Modified SMOR Using Sparsity Aware Distributed ...

Jour of Adv Research in Dynamical & Control Systems, Vol. 9, No. 6, 2017

Modified SMOR Using Sparsity Aware Distributed Spectrum Map for Enhanced Opportunistic Routing in Cognitive Radio Adhoc Networks Hesham Mohammed Ali Abdullah, Research Scholar, PG & Research Department of Computer Applications, Hindusthan College of Arts & Science, Coimbatore. E-mail:[email protected] Dr.A.V. Senthil Kumar, Director, PG & Research Department of Computer Applications, Hindusthan College of Arts & Science, Coimbatore. E-mail:[email protected]

Abstract--- Opportunistic routing in the Cognitive Radio Adhoc Networks (CRAHN) has been one of the most sought after research area. Opportunistic spectrum access without much interference is the major concept in the routing paradigm. However achieving such behaviour is not an easy task. It can be achieved only when the spectrum is allocated in distributive manner. The general concept is that when the accuracy of the spectrum mapping improves, there is progressive change in the accuracy of the routing. Hence in this paper, the Sparsity of the spectrum map is exploited in a distributive manner. The distributed spectrum map is achieved by using the Sparse Spectrum Gaussian Process Regression. This approach exploits the accuracy of opportunistic routing by improving the spectrum map. In this approach, the Modified SMOR is utilized but with the distributed spectrum map and by also considering the Bit Error Rate (BER) and path loss ratio along with the network throughput and transmission delay for routing analysis. The evaluation comparisons are made in terms of end-to-end delay, BER, throughput and path loss ratio. The results indicate that the proposed model of Sparsity aware distributed spectrum map improves the routing performance of the Modified SMOR in CRAHNs. Keywords--- Cognitive Radio Adhoc Networks, Opportunistic Routing, Opportunistic Spectrum Access, Distributed Spectrum Map, Sparsity.

I. Introduction Cognitive radio Ad hocNetwork (CRAHN) [1] is a type of remote correspondence in which a handset can astutely recognize correspondence channels which are being used and which are not, and in a flash move into unused channels while staying away from possessed ones. This upgrades the utilization of accessible radiofrequency spectrum while impedance limited to different clients. CR innovation is a worldview for remote correspondence in which transmission or gathering parameters of network or remote hub are changed to convey maintaining a strategic distance from impedance with authorized or unlicensed clients [2]. CRAHN is thought to be the fate of remote specialized gadgets which will display a versatile and shrewd conduct as far as various operational parameters, for example, transporter frequency determination, transmit control, regulation sort, and so on. There are various difficulties in the acknowledgment of cognitive radio networks from research, specialized, and plans of action point of view [3]. Managing every one of these difficulties is similarly vital, nonetheless, the exploration and specialized difficulties concerning continuous spectrum detecting [4], multi-channel spectrum sharing/ administration, growing new conventions and guidelines, and so on is the focus of the research group as these frame the fundamental building pieces to the acknowledgment of cognitive radio networks. Spectrum detecting has gotten the consideration of cognitive radio research exertion more than some other viewpoint principally as a result of the significance of the discovery of spectrum openings and evasion of impedance with the primary clients [5]. There are be that as it may, various difficulties in acknowledgment of constant spectrum detecting which incorporates restricted equipment abilities of cognitive radios, elements of signal spread in outside, hidden terminal problem, and so on. The theme of opportunistic spectrum access for cognitive radios has as of late pulled in generous research enthusiasm for wireless research group as another worldview for defeating the problem of spectrum congestion and underutilization because of static spectrum portion [6]. As pointed by the FCC (of US) in [7], a significant part of the spectrum reasonable for wireless communications has been authorized to primary clients. In numerous applications, sporadic channel access by authorized clients prompts underutilization of their assigned spectra. By

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enabling optional clients to opportunistically get to the data transfer capacity allotted to however underutilize by primary clients, cognitive radio networks have been championed as an empowering new innovation to beat the present spectrum shortage caused from the conventional static spectrum distribution. The fundamental test of opportunistic spectrum get to (OSA) [8] is the need to guarantee that cognitive clients keep away from inconvenient interruption to authorized client communications when they are dynamic. Enhancing the precision of designating the spectrum by means of spectrum guide can improve the opportunistic routing process. Previously, the Spectrum-Map-empowered Opportunistic Routing (SMOR) [9] has been modified by diffusion approximation based Markov chain modelling and sparse approximation based stochastic geometry. This modification was done in part to improve the opportunistic routing performance. In this paper, it is stated that the accurate mapping of spectrum can further improve the routing and hence the Sparsity aware distributed spectrum map is introduced into the Modified SMOR. The Sparsity is exploited by employing Sparse Spectrum Gaussian Process Regression. This approach examines the transmission delay, network throughput and BER for the efficient network analysis. Thus the proposed model named as Sparsity aware Distributed Spectrum map for Modified SMOR can improve the performance of CRAHN. The remainder of the article is organized as follows: section 2 describes some the recent research works related to the CRAHN routing. Section 3 describes the proposed system model; section 4 describes the distributed spectrum map while the section 5 describes the architecture of Sparsity aware distributed spectrum map. Section 6 illustrates the exploitation of Sparsity using the proposed Gaussian process model and the section 7 presents the evaluation results. Section 8 makes a conclusion of the proposed research model.

II. Related Works Routing protocols for CR networks ought to use the adaptability of CRs and address the basic difficulties that don't exist in the customary wireless networks. Various routing conventions for CR networks are accessible in the writing. The routing conventions for the CR networks can be classified by the number and the use of radios. A few conventions accept a devoted radio for control and another radio for information. These conventions depend on the idea of the presence of a common control channel (CCC) and the control radio is utilized for trading the control message just [10]−[14]. Be that as it may, the extra control radio in every CR client is probably going to be hurtful for vitality compelled CR networks. The plans utilizing a solitary radio for both information and control message trade are accessible in [15] and [16], yet the key issue with those conventions is control message must be communicated to every single accessible channel, which is tedious and increment the network overhead. Moreover, routing plan with various radios for CR networks comprising of opportunistic connections is accessible in [17]. Because of to a great degree dynamic nature of CR connections, customary routing is not achievable to keep up endto-end routing table for CRAHNs. A nearby on-request routing is displayed for the sensible at sensible routing deferral to course bundles. Despite the fact that, throughput can be expanded utilizing various radios yet it is not appropriate for vitality limitation CR network. Routing in CRAHN can be figured as a worldwide improvement issue with the channel-connect portion for information streams in the network [18]. Xin et al. [19] propose a layered chart to delineate the topology of CRN in a preview and apportion different connections over orthogonal channels to upgrade the movement throughput by building up a close ideal topology. Dish et al. [20] proposed a joint booking and routing plan as indicated by the long haul measurements of the connection transmission quality for SUs. Gao et al. [21] build up a stream routing plan which mitigates the all-inclusive asset for multicast sessions in multi-hop CRN. These chips away at cognitive routing foreordain a conclusion to-end transfer way in CRN in view of the worldwide network data. Be that as it may, the channel states of optional connections are exceptionally reliant on PUs' exercises in CRN. SUs generally need to track the channel status by periodic sensing [22] or field estimations [23]. At the point when the channel status changes source hubs need to re-ascertain a way. Khalif et al. [24] demonstrate that the included calculation and communication overhead for re-building routing tables for all streams is nontrivial, particularly when the channel status changes as often as possible. Contrasted and unified planning, distributed opportunistic routing is more appropriate for a dynamic CRN since SUs can choose the following jump hand-off to adjust to the varieties of nearby channel/interface conditions [25], [26]. Rather than utilizing a settled transfer way, a source hub communicates its information to neighbouring hubs, and chooses a hand-off in light of the got reactions under current connection conditions. Liu et al. [27] propose to apply an opportunistic routing calculation in CRN where the sending choice is made under the privately distinguished spectrum get to circumstances. Up until now, most opportunistic routing conventions have been contemplated in a solitary channel situation. In a multi-channel framework, the channel determination and transfer interface arrangement may present

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additional deferral, which corrupts the execution of the network. Step by step instructions to expand opportunistic routing in a multi-channel CRN is as yet an open research issue. It is likewise perceived that with accessible confinement administrations, geographic routing can accomplish low multifaceted nature and high adaptability under unique connection conditions in different wireless networks, for example, wireless work networks [28], impromptu networks [29] and vehicle communication networks [30]. With geographic routing, a hub chooses a transfer hub that is nearer to the goal for accomplishing separation progresses in each jump. Chowdhury and Felice [31] acquaint geographic routing into CRAHN with figure a way with the insignificant latency. Be that as it may, their work still concentrates on building routing tables and in this manner is not appropriate for dynamic CRN. Considering a kind elements of CRAHN, it is fundamental to outline a distributed opportunistic routing calculation by firmly coupling with physical layer spectrum detecting and MAC layer spectrum sharing to adjust to the network elements in CRAHN. This proposed research work aims at improving accuracy of spectrum map which in turn can improve opportunistic routing.

III. System Model Consider a CRAHN network comprising of M PUs and N CRAHN clients. PUs is permit holders for particular spectrum groups, and can possess their doled out spectrum at whatever time and for any length. Consider the spectrum comprising of C non-covering channels, which are authorized to PUs. The data transfer capacity of channel (c = 0, 1, 2, … , C−1) is indicated by Bc. Figure 1 shows the system model of the proposed routing model. Data Packets

Feedback

PU-TX

PU-RX

SU-Rx SU-Rx

SU-TX SU-TX Figure 1: System Model Considering the dynamic spectrum nature of the CRAHN networks, a CCC is utilized by all CRAHN clients for spectrum get to, which is constantly accessible. This CCC might be claimed by the CRAHN specialist co-op. Each of C channels is displayed as an ON-OFF source rotating between ON (active) state and OFF (idle) state determining whether the PU signs are utilizing a channel or not, separately. The CRAHN clients can use the OFF time to transmit their own movement. It is expected that all CRAHN clients are furnished with a solitary half-duplex CRAHN device. For getting to a channel, a CRAHN client must detect channels to start with, and can get to the channels just if any of these C channels is not being utilized by PUs. It is additionally accepted that each CRAHN

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client has enough ability of precisely detecting the nearness of PU on any channel and monitoring a rundown of channels accessible for transmission. An entire CRAHN topology includes a CR source (CRS), a CR destination (CRD), a couple of CRs as the cooperative relay nodes(CRRs) that can forward the packet(s) from CRS to CRD. As a result of the communicate nature for every transmission by methods for a comparative medium in wireless communication, CR-Tx may catch the transmission from a PU-Rx's region, while PU devices are in RTS-CTS or some other control flagging period. CR-Tx at that point predicts the separation n to the PU-Rx from the channel show for PU-Rx to CR-Tx and sends the separation into the other hand channel demonstrate (i.e., from CR-Tx to PU-Rx) to get its check to PU-Rx.

IV. Distributed Spectrum Map (DSM) The main purpose of DSM is to encourage radio spectrum usage and administration in a cognitive radio specially appointed network by building up a specific level of spectrum mindfulness in the network in a distributed way. DSM is a distributed database-empowered spectrum delineate creates on the possibility of spectrum mapping introduced in [32] for TV whitespace cognitive radio networks and joins the thought of spectrum suggestions exhibited in [33] [34]. DSM amplifies the past work by indicating the engineering for a distributed spectrum outline a cognitive motor, programming characterized radio stage, data dispersals and combination part to combine distributed spectrum detecting, and a database with little impression for putting away continuous and memorable spectrum data. The DSM is basically a learning base executed by means of a distributed database that gives continuous and in addition noteworthy data about the frequency channels a cognitive radio can access. Three levels of spectrum data are considered in the engineering of DSM concerning the diverse planning necessities of cognitive radio capacities. The primary level of spectrum data is gotten from constant spectrum detecting performed by individual cognitive radio hubs. Continuous spectrum data is required for cognitive radio capacities which have stringent planning imperatives, for example, medium get to conventions which perform spectrum imparting to other auxiliary clients and furthermore need to respond rapidly to the presence of a primary client in a channel utilized by cognitive radio at a specific point in time. The second level of spectrum data bolsters non-constant spectrum data prerequisites, for example, channel suggestions [33] or spectrum handover activated by application layer nature of administration parameters. The data from first level might be moved to the second level after some deliberation after some time. The third level of spectrum data identifies with memorable spectrum data and can be utilized while starting another application and additionally spectrum handovers relying on an application's necessities. The notable data is gathered after some time from the physical spectrum detecting and from social event more elevated amount spectrum data from different companions in the network in this way supporting distributed spectrum detecting for non-real time necessities. Another wellspring of more elevated amount spectrum data can be extraordinary portable scout hubs having particular equipment detecting capacity which could meander in the network, sense spectrum utilizing specific equipment, and communicate more elevated amount data to optional hubs in the network. The noteworthy spectrum data can dole out a specific estimation of trust to individual channels contingent upon how much primary client action is seen in that specific channel e.g. a channel with higher primary client movement can be allotted lower trust esteem and the other way around. Such data is vital for a cognitive radio for accomplishing a coveted level of nature of administration and impedance evasion with primary clients. In a distributed impromptu network, the hubs might be heterogeneous and have diverse detecting and transmission abilities. The individual ability of a cognitive radio as far as detecting and transmission consequently decides its individual asset recognizable proof limit and convenience in building up a general network bolster engineering through sharing of data with its less skilled associates. Considering the troubles of real time spectrum detecting, it just bodes well to have an information base about the channels and trait a specific level of trust to each channel for future access.

V. Sparsity Aware Distributed Spectrum Map Architecture The main components of the proposed DSM and the overall node architecture are shown in Figure 2. The parts of DSM are highlighted by bounding them in the dashed lines. Other fundamental segments of the present execution structure incorporate a Cognitive Engine, Software Defined Radio, and USRP2 based radio front-end. The strong lines speak to the between associations among the segments for information and control. Cognitive motor is the focal learning and basic leadership part and has tight reconciliation with DSM. The primary reason for the cognitive motor is to encourage cross-layer engineering for cognitive radio. It acknowledges contributions from applications regarding nature of administration necessities and from protocol stack as far as particular protocol prerequisites

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working at various layers. The cognitive motor uses the data of DSM for settling on spectrum choices relating to the distinctive necessities. The principle controlling and interfacing substance inside the DSM design is the Spectrum Map Manager (SMM). The cognitive motor submits solicitations to SMM specifically through its own interface to SMM and gets essential spectrum data accordingly, which might be put away in either notable or real time database relying on the way of the demand from cognitive motor. For instance, if the cognitive motor requires data for start of another application with particular nature of administration prerequisites, spectrum data from the noteworthy database are given containing the aggregated level of trust allotted to the utilized channels. Then again, if cognitive motor needs to serve the necessities of a protocol with stringent planning prerequisites, for example, if there should be an occurrence of spectrum handover caused by primary client appearance, data from constant database is given. The explanation behind this separation is that despite the fact that the noteworthy database may relegate a decent trust an incentive to a specific channel, just the present status of that channel matters progressively spectrum decisions. CRAHN Applications

Sparsity information

Hdb Spectrum Map Manager

Cognitive Engine

Rdb

Spectrum Map Manager

USRP2

Sharing & Acquisition

IRIS Platform

Figure 2: Sparsity Aware Distributed Spectrum Map Architecture The SMM interface for cognitive radio applications is accommodated guide access to the conduct and elements of DSM. A GUI application can be utilized to control the conduct and diverse parameters of DSM which may incorporate undertakings, for example, restricting the spectrum mapping to certain scope of frequency channels or changing the collection and combination calculation utilized as a part of the combination segment. In principle, a cognitive radio can get to any frequency channel it finds empty by a primary client. Practically speaking nonetheless, the quantity of channels utilized by a cognitive radio will be constrained to certain range. In the first place, this impediment can be forced by the equipment capacities of the handset utilized by the cognitive radio which might be fit for transmission in a specific frequency go as it were. The second restriction can originate from network arrangement which figures out which frequency groups are took into account access by auxiliary clients at a specific area or point in time. Accordingly, the spectrum mapping capacities will likewise be restricted to a specific frequency extend since expecting generally, the quantity of conceivable channels could be overpowering making spectrum sharing and administration amazingly troublesome. Restricting the continuous spectrum mapping to certain frequency range may likewise be an imperative forced for on a cognitive radio for versatile detecting so as to farthest point the time it accepts to distinguish spectrum opportunities.

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The SMM segment is likewise in charge of controlling the conduct of sharing and securing segment of DSM which is in charge of sharing its more elevated amount of spectrum data with different companions and assemble their spectrum data and pass it on to combination part for deliberation. Rdb and Hdb in Figure 2 speak to the constant and authentic databases utilized for spectrum data stockpiling. The sharing of spectrum data with different companions can take after a coordinated succession or be done on demand of other optional clients relying on the prerequisites. The spectrum sharing and securing part is interfaced straightforwardly with IRIS [35] programming characterizes radio (SDR) stage which thus is associated with USRP2 radio frontend. The IRIS SDR stage bolsters real time reconfiguration of the transmission parameters and in this manner underpins spectrum handover to a channel gotten from the spectrum map.

VI. Exploiting Sparsity Using Sparse Spectrum Gaussian Process Regression A Sparse spectrum Gaussian process (GP) estimate perspective of the proposed model is used, which demonstrates how it can be comprehended as a computationally proficient guess to any GP with stationary covariance work. A non-specific GP with stationary covariance capacity is taken and Sparsity is applied on its powerspectral density to get a scanty GP that approximates the full GP. The power spectral density (or power spectrum)𝑆𝑆(𝑠𝑠) of a stationary random process communicates how the power is distributed over the frequency area. For a stationary GP, the power is equivalent to the earlier fluctuation 𝑘𝑘(𝑥𝑥, 𝑥𝑥) = 𝑘𝑘(0) = 𝜎𝜎02 . The frequency vector s has an indistinguishable length D from the info vector x. The d-th component of s can be deciphered as the frequency related to the d-th input measurement. The Wiener-Khintchine hypothesis expresses that the power spectrum and the autocorrelation of the irregular procedure constitute a Fourier sets. For our situation, given that f (•) is drawn from a stationary Gaussian process, the autocorrelation capacity is equivalent to the stationary covariance function, and Τ

Τ

𝑆𝑆(𝑠𝑠) = ∫ℝ𝐷𝐷 𝑒𝑒 −2𝜋𝜋𝜋𝜋𝜋𝜋 𝜏𝜏 𝑘𝑘(𝜏𝜏)𝑑𝑑𝑑𝑑 (1) 𝑘𝑘(𝜏𝜏) = ∫ℝ𝐷𝐷 𝑒𝑒 2𝜋𝜋𝜋𝜋𝜋𝜋 𝜏𝜏 𝑆𝑆(𝑠𝑠)𝑑𝑑𝑑𝑑, There are two identical portrayals for a stationary Gaussian process: the customary one as far as the covariance function in the (input) space area, and a maybe less common one as the power spectrum in the frequency area. Bochner's hypothesis expresses that any stationary covariance function k(t) can be spoken to as the Fourier change of a positive limited measure. This implies the power spectrum is a positive limited measure, and specifically that it is relative to a likelihood measure 𝑆𝑆(𝑠𝑠) ∝ 𝑝𝑝𝑆𝑆 (𝑠𝑠). The proportionality consistent can be specifically gotten by assessing the covariance function at t = 0. The relation is acquired as:

(2) 𝑆𝑆(𝑠𝑠) = 𝑘𝑘(0)𝑝𝑝𝑆𝑆 (𝑠𝑠) = 𝜎𝜎02 𝑝𝑝𝑆𝑆 (𝑠𝑠) The fact is that the S(s) is proportional to a multivariate probability density in s to rewrite thecovariance function as an expectation 𝑘𝑘�𝑥𝑥𝑖𝑖 , 𝑥𝑥𝑗𝑗 � = 𝑘𝑘(𝜏𝜏) = ∫ℝ𝐷𝐷 𝑒𝑒 2𝜋𝜋𝜋𝜋𝜋𝜋

Τ �𝑥𝑥

𝑖𝑖 −𝑥𝑥 𝑗𝑗 �

𝑆𝑆(𝑠𝑠)𝑑𝑑𝑑𝑑 = 𝜎𝜎02 ∫ℝ𝐷𝐷 𝑒𝑒 2𝜋𝜋𝜋𝜋𝜋𝜋

Τ 𝑥𝑥

�𝑒𝑒 2𝜋𝜋𝜋𝜋𝜋𝜋

Τ 𝑥𝑥

∗

� 𝑆𝑆(𝑠𝑠)𝑑𝑑𝑑𝑑 = 𝜎𝜎02 𝔼𝔼𝑝𝑝𝑝𝑝 [𝑒𝑒 2𝜋𝜋𝜋𝜋𝜋𝜋

Τ 𝑥𝑥

�𝑒𝑒 2𝜋𝜋𝜋𝜋𝜋𝜋

Τ 𝑥𝑥

∗

�]

(3) where𝔼𝔼𝑝𝑝𝑝𝑝 denotes expectation with respect to𝑝𝑝𝑆𝑆 (𝑠𝑠) and superscript asterisk denotes complex conjugation.This last expression is a correct extension of the covariance function as the desire of a result of complex exponentials concerning a specific dissemination over their frequencies. This vital can be approximated by straightforward Monte Carlo by taking a normal of a couple tests comparing to a limited arrangement of frequencies, called as phantom focuses. Since the power spectrum is symmetric around zero, a legitimate Monte Carlo methodology is to test frequencies dependably as a pair{𝑠𝑠𝑟𝑟 , −𝑠𝑠𝑟𝑟 }. This has the upside of protecting the property of the correct extension. This merging outcome can likewise be expressed as takes after: A stationary GP can be viewed as a neural network with interminably many shrouded units and trigonometric actuations if autonomous priors taking after Gaussian and𝑝𝑝𝑆𝑆 (𝑠𝑠) appropriations are put on the output and input weights, respectively. 𝑖𝑖

𝑗𝑗

𝑖𝑖

𝑗𝑗

The primary objective of sparse approximations is to lessen the computational weight while holding however much prescient exactness as could reasonably be expected. Inspecting from the ghastly thickness constitutes a method for building a sparse guess. Notwithstanding, it is suspected that substantially sparser models can be acquired if the unearthly frequencies are found out by upgrading the minor probability. In light of this thought, the Sparsity is abused.

The proposed calculation utilizes conjugate inclinations to improve the minor probability as for the unearthly focuses {𝑠𝑠𝑟𝑟 } and the hyper parameters 𝜎𝜎02 , 𝜎𝜎𝑛𝑛2 , and {𝑙𝑙1 , 𝑙𝑙2 , … . , 𝑙𝑙𝐷𝐷 }. Improving concerning the length scales notwithstanding the spectral focuses is successfully an overparameterization, yet this repetition demonstrates supportive in staying away from undesired neighbourhood minima.

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As is regular with this sort of enhancement, the issue is non-raised and one can't hope to locate the worldwide ideal. The objective of the streamlining is to locate a sensible neighbourhood ideal. Taking in the ghostly frequencies by advancement leaves from the first inspiration of approximating a full GP. The streamlining stage represents a danger of over fitting. Be that as it may, the extra adaptability can possibly enhance execution since it permits taking in a covariance function reasonable to the current issue.Based on this Sparsity aware distributed spectrum map, the Modified SMOR-1 and Modified SMOR-2 are designed.This model of SMOR can enhance the accuracy of opportunistic routing with enhanced network performance and less delay.

VII. Performance Evaluation The simulation of the Sparsity aware distributed spectrum mapenabled Modified SMOR-1 (SDS-M-SMOR-1) and Modified SMOR-2 (SDS-M-SMOR-2)are done using MATLAB tool. The performance of the modified SMOR algorithms is compared with that of the existing SMOR algorithms in terms of end-to-end delay (EED), BER, Throughput and path loss ratio. 0.2 SMOR1 Modified SMOR-1 SDS-M-SMOR-1

0.18 0.16

EED (msec)

0.14 0.12 0.1 0.08 0.06 0.04 0.02

1

1.5

2

2.5 3 3.5 lambda (packets/sec)

4

4.5

5

Figure 3: End to End Delay vs. Lambda for Regular CRAHN 0.1 SMOR1 Modified SMOR-1 SDS-M-SMOR-1

0.09 0.08

EED (msec)

0.07 0.06 0.05 0.04 0.03 0.02

0

5

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20 SNR(dB)

25

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Figure 4: End to End Delay vs. SNR for Regular CRAHN The end-to-end packet delay is defined as the latencyincurred by a data packet between the generation time at thesource and the arrival time at the destination.Figure 3 shows the EED comparison against the lambda packets while figure 4 shows EED against SNR for the SMOR-1, Modified SMOR-1 and SDS-M-SMOR-1 for regular

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CRAHN. The effect of lambda and SNR on the average end-to-end packetdelay is shown in this figure. SDS-MSMOR shows lowerdelay in all level of the offered load because of the networkrecourses is static in this scenario. M-SMOR and SMOR showscomparatively higher delay because of the limited spectrumrecourses availability.It is seen that the SDS-M-SMOR-1 has less delay than the Modified SMOR-1 and SMOR-1 due to the concepts of Sparsity in the spectrum map analysis. 0.2 SMOR2 Modified SMOR-2 SDS-M-SMOR-2

0.18 0.16

EED (msec)

0.14 0.12 0.1 0.08 0.06 0.04 0.02

1

1.5

2

2.5 3 3.5 lambda (packets/sec)

4

4.5

5

Figure 5: End to End Delay vs. Lambda for Large Scale CRAHN 0.1 SMOR2 Modified SMOR-2 SDS-M-SMOR-2

0.09

EED (msec)

0.08

0.07

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0

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20 SNR(dB)

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Figure 6: End to End Delay vs. SNR for Large Scale CRAHN Figure 5 shows the EED comparison against the lambda packets while figure 6 shows EED against SNR for the SMOR-2, Modified SMOR-2 and SDS-M-SMOR-2 for large scale CRAHN. It is seen that the SDS-M-SMOR-2 has less delay than the Modified SMOR-2 and SMOR-2 in both the cases. It is due to the usage of the sparse spectrum Gaussian process regression of the mathematical analysis.

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0.08 SMOR1 Modified SMOR-1 SDS-M-SMOR-1

0.07

0.06

BER

0.05

0.04

0.03

0.02

0.01

1

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2.5 3 3.5 lambda (packets/sec)

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Figure 7: BER vs. Lambda for Regular CRAHN 0.45 SMOR1 Modified SMOR-1 SDS-M-SMOR-1

0.4 0.35

BER

0.3 0.25 0.2 0.15 0.1 0.05 0

0

5

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20 SNR(dB)

25

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40

Figure 8: BER vs. SNR for Regular CRAHN Figure 7 shows the BER comparison against the lambda packets while figure 8 shows BER against SNR for the SMOR-1, Modified SMOR-1and SDS-M-SMOR-1 for regular CRAHN. It is seen that the SDS-M-SMOR-1 has less BER than the Modified SMOR-1 and SMOR-1. In SMOR and M-SMOR, increasing the length of training sequence increases bit error rate as signal to noise ratio value is very less. However, on increasing the signal to noise ratio the bit error rate reduces which is obvious due to the impact created by SNR value which in turn analyses the performance of a cognitive radio system.This proves the Sparsity exploited in the proposed model reduces the performance degradation through improved accuracy of opportunistic routing.

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0.08 SMOR2 Modified SMOR-2 SDS-M-SMOR-2

0.07

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BER

0.05

0.04

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0.01

1

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3.5 3 2.5 lambda (packets/sec)

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Figure 9: BER vs. Lambda for Large Scale CRAHN 0.4 SMOR2 Modified SMOR-2 SDS-M-SMOR-2

0.35 0.3

BER

0.25 0.2 0.15 0.1 0.05 0

0

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25

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Figure 10: BER vs. SNR for Large Scale CRAHN Figure 9 shows the BER comparison against the lambda packets while figure 10 shows BER against SNR of the SMOR-2, Modified SMOR-2and SDS-M-SMOR-2 for large scale CRAHN. It is seen that the SDS-M-SMOR-2 has less BER than the Modified SMOR-2 and SMOR-2 in both the cases. This can be associated with the sparse spectrum Gaussian process regression based Sparsity exploitation. 1

Throughput (Packets/sec)

0.98 SMOR1 Modified SMOR-1 SDS-M-SMOR-1

0.96

0.94

0.92

0.9

0.88

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2.5 3 3.5 lambda (packets/sec)

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Figure 11: Throughput for Regular CRAHN

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1

Throughput (Packets/sec)

0.98

0.96 SMOR2 Modified SMOR-2 SDS-M-SMOR-2

0.94

0.92

0.9

0.88

1

1.5

2

2.5 3 3.5 lambda (packets/sec)

4

4.5

5

Figure 12: Throughput for Large Scale CRAHN Figure 11 shows the throughput comparison for the SMOR-1, Modified SMOR-1 and SDS-M-SMOR-1 while figure 12 shows throughput comparison for the SMOR-2, Modified SMOR-2and SDS-M-SMOR-2 for regular and large scale CRAHN respectively. In the figures, measured the aggregate throughput (the sumof individual routes throughput), which is defined as the datatraffic received by the destinations in Mbps by varying theoffered load. When the offered load increases, aggregate throughput increases up to messagegeneration rate and then slightly decreases. It is seen that the SDS-M-SMOR-1 and SDS-M-SMOR-2 has higher throughput values. One point is noted here that even though we have multiple channels with 2 Mbpsdata rate each but the obtained aggregate throughput is closedto 2 Mbps. This is because of the multi-hop routing nature.Another reason is that for the PUs activities, we are unable toutilize the whole resources of the multichannel we have.In any cases, if a link supports lower data rate, then theaggregate throughput of all routes passing through that link isdecreased. 0.9 SMOR1 Modified SMOR-1 SDS-M-SMOR-1

Path Loss Ratio

0.85

0.8

0.75

0.7

0.65

1

1.5

2

2.5 3 3.5 lambda (packets/sec)

4

4.5

5

Figure 13: Path Loss Ratio for Regular CRAHN

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0.8 0.78 0.76

Path Loss Ratio

0.74 0.72

SMOR2 Modified SMOR-2 SDS-M-SMOR-2

0.7 0.68 0.66 0.64 0.62

1

1.5

2

2.5 3 3.5 lambda (packets/sec)

4

4.5

5

Figure 14: Path Loss Ratio for Large Scale CRAHN Figure 13 & 14 shows the Path loss ratio for regular and large scale CRAHN respectively. As the lambda increases, the packet loss ratio increases. It is obvious that the packet loss ratio in buffer is always zero. While under the packet discarding policy, an increase in lambdacauses a higher waiting probability, which leads to more discarded packets. From this evaluation it is proved that the proposed model of SDS-M-SMOR is significantly efficient than the existing models as the Sparsity analysis improves its performance.

VIII. Conclusion This paper suggested the use of Sparsity exploitation for the improvement of routing by Modified SMOR in CRAHN. The analysis of the routing performance has been improved by considering the BER and path loss ratio. The sparse spectrum Gaussian process regression used for exploiting the Sparsity function improves the accuracy of mapping distributed spectrum. The proposed SDS-M-SMOR models has less values of delay, BER, path loss ratio while has higher values of throughput for both regular and large scale CRAHN.Though distributed spectrum mapping is utilized in this work, it is of smaller category. In the future, more broad distributions will be exploited along with considering different policies of the spectrum sensing in CRAHN.

References [1] [2] [3] [4] [5] [6] [7]

[8] [9] [10] [11] [12]

Haykin, S. Cognitive radio: brain-empowered wireless communications. IEEE journal on selected areas in communications 23 (2) (2005) 201-220. Akyildiz, I.F., Lee, W.Y. and Chowdhury, K.R. CRAHNs: Cognitive radio ad hoc networks. AD hoc networks 7 (5) (2009) 810-836. Cormio, C. and Chowdhury, K.R. A survey on MAC protocols for cognitive radio networks. Ad Hoc Networks 7 (7) (2009) 1315-1329. Nie, N. and Comaniciu, C. Adaptive channel allocation spectrum etiquette for cognitive radio networks. Mobile networks and applications 11 (6) (2006) 779-797. Chen, R., Park, J.M. and Bian, K. Robust distributed spectrum sensing in cognitive radio networks. The 27th Conference on Computer Communications INFOCOM, 2008, 1876-1884. Akyildiz, I.F., Lee, W.Y., Vuran, M.C. and Mohanty, S. NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer networks 50 (13) (2006) 2127-2159. Zhao, Q., Tong, L., Swami, A. and Chen, Y. Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework. IEEE Journal on selected areas in communications 25 (3) (2007). Streeter, T. Policy discourse and broadcast practice: the FCC, the US broadcast networks and the discourse of the marketplace. Media, Culture & Society 5 (3-4) (1983) 247-262. Zheng, H. and Peng, C. Collaboration and fairness in opportunistic spectrum access. IEEE International Conference on Communications, 2005, 3132-3136. Lin, S.C. and Chen, K.C. Spectrum-map-empowered opportunistic routing for cognitive radio ad hoc networks. IEEE Transactions on Vehicular Technology 63 (6) (2014) 2848-2861. Cheng, G., Liu, W., Li, Y. and Cheng, W. Joint on-demand routing and spectrum assignment in cognitive radio networks. IEEE International Conference on Communications, 2007, 6499-6503. Sampath, A., Yang, L., Cao, L., Zheng, H. and Zhao, B.Y. High throughput spectrum-aware routing for cognitive radio networks. Proc. of IEEE Crowncom., 2008.

ISSN 1943-023X

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[13] [14]

[15] [16]

[17] [18] [19]

[20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35]

Talay, A.C. and Altilar, D.T. RACON: a routing protocol for mobile cognitive radio networks. Proceedings of the ACM workshop on Cognitive radio networks, 2009, 73-78. Ding, L., Melodia, T., Batalama, S.N., Matyjas, J.D. and Medley, M.J. Cross-layer routing and dynamic spectrum allocation in cognitive radio ad hoc networks. IEEE Transactions on Vehicular Technology 59 (4) (2010) 1969-1979. Fujii, T. Multi-band routing for ad-hoc cognitive radio networks. Conference on SDR, 2006. Ma, H., Zheng, L. and Ma, X. Spectrum aware routing for multi-hop cognitive radio networks with a single transceiver. 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2008, 1-6. Chen, K.C., Cetin, B.K., Peng, Y.C., Prasad, N., Wang, J. and Lee S. Routing for cognitive radio networks consisting of opportunistic links. Wireless Communications And Mobile Computing 10 (4) (2010) 451-466. Cesana, M., Cuomo, F. and Ekici, E. Routing in cognitive radio networks: Challenges and solutions. Ad Hoc Networks 9 (3) (2011) 228-248. Xin, C., Xie, B. and Shen, C.C. A novel layered graph model for topology formation and routing in dynamic spectrum access networks. First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005, 308-317. Pan, M., Zhang, C., Li, P. and Fang, Y. Joint routing and link scheduling for cognitive radio networks under uncertain spectrum supply. IEEE Proceedings INFOCOM, 2011, 2237-2245. Gao, C., Shi, Y., Hou, Y.T., Sherali, H.D. and Zhou, H. Multicast communications in multi-hop cognitive radio networks. IEEE Journal on Selected Areas in Communications 29 (4) (2011) 784-793. Kim, H. and Shin, K.G. Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks. IEEE transactions on mobile computing 7 (5) (2008) 533-545. Wellens, M., Riihijarvi, J. and Mahonen, P. Evaluation of adaptive MAC-layer sensing in realistic spectrum occupancy scenarios. IEEE Symposium on New Frontiers in Dynamic Spectrum, 2010, 1-12. Khalifé, H., Malouch, N. and Fdida, S. Multihop cognitive radio networks: to route or not to route. IEEE network 23 (4) (2009). Zeng, K., Yang, Z. and Lou, W. Location-aided opportunistic forwarding in multirate and multihop wireless networks. IEEE Transactions on Vehicular Technology 58 (6) (2009) 3032-3040. Biswas, S. and Morris, R. ExOR: opportunistic multi-hop routing for wireless networks. ACM SIGCOMM Computer Communication Review 35 (4) (2005) 133-144. Liu, Y., Cai, L.X. and Shen, X. Joint channel selection and opportunistic forwarding in multi-hop cognitive radio networks. Conference on Global Telecommunications, 2011, 1-5. Karp, B. and Kung, H.T. GPSR: Greedy perimeter stateless routing for wireless networks. Proceedings of the 6th annual international conference on Mobile computing and networking, 2000, 243-254. Abdrabou, A. and Zhuang, W. A position-based QoS routing scheme for UWB mobile ad hoc networks. IEEE Journal on Selected Areas in Communications 24 (4) (2006) 850-856. Abdrabou, A. and Zhuang, W. Statistical QoS routing for IEEE 802.11 multihop ad hoc networks. IEEE Transactions on Wireless Communications 8 (3) (2009) 1542-1552. Chowdhury, K.R. and Felice, M.D. Search: A routing protocol for mobile cognitive radio ad-hoc networks. Computer Communications 32 (18) (2009) 1983-1997. Yoon, S., Lim, K. and Kim, J. Cross-layer dynamic spectrum map management framework for white space applications. EURASIP Journal on Wireless Communications and Networking 1 (2010) 870976. Chen, X., Huang, J. and Li, H. Adaptive channel recommendation for dynamic spectrum access. IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), 2011, 116-124. Li, H. Customer reviews in spectrum: recommendation system in cognitive radio networks. IEEE Symposium on New Frontiers in Dynamic Spectrum, 2010, 1-9. Sutton, P.D., Lotze, J., Lahlou, H., Fahmy, S.A., Nolan, K.E., Ozgul, B. and Doyle, L.E. Iris: an architecture for cognitive radio networking test beds. IEEE communications magazine 48 (9) (2010).

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