Cooperative Wireless Energy Harvesting and Spectrum Sharing in …iot.korea.ac.kr/file/ProfMinhojo/Cooperative Wireless... · 2017-07-17 · as transmission relays to enhance PUs’

SPECIAL SECTION ON GREEN COMMUNICATIONS AND NETWORKING FOR 5G WIRELESS

Received May 13, 2016, accepted May 26, 2016, date of publication June 9, 2016, date of current version July 22, 2016.

Digital Object Identifier 10.1109/ACCESS.2016.2579598

Cooperative Wireless Energy Harvesting andSpectrum Sharing in 5G NetworksHONGYUAN GAO1, (Member, IEEE), WALEED EJAZ2, (Senior Member, IEEE),AND MINHO JO3, (Member, IEEE)1College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China2Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada3Department of Computer and Information Science, Korea University, Sejong Metropolitan City 30019, South Korea

Corresponding author: M. Jo ([email protected])

This work was supported in part by the National Natural Science Foundation of China under Grant 61102106 and Grant 61571149, in partby the Heilongjiang Post-Doctoral Fund under Grant LBH-Z13054, in part by the Fundamental Research Funds for the Central Universitiesunder Grant HEUCF150817 and HEUCF160808, in part by the Special China Post-Doctoral Science Foundation under Grant 2015T80325,and in part by the China Scholarship Council.

ABSTRACT In this paper, we propose a novel best cooperative mechanism (BCM) for wireless energyharvesting and spectrum sharing in 5G networks. Data transfer and energy harvesting are finished in thedesigned timeslot mode. In the proposed BCM, secondary users (SUs) harvest energy from both ambientsignals and primary user’s (PU’s) signals. In addition, the SU’s can act as relay for PUs and harvest energyfrom PU signals simultaneously. The proposed mechanism allows optimal time duration for data transferwithin the timeslot. We formulate an optimization problem based on the proposed BCM with an objectiveto maximize throughput of PUs and SUs with constraints on data rate and energy harvest save ratios. Theeffectiveness of the proposed cooperative mechanism is verified by simulations, and it is considered as animportant stepping stone for future research in this domain.

INDEX TERMS Cooperative, energy harvesting, optimization, 5G networks.

I. INTRODUCTIONThe rapid growth of mobile devices and modern communica-tion applications result in the explosive demand for wirelessdata. Communication networks have to face several chal-lenges such as spectrum sharing and energy scarcity to dealwith the dramatic increase in wireless data which shift thefocus of research directions to fifth generation (5G) networks.To address these challenges wireless design engineers needto come up with energy and spectrum management solu-tions in 5G networks. The key technologies for 5G networksare massive MIMO, energy-efficient communications, cog-nitive radio networks (CRNs), visible light communication,etc [1]–[3]. In general, the aim of 5G networks is to providehigh data rate and guaranteed quality-of-service (QoS) [4].

Wireless energy harvesting and spectrum sharing in5G networks have attracted growing attention to solving theproblems caused by the high demand for data and users.Compared to a conventional battery-powered communicationsystems, wireless energy harvesting can provide an unlimitedenergy supply from ambient radio-frequency (RF) signalswhich can significantly alleviate energy efficiency [5]–[7].Simultaneous wireless information and power transfer for

two-hop OFDM relay system is proposed in [8], where relayharvest energy from transmitted RF signals by source andthen assist information transfer from source to destinationusing energy harvested. In [9], a simultaneous wireless infor-mation and power transfer scheme based on opportunisticcommunications is proposed to resolve practical challengesin interference alignment networks, which is also a potentialtechnology for 5G networks. In order to present a unifiedframework of wireless energy harvesting and data transmis-sion in interference alignment networks, a power splittingoptimization algorithm is proposed to improve network per-formance [10]. Therefore, wireless energy harvesting andpower transfer are key technologies in 5G networks.

On the other hand, spectrum sharing ensures the coverageof 5G network everywhere and all the time. It can supporta large number of mobile users and ubiquitous applica-tions [11], [12]. Cognitive radio (CR) technology is consid-ered as a promising technology in 5G wireless systems tosolve the lack of spectrum through spectrum sharing. The CRtechnique allows unlicensed/ secondary users (SUs) to sharethe idle spectrum of licensed/ primary users (PUs). A public-private spectrum sharing scheme is proposed in order to

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accelerate 5G Quality of Experience (QoE) [13]. A statisticalentropy-based methodology is proposed from an informationtheory perspective to measure the degree of predictabilityin real-world radio spectrum state dynamics [14]. However,CR devices need to consume more energy to perform exclu-sive functionalities such as sensing and sharing [15]. Thus,it is more important to apply wireless energy harvestingmethod in addition to spectrum sharing to improve bothenergy and spectrum efficiency.

Cooperative communication in CRNs allows SUs toactively help each other to look for opportunities in orderto achieve better throughput [16]. In this paper, cooperativecommunication means that when PUs are busy, SUs can workas transmission relays to enhance PUs’ transmission abilities(or throughput). This result in more data transmission oppor-tunities for SUs because PUs can complete their data trans-mission more rapidly and they release the licensed spectrumfor SUs earlier than the traditional case. However, most ofthe existing cooperative communication mechanisms amongusers (devices) in CRNs have realized that the cooperation ismainly used for data transfer or spectrum sharing, and do notcompletely consider simultaneous wireless energy harvestingand data transfer for the whole CR system.

In this paper, we consider a 5G network that consists ofmultiple primary networks. We propose a best cooperationmechanism (BCM) for cooperative 5G network that canharvest energy and transmit data simultaneously in times-lot mode. A timeslot is divided into several flexible timeintervals to carry out data transfer or/and energy harvesting.Different from the traditional cooperative mechanismin CRNs, in which SUs only cooperate with each other, inour proposed BCM, SUs cooperate with both SUs and PUs.Both SUs and PUs can harvest energy from ambient radiosignals. The proposed BCM is the complete study on wirelessenergy harvesting and spectrum sharing in 5G networks,which aims to improve the throughput of PUs and SUs.We formulate a maximal throughput problem for the coexis-tence scenario of energy harvesting and data transfer in orderto prove the feasibility and efficiency of proposed BCM. Themain contributions of this paper are can be summarized asfollows:

1) We propose a novel BCM in which SUs act as arelay for simultaneous improvement of throughput andenergy efficiency of SUs and PUs in timeslot mode.

2) We introduce a flexible time intervals for data trans-mission in our proposed mechanism. PU and SU canharvest energy from ambient signals to transmit infor-mation with the proposed BCM.

3) We formulate an optimization problem for whichobjective is to maximize throughput with constraintson data transmission in a slot. Cat swarm optimization(CSO) is used to solve the formulated optimizationproblem.

4) We conduct simulations to solve the optimization prob-lem in proposed mechanismwhich verifies the feasibil-ity and superiority of proposed mechanism.

This paper is organized as follows: In Section II, the relatedworks of energy harvesting, spectrum sharing, and coopera-tive mechanism for communications systems are introduced.In Section III, a system model for wireless energy harvestingand spectrum sharing in cognitive 5G networks is presented.Then, in Section IV, we detailed our proposed novel BCMwhich operates in timeslot mode and an optimization problemis formulated to maximize throughput, and CSO algorithm isused to solve the formulated problem. In Section V, the simu-lation results are presented and comparison of proposed BCMis done with two existing cooperative schemes in literature.Finally, the conclusion is drawn in Section VI followed byfuture work and challenges.

II. RELATED WORKA. WIRELESS ENERGY HARVESTING ANDSPECTRUM SHARING IN 5G NETWORKSWireless energy harvesting and spectrum sharing are promis-ing technologies in 5G networks for prolonged device life-time and spectrum efficiency respectively. In [17], authorspresented an integrated architecture for spectrum and energyharvesting to deal with the challenges (such as data raterequirement) posed by modern and sophisticated applica-tions. A spectrum and energy control mechanism is alsoproposed that attracts cooperative sensing participants alongwith the capability of energy harvesting. An optimizationproblem is formulated to maximize the energy efficiency ofenergy harvesting cooperative networks in [18]. A decode-and-forward relay-based cooperative network is consideredin which self-energy recycling is powered by wireless energyharvesting. In [19], authors designed a CRN with wirelessenergy harvesting with a goal to achieve spectrum and energyefficiency for SUs. The objective is to maximize the totalthroughput with constraints on energy causality and collision.A novel data transfer protocol was developed in a two-wayrelay network to improve the outage probability for the pri-mary network in [20]. The energy-efficiency of the wholesystem is analyzed in order to set up proper parameters tomaximize it. In [21], SUs harvest energy fromPU transmittersand store harvested energy to transmit data. The relationshipbetween SU throughput and primary transmitter density wasgiven in a stochastic geometry model. In order to obtainthe maximal throughput of SUs under energy neutrality con-straint and fading channel conditions, a channel selectionmethod is proposed in [22] for energy harvesting in CRNs.In [23], a stochastic geometry model based on the distributionof PUs and SUs is designed to maximize secondary networkthroughput under the given outage probability constraintsin the designed CRNs. A channel access policy for energyharvesting in CRNs to maximize expected long term through-put [24]. In [25], an energy harvesting CR system basedon slotted mode is proposed, in which only SU can harvestenergy from the ambient environment and system parameterscan be optimized for the ‘‘harvesting-sensing-throughput’’tradeoff.

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B. COOPERATIVE MECHANISM FOR ENERGYHARVESTING AND SPECTRUM SHARINGIN 5G NETWORKSA cooperative mechanism is a key technology to improve theefficiency of spectrum sharing in 5G networks that givesmoreopportunities to SUs to transmit on licensed spectrum, not inuse of PUs. Compared to non-cooperative spectrum sharingtechnology, cooperative mechanisms have more advantagesand have attracted much attention of researchers. In [26]authors studied the cooperative spectrum sharing with wire-less energy harvesting. An optimization problem is formu-lated to maximize the throughput with constraints on theperformance of primary systems. The system performanceis analyzed using the stochastic geometry theory. In [27],authors considered two cooperative relay models in a cogni-tive femtocell network where SUs can make smart decisionswhen they want to obtain maximal throughput by a gener-alized Lyapunov optimization algorithm. Beamforming andmultiple antennas for SUs can help cognitive cooperation forthe PUs. In [28], a zero-forcing (ZF) beamforming methodfor multi-antenna cognitive base stations is proposed to helpa primary system to gain the upper bound in transmitting itsown signals. It is important to design a cooperative spectrumsensing scheme to tackle errors and obtain the requirementsof PUs and SUs while designing antenna weights and makingpower allocation. In [29], the authors mentioned an over-lay spectrum sharing scheme where the PU leases half of

its timeslots to the SUs, in return SUs cooperative relayingthe PU’s data using amplify and forward scheme. The SU’santenna weight design and power allocation scheme is pro-posed to tackle a certain error or rate design criterion for bothPUs and SUs. To solve the cooperative decentralized stochas-tic optimization problem of cooperative multiuser CRNs,a decentralized learning algorithm is proposed by [30] toobtain the optimal channel access policies. To boost theprimary system’s performance by relaying and providingmore opportunities for the secondary system to access thespectrum, three schemes are proposed by [31] for energy har-vesting CR system. In [32] and [33], the optimal cooperationprotocols are proposed for energy harvesting CR system withtimeslot structure to obtain the maximal throughput.

Compared to the previous research, the main features ofour work is that we do not only propose novel BCM forwireless energy harvesting and spectrum sharing in cognitive5G networks, but also design a new cooperation strategy thatmaximizes the throughput of both SUs and PUs by usingtimeslots and CSO to solve new throughput formulas.

III. SYSTEM MODELWe consider a cognitive 5G network that consists of a PUnetwork and an SU system. The PU networks include aprimary transmitter (PT) and a primary receiver (PR) whilethe SU system includes a secondary transmitter (ST) and asecondary receiver (SR). Fig. 1 (a) shows the cooperative

FIGURE 1. Cooperative and non-cooperative mechanisms in cognitive 5G networks: (a) cooperation in one-to-one;(b) non-cooperation in one-to-one; (c) cooperation with multiple-to-one; (d) cooperation with one-to-multiple.

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cognitive 5G network for spectrum sharing in a one-to-one(with one PT/PR and one ST/SR) which is considered in ourproposed BCM. It can be seen that ST 1 act as a relay forthe PU system, i.e., PT 1 and PR 1. Thus, ST 1 and SR 1 canshare the licensed spectrum in return for ST 1’s relay for PT 1and PR 1. Fig. 1 (b) is no-cooperative cognitive 5G networkbecause SR does not play a role as a relay for the PUs.Fig. 1 (c) and (d) are for our future research ideas.In Fig. 1 (c), multiple secondary transmitters (ST 2 and ST3)are used as relay and in Fig. 1 (d), a single secondary transmit-ter (ST 4) is used for relay for multiple primary transmitters(PT 3 and PT 4). This future work will be discussed morein Section VI.

In every timeslot, the PU will transmit data through thelicensed channel to the intended receiver. After the PU fin-ishes data transmission, the PU begins to harvest energy fromambient signals and use an energy storage device to storeenergy for PU’s data transmission for the next timeslot. Forthe first timeslot, the energy storage device of the PU needs tobe charged in advance. However, for the following timeslots,the PU uses the energy harvested during the last timeslot.In order to avoid collision between the SU and the PU, theSU is not allowed to use the licensed spectrum to transmit itsown data when the PU utilizes the licensed channel becausethe PU has priority. Moreover, the SU harvest energy fromambient radio signals first when the licensed channel is used.In this paper, the SU’s transmitter abides by a save-then-transmit protocol for energy half-duplex constraints. That isbecause the rechargeable energy storage device practicallycannot charge and discharge at the same time.We assume thatin every timeslot, SU spends a part of a time in the slot forharvesting energy to be consumed for its data transmission.

In the proposed BCM, when the PU transmit its data, theSU can also work as a cooperative relay to help the PU’sdata transmissions. This cooperative relay of SU not onlycan improve the throughput of the PU, but also can finishPU data transmission earlier which in turn provide moreopportunities for sharing spectrum for SU’s transmission. Thecooperative communication between the PUs and the SUswhile considering the SU’s relay role can potentially increasethe throughput of both SU’s and PU’s. During the cooperativeprocess, the SU should harvest energy in a timeslot mode inorder to help PUs to relay its data.

IV. BEST COOPERATIVE MECHANISM (BCM) USINGTIMESLOT FOR ENERGY HARVESTING ANDSPECTRUM SHARINGWe propose a novel BCM using timeslots for energy har-vesting and spectrum sharing in 5G network. The proposedcooperative mechanism allows PU and SU to harvest energyin three modes: the SU harvests energy from ambient signals,the SU harvests energy from the PUs, and the PUs harvestenergy from ambient signals, as shown in Fig. 2. Thus,BCM allows SU to harvest energy from both sources, i.e.,PU signals and ambient signals. Unlike the existing work, thePU is no longer restricted to transmit a fixed amount of data

FIGURE 2. An example of the proposed timeslot structure with BCM forenergy harvesting and spectrum sharing.

in a timeslot. Further, simultaneous data and energy transfertechnology from PU to SU is proposed in the cooperativemechanism, i.e., SU can receive data and harvest energyfrom PU simultaneously in the blue/magnet color part ofSU timeslot in Fig. 2. The above three different ideas aresignificantly outstanding cooperative mechanisms comparedto the existing methods. A detailed list of symbols used andtheir description is provided in Table 1.

In the BCMusing timeslot mode shown in Fig. 2, a timeslotduration T is divided into three parts according to differenttransactions, which can be explained as follows:• At time interval (0, ρ1T ], when PU transmits its datadirectly in the non-cooperative style without using SU’sas relay indicating the red color of PU timeslot. ρ1 isthe SU’s energy harvest save ratio from ambient signals.At this time, SU needs to harvest energy from ambientradio signals and PU signals indicated in the aqua colorof SU timeslot.

• At time interval (ρ1T , (ρ1 + 2ρ2)T ], the SU works as acooperative relay for the PU to improve the throughputand to finish the PU’s transmission earlier than normaltime. ρ2 is the SU’s energy harvest save ratio fromPU signals. In the first half of the cooperative interval(ρ1T , (ρ1 + ρ2)T ], when the PU’s transmit its data tothe receiver in blue color of PU timeslot, the SU receivesdata and harvests energy simultaneously in blue/magnetcolors of SU timeslot. In the second half of the coopera-tive interval ((ρ1 + ρ2)T , (ρ1 + 2ρ2)T ], while the PUharvests energy from ambient signals in yellow colorof PU timeslot, the SU relays the PU’s data to the PUreceiver in the dark cyan color of SU timeslot.

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TABLE 1. Description of the symbols used in the BCM.

• At time interval ((ρ1 + 2ρ2)T ,T ], after the PU com-pletes the transmission in blue color of PU timeslot,then the idle licensed channel (shown in yellow colorof PU timeslot) can be used by the SU and the SU willtransmit its own data by using the idle licensed channelin green color of SU timeslot. Through all this, the PUkeeps harvesting energy from ambient sources, and theharvested energy of the PU will be used in the nexttimeslot. Since we focus on short-term optimization ofthroughput, we assume that the SUs have to use up theharvested energy during each timeslot.

For the proposed BCM, we formulate an optimizationproblem with an objective to maximize throughput of bothPUs and SUs. We introduce the energy harvest save ratiodefinition which is a portion of energy harvesting time withina timeslot. Let Rpt and Rst represents throughput of thePU and the SU in each timeslot respectively. Rpt dependson Rp and Rc, where Rp represents PU’s instantaneousnon-cooperative transmission rate during one timeslot andRc denotes PU’s instantaneous cooperative transmission rateduring one timeslot. Rp is affected by the SU’s energy harvestsave ratio from ambient signals (ρ1), the SU’s energy harvestsave ratio from PU signals (ρ2), the channel-power-gain-to-noise-power ratio of the transmitter and receiver of PU (γp),

and current available energy which including energy supplyof energy equipment (can be equal to 0) and harvested energyof the last timeslot. Rp can be expressed as

Rp = log2

[1+

(Yp +

Xp(1− ρ1 − ρ2)ρ1 + ρ2

)γp

], (1)

where ρ1 and ρ2 are SU’s energy harvest save ratio fromambient signals and PU signals in last time slot respectively.Yp represents energy supply rate of PU andXp represents PU’senergy harvest rate in the last timeslot.Rc has a relation with energy harvesting of the last times-

lot, channel-power-gain-to-noise-power ratio between PU’stransmitter and SU’s transmitter (γs), channel-power-gain-to-noise-power ratio of the link between SU’s transmitterand PU’s receiver (rp), SUs allocated power (ws) for thecooperative relay. Rc can be expressed as

Rc =12min{log2

[1+

(Yp +

Xp(1− ρ1 − ρ2)ρ1 + ρ2

)s

],

log2

[1+

(Yp +

Xp(1− ρ1 − ρ2)ρ1 + ρ2

)p + srp

]}.

(2)

Rs represents SU’s instantaneous non-cooperative trans-mission rate. In the designed BCM, SU’s cooperative trans-mission rate term is not needed because the SU only receivesthe data from the PU as a relay in the cooperative mode.Rs has a relation with SU’s energy harvest rate (Xsp) fromthe PU’s signal, SU’s energy harvest rate (Xs) from ambientsignals, channel-to-noise power gain ratio between the SU’stransmitter and receiver (rs). Rs can be expressed as

Rs = log2

[1+

(Xsρ1 + Xspρ2 − 2ρ2ws

1− ρ1 − 2ρ2

)rs

]. (3)

We formulate an optimization problem for maximizationof throughput of the SU and the PU in each timeslot as:

maxρ1,ρ2,ws

: Rpt + Rst ,

Subject to : Xsρ1 + Xspρ2 − 2ρ2ws ≥ 0,

1− ρ1 − 2ρ2 ≥ 0,

Rpt ≥ Qpmin, (4)

where Rst = (1 − ρ1 − 2ρ2)Rs and Rpt = ρ1Rp + 2ρ2Rc.Qpmin represents PU’s minimal data transmission amount ineach timeslot. The first constraint refers to energy causalityconstraint that the energy used for relay transmission shouldbe less than the energy harvested by the SU. For the SU, theenergy harvested in the current timeslot should be consumedby SU in the current timeslot. In the case of PU, the energyharvested by PU in the last timeslot should be consumed in thecurrent timeslot. The second constraint refers to the coopera-tive transmission duration and the non-cooperative transmis-sion duration must be less than the duration of the timeslot.

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In the third constraint, the throughput of PU must be biggerthan the PU’s minimal objective throughput.

We assume that SU should not start its data transmissionbefore PU finishes its data transmission. In other words,SU cannot share the licensed channel when the channel isoccupied by PU. Under all these constraints, we need to findthe best values for energy harvest save ratios for SUs, i.e.,ρ1 and ρ2, and allocated power for the cooperative relay (ws)to maximize the sum rate of PU’s and SU’s, i.e., sum ofRpt and Rst .We solve the optimization problem for our proposed BCM

using CSO which is an optimization algorithm. CSO is anopportunistic method with fast global convergence [34]. Theinitial population of CSO was created randomly from thedefinition domain. In CSO, each cat’s position represents apotential solution for a cooperative spectrum sharing sys-tem in 5G networks. The target of the maximal throughputoptimization using CSO is the maximization of the objectivefunction. If a cat’s position (a latent solution) satisfies theconstraints, the fitness of the cat is equal to objective functionvalue. If the cat’s position does not satisfy the constraint con-ditions, a penalty factor is used for the constraint optimizationproblem to guarantee the convergence, i.e., if the solutiondoes not satisfy constraints then it will be eliminated by catswarm in the future, and the fitness of the cat is equal tothe multiplication of objective function value and the penaltyfactor which is set as 0.1. The description of CSO for theBCM is given in Algorithm 1. For the detailed evolutionaryprocess of CSO, see [34].

V. PERFORMANCE ANALYSISIn this section, we evaluate the performance of proposedBCM. For simulations, we use a cognitive 5G network aPU network and an SU system. We consider a scenario forspectrum sharing and energy harvesting in a one-to-one way(with one PT/PR and one ST/SR) as shown in Fig. 1 (a).However, we can extend this prototype simulation modelto a more realistic model. We compare our proposed BCMwith the existing schemes presented in [33]. From [33], thenon-cooperative mode is named as Type A and the simplecooperative mode is named Type B. In Type A, SU starts itstransmission only after PU finishes its transmission to PU’sreceiver. In Type B cooperative protocol, SU can cooperatewith PU by a save ratio of battery and allocate battery powerfor the throughput of SU.

We use the following simulation parameters for CSO:constant factor (c = 2), counts of dimensions to change(CDC=0.5), seeking memory pool (SMP=6), seeking arange of selected dimension (SRD = 0.2), and mode ratio(MR=0.8). Inertia weight (wt ) will decrease from 0.9 to 0.4and population size (H ) is set to 300. The maximal iterationnumber is set to 100. Detailed simulation parameters aregiven in Table 2. Some parameters definition and processesare adopted from [34] and [35]. For type A and type B,the throughput of PU must be fixed which is the minimumrequired, i.e., Qpmin = 4. For the sake of simplicity, we set

Algorithm 1 Generale Description of CSOAccording to the system requirements, define the objectivefunction and solution space for CSOInitial a population of H cats with random variants whichincludes velocities and positionsvti = (vti1, v

ti2, . . . , v

tiD), x

ti = (x ti1, x

ti2, . . . , x

tiD), i =

1, 2, . . . ,HCompute the fitness of every cat and store the global bestposition ptb = (ptb1, p

tb2, . . . , p

tbD)

while (t <Maximal Generation) dofor i = 1 : H doif (rand <MR) then% rand represents uniform random variant between0 and 1Use seeking mode to generate new solutionsMake SMP copiesAllowSMP-1 copies tomutation based on SRD andCDCCompute the fitness of every copy and choose thebest copy to update the position of the cat

elseUse tracing mode to generate new velocities andpositionsvt+1i = wtvti + c× rand × (ptb − x

ti )

x t+1i = x ti + vt+1i

end ifend forFind current global best position pt+1b

end while

TABLE 2. Simulation parameters for CSO.

T = 1 and Xp = Xp. For the proposed BCM, we assumethat PU’s minimal throughput equal or superior to type A andtype B in each timeslot for PU’s throughput.

In addition to special explanation, the environmentalparameters of cognitive 5G network are set as the same forBCM, Type A, and Type B methods in simulation experi-ments. Unless specifically stated, we set γp = 0.4, γs = 100,rp = 200, rs = 40, Xp = 0, 50, 100, 150, Yp = 100,Xsp = 30, Xs = 20:4:60, and ws ∈ [10, 200].

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FIGURE 3. Relationship of SU’s throughput and energy harvesting rateof SU (a) Sum throughput of both SU and PU, and (b) SU’s throughput.

A. PU’s ENERGY PROVIDED BY ENERGY HARVESTINGAND FIXED ENERGY RESOURCEFig. 3 (a) and (b) show achievable sum throughput of bothPU and SU, and achievable throughput of SU versus energyharvesting rate of SU respectively for BCM (with varyingPU rate), Type A, and Type B. It is obvious that the proposedBCM can satisfy the throughput requirement of the PU.We can also notice that the achievable throughput of SUwill be increasing with the increase of energy harvestingrate of the PU and the SU. We can see that even energyharvesting rate of PU is 0, the proposed BCM is superiorto the traditional non-cooperative and cooperative schemes.Thus, the proposed BCM is an efficient scheme for wirelessenergy harvesting and spectrum sharing in 5G networks.

Fig. 4 (a) and (b) give maximal throughput simulationresults in 30 timeslots with the different probability dis-tribution of energy harvesting rate and channel condition.We set the variables as Xp ∼ 0(40, 5), Xs ∼ 0(20, 5),Xsp ∼ 0(10, 5), γs ∼ EXP(200), rp ∼ EXP(100), andrs ∼ EXP(40), where 0 represents the Gamma

FIGURE 4. Comparison of throughput traces for three differentmechanisms (a) Sum throughput traces of both SU and PU, and(b) Throughput traces of SU.

distribution function and EXP represents the exponentialdistribution function, since a Rayleigh fading channel isconsidered during the simulations.We can notice that the pro-posed cooperative scheme is superior to the traditional non-cooperative scheme Type A and simple cooperative schemeType B. Compared to Type A and Type B, our proposedmechanism can harvest more energy through both PU andSU and thus more power can be used for data transmission ofSU and PU and for cooperative relay of SU.

Fig. 5 (a) and (b) show achievable sum throughput of bothPU and SU, and achievable throughput of SU versus energyharvesting rate of PU respectively for BCM, Type A, andType B. We set the variables as Yp =40:6:100, Xp = 100,Xs = 40, and Xsp = 40. It is obvious that the achievablethroughput of BCM is superior to other two methods. It isbecause of that our proposed BCM can harvest more energythrough both PU and SU and thus more power can be usedfor data transmission of SU and PU, and for the cooperativerelay of SU. In addition, it can be concluded that if energy

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FIGURE 5. Comparison of throughput in different energy supply rate ofPU for three mechanisms (a) Sum throughput of both SU and PU, and(b) SU’s throughput.

harvesting rate of PU and SU are fixed in some special envi-ronment, we can improve battery capacity of PU to transmitbig data of SU. For SU, when Yp = 40, the throughput ofBCM is about 5 times of Type A.

From Figs. 3-5, we can see that the proposed BCM cansatisfy the requirement of both PU and SU. Compared toexisting methods, our proposed BCM can harvest moreenergy through both PU and SU and thus more power canbe used for data transmission of SUs, PUs, and for thecooperative relay of SUs. We also can obtain satisfactorySU throughput by setting of PU’s minimal transmission data.So, energy harvesting and data transmission have an intimaterelationship, the BCM can resolve the conflict of energyharvesting and data transmission by resolving the optimalparameters.

B. PU’s ENERGY PROVIDED BY ONLYENERGY HARVESTINGHere, the PU energy is provided by energy harvesting andthere is no need of additional energy equipment (i.e., Yp = 0).

We use BCM to harvest energy for PU and SU. For Type Aand Type B, PU must be supplied energy by the traditionalpowered system (Yp = 100) and only SU can harvest energyfrom ambient signals.

Fig. 6 (a) and (b) show achievable sum throughput of bothPU and SU, and achievable throughput of SU versus energyharvesting rate of SU from ambient signals and PU’s energyharvesting rate respectively for BCM (with varying Xp),Type A, and Type B. We set the variables as Xp =120, 170, 220, 270, andXsp = 30. If energy harvesting rate ofPU is high, the achievable throughput of BCM is superior tothe existing Type A and Type B methods. It is because of thefact that PU in proposed BCM can harvest energy in idle timeto speed PU’s and SU’s data transmission. So, it is feasiblethat PU and SU can work well by energy harvesting withoutadditional energy supply equipment.

FIGURE 6. Comparison of throughput traces for three mechanisms(a) Sum throughput of both SU and PU, and (b) SU’s throughput traces.

Fig. 7 (a) and (b) give maximal throughput simula-tion results in different channel-power-to-noise-power ratioof the transmitter and receiver of PU (γp) for BCM,

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FIGURE 7. Comparison of throughput with different Xs for threemechanisms (a) Sum throughput of both SU and PU, and (b) SU’sthroughput.

Type A, and Type B. We set the variables γp = 0.2:0.03:0.5,γs = 60, rp = 80, Xp = 200, Xs = 40, and Xsp = 40. It isobvious that the achievable throughput of BCM is superior tothe other two methods. For higher values of γp, Type B hasthe same throughput as Type A, and the cooperative relay ofType B does not produce good result. However, the proposedBCM has obvious performance advantages. And it is easy tounderstand that BCM is superior to other two mechanismsin the same simulation environment with different channel-power-to-noise-power ratio of the transmitter and receiverof PU.

Fig. 8 (a) and (b) give maximal throughput simulationresults in different channel-power-gain-to-noise-power ratioof the transmitter and receiver of SU for BCM, Type A,and Type B. We set the variables as rs = 10:3:40,γs = 110, Xp = 200, Xs = 40, and Xsp = 40. It is obviousthat the achievable throughput of BCM is superior to othertwo methods, because PUs and SUs in BCM can harvestmore energy for data transmission and thus, obtain excellent

FIGURE 8. Comparison of throughput with different rs for threemechanisms (a) Sum throughput of both SU and PU, and (b) SU’sthroughput.

throughput performance which has relationship with powerof PU and SU. The channel condition is a key factor for datatransmission. Thus, in order to obtain high throughput in thesame channel conditions, BCM should be used in 5G systems,and BCM can transmit more data in high noise environmentthan other existing Type A and Type B methods in low noiseenvironment.

Fig. 9 (a) and (b) give maximal throughput simulationresults in different energy harvesting rate of SU from PU forBCM, Type A, and Type B. We set the variables as Xp = 100,Xsp = 10:3:40, and Xs = 20, 25, 30, 35. It can be seenthat the achievable throughput will be increasing with theincrease in SU’s energy harvesting rate from ambient signalsand fromPU signals. Themaximal throughput has an intimaterelationship with two kinds of SU’s energy harvesting rate.We can see that the achievable throughput increase with theincrease of energy harvesting rate of SU. In order to obtainmore throughput, we need to use the high rate of SU’s energyharvesting. When SU’s energy harvesting rate from ambient

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FIGURE 9. Relationship of SU’s throughput and different Xsp (a) Sumthroughput of both SU and PU, and (b) SU’s throughput.

signals is fixed, we can control the SU’s energy harvest ratefrom PU’s signals to speed data transmission rate of SU tosome extent.

Fig. 10 (a) and (b) gives maximal throughput simulationresults in different energy harvesting rate of SU from ambi-ent signals and channel-power-gain-to-noise-power ratiobetween SU’s transmitter and PU’s receiver. We set the vari-ables as γp = 1, γs = 200, rp = 60:10:160, Xp = 100,Xs = 20, 25, 30, 35, and Xsp = 20. It is obvious that theachievable throughput increase with the increasing of SU’senergy harvesting rate and rp. We can obtain more through-put by improving SU’s energy harvesting rate and channelcondition of rp.According to Figs. 6-10, it is obvious that the pro-

posed optimal cooperation scheme can satisfy the throughputrequirement of PU by energy harvesting.We not only give thecomparison of the proposed BCM and previous mechanisms,but also show the relationship of throughput and systemparameters. Compared to previous methods and mechanisms,

FIGURE 10. Relationship of SU’s throughput and different rp for threemechanisms (a) Sum throughput of both SU and PU, and (b) SU’sthroughput.

we can obtain excellent performance, because the proposedmechanism can harvest more energy through both PU and SUand thus more power can be used for data transmission of SUand PU and for cooperative relay of SU. It should be noticedthat 5G networks based on BCM can attain excellent perfor-mance without additional energy supplication equipment inthis section.

VI. CONCLUSION AND FUTURE RESEARCH CHALLENGESIn this paper, we considered wireless energy harvesting andspectrum sharing in 5G networks which operate in timeslot.In our proposed mechanism using timeslot, SU can har-vest more energy from both ambient signals and PU sig-nals when compared to the traditional mechanisms. Further,SUs actively cooperate with PUs to relay their signals, andsimultaneously work as relay and harvest energy to gainmore opportunities for transmission and harvesting. Theproposed mechanism also offer flexible size of transmitteddata. We formulated an optimization problem to maximizethe throughput of both SUs and PUs and solve it using

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CSO algorithm. The proposed mechanism has been verifiedto be superior to the traditional schemes by simulation. Thusthe proposed mechanism can be an important step stone ofdoing more perspective research work in the future.

There are many research problems in this area which areyet to be studied, we highlight some interesting researchtopics for cooperationmechanism in 5G networks energy har-vesting and wireless information-energy transmission. Sincethe proposed mechanism has proved to obtain the maximalthroughput, we can extend the current prototype to the realworld problem by increasing the number of SUs and PUs, andby providing more detailed channel information. As shownin Fig. 1, there are more diversified network models for PUsand SUs in an actual system. Fig. 1 (c) and (d) are the casesof different cognitive 5G models for our future research.In Fig. 1 (c), called ‘‘cooperation with multiple-to-one,’’multiple secondary transmitters (ST 2 and ST3), are usedfor relay. A single primary transmitter PT2 cooperates withtwo secondary transmitters ST2 and ST3 for the relay. In thiscase, we expect that the throughput of PU can be increased.In Fig. 1 (d), a single secondary transmitter (ST 4) is usedas a relay for multiple primary transmitters (PT 3 and PT 4).In this case, the secondary transmitter ST4 can have higheropportunities of sharing the licensed channel and thus thesecondary transmitter can have higher capacity. However, ifthe number of relay requests by two primary transmitters isincreased, the throughput of the secondary transmitter can bedecreased. The mix model of cooperation with multiple-to-one and cooperation with one-to-multiple can be consideredas another practical cognitive 5G network case for the futureresearch.

In this research, we didn’t verify how much the proposedmechanism harvests energy efficiently. Thus this specifictopic can be considered to be future research work.

ACKNOWLEDGMENTH. Gao was an invited visiting professor under supervision ofProf. Minho Jo in IoTCoN Lab, Department of Computer andInformation Science, Korea University, Sejong Metropolitan,South Korea, from 2015 through 2016.

REFERENCES[1] M. Iwamura, ‘‘NGMN view on 5G architecture,’’ in Proc. IEEE 81st

Veh. Technol. Conf. (VTC Spring), Glasgow, Scotland, May 2015,pp. 1–5.

[2] H. Droste et al., ‘‘TheMETIS 5G architecture: A summary ofMETISworkon 5G architectures,’’ inProc. IEEE 81st Veh. Technol. Conf. (VTC Spring),Glasgow, Scotland, May 2015, pp. 1–5.

[3] P. K. Agyapong, M. Iwamura, D. Staehle, W. Kiess, and A. Benjebbour,‘‘Design considerations for a 5G network architecture,’’ IEEE Commun.Mag., vol. 52, no. 11, pp. 65–75, Nov. 2014.

[4] N. U. Hasan, W. Ejaz, N. Ejaz, H. S. Kim, A. Anpalagan, and M. Jo,‘‘Network selection and channel allocation for spectrum sharing in 5Gheterogeneous networks,’’ IEEE Access, vol. 4, pp. 980–992, 2016.

[5] S. Sudevalayam and P. Kulkarni, ‘‘Energy harvesting sensor nodes: Sur-vey and implications,’’ IEEE Commun. Surveys Tuts., vol. 13, no. 3,pp. 443–461, Sep. 2011.

[6] W. Ejaz, S. Kandeepan, and A. Anpalagan, ‘‘Optimal placement andnumber of energy transmitters in wireless sensor networks for RF energytransfer,’’ in Proc. IEEE 26th Annu. Int. Symp. Pers., Indoor, Mobile RadioCommun. (PIMRC), Aug./Sep. 2015, pp. 1238–1243.

[7] G. Du, K. Xiong, Y. Zhang, and Z. Qiu, ‘‘Outage analysis and optimiza-tion for time switching-based two-way relaying with energy harvestingrelay node,’’ KSII Trans. Internet Inf. Syst., vol. 9, no. 2, pp. 545–563,Feb. 2015.

[8] X. Di, K. Xiong, Y. Zhang, and Z. Qiu, ‘‘Simultaneous wireless infor-mation and power transfer in two-hop OFDM decode-and-forward relaynetworks,’’ KSII Trans. Internet Inf. Syst., vol. 10, no. 1, pp. 152–167,Jan. 2016.

[9] N. Zhao, F. R. Yu, and V. C. M. Leung, ‘‘Opportunistic communicationsin interference alignment networks with wireless power transfer,’’ IEEEWireless Commun., vol. 22, no. 1, pp. 88–95, Feb. 2015.

[10] N. Zhao, F. R. Yu, and V. C. M. Leung, ‘‘Wireless energy harvesting ininterference alignment networks,’’ IEEE Commun. Mag., vol. 53, no. 6,pp. 72–78, Jun. 2015.

[11] A. El-Mougy, M. Ibnkahla, G. Hattab, and W. Ejaz, ‘‘Reconfigurablewireless networks,’’ Proc. IEEE, vol. 103, no. 7, pp. 1125–1158,Jul. 2015.

[12] W. Ejaz and M. Ibnkahla, ‘‘Machine-to-machine communications incognitive cellular systems,’’ in Proc. IEEE Int. Conf. UbiquitousWireless Broadband (ICUWB), Montreal, QC, Canada, Oct. 2015,pp. 1–5.

[13] J. Mitola et al., ‘‘Accelerating 5G QoE via public-private spectrum shar-ing,’’ IEEE Commun. Mag., vol. 52, no. 5, pp. 77–85, May 2014.

[14] G. Ding et al., ‘‘On the limits of predictability in real-world radio spectrumstate dynamics: From entropy theory to 5G spectrum sharing,’’ IEEECommun. Mag., vol. 53, no. 7, pp. 178–183, Jul. 2015.

[15] W. Ejaz, G. A. Shah, N. Ul Hasan, and H. S. Kim, ‘‘Energy and throughputefficient cooperative spectrum sensing in cognitive radio sensor networks,’’Trans. Emerg. Telecommun. Technol., vol. 26, no. 7, pp. 1019–1030,Jul. 2015.

[16] Q. Zhang, J. Jia, and J. Zhang, ‘‘Cooperative relay to improve diver-sity in cognitive radio networks,’’ IEEE Commun. Mag., vol. 47, no. 2,pp. 111–117, Feb. 2009.

[17] Y. Liu, Y. Zhang, R. Yu, and S. Xie, ‘‘Integrated energy and spectrumharvesting for 5G wireless communications,’’ IEEE Netw., vol. 29, no. 3,pp. 75–81, Jun. 2015.

[18] S. Hu, Z. Ding, Q. Ni, W. Yu, and Z. Song, ‘‘Energy efficiency in energyharvesting cooperative networks with self-energy recycling,’’ in Proc.IEEE 20th Int.WorkshopComput. AidedModellingDesignCommun. LinksNetw. (CAMAD), Guildford, U.K., Sep. 2015, pp. 59–63.

[19] S. Park, H. Kim, and D. Hong, ‘‘Cognitive radio networks with energyharvesting,’’ IEEETrans.Wireless Commun., vol. 12, no. 3, pp. 1386–1397,Mar. 2013.

[20] Z. Wang, Z. Chen, Y. Yao, B. Xia, and H. Liu, ‘‘Wireless energy harvestingand information transfer in cognitive two-way relay networks,’’ in Proc.IEEE Global Commun. Conf. (GLOBECOM), Dec. 2014, pp. 3465–3470.

[21] S. Lee, K. Huang, and R. Zhang, ‘‘Cognitive energy harvesting and trans-mission from a network perspective,’’ in Proc. IEEE Int. Conf. Commun.Syst. (ICCS), Nov. 2012, pp. 225–229.

[22] J. J. Pradha, S. S. Kalamkar, and A. Banerjee, ‘‘ Energy harvesting cog-nitive radio with channel-aware sensing strategy,’’ IEEE Commun. Lett.,vol. 18, no. 7, pp. 1171–1174, Jul. 2014.

[23] S. Lee, R. Zhang, and K. Huang, ‘‘Opportunistic wireless energy harvest-ing in cognitive radio networks,’’ IEEE Trans. Wireless Commun., vol. 12,no. 9, pp. 4788–4799, Sep. 2013.

[24] T. T. Nguyen and I. Koo, ‘‘Throughput maximization for a primary userwith cognitive radio and energy harvesting functions,’’KSII Trans. InternetInf. Syst., vol. 8, no. 9, pp. 3075–3093, Sep. 2014.

[25] S. Yin, Z. Qu, and S. Li, ‘‘Achievable throughput optimization in energyharvesting cognitive radio systems,’’ IEEE J. Sel. Areas Commun., vol. 33,no. 3, pp. 407–422, Mar. 2015.

[26] C. Zhai, J. Liu, and L. Zheng, ‘‘Cooperative spectrum sharing with wirelessenergy harvesting in cognitive radio network,’’ IEEE Trans. Veh. Technol.,vol. PP, no. 99, pp. 1–13, Jul. 2015.

[27] R. Urgaonkar and M. J. Neely, ‘‘Opportunistic cooperation in cogni-tive femtocell networks,’’ IEEE J. Sel. Areas Commun., vol. 30, no. 3,pp. 607–616, Apr. 2012.

[28] S. H. Song, M. O. Hasna, and K. B. Letaief, ‘‘Prior zero forcing for cogni-tive relaying,’’ IEEE Trans. Wireless Commun., vol. 12, no. 2, pp. 938–947,Feb. 2013.

[29] R. Manna, R. H. Louie, Y. Li, and B. Vucetic, ‘‘Cooperative spectrumsharing in cognitive radio networks with multiple antennas,’’ IEEE Trans.Signal Process., vol. 59, no. 11, pp. 5509–5522, Nov. 2011.

VOLUME 4, 2016 3657


[30] D. T. Hoang, D. Niyato, P. Wang, and D. I. Kim, ‘‘Performance optimiza-tion for cooperative multiuser cognitive radio networks with RF energyharvesting capability,’’ IEEE Trans. Wireless Commun., vol. 14, no. 7,pp. 3614–3629, Jul. 2015.

[31] G. Zheng, Z. Ho, E. A. Jorswieck, and B. Ottersten, ‘‘Information andenergy cooperation in cognitive radio networks,’’ IEEE Trans. SignalProcess., vol. 62, no. 9, pp. 2290–2303, May 2014.

[32] S. Park and D. Hong, ‘‘Achievable throughput of energy harvesting cog-nitive radio networks,’’ IEEE Trans. Wireless Commun., vol. 13, no. 2,pp. 1010–1022, Feb. 2014.

[33] S. Yin, E. Zhang, Z. Qu, L. Yin, and S. Li, ‘‘Optimal cooperation strategyin cognitive radio systems with energy harvesting,’’ IEEE Trans. WirelessCommun., vol. 13, no. 9, pp. 4693–4707, Sep. 2014.

[34] S. K. Saha, S. P. Ghoshal, R. Kar, and D.Mandal, ‘‘Cat swarm optimizationalgorithm for optimal linear phase FIR filter design,’’ ISA Trans., vol. 52,no. 6, pp. 781–794, Nov. 2013.

[35] L. Pappula and D. Ghosh, ‘‘Linear antenna array synthesis using cat swarmoptimization,’’ AEU-Int. J. Electron. Commun., vol. 68, no. 6, pp. 540–549,Jun. 2014.

HONGYUAN GAO received the B.S. degree fromthe Department of Mechanical Design, JiamusiUniversity, China, in 1999, and the Ph.D. degreefrom the Department of Communication andInformation Systems, College of Information andCommunication Engineering, Harbin EngineeringUniversity, China, in 2010. He has been a Vis-iting Research Professor with the Department ofComputer and Information Science, Korea Univer-sity, Sejong Metropolitan City, South Korea, from

2015 to 2016. He is currently an Associate Professor with the College ofInformation and Communication Engineering, Harbin Engineering Univer-sity, China. Areas of his current interests include wireless energy harvestingcommunications, intelligent computing, software radio, radio signal recog-nition and classification, cognitive radio, LTE-Unlicensed, HetNets in 5G,communication theory and image processing, and massive MIMO.

WALEED EJAZ (S’12–M’14–SM’16) received theB.Sc. and M.Sc. degrees in computer engineer-ing from the National University of Sciences andTechnology, Islamabad, Pakistan, and the Uni-versity of Engineering and Technology, Taxila,Pakistan, and the Ph.D. degree in information andcommunication engineering from Sejong Univer-sity, South Korea, in 2014. He worked in top engi-neering universities in Pakistan and Saudi Arabiaas a Faculty Member. He was a Post-Doctoral

Fellow with Queen’s University, Kingston, Canada. He is currently aSenior Research Associate with Ryerson University, Toronto, Canada. Hiscurrent research interests include Internet of Things, energy harvesting,5G cellular networks, and mobile cloud computing. He is currently servingas an Associate Editor of the Canadian Journal of Electrical and ComputerEngineering, KSII Transactions on Internet and Information Systems, andthe IEEE Access. In addition, he is handling the special issues in IETCommunications, the IEEE ACCESS, and the Journal of Internet Technology.He also completed certificate courses on Teaching and Learning in HigherEducation from the Chang School, Ryerson University.

MINHO JO (M’07) received the B.A. degreefrom the Department of Industrial Engineering,Chosun University, South Korea, in 1984, andthe Ph.D. degree from the Department of Indus-trial and Systems Engineering, Lehigh University,USA, in 1994. He is currently a Professor withthe Department of Computer and Information Sci-ence, Korea University, Sejong Metropolitan City,South Korea. He is one of the founders of SamsungElectronics LCD Division. Areas of his current

interests include LTE-Unlicensed, cognitive radio, IoT, HetNets in 5G,green (energy efficient) wireless communications, mobile cloud computing,network function virtualization, 5G wireless communications, optimizationand probability in networks, network security, and massive MIMO. He isthe Founder and Editor-in-Chief of the KSII Transactions on Internet andInformation Systems (SCI and SCOPUS indexed). He receivedwith HeadongOutstanding Scholar Prize 2011. He was the Vice President of the Instituteof Electronics and Information Engineers, and was the Vice President of theKorea Information Processing Society. He is an Editor of the IEEE Wire-less Communications, an Associate Editor of the IEEE INTERNET OF THINGS

JOURNAL, an Associate Editor of Security and Communication Networks, andan Associate Editor of Wireless Communications and Mobile Computing.

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Cooperative Wireless Energy Harvesting and Spectrum Sharing in …iot.korea.ac.kr/file/ProfMinhojo/Cooperative Wireless... · 2017-07-17 · as transmission relays to enhance PUs’