[ieee icc 2014 - 2014 ieee international conference on communications - sydney, australia...

GA-Based Frequency Selection Strategies forGraphene-Based Nano-Communication Networks

Armita Afsharinejad∗, Alan Davy∗, Brendan Jennings∗, Sasitharan Balasubramaniam†∗TSSG, Waterford Institute of Technology, Ireland

Email: [email protected], [email protected], [email protected]†Tampere University of Technology, Finland

Email: [email protected]

Abstract—We propose and evaluate a number of of frequencyselection strategies for nano-scale devices using graphene-basednano-antennas (“graphennas”), which operate in the Terahertzband. The strategies take into account the limitations of Terahertzchannel and aim to optimize the overall network transmissionrate of a network of nano-devices, while maximizing variousobjectives. We investigate the trade-off between cases where: 1)frequency duplication within the network is allowed or prevented;2) limiting the spread of frequencies over the entire Terahertzrange is required; and 3) balancing the load between the networksink nodes is required. We compare the network performancefor the different objectives proposed against a random frequencyselection strategy. Our simulation study demonstrates the effi-ciency of the proposed algorithms and indicates their usefulnessin different application scenarios.

I. INTRODUCTION

The area of nano-network communications seeks to enablecommunication between devices at the nano-scale. Nano-scalecommunications can provide a wide range of applicationsin the fields of military, environmental and medical systems[1]. Proposals to enable communication between nano-deviceshave predominately focused on molecular communicationor on electromagnetic wave propagation. The latter can beachieved through the fabrication of nano-scale graphene-basedantennas (“graphennas”) which resonate within the Terahertzband (0.1-10 THz). The theoretical bandwidth available toa graphene-based electromagnetic channel can be quite high(in the order of Tbps). However, many constraints emergewithin this channel that are not present at lower frequencies.One such constraint is molecular absorption of electromagneticwaves and the subsequent generation of noise. The introducedmolecular absorption and noise are frequency selective—their intensity varies over different frequencies across theTerahertz channel [2]. These variations, which depend on thetransmission frequency and the channel composition, can leadto fluctuations in the channel capacity. Therefore, throughappropriate selection and allocation of specific transmissionfrequencies for nano-devices, the channel capacity and in turnthe network performance can be maximized.

We proposed and evaluate a number of frequency selec-tion strategies that aim to maximize network capacity for aset of nano-devices communicating within a nano-network,while ensuring various objectives are maximized. The strate-gies optimize channel capacity through selecting appropriatetransmission frequencies for each nano-device. We investigatestrategies where frequency overlapping is allowed/prevented,

the Terahertz spectrum is efficiently used and nano-deviceassignment to a limited set of sink nodes are balanced. Weanalyse the performance of the strategies based on overallsuccessful packet transmissions and network capacity, findingthat there is a trade-off between overall network capacity,spectrum efficiency and non-overlapping frequency selectionwithin the nano-network.

The paper is organized as follows: §II discusses relevantliterature in the area of electromagnetic communication basedon graphene and the related constraints of nano-scale devices.§III presents our nano-network system model and associatedassumptions. §IV presents the frequency selection strategies,whilst §V presents the results of our simultion study. Finally§VI summarises the paper and outlines directions for futurework.

II. BACKGROUND

There are a number of attributes that must be consideredwhen developing communication protocols for wireless nano-networks. These include energy storage and power consump-tion. Another factor that is crucial is the efficient use of thecommunication channel, in this case the THz channel. In thefollowing sections, we discuss recent research on the theoret-ical modeling of the nano-device power and communicationblocks and envisaged topologies of nano-networks.

A. Power constraints of nano-devices

To increase the nano-network longevity, a piezoelectricnano-generator power block is proposed for nano-devices byJornet and Akyildiz [3]. Such a power block is capable ofharvesting the required energy from the environment throughconversion of mechanical energy into electrical energy. Con-sidering the size of a nano-device, a limited energy in theorder of a few pJ can be stored on it. So, based on theenergy restrictions a few hundred bits can be transmitted ineach interaction [3]. To consume this limited energy efficiently,Jornet and Akyildiz [4] propose a data modulation techniquecalled Time Spread On-Off Keying (TS-OOK) for nano-devicecommunication. This technique is based on the transmission ofultra short pulses to present a logical 1 and the silent periodsto indicate a logical 0; where 0s and 1s are separated overtime by fixed periods which are much longer than the pulseduration. By presenting a logical 0 with silence periods, theprobability of collision and errors in bit detection is reduced,also nano-devices can preserve more energy [5].

IEEE ICC 2014 - Selected Areas in Communications Symposium

978-1-4799-2003-7/14/$31.00 ©2014 IEEE 3642

B. Channel constraints in the Terahertz band

The graphenna is proposed as the communication block ofa nano-device [6]. This is deemed a more suitable materialthan a metallic antenna as the length of such nano-antennais a few hundred nanometers and it can be in the form ofa nano-dipole by using a Carbon Nano-Tube (CNT), or anano-patch by deploying a Graphen Nano-Ribbon (GNR).Regardless of its shape, such an antenna is capable of operatingin the Terahertz frequency band. However, there are associatedchallenging properties of the THz band, which are molecularabsorption and its related noise. Molecular absorption indicatesthe loss in signal energy as it collides with other molecules inthe transmission medium. These collisions cause the collidedmolecules to vibrate which in turn introduce a source ofnoise in the communication channel. Jornet and Akyildiz [7]model the THz channel is modeled in terms of path lossand noise. The path loss can be considered as the additionof spreading loss and the molecular absorption loss. Basedon these models, the observed absorption and noise dependmostly on the transmission frequency, distance, bandwidthand channel compositions and their concentration, temperatureand pressure. These unique properties of THz channel canhave a considerable effect on the channel capacity, effectivetransmission range and generally the network performance.Moreover, it is shown in [7] that the presence of watermolecules in the medium has the greatest impact on the levelof molecular absorption within the channel.

C. Frequency tuning of graphennas

As molecular absorption can occur randomly across theTerahertz band, it is vital that care is taken when selecting asuitable frequency for transmission as this phenomenon candirectly impact on the capacity of the channel. Generally,the resonance frequency of a graphene-based nano-antennacan be tuned statically or dynamically [8]. In this paper,we focus on dynamic frequency tuning methods, as suchapproaches are relevant to selective frequency tuning. Dynamicfrequency tuning can be accomplished through varying thetemperature or the electrical conductivity of the graphenna.Electrical conductivity of a graphenna is a function of itschemical potential. The latter parameter can be manipulatedthrough doping or by applying an electrostatic bias voltage tothe graphenna [8]. Specifically, the required energy (E(.)) toshift the resonance frequency (f ) of a 5×0.5 μm2 graphennapatch can be approximated as:

E(f) =0.048× f2 − 0.005× f − 0.033

∀f > 0.918 THz(1)

Llatser et al. [9] report a slight shift of the operationalfrequency to the higher frequencies through the increase ofthe temperature. The effect of a bias voltage on the resonancefrequency of a graphene-stacked dipole antenna is studiedby Tamagnone et al. [10], who show that, by applying abias voltage in the range of 0-3V to the graphene stack itschemical potential is increased in the range of 0-0.2eV, henceits operational frequency is shifted to the upper bands of theTHz range.

III. SYSTEM MODEL

We now define our system model assumptions that formthe basis of our experimental analysis. Each nano-device iscomposed of a power block and a communication block alongwith relevant sensor, processing and storage units. We are onlyinterested in the power unit and communication unit as theirproperties will directly affect the overall performance of thenano-network.

A. Power Block

In this scenario we use the power model defined in equation(2) to model the power block of each nano-device. We assumethat the charging frequency of the Zinc Oxide nano-wires isequal to 50Hz while we use 100 femtosecond Gaussian longpulses for the transmission of bits. We also assume that ifthe nano-device has enough energy it transmits/receives somepackets and loses part of its energy, otherwise it will wait toharvest enough energy to be able to communicate. We assumethat when fully charged the nano-device capacitor can storeup to 800pJ. In our system model, each nano-device uses 1 pJto transmit and 0.1pJ to receive a bit. This energy is enoughto transmit 1600 bits in each event detection round and afterdraining the energy it takes them 50s to get the full charge andbeing able to interact with the environment again. This is basedon employing the TS-OOK modulation technique proposedin [4]. To ensure successful packet transmission we ensurethat each node is scheduled to transmit data every 50s whichequates to an average of 32bps per node. The total energystored in a nano-capacitor, Ecap, can be calculated as:

Ecap(ncyc) =1

2Ccap

(Vg(1− e

(−ncycΔQ

VgCcap))

)2

(2)

where ncyc is the number of compress-release cycles, Ccap

is the capacitance of the nano-capacitor, ΔQ is the harvestedenergy in a cycle and Vg is the equivalent voltage generatedby the nano-wires.

B. Communication Block

To enable communication between nano-devices within thenetwork and sink nodes, we assume the use of a graphene-based plasmonic nano-antenna or graphenna which is com-posed of a 2 dimensional graphene patch over a dielectricsubstrate [8]. To model the capacity of the THz channel,the transmission bandwidth is divided into narrow frequencywindows with central frequency fi and a width equal toΔf = fi− fi−1 where i = 1, 2, · · · , N . In this way the powerspectral density of the colored noise can be considered flatin each window. So, the total capacity can be calculated bysumming up the capacity of narrow frequency windows overthe whole Terahertz band as:

Cap(d,W ) =∑i

Δf log2

[1 +

Sp(fi)P−1loss(fi, d,W )

N(fi, d,W )

](3)

where d is the distance, Ploss(.) is the total path loss, Wis the ratio of water in the medium, Sp(.) and N(.) are thepower spectral density of the transmitted signal and noiserespectively. We assume that each of the nano-devices willtransmit within the (1 - 2 THz) range, each over a sub-channelfrequency window of 50 GHz.


3643

Internet

Cluster 1

Cluster 2 Cluster N

User Terminal

Micro-device

Nano-device

Figure 1: Nano-network topology.

C. Nano-Network Conditions

The network topology under study follows a hierarchicalstructure in which each nano-device will transmit to a desig-nated micro-device over a single hop [11]. Micro-devices arededicated devices with ample power and are capable of per-forming complex tasks. The micro-devices diffuse the collectedinformation and relay it to higher layers, e.g. a user terminal ora gateway (figure 1). The micro-devices manage and schedulethe nano-devices in a coordinated manner. The nano-deviceswill be distributed using a normal random distribution throughthe medium, which is made up of a percentage of watermolecules and a regular composition of air.1 The temperatureand pressure values of the environment are constant andnormalized based on recommendations in [7].

IV. FREQUENCY SELECTION STRATEGIES

Given that molecular absorption is frequency selectiveacross the Terahertz band, we investigate three frequencyselection strategies that seek to maximize overall networkperformance. We measure network performance both as theoverall capacity of the network and as the ratio of successfulpacket transmissions of nano-node devices to their associatedsink micro-nodes. We analyze the performance of differentfrequency selection strategies where: i) the duplication offrequencies is allowed or prevented, ii) there is an efficientuse of the THz spectrum and iii) nano-device clusters areevenly balanced between the available micro-nodes to ensuremicro-nodes are not over loaded with data. The following willformally define our objectives and discuss the strategy that weemploy to find a solution.

A. Load balancing

For this objective we wish to balance the load of micro-devices by evenly assigning nano-devices within the networkbetween each of them. This objective can be formulated as:

Obj1 = Max(1/

S∑j=1

1

Nj) (4)

where S is the number of micro-devices in the network andNj is the total number of nano-devices assigned to the jthmicro-device. This objective will ensure that the distributionof nano-devices among micro-devices is fair.

1Nitrogen, Oxegen, Argon, Carbon Dioxide and water vapor

B. Frequency Selection

Our second objective (Obj2) tries to tune the frequencyof nano-devices based on the channel conditions. For thisobjective we can consider three different strategies, as follows:

1) Capacity maximization with permitted frequency over-lapping: Through this strategy, the objective is maximizing theoverall channel capacity through maximizing the capacity foreach individual nano-device. This goal can be achieved throughdynamically tuning the operational frequency of nano-devicesto the most clear frequency, regarding the channel conditions.Here, the frequency overlapping is allowed so, among the se-lected frequencies, duplication, or even convergence to the bestfrequency, can be observed. To avoid the probable collisions inthis case, a time division multiple access algorithm should beemployed which prevents the transmission of the nano-nodeswith the same frequency at the same time. This objective canbe formulated as:

Obj2 = Max[N∑i=1

Cap(fri, di,Wi)] (5)

where fri is the operational frequency of the ith nano-device;di is the distance between the ith nano-device and the sinknode; Wi is the ratio of the water for the ith nano-device;Cap(.) is channel capacity based on equation (3), N is thenumber of nano-devices of the network.

2) Capacity maximization with constraint-based frequencyduplication prevention: This strategy is similar to the previousstrategy with the difference that it prevents the overlapping offrequency through defining constraints. This strategy is wellsuited to a frequency division multiple access algorithm tomange transmissions within the nano-network. This objectivecan be formulated with the same parameters as:

Obj2 = Max[

N∑i=1

Cap(fri, di,Wi)]

s.t.

fri �= frj ∀j = 1 · · ·N, i �= j

(6)

3) Capacity maximization with swarm-based frequency du-plication prevention: Providing the same goal as the secondstrategy, this strategy ensures that no two frequencies areoverlapping and also that there is an efficient use of the THzspectrum. For this objective, we assume that all nodes in thenetwork are within range of each other, i.e. a fully connectednetwork. The following equation shows the formulated formof this objective; the first term tries to maximize the capacityand the second term tries to avoid collisions and frequencyoverlapping among nano-devices, while keeping the range offrequencies limited. This term is inspired from a swarmingmechanism proposed by Gazi and Passino [12].

(7)Obj2 = Max[

N∑i=1

Cap(fri, di,Wi)

− 1

2

N∑i=1

N∑j=1,j �=i

g(frj − fri)]


3644

In this equation all the parameters are the same as describedabove and g(.) is a function representing attraction and repul-sion between frequencies with the following format:

g(frj − fri) = (ga(frj − fri)− gr(frj − fri))|frj − fri|(8)

where ga(.) and gr(.) respectively represent attraction andrepulsion functions. Here we consider a constant attraction anda bounded repulsion function as follows:

ga(frj − fri) = CA, CA > 0, ∀(frj − fri)

gr(frj − fri) =CR

(frj − fri)2, CR > 0, ∀(frj − fri)

(9)

This approach can be beneficial for reducing the power con-sumption of nano-networks.

C. Genetic Algorithm based Optimization

As the frequency selection problem is multi-objective,we believe that this problem can be solved by employing aGenetic Algorithm (GA) approach; we use a weighted summultiple-objective GA. We define each of the objectives by afunction, and then assign a weight, wi, to each of the objectivesfunction based on its importance. By summing up the weightednormalized objective functions, we can convert our probleminto optimizing a single objective as follows:

Final Obj = w1 ×Obj1 + w2 ×Obj2 (10)

In line with this approach our GA-based algorithm is specifiedin Alg. 1:

Algorithm 1 pseudo-code for frequency tuning algorithm

Inputs:Freq: Intended frequency range, α: Population size, β: Elitism rate,γ: Mutation rate, δ: Number of iterations,Outputs:Solutions in (Mn,fr) format whereMn: Micro-device number, Fr: Selected frequencyMain algorithm:

1: Generate α random pair of solutions (Mn,Fr);2: Save them in population Pop;3: for i = 1 to δ do4: Evaluate Pop based on objectives;5: Select α× β of best solutions in Pop;6: Save them in population Pop1;7: Crossover population of Pop1;8: Mutate population of Pop1 with rate γ;9: Evaluate Pop1 based on objectives;

10: Update Pop with best solutions in Pop1;11: end for12: Return the Pop as best solutions

Upon running, this algorithm randomly divides the nano-devices into clusters by assigning a random micro-device toeach of them. Then, it assigns a random operational sub-frequency in a predefined boundary, to each of nano-devices.Following this step, the efficiency of the frequency and micro-device assignments are evaluated based on the defined ob-jectives. In the next phase, the algorithm tries to improvethe objectives by assigning different frequencies and micro-devices to each nano-device through crossovers and mutations.

Then it evaluates the new assignments and replaces the oldsolutions with part of the new improved solutions. This loopcontinues for a certain number of iterations to assure thatthe bests solutions have been found. The final output of thisalgorithm defines the best micro-node and frequency for eachof the nano-devices to maximize the total channel capacitywhile balancing the load between each of the micro-devices.These assignments are dictated to the nano-nodes by the micro-devices, to dynamically tune their antennas. Then the micro-devices in each cluster start a frequency hopping processto collect the data from nano-devices based on the dictatedfrequencies.

V. EXPERIMENTAL EVALUATION

We now analyze the proposed strategies based on therelated channel and power models and the associated nano-network conditions. To evaluate the strategies we considerthe successful transmission probability, the distribution ofnano-devices among clusters and total channel capacity asperformance metrics. We compare the strategies to that of arandom assignment approach. For simplicity we term the in-troduced strategies as Normal Optimization, Constraint-BasedOptimization and Swarm-Based Optimization respectively andrandom assignment solution as Random-Based.

For the simulation scenario we consider a 5m2 area asour measurement environment with a water vapor contentranging between 0.1% to 50%. We set a minimum frequencychannel distance between frequencies as 1GHz. Based onthe recommendations in swarming mechanism literature andalso the minimum frequency distance, we set our swarmingconstants as CR = 2 and CA = 1. For each of our experimentswe will vary a combination of parameters including numberof nano-devices, concentration of water vapor and number ofmicro-devices and analyze the performance of the network.

A. Successful transmission probability for a fixed ratio ofwater vapor

In the first scenario we consider a medium composed of50% water vapor, 100 nano-devices and 5 micro-devices whichare randomly scattered in the area. We simulate the success-ful packet transmission ratio for three types of assignmentsin Figure 2. Here, we consider the successful transmissionprobability merely as the effect of the molecular absorptionand noise on the channel between the nano-device and its as-signed micro-device in the medium (in this scenario, frequencyduplication in the random approach is prevented).

As can be seen in Figure 2, following a transient period ofabout 50 seconds, the successful transmission probability staysalmost constant and it remains virtually unchanged above 100seconds. This results demonstrates that the Constraint-Basedstrategy chooses the best set of transmission frequencies toensure the channel quality of the nano-devices is of sufficientquality to transmit data. On the other hand, the Swarm-Basedstrategy ranks second as it tries to find the best frequencieswhile ensuring limited frequency spread.

B. Successful transmission probability for different ratios ofwater vapor

In the next experiment, we extend our simulation analysiswith different ratios of water vapor in the area. In figure 3,


3645

0 50 100 150 200 250 3000.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time [second]

Succ

essf

ul T

rans

mis

sion

Pro

babi

lity

Swarm Based optimizationConstraint Based optimizationRandom Based

Figure 2: Successful transmission probability as a function of time,while 100 nano-devices are communicating with one of 5 randommicro-devices according to the Random-Based, Swarm-Based Opti-mization and Constraint-Based Optimization, with 50% water vapor.

0.1% 1% 10% 30% 40% 50%0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

H2O Ratio

Succ

essf

ul T

rans

mis

sion

Pro

babi

lity

Swarm Based optimizationConstraint Based optimizationRandom Based

Figure 3: Comparison of the average successful transmission prob-ability between the case that 100 nano-devices are with one of 5random micro-devices at a Random-Based, Swarm-Based Optimiza-tion and Constraint-Based Optimization in a medium of varying watervapor of between 0.1% to 50%.

the average successful transmission probability is depicted andcompared between three mentioned cases. As it can be seen,the Constraint-Based strategy outperforms the two other ap-proaches for all the ratios of water vapor. Based on this figure,for the water vapor ratios below/equal to 10%, Swarm-Basedstrategy and Constraint-Based strategy guarantee a successfultransmission probability equal to 1.

C. Load balancing amongst clusters

The distribution of nano-devices between clusters/micro-nodes is shown in Figure 4 while the medium contains 50%water vapor. We analyze load balancing between 5 and 10micro-devices for 100 nano-devices and compare the Random-Based and Swarm-Based approaches. We compare only thesetwo strategies as the Constraint-Based and Normal strategiesalso share this objective. We can observe that the Swarm-Based approach tries to balance the distribution of nano-devices between clusters for both number of micro-devices.

D. Capacity and frequency distribution comparison for differ-ent approaches

Table I compares the capacity and the frequency distri-bution for the proposed strategies. In this scenario, the ratioof water vapor is 50%, there exist 5 micro-devices in thenetwork and the number of nano-devices varies between 25to 200. It can be observed that the Normal strategy has thehighest capacity for all numbers of nano-nodes. This premium

1 2 3 4 50

5

10

15

20

25

30

Cluster Number

Num

ber o

f Nan

ode

vice

s

Swarm Based OptimizationRandom Based

(a)

1 2 3 4 5 6 7 8 9 100

2

4

6

8

10

12

14

16

Cluster Number

Num

ber o

f Nan

ode

vice

s

Swarm Based OptimizationRandom Based

(b)

Figure 4: Comparison of the distribution of 100 nano-devices be-tween 5 (a); and 10 micro-devices (b) for Random-Based and Swarm-Based Optimization approaches in a medium of 50% water vapor.

comes with the cost of high rates of overlapping frequencies.Comparing the Swarm-Based strategy and Constraint-Basedstrategy, it can be concluded that the latter always has a highercapacity. This premium comes at the cost of a wider spread offrequencies over the desired band. It can be observed that theSwarm-based strategy has a higher rate of frequencies aroundthe central frequency while sacrificing the total capacity.

E. Frequency distribution and power consumption comparisonfor different approaches

Figure 5, shows the distribution of frequencies over 1-2THz for all the proposed approaches. In this scenario, 50 and100 nano-devices are considered and the medium is composedof 50% of water vapor. In all the plots it can be observed thatthe second quartile has the highest density of frequencies. Itshows that not all the frequency ranges in the specified bandcan satisfy required objectives. It also can be observed that theNormal strategy tends to limit the spread of frequencies more.As a consequence, it might lead to the cases where all nano-devices converge to the best frequencies, causing frequencyoverlapping. It can be seen that Swarm-Based strategy is abetter solution in comparison with Constraint-Based strategy,regarding the spread of frequencies.

Based on frequency distributions and equation 1, the powerconsumption is 4.61eV for the Normal-Based strategy, 6.09eVfor the Constraint-Basde strategy and 5.61eV for the Swarm-Based Strategy. The Constraint-Based strategy has the highestand Normal strategy has the lowest power consumptions andSwarm-Based Optimization is located in between. This obser-vation is based on the proximity of the selected frequencies ineach of the approaches. It can be concluded as the frequenciesare more spread, more energy is consumed for frequencytuning/hopping and vice versa.

VI. CONCLUSION

We have presented an analysis of a set of frequency selec-tion strategies for graphenna nano-communication through thedevelopment of a GA-based optimization algorithm. Tuningdynamically within a nano-network seeks to improve the over-all network performance; we have demonstrated that there is aclear trade-off between network capacity and energy efficiency.By ensuring nano-node frequencies swarm around a centraloptimal frequency, energy consumption of frequency hopping


3646

Table I: Comparison between four frequency assignment approaches.

Approach Number of nodes Total capacity Central frequency [GHz] Nodes in 200 GHz proximityof central frequency

Frequency duplication ratio

Normal 4.14E+12 1247 88% 24%Constraint-Based 25 3.83E+12 1226 88% 0%Swarm-Based 3.53E+12 1187 100% 0%Random-Based 4.11E+10 1433 40% 0%





1000

1100

1200

1300

1400

1500

1600

1700

1800

1900

2000

Swarm Based Constraint based Normal

Fre

quen

cy [G

Hz]

(a)

1000

1100

1200

1300

1400

1500

1600

1700

1800

1900

2000

Swarm Based Constraint based Normal

Fre

quen

cy [G

Hz]

(b)

Figure 5: Distribution of frequencies in the desired band for threeapproaches with 50 (a); and 100 (b) nano-devices.

can be reduced. Where energy efficiency is not an issue, net-work capacity can be increased by assigning frequencies acrossthe spectrum. Finally, in the case of overlapping frequencies,network capacity can be maximized while at the same timeminimizing energy consumption. In the future, we wish toinvestigate scheduling algorithms appropriate for nano-networkcommunication within the frequency and time domain.

VII. ACKNOWLEDGMENT

This work was partially funded by (i) the Irish HigherEducation Authority under the Programme for Research inThird Level Institutions (PRTLI) cycle 5, which is co-fundedby the European Regional Development Fund (ERDF), via theTelecommunications Graduate Initiative (http://www.tgi.ie),(ii) Science Foundation Ireland via the FAME strategic re-search cluster (grant no. 08/SRC/I1403), and (iii) the FiDiProprogram of Academy of Finland (Nano communication Net-works), 2012-2016.

REFERENCES

[1] I. F. Akyildiz, F. Brunetti, and C. Blazquez, “Nanonetworks: Anew communication paradigm,” Computer Networks, vol. 52, no. 12,pp. 2260–2279, 2008.

[2] J. Jornet and I. Akyildiz, “Channel modeling and capacity analysis forelectromagnetic wireless nanonetworks in the terahertz band,” IEEETransactions on Wireless Communications, vol. 10, no. 10, pp. 3211–3221, 2011.

[3] J. Jornet and I. Akyildiz, “Joint energy harvesting and communicationanalysis for perpetual wireless nanosensor networks in the terahertzband,” IEEE Transactions on Nanotechnology, vol. 11, no. 3, pp. 570–580, 2012.

[4] J. Jornet and I. Akyildiz, “Information capacity of pulse-based wirelessnanosensor networks,” in IEEE Communications Society Conference onSensor, Mesh and Ad Hoc Communications and Networks (SECON),pp. 80–88, 2011.

[5] J. Jornet and I. Akyildiz, “Low-weight channel coding for interferencemitigation in electromagnetic nanonetworks in the terahertz band,” inIEEE International Conference on Communications (ICC), pp. 1–6,IEEE, 2011.

[6] J. Jornet and I. Akyildiz, “Graphene-based nano-antennas for electro-magnetic nanocommunications in the terahertz band,” in the FourthIEEE European Conference on Antennas and Propagation (EuCAP),pp. 1–5, 2010.

[7] J. Jornet and I. Akyildiz, “Channel capacity of electromagnetic nanonet-works in the terahertz band,” in IEEE International Conference onCommunications (ICC), pp. 1–6, 2010.

[8] I. Llatser, C. Kremers, D. N. Chigrin, J. M. Jornet, M. C. Lemme,A. Cabellos-Aparicio, and E. Alarcon, “Radiation characteristics oftunable graphennas in the terahertz band,” Radioengineering, vol. 21,no. 4, pp. 946–53, 2012.

[9] I. Llatser, C. Kremers, A. Cabellos-Aparicio, J. Jornet, E. Alarcon, andD. Chigrin, “Graphene-based nano-patch antenna for terahertz radia-tion,” Photonics and Nanostructures-Fundamentals and Applications,2012.

[10] M. Tamagnone, J. S. Gomez-Dıaz, J. R. Mosig, and J. Perruisseau-Carrier, “Reconfigurable terahertz plasmonic antenna concept using agraphene stack,” Applied Physics Letters, vol. 101, p. 214102, 2012.

[11] S. Balasubramaniam and J. Kangasharju, “Realizing the internet of nanothings: Challenges, solutions, and applications,” Computer, vol. 46,no. 2, pp. 62–68, 2013.

[12] V. Gazi and K. M. Passino, “Stability analysis of social foragingswarms,” IEEE Transactions on Systems, Man, and Cybernetics, PartB: Cybernetics, vol. 34, no. 1, pp. 539–557, 2004.


3647

[ieee icc 2014 - 2014 ieee international conference on communications - sydney, australia...

Documents