Intern Report Work

8/20/2019 Intern Report Work

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1. P ROJECT W ORK

1.1 Literature Survey on Multi-hop Cognitive Radio Networks

1.1.1 Cognitive RadioCognitive radio networks (CRNs) are composed of cognitive, spectrum-agile devices capable ofchanging their configurations on the fly based on the spectral environment.

Cognitive networks are initiated by the apparent lack of spectrum under the current spectrummanagement policies. Current wireless networks are regulated by governmental agencies mainlyaccording to a fixed spectrum assignment policy.

Use of wireless technologies operating in unlicensed bands, especially in the ISM band, has been prolific with a wide range of applications developed in different fields (e.g,WLANs, mesh

networks, personal area networks, body area networks, sensor networks, etc.), which causedovercrowding in this band. Since most of the bands have been licensed, and the unlicensed bandsare also rapidly filling up, it would appear that a spectral crisis is approaching.

Usage of licensed spectrum is quite uneven and many spectrum bands allocated through staticassignment policies are used only in bounded geographical areas or over limited periods of time,and that the average utilization of such bands varies between 15% and 85%.

The above mentioned capability opens up the possibility of designing flexible and DynamicSpectrum Access (DSA) strategies with the purpose of opportunistically reusing portions of the

spectrum temporarily vacated by licensed primary users.With DSA, unlicensed users may use licensed spectrum bands opportunistically in a dynamic andnon-interfering manner.

Resulting so-called Cognitive Radio (CR) transceivers have the capability of completely changingtheir transmitter parameters (operating spectrum, modulation, transmission power, andcommunication technology)

– Sense a wide spectrum range

– Dynamically identify currently unused spectrum blocks for data communications – Intelligently access the unoccupied spectrum called Spectrum Opportunities (SOP).

Bands licensed to primary users could, under certain negotiable conditions, be shared with non- primary users without having the primary licensee release its own license. Whether the primaryusers would be willing to share their spectrum would depend on a number of factors, including theimpact on their own communication. The application of cognitive networks, however, is not limited


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to just fixing the current spectrum licensing. Other applications abound in shared spectra, such asthe ISM band (where different devices need to coexist without inhibiting each other), sensornetworks (where the sensors may need to operate in a spectrum with higher power devices), andcurrent services such as the cellular network (where the operator may want to offer different levelsof services to different types of users).

So in general, Cognitive radios — wireless devices with reconfigurable hard-ware and software(including transmission parameters and protocols) and are capable of delivering what thesesecondary devices would need: the ability to intelligently sense and adapt to their spectralenvironment. By carefully sensing the primary users‘ presence and adapting their own transmissionto guarantee a certain performance quality for the primary users,

1.1.2 Characteristics of Cognitive Radio

Cognitive capability

– Ability of the radio technology to capture or sense the information from its radioenvironment.

– Through this capability, the portions of the spectrum that are unused at a specific time orlocation can be identified. Consequently, the best spectrum and appropriate operating parameterscan be selected.

Reconfigurability

– The cognitive capability provides spectrum awareness whereas reconfigurability enables

the radio to be dynamically programmed according to the radio environment.

– The cognitive radio can be programmed to transmit and receive on a variety of frequenciesand to use different transmission access technologies supported by its hardware design.

1.1.3 Multi Hop Cognitive Scenario

Different scenarios of multi-hop wireless networks

– Cognitive Wireless Mesh Networks featuring a semi-static network infrastructure. – Cognitive radio Ad Hoc Networks (CRAHNs) characterized by a completely self-

configuring architecture, composed of CR users which communicate with each other ina peer to peer fashion through ad hoc connections.


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1.1.4 Classical Ad hoc Vs Cognitive Radio Ad hoc Networks (CRANHs)

The changing spectrum environment and the importance of protecting the transmission of thelicensed users of the spectrum mainly differentiate classical ad hoc networks from CRAHNs.

Choice of transmission spectrum – In CRAHNs, the available spectrum bands are distributed over a wide frequency range,

which vary over time and space. Thus, each user shows different spectrum availabilityaccording to the primary user (PU) activity.

– A key distinguishing factor is the primary consideration of protecting the PU transmission,which is entirely missing in classical ad hoc networks.

Topology control

– Ad hoc networks lack centralized support. Hence rely on local coordination to gathertopology information. In classical ad hoc networks, this is easily accomplished by periodic

beacon messages on the channel.

– In CRAHNs, as the licensed spectrum opportunity exists over large range of frequencies,sending beacons over all the possible channels is not feasible. Thus, CRAHNs are highly

probable to have incomplete topology information, which leads in an increase in collisionsamong CR users as well as interference to the PUs.

Multi-hop/multi-spectrum transmission

– CRAHNs consist of multiple hops having different channels according to the spectrumavailability. Thus, CRAHNs require collaboration between routing and spectrum allocationin establishing these routes. The spectrum switches on the links are frequent based on PUarrivals.

– As opposed to classical ad hoc networks, maintaining end-to-end QoS involves not only thetraffic load, but also how many different channels and possibly spectrum bands are used inthe path, the number of PU induced spectrum change events, consideration of periodicspectrum sensing functions, among others.

Distinguishing mobility from PU activity – In CRAHNs, a node may not be able to transmit immediately if it detects the presence of a

PU on the spectrum, even in the absence of mobility.

– Thus, correctly inferring mobility conditions and initiating the appropriate recoverymechanism in CRAHNs necessitate a different approach from the classical ad hoc networks.


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1.1.5 Spectrum Management Framework for Cognitive Radio Ad HocNetworks

In order to adapt to dynamic spectrum environment, the CRAHN necessitates the spectrum-awareoperations, which form a cognitive cycle.

As shown in Fig.1, the steps of the cognitive cycleconsist of four spectrum management functions:spectrum sensing, spectrum decision, spectrumsharing, and spectrum mobility [2]. To implementCRAHNs, each function needs to be incorporatedinto the classical layering protocols.

Spectrum Sensing — A CR user can be allocatedto only an unused portion of the spectrum.

Therefore, a CR user should monitor the availablespectrum bands, and then detect spectrum holes.Spectrum sensing is a basic functionality in CRnetworks, and hence it is closely related to otherspectrum management functions as well as layering

protocols to provide information on spectrumavailability.

Spectrum Decision — Once the available spectrums are identied, it is essential that the CRusers select the most appropriate band according to their QoS requirements. It is important to

characterize the spectrum band in terms of both radio environment and the statistical behaviors ofthe PUs. In order to design a decision algorithm that incorporates dynamic spectrum characteristics,we need to obtain a priori information regarding the PU activity. Furthermore, in CRAHNs,spectrum decision involves jointly undertaking spectrum selection and route formation.

Spectrum Sharing — Since there may be multiple CR users trying to access the spectrum, theirtransmissions should be coordinated to prevent collisions in overlapping portions of the spectrum.Spectrum sharing provides the capability to share the spectrum resource opportunistically withmultiple CR users which includes resource allocation to avoid interference caused to the primarynetwork. For this, game theoretical approaches have also been used to analyze the behavior ofselsh CR users. Furthermore, this function necessitates a CR medium access control (MAC)

protocol, which facilitates the sensing control to distribute the sensing task among the coordinatingnodes as well as spectrum access to determine the timing for transmission.

Spectrum Mobility — If a PU is detected in the specic por tion of the spectrum in use, CRusers should vacate the spectrum immediately and continue their communications in another vacant

portion of the spectrum. For this, either a new spectrum must be chosen or the affected links may be

Fig. 1 — Cognitive Cycle


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circumvented entirely. Thus, spectrum mobility necessitates a spectrum handoff scheme to detectthe link failure and to switch the current transmission to a new route or a new spectrum band withminimum quality degradation. This requires collaborating with spectrum sensing, neighbordiscovery in a link layer, and routing protocols. Furthermore, this functionality needs a connectionmanagement scheme to sustain the performance of upper layer protocols by mitigating the inuenceof spectrum switching.

1.1.6 Some Open Ended Problems

Switching Delay Management — The spectrum switching delay is closely related to not onlyhardware, such as an RF front-end, but also to algorithm development for spectrum sensing,spectrum decision, link layer, and routing. Desirable to design spectrum mobility in a cross-layerapproach to reduce the operational overhead among each functionality and to achieve a fasterswitching time.

Support of Asynchronous Sensing — Each user has independent and asynchronous sensingand transmission schedules in CRAHNs. It can detect the transmissions of other CR users as well asPUs during its sensing period. Energy detection is the most commonly used method for spectrumsensing. With this CR user cannot distinguish the transmission of CR and PUs, and can detect onlythe presence of a transmission. As a result, the transmission of CR users detected during sensingoperations causes false alarm in spectrum sensing, this leads to an increase in spectrumopportunities. Thus the most important issue in spectrum sensing is how to coordinate the sensingcooperation of each CR user to reduce these false alarms.

Optimization of Cooperative Sensing — Requesting the sensing information from several CR

users improves the accuracy but also increases the network traffic. This also results in higherlatency in collecting this information due to channel contention and packet retransmissions. Thus,these factors which must be optimized for correct and efficient sensing.

Choice of spectrum for CCC — Most of the current CR MAC protocols assume an out-of-bandCCC 1. For this, learning based techniques need to be devised so that the best spectrum thatguarantees continued use even in the presence of PU activity can be determined. For single-transceiver systems, this may involve frequent switching of the radio transceiver from the CCC tothe operational channel that adds a finite cost in the form of switching time. This time must beaccounted for in the CCC design. Novel techniques like ultra-wideband (UWB) may also be used to

realize an ‗always on‘ CCC as in [ 3]

Determining the CCC coverage — The area of coverage of the CCC depends upon the extentof the region that displays correlated PU behavior. For the approaches that use a cluster-likearchitecture with a common CCC shared among its members, the coverage of the CCC is the same

1 Out-of band CCC ensures Global Coverage i.e., Dedicated spectrum assigned as a constant through the network


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as the footprint of the cluster region, which restricts the design flexibility. A key challenge here isthe collection of the network information with minimum coordination and mapping these observedfactors to a physical region where a common CCC may be used.

Opportunistic Forwarding — If cognitive radios try to access over highly active and rarelyavailable primary bands, opportunistic forwarding without pre-established routing is to be explored[4]. In such highly dynamic and often disconnected environment, every sent packet may be forcedto follow a different path based on the primary bands availability. Therefore opting for a completeopportunistic solution, where every packet can be sent and forwarded over opportunisticallyavailable channels constitutes a potential solution. Using this feature can reduce the complexity ofestablishing end-to- end routes and increase the efciency of the proposed solutions.

The Common Control Channel Problem — Firstly, a dedicated channel for control signals iswasteful of channel resources. Secondly, a control channel would get saturated as the number ofusers increases in multi-hop network .

Multi-channel Hidden Terminal Problem — The multi-channel hidden terminal problem wasidentified in [5] for multi-channel networks. The same problem is extended to a cognitive networkenvironment in [6]. In [6] this problem is addressed by allocating special time slots. In these timeslots the communicating pair of nodes gets updated from its neighboring nodes about any potentialhidden terminals in their vicinity. Though, the problem is solved successfully using this method, aCCC is still used for control signal exchange.

Both the above problems are addressed in [7] (SYN – MAC) by dividing the total time into fixed-time intervals, each representing one of the available channels. At the beginning of each time slot,

all nodes in the network listen to a channel which the time slot represents for exchanging controlsignals. Thus, all nodes in the network are synchronized .


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1.2 Joint Power Control, Scheduling and Routing for Multi-hopCognitive Radio Networks

1.2.1 System Model

Consider an node secondary network with time varying channels. Let, - denote the matrix process of channel states, where each column contains the channel statesof a particular channel as elements and represents the current state of channel on link (representing, for example attenuation values and/or noise levels).Time is slotted with slots

normalized to integral units * +. We assumed that channels hold their states for theduration of time slot, and are known to the network controller at the beginning of each slot.Every time slot, the controller determines transmission rates on each link by allocating power

matrix

. / , - subject to a total power constraint

∑∑ for all nodes .In other way we can say that the nodes are power constrained in the form , where is acompact set of acceptable power allocations that includes the power limits for node.Link rates are determined by a corresponding rate-power curve,

. / , - .

We also assume that all the packets have fixed lengths and the transmission rates are restricted tointegral multiples of the packet-length/time slot quotient.

In general, the transmission rate over a link of the network depends upon the full matrix of power allocation decisions. This is because the communication rates over the link may beinfluenced by the interference from other transmissions on the same channel or from other channelsitself.

To calculate capacity of the network, we aim to study this problem in the SINR (signal-to-interference-and-noise-ratio) model. In this model, concurrent transmissions are allowed andinterference (due to transmissions by non-intended transmitter) is treated as noise. Moreover, theachieved transmission capacity is also a function of SINR (via Shannon‘s formula).


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The achievable data rates could be approximated by using the SINR in the capacity formula for aWhite Gaussian Noise (WGN) channel, where the SINR over link is dened as the attenuated signal

power divided by the total interference at node.

Rate-Power Curve —

. / 4 ∑ ∑ 5 where represent the noise coefficient associated with particular channel and with the

particular channel state. As we are considering cognitive environment we will define as

Channel gain for channel

on lin

if the channel is not used y the primary

if the channel is used y the primary

Here we are using Interference Avoidance 2 approach.

1.2.2 Control Decision Variables and the Queueing Equation

Each network node maintains a set of output queues for storing data according to its destination.For convenience we classify all data flowing through the network as belonging to a particular

commodity

* +, representing the destination node for the data. We define as

the rate offered to commodity traffic along link and channel during the time slot .

Power Al location : Choose such that . Routing/Scheduling : Choose such that . / , -

Let represent the amount of commodity bits that arrive exogenously to the network atnode during slot . Let represent the current backlog of bits in node destined for node .

The process updates according to the following queueing dynamics.

2 Interference-Avoidance: A cognitive user senses the time/frequency \white spaces" and opportunistically transmitsover the detected spaces


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The above expression is an inequality instead of equality because endogenous arrivals may be less

than ∑∑ if neighbouring nodes have little or no commodity data to transmit. The goalof the controller is to maintain low backlog and thereby stabilize the system.

1.2.3 Network Capacity Region

Network Capacity

The network capacity region is the closure of the set of all rate matrices that can be stablysupported over the network, considering all possible algorithms (possibly those with full knowledgeof future events).In [7] it is shown that stabilizing policies do not require knowledge of futureevents and, hence, such knowledge does not expand the region of stabilizable rates.

. /

is defined as the set of node-to-node transmission rate matrices the resulting transmission rateover a given link is averaged over all possible channel states.

{ }, where E is the expectation{ } ⋃


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Flow Equations

The capacity region is the set of all input rate matrices for which there exist multi-commodityow varia les 23 satisfying

where and for some

Note that inequalities (6 ) constrain ow varia les to be nonnegative and to be efcient , in that no

node routes data to itself and no node re-injects the delivered data back into the network. Inequality(7) is a conservation constraint that ensures the total ow of commodity data into a given node isless than or equal to the total ow out of that node, provided that node is not the destination.Constraint (8) ensures that the total ow o ver any link does not exceed the link capacity.

Thus, a rate matrix is in the capacity region if there exists a matrix that denes lincapacities in a network, such that there exist multi-commodity ow variables 23.1.2.4 Centralized AlgorithmOur main problem is to choose which commodity of packets must be sent in each link and to choosethe channel which is to be used. This entire algorithm used to allot the channel and power beforethe beginning of each time slot. Every time slot the network controller observes the state

matrix and the matrix of queue backlogs . / and performs routing and powercontrol by using this algorithm.

Let is the initial rate matrix where each column represents one of the permutations of channels

and each element is assigned with a channel and the rate of Transmission.


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( ) ( ) ( )

Where in the superscript represents one of the permutations and subscript represents thechannel to which that rate is assigned.

Taking each column of separately i.e., considering one permutation, we now have the rates toselect which commodity to be transmitted through link .

The differential backlog between nodes and of the queues that belong to commodity during

time slot , is defined as

, - where represents the current backlog of bits in node destined for node .

Now maximize the differential backlog between nodes and over all commodities to obtain thetransmit commodity

, -0 1

If any node does not have enough bits of a particular commodity to send over all of its entire

outgoing links requesting that commodity, null bits are delivered . The maximum differential backlog between nodes and , is given by

01 Where in superscript ‗ ‘ is the indicator that this is the iteration. Here as it is the firstiteration .Assuming that these transmit commodity bits are transmitted according to the rate in the , we nowupdate the differential backlog by subtracting the rate from it.

where


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Where in , 3 and are the variables that indicate that channel and the permutation.Using this updated value we will check for the max differential backlog again. The new maximum

differential backlog will be . We also assume that this transmit commodity bits aretransmitted according to the rate and the channel given by the permutation. Again we will repeat the

procedure to find the new maximum differential backlog. This procedure is repeated until all thechannels in a single permutation are assigned to the updated transmit commodity with thecorresponding rates given by the rate matrix .

All the s are stored in the row matrix . Note the in , it implies that everyelement of is calculated during the same time slot. Here we are not actually sending the packets, we are just assuming that they are sent and using that assumption we are calculating thenew maximum differential backlogs 4.

Now that we have the matrix we will calculate another matrix

defined as

where is the column of . Matrix is a column matrix containing each element as. So it is a dimension matrix (since m denotes the column of , it indicates the

permutation).

We now find which permutation is going to give the maximum product of bymaximizing over all .

Now choose a matrix such that

Now d ene transmission rates as follows:

. /if

and

otherwise Using these rates we will calculate the entire algorithm to allocate resources in the next slot.

3 is an element of the matrix 4 Reminder - The goal of the controller is to maintain low backlog and thereby stabilize the system .


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Initialization

1. Let are running variables with both initially set to .2. represent a rate matrix where each column represents one of the permutations of

channels. is given initially.

Main Algorithm

1. , - 2. , -

and 0 1 3. update using

4. if go to (5)else go to (2)

Note: Step (2) and (3) are to find all

5. Consider another matrix

, in this row matrix will all be elementsis the column of .

6. if Go to (7)else , go to (2)

7.

8. Dening transmission rates

. / if and otherwise


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Table-1 : Some symbols used in the algorithms and their definitions.

Symbol Definition Total number of nodes in the network

Total number of Channels

Current differential backlog between nodes and destined to commodity

Current backlog of bits in node destined for node

Flow variable for the link for commodity

Channel gain for the link

Rate allotted for the link in channel Noise coefficient associated with particular channel and with the particular

channel state

Amount of exogenous arrivals that belong to commodity that arrive to thenetwork at node during slot

Rate matrix where each column represents one of the permutations of channelsand each element is assigned with a channel and the rate of Transmission

Product matrix in which each column is given by

Allocated power matrix

It is an element of the matrix where and are the variables indicating thechannel and the permutation.

It is the transmit commodity


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Simple Case

Considering a simple case of a secondary network where there are only 3 destinations and only 3channels. We take two nodes and . The number on the three links indicates three channels andthe numbers on the queues indicate to which commodity they belong.

Fig 2 — Model link with nodes having the queues Let the initial rate matrix is given with rate in terms of packets/slot.

We will consider the first permutation i.e., first column of the above matrix.

Let the initial backlogs at nodes are given as , and

From the initial backlogs we can calculate initial differential backlogs as

As we can see maximum differential backlog is ,5 according to the permutation channel 1 isassigned for this transmit commodity and rate will be subtracted from

After this iteration we will get . Once again we see that commodity 1 ishaving maximum backlog. The above process is repeated.

After 2 nd iteration we will get (since, the rate subtracted is 3). Now the

transmit commodity will be 2 since it has maximum differential backlog.

Repeating the above procedure for 3 rd iteration we will get (since, the ratesubtracted is 1). Now we can see that all the all the queues are having nearly same differential

backlog which proves that this algorithm will try to maintain stability.

5 Which implies transmit commodity is 1.


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1.2.5 Distributed Algorithm

The centralized algorithm of the previous section involves solving a constrained optimization problem every time slot, where current channel states and queue backlogs appear as parameters inthe optimization. Here, we consider decentralized implementations, where users attempt tomaximize the product in (14) by exchanging information with their neighbours.

Consider a network with rate-power curve described by the function given in (3). This network hasdependent, interfering channels, and the associated optimization problem (14) is nonlinear, non-convex, and difficult to solve even in a centralized manner. Here, we provide a simple decentralizedapproximation, where nodes use a portion of each time slot to exchange control information withneighbours.

Algorithm

1) At the beginning of each slot, nodes randomly decide to transmit with probability q. Alltransmitting nodes send a control signal of power , where is some globally known scalingfactor designed to limit power expended by the control signal.

2) Define as the set of all transmitting nodes. Each node measures its total resultinginterference,

∑ , where is the channel and sends this scalar quantity over a control channel to all neighbours.

3) Using knowledge of the interference, attenuation values, and queue backlogs associated with allneighbouring nodes, each transmitting user decides to transmit using full power to the singleneighbour who maximizes the function

4 5 Note that this allows nodes to receive from multiple transmitters simultaneously, with rates thatcorrespond to the effective SIR of each transmission. The constraint that a transmitting node cannotsimultaneously receive can easily be incorporated by setting

(or,

equivalently, ), for all transmitting nodes Initially we will assume some power levels and some nodes to transmit. From there we willcalculate interference using the formula (18). Then using these interference we will calculate theinitial rates using the expression (19).


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In the expression (19) can be found using the first four steps of the centralized algorithm. Thedifference will be the updating of maximum differential backlogs. In distributed algorithm we haveto subtract the rates which are calculated above using the knowledge of neighbours which isdifferent from the centralized case where matrix was used. In centralized case we have globalknowledge i.e., we know the entire networks rate distributions.

1.2.6 Simulations

Due to the lack of time simulations are done only for the distributed algorithm.

System Model

Total number of nodes = N=10 ;

Probability of transmission = transProb = 0.3 ;

Total power is normalized = = pTotal = 1 ;

Assumed power levels powerLevels=[0 .25 0.5 1];

Total number of channles = = numChannels=3;

Initial assumed interference as Rayleigh distribution

channelOne=raylrnd(1,N,N);

channelTwo=raylrnd(2,N,N);

channelThree=raylrnd(3,N,N);

Exogenous arrivals are Poisson distributed

arrivalExo=poissrnd(arrivalRates);

We varied the arrivals rates and checked whether the queues are blowing up or not for that arrivalrate.


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1.3 Results

Sample Result — 1

arrivalRates=0.25*ones(N,N); After 10000 iterations

queueArray =

0 2438 2483 2515 2479 2447 2417

2474 0 2409 2292 2448 2422 2415

2391 2406 0 2438 2459 2378 2414

2407 2362 2469 0 2408 2477 2378

2478 2411 2429 2421 0 2361 2363

2373 2439 2478 2460 2498 0 2410

2428 2424 2502 2489 2348 2338 0

2392 2397 2336 2368 2397 2378 2445

2458 2440 2442 2453 2436 2419 2474

2396 2331 2429 2479 2373 2410 2415

Sample Result — 2

arrivalRates=0.0025*ones(N,N); After 10 000 iterations

queueArray =

0 0 0 0 0 1 1 1 1 1

1 0 0 1 0 1 1 1 1 3

1 1 0 0 1 1 1 1 1 1

1 1 0 0 1 1 1 1 1 1

1 1 0 0 0 1 1 1 1 1

1 1 1 0 1 0 1 0 1 1

1 0 1 0 0 1 0 0 0 0

1 0 0 0 1 2 1 0 1 1

1 1 0 0 1 1 1 0 0 1

0 0 2 3 2 3 2 0 2 0

2384 2407 2367

2492 2422 2392

2480 2423 2363

2387 2373 2358

2394 2397 2373

2474 2431 2427

2365 2456 2457

0 2415 2424

2351 0 2389

2304 2699 0


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Sample Result — 3

arrivalRates=0.2*ones(N,N); After 10 000 iterations

queueArray =

0 1939 1954 1884 1968 1966 1929

1884 0 1950 1937 1924 1846 1925

1928 1948 0 1933 1889 1867 1994

1907 1926 1935 0 1938 1954 1882

1892 1931 1960 1899 1 1904 1923

1904 1865 1930 1972 1909 0 1951

2044 1953 1976 1907 1937 1870 0

1952 1933 1979 2034 1891 1975 1885

1934 1896 1907 1865 1969 1944 1904

1929 1939 1850 1790 1870 1959 1900

1988 1917 1903

1853 1911 1910

1912 1861 1989

1927 1910 1934

1937 1850 1938

1956 1909 1905

1935 1911 1956

0 1906 1902

1933 0 1920

1799 2219 0

Rate fraction with respect to 0.5

Averageoccupancy

E[U]


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2. ConclusionThe project work involved a comprehensive literature survey on multi-hop cognitive radio networksregarding open research problems in the corresponding field. Work was mainly focused on jointoptimization of power control, scheduling and routing for multi-hop cognitive radio networks. Anovel algorithm based on queue backlogs was proposed for dynamic resource allocation inmultichannel, multi-hop cognitive radio networks. The algorithm was developed for a fixednetwork topology with centralized control as well as distributed control. Implementation of thedistributed algorithm was done in MATLAB.

The graph is shown in the above section is for the distributed case simulation. Similar graph can be plotted in the centralized case and both can be compared. It should come out that the centralizedcase is more optimal solution as it has the knowledge of the entire network and the optimizationwas carried globally. Whereas in distributed case the optimization is suboptimal as the optimizationis carried at each node and the knowledge at a particular node is local.

This problem has been taken as a PhD problem by Ashok Krishnan where his main aim would beto solve this non-linear non-convex optimization problem using different optimization techniquesand proper changes in the optimization problem to make it convex.

The cognitive scenario we considered was the simplest case 6 where the secondary user will nottransmit if there is any primary user transmission going on in its channel. This algorithm can beextended to number of cases like Interference control, Interference mitigation [10]. Differentsecondary network topologies can also be implemented using this algorithm.

6 Simplest case —Interference Avoidance


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3. Work Experience

Networking — Internship in IISc helped me in gaining professional contacts. I came to

know some of my gu ide‘s research collaborators who are currently working as Professors insome of the renowned universities in United States. This will be helpful in pursuing higherstudies abroad.

Tackling Real Time Problems — Internship enabled me to develop specific skillswhich are required while tackling with real time problems. Regular discussions with myguide helped me not only think in theoretical perspective but also to think about real timefeasibility.

Future Perspective — Working with PhD students and M.E students will be helpful inorder to make proper choices for my further studies.

Life in a Research Institution — Through this internship I have come to know howPhD students work in their labs in order to meet deadlines for conferences and how theyinteract with the professor.

Area of Interest — Before this internship I had a broad area of interest in my mind.This intern helped me in narrowing down my area of interest.

About Project Assistant — Before this internship I never knew that there was a position called project assistant where some of them are B.Techs who came there to assistthe on-going project as well as to gain some useful recommendations for their higherstudies.


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4. References

[1] N. Devroye, M. Vu and V. Taro h, ―Cognitive Radio Networ s,‖ IEEE Signal ProcessingMagazine, vol. 25, no. 6, pp. 12-23, November 2008. (Invited)

[2] Ian F. Akyildiz, Won-Yeol Lee, Mehmet C. Vuran, and Shantidev Mohanty. 2006. NeXtgeneration/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput.

Netw. 50, 13 (September 2006), 2127-2159.

[3] Ahmed Masri, Carla-Fabiana Chiasserini, Claudio Casetti, Alberto Perotti, Common ControlChannel Allocation in Cognitive Radio Networks through UWB Multi-hop Communications,The first Nordic Workshop on Cross-Layer Optimization in Wireless Networks at Levi,Finland, April 7-9, 2010.

[4] H. Khalife, et al., "Multihop cognitive radio networks: to route or not to route," Network, IEEE,

vol. 23, pp. 20-25, 2009.

[5] J. So, N. Vaidya; ―Mu lti-Channel MAC for Ad Hoc Networks: Handling Multi-ChannelHidden Terminals Using A Single Transceiver'‖, Proc. ACM Mo iHo 2004.

[6] Hao Nan, Sang-jo Yoo and Tae- In Hyon, ―Distri uted Coordinated Spectrum Sharing MACProtocol for Cognitive Radio‖, IEEE I nternational Symposium on New Frontiers in DynamicSpectrum Access Networks, pp. 240 – 249, April 2007.

[7] Y.R. Kondareddy, P. Agrawal, Synchronized MAC Protocol for multihop cognitive radionetworks, in: Proceedings of the IEEE International Conference on Communication (ICC),

May 2008, pp. 3198 – 3202.

[8] S. Yi and Y. T. Hou, ―A Distri uted Optimization Algorithm for Multi -Hop Cognitive Radio Networ s,‖ in INFOCOM 2008. The 27th Conference on Computer Communications. IEEE,2008, pp. 1292-1300.

[9] I. F. Aky ildiz, W. Y. Lee, and K. R. Chowdhury, ―CRAHNs: Cognitive Radio AdHoc Networ s,‖ in Elsevier Ad Hoc Networ s Journal, vol. 7,no. 5, pp. 810 -836, July 2009.

[10 ] E. Hossain, L. Le, N. Devroye and M. Vu, ―Cognitive Radio: From Theory to Practical Network Eng ineering,‖ to appear in Advances in Wireless Communications, V. Taro h, I.F.Blake, A. Gulliver Ed., Springer, 2009.

[11] M. Neely, E. Modiano, and C. Rohrs. Dynamic power allocation and routing for time-varyingwireless networks. IEEE JSAC,23(1),2005.

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