICIENT DISTRIBUTED CHANNEL ALLOCATION ALGORITHM FOR SPECTRUM SENSING TO AVOID INTERFERENCE IN COGNITIVE RADIO NETWORKS Mr.M.Kannan 1 Dr.B.Rosiline