Energy Harvesting Technique Utilizing Resource Allocation Algorithm in 5G Wireless Channel.45073

The results of the Simulation are the estimated number of information decoding rate subcarriers and the Energy harvesting subcarriers at the final receiver of the relay scenario based on

978-1-7281-3841-1/19/$31.00 ©2019 IEEE

Energy Harvesting Technique Utilizing Resource Allocation Algorithm in 5G Wireless Channel

Hesham Aldhanhani

Faculty of Engineering and Information

Technology

University of Technology Sydney

Sydney,Australia

Hesham.Aldhanhani@student.uts.edu.au

Kumbesan Sandrasegaran

Faculty of Engineering and Information

Technology University of Technology Sydney

Sydney,Australia Kumbesan.Sandrasegaran@uts.edu.au

Sinh Cong Lam

Faculty of Electronics and Telecommunications Vietnam National University

Hanoi,Vietnam SinhCong.Lam@student.uts.edu.au

Abstract— The technology utilized in Energy harvesting

from wireless channels of cellular communication is one of the

critical aspects of future generation of cellular

telecommunication, as it will be one of its alternatives for

conserving power, by transmitting power to devices through

the cellular networks There have been numerous techniques

that were proposed in 5G cellular technology for sustainability

and enhancing the lifespan of centralized power sources in

mobile and future IoT devices Throughout this research

paper, a literature review of the main current advancements of

EH technology and SWIPT techniques will be presented as well

as its achievements An implementation of one of the Energy

Harvesting techniques which is the SWIPT, in a cooperative

relay scenario is presented in this paper and it is applied with a

proper simulation tool The simulation will mainly be

implemented in MATLAB, which is divided into several

functions computing the essential primary values for the

proposed EH network Firstly, the relay scenario that

comprises a base station and two receivers with one receiver

acting as a relay station while the other act as the final receiver

for both transmissions Secondly, the SWIPT technique is

implemented in the Relay stations, where the AF protocol is

utilized Finally, the last receiver will receive both signals

which represent the information decoding and the EH signals

from the relay stations The evaluation of the simulation results

will be discussed in the final stages of the research The

outcome of this research will be the simulation results in which

the estimation of the EH efficiency is evaluated based on two

factors including; the number of EH subcarriers in the final

receiver, and the average allocated power for EH transmission

received by the final receiver The idea behind this research

paper was to search for an efficient method to harvest energy

from the next nominated waveform of the 5th generation

mobile communication OFDM The anticipated outcome of this

paper is beneficial in minimizing the cellular operation cost,

extending the user equipment (UE) battery life, and creating

an overall conserving green communication

Keywords— Energy Harvesting, SWIPT, OFDMA, Relay

cooperative network, 5G

I.INTRODUCTION

The conventional cellular communication has incurred

significant technical development based on recent studies

There will likely be 5th generation mobile technology

released by 2020 This advancement will take part in various

application extended not only for person-to-person

communication but also for communications between

machines The vision of communication involving the 5th

generation of cellular communication is to integrate the

capabilities of high capacity communication, high speed, ubiquitous connectivity, termination of latency, and a wide range of services These services comprise smart power grids and smart homes utilizing M2M communication, all with advanced security It is expected that the communication traffic will increase, due to the expansion of various mobile terminals It is also estimated that download and upload volumes would reach Exabyte ( per months, which will result in the extension of IP addressing schemes Achieving this goal through the use of the current 4G and 5G architectural networks, which are not sustainable, would result in an inevitable energy crisis with economic and environmental concerns [1] The foreseen disasters would be the increase in the operating costs as well

as the emissions of the carbon-based power sources

It is anticipated that the emissions would increase,

as the majority of the information and communication sector would go wireless [2] One of the aspects of the 5th generation mobile communications is anticipated to have self-sustainability capability, by absorbing the power from the transmission in the air and convert it into energy to replenish the mobile batteries This process will enable having much greener communication technology It will eventually reduce the consumption of traditional batteries and extend battery life by exploiting the harvested energy from wireless channels The aforementioned has led to the incentives to research for a method to harvest energy and to analyze the efficiency of Harvested Energy from the radio resources available The primary aim of this paper is to use the available knowledge to formulate a hypothesis to harvest energy from Base stations transmission in a relay scenario The supposition will be elaborated by creating a model of a cooperative EH simulation tool Moreover, it will analyze the efficiency of the resulting energy measurements in the simulation The simulation will be implemented in MATLAB using the available online functions or, specific functions will be designed to meet the requirements of the research All communication parameters are available in MATLAB as well as the ability to customize a function based on the purpose of the simulation is also possible

An investigation of the various Energy harvesting techniques combined with different frequency access scheme will be explored in the literature These techniques comprise the SWIPT, the time and power switching circuit, and the received power splitter Moreover, the resulted simulation will be carried out using the OFDM frequency access scheme specifically, which is also the current

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candidate for the 5G cellular communication This OFDM

scheme is deployed in a Relay cooperative

telecommunication scenario that is depicted by the authors

in [3]; this is an appropriate scenario to investigate further

how the SWIPT technique is operating individually in a

Relay station From their literature, there are three stations

in which one of them is the primary transmitter (PT), and

the other two stations are the secondary transmitter (ST)

acting as the Relay station, and finally, the primary receiver

(PR) receiving both transmissions of the Relay and the

Primary Transmitter The results of the Simulation are the

estimated number of information decoding rate subcarriers

and the Energy harvesting subcarriers at the final receiver of

the relay scenario based on the iteration algorithm proposed

The percentage of EH subcarriers received is based on the

communication deployments purposes where certain areas

of communication nodes require information decoding

rather than EH subcarriers, in which parameters of the EH

simulation is standardized While other communication

deployments require EH subcarriers, where the parameters

of the simulation are maximized for EH subcarrier, hence

will require having EH retransmission increased from the

secondary transmitter (ST) The amount of energy harvested

is measured and averaged; it will be based on the number of

EH subcarrier at the final receiver of the cooperative

network The amount of energy harvested will be evaluated

based on efficiency and energy flow rate and is related to

the fundamental parameters of the simulation; they are

assigned based on the telecommunication scenario of

whether to optimize the simulation for information decoding

or energy harvesting

Throughout this research paper, a literature review will

exhibit existing work exploring various factors affecting the

EH methods and techniques, which will aid the scope of the

research The theoretical content of the EH is elaborated

from [3], their work mainly comprises emphasizing the

SWIPT technique in a cooperative relay network for an

OFDM 5G communication system Comprehension of their

work will lead to creation of functional block diagram of the

research simulation This simulation will examine the

ability to maximize the Energy harvesting subcarrier at the

first receiver of the relay network Further, the relevance of

the expected results to the research scope is discussed The

conclusions are drawn highlighting the possible long-term

benefits of energy conservation to the applied field of

communication engineering

II.LITERATURE REVIEW

The Literature search regarding this field is divided into

five main aspects of telecommunications These aspects are

essential when decide which technology should be utilized

for which telecommunication scenario The first aspect is

the frequency access scheme, which is used to access the

spectrum provided It impacts on how the UEs receive

signals from the spectrum provided The concept of

orthogonality in OFDMA systems supports the efficiency of

the range by stacking the subcarriers on to each other, to

achieve orthogonality It is then essential to investigate other

frequency access schemes to have an overall comprehension

of which plan is appropriate for which scenario This has led

to the findings in [4]in which the authors have proposed a

system with NON-orthogonal based 5G communication

with applied SWIPT technique In their system design, they

have utilized the power domain NOMA in which it allocates different power level to different User terminals This will enable the UEs to achieve their performance gain Therefore, the users are multiplexed in the power domain The dynamic power splitting which they have developed is for the downlink NOMA system; in their scenario, the UEs harvest energy from the power spliced received signal that is applied by the SWIPT technique Their results concluded that their situation could only be used in a condensed network with users and base stations to achieve the required QoS Moreover, the dense network will suffer from interferences, such as Inter-cell interference that is essential

to be considered This is evidence that the frequency access scheme is a factor which decides the type of scenario deployed In this case, it is recommended to utilize the OFDMA instead; due to the fact the OFDMA eliminates the

ISI interference

Another article reviewed in [5], in which the authors have implemented the NOMA access scheme in a supportive network The authors have also exploited the SWIPT technique in a NOMA access scheme to enhance the terminals with an active link, to relay the terminals with a weak link their user information in a Full Duplex mechanism Despite that, the author aimed to utilize beam forming of wave propagation along with NOMA and SWIPT in a Relay attempt, yet the terminals would suffer from the self-interference due to full duplex communication They have presented three cases for their one model solution consisting of a base station, weak link terminal, and a robust link terminal The first case is with the perfect Signal Interference Cancellation (SIC), where the strong user SI is cancelled The second case is the ideal SI cancellation, and

SI harvesting in which the energy harvested is from the SI with a ratio as well as the power transmitted is also collected

as the sum of the SI signals and base station signals The third case is imperfect SI cancellation and SI harvesting, where the imperfect SI produces distortions in the signal, non-linearity as well as residual SI signals Their numerical results demonstrated that in a NOMA cooperative network, higher sum rate of messages than that of a non-cooperative system Further, the energy harvested rate has increased that

is powering the relay station due to the utilization of the Full Duplex (FD) relay scenario Also, the FD mechanism reduces the negative drawback of the SI, which makes the

FD more efficient Moreover, the energy harvested from the information decoding utilizing the perfect SI cancellation is their best and efficient solution These results proved that the NOMA is best used in a cooperative network while the OFDMA is best suited for allocation each node with a subcarrier Nevertheless, the OFDMA should be tested in a cooperative network to level out the efficiency of harvested energy

The second aspect that affects EH efficiency is the

hardware realization of the transceivers The variation in

the aims of the purposes of the communication will serve in the alteration in the interior designs of the transceivers used

in the technology Not only is the need to marginalize the data transmission through the allocation of radio resources is essential, but also the need to allocate radio resources for

EH shows a significant change in gains of energy efficiency The need to examine a transceiver design is hence, critical

to the scope of the project According to [6], they have proposed a transceiver design in which the DL channel is

divided into two parts, one which is applied for the data

transmission/information decoding, and the other is for

Energy Harvesting (EH) In their scenarios, they have

exploited the interferences of channels in (MIMO) devices

consisting of K pairs of IoT/D2D Full-Duplex (FD) nodes,

with Simultaneous Wireless Information and Power

Transfer (SWIPT) technique to harvest energy These

findings can aid the research scope into further

comprehending how the SWIPT technique is applied and is

operating from hardware perspectives

Another similar design proposed in [7], presented a

couple of receiver designs to accommodate the EH circuit

and enabling the (SWIPT) technique, it is essentially a

time-switching (TS) and the power-time-switching (PS) design The

TS design functions by switching between the EH mode and

data transmission mode at specific points of time, on the

other hand, the (PS) design is similar to the approach in [6],

where it splits the received signal into two streams which

are the decoding of the received signal and the EH

processing circuit concurrently The results illustrated by [7]

were that the Line of Sight (LoS) is of significant effect in

the harvested energy for both the BS and the UE because

they were utilizing the mm-Wave in their simulation

scenario The mentioned attenuation will specify space

propagation constant that will be introduced in the

simulation tool system, combined with Gaussian white noise

with variance , this will give the simulation an actual

best or worst communication condition which will affect the

communication link Thus, changing the information

decoding subcarrier and the EH subcarriers, assuming that

there is a line of sight in between the cooperative nodes of

the simulation

The next aspect is the Resource Allocation algorithm

utilized in EH applications The primary step into achieving

the principal aims of the research is the implementation of

an algorithm for radio resource allocation for the sole

purpose of EH in Base stations The study has led to the

findings of [8] and [9], who have considered an algorithm

utilized in OFDMA frequency access scheme, which is a

practical access scheme that minimizes the

Inter-Symbol-interference (ISI) and ensures orthogonally of subcarriers

The literature by [8] and [9] have proposed an algorithm that

comprises information decoding and energy harvesting in

which they cannot occur at the same time Instead, one

transmitter can select a single receiver for information

transfer while the other receivers use unwanted signals for

EH They have assumed that the number of receivers is ten

considering sub channels of 32, and uniform distribution of

distances between the base stations and the UEs The

simulation results show that there is an increase in the

resource allocation elements for maximizing energy

efficiency by increasing the number of receivers This result

is consistent with the mobile cellular theories because when

the number of users increases, more subcarriers are

allocated, and more resource elements are allocated per UE

from the radio frame

The literature by [9], describes a special iterative

algorithm of recourse allocation using a computed policy

based on the available channel state information (CSI),

which is a deterministic factor for channel capacity

scheduling between the base station and the UEs,

considering K users and subcarriers The proposed system

consists of an increment of users from 2 to 20 UEs The

MATLAB simulation results showed that increasing the number of idle users will result in more radiated power This will lead to more energy harvesting process, while an active user does not receive harvest energy signals Also, the number of UEs is in proportional to the harvested energy of the overall network The regime of algorithms governing the affectivity of communication is difficult to declare one optimum algorithm for energy harvesting or data transmission This is because the algorithm is merely a factor of management of radio resources in which it can utilize and exploit any resources for either EH subcarrier or data subcarriers Nevertheless, an algorithm should be developed to serve the purpose of communications deployments, whether the area of implementation is prioritized for either EH or data transmission

The next research area linked to the research gap of the

literature scope is the propagation medium in which it

transfers the energy accurately, new mm-wave that will be utilized in the 5G cellular network The motivation has been exploited to investigate literature by [10]and [11], to further identify the nature, the behavior, and the characteristics of the mm-Wave In both works of literature, it is elaborated that the mm-Wave depicts a wavelength suitable for information and energy transfer, due to its robustness However, it is very susceptible to blockage of buildings and materials The first literature has proposed a simulation of wireless power transfer in mm-Wave with the human body

as a hindrance In their simulation scenario, they have modelled the obstruction as a blocked communication node Therefore, they have taken into considerations the two types

of communication nodes, namely; the energy harvester power beacon leveraging both frequencies of UHF and mm-Wave

The simulation results are expressed as the probability of energy convergence vs the network density It is explained that there is an increase in the energy convergence probability by reducing the physical blocks (PBs) in transmission as the TDMA is utilized in this case Hence, the overall performance is that the mm-Wave outperforms the UHF in terms of energy harvesting more than penetration The other literature has introduced a Hybrid data and energy Access Point (HAP), like base stations and multiple of UEs in transmission, with the presence of rain attenuations The demonstrated results note that increasing rain factor in the simulation parameter yields less energy harvested by the UEs It further suggested that deployment

of a massive MIMO system could remedy the performance whenever rain attenuations occur in communications transmissions

As for final research area, which is the crucial element in deciding whether the EH is practical or not, is the

Efficiency of Harvested Energy This will be in the form

of a percentage of the EH, which is considered as a trade-off between the information transfer and the energy transfer A new approach by [12], in which they have tackled the mentioned trade-off and suggested an outer element to the information/energy network to optimize both the information and energy transfer from base stations to UEs It

is an Energy harvesting from nature (e.g., solar panels) to create a hybrid system of SWIPT cellular and natural energy sources exchanging information and energy simultaneously The simulation results by [12]; highlighted three different strategies, where there is a full power transmission, zero

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power transmission and a mix of information and power

transmission The proposed hybrid system is practical as the

simulation results prove scalable capabilities in EH

scenarios It is then concluded that their method of

allocating the appropriate percentage of EH could be of

benefit in this research scope Correspondingly, this

percentage will be the deciding factor in which the EH

technology acceptable or not

III.METHODOLOGY

A Problem Modelling

The Methods utilized in this paper are attempting to

recreate and reverse-engineer the results achieved by the

authors [3], once similar readings are obtained The effort to

manipulate the parameters of the simulation is essential to

serve the main goal, which is to maximize the EH subcarrier

at the primary receivers (PR) In their scenario, they have

utilized a cooperative network with three nodes, the first

transmitting node (PT) serves as the base station

transmitting signals to both nodes ST and PR, where ST

serves as the Relay station and PR as the final Receiver of

the network They have stated that they have utilized an

optimization solution to maximize the information decoding

rate on of the mobile terminal PR while the other mobile

terminal ST is optimized for information decoding and

conveying the remaining of its subcarriers as Energy

Harvesting transmission The following figure is depicting

their scenario:

Figure 1 Communication Scenario in [3]

The objective of this paper is to use the methods in [3] in

order to optimize a simulation for maximizing the harvested

energy at ST with Relaying, in which the remaining EH

subcarriers are transmitted to the Primary receiver PR are

increased Consequently, it is essential to investigate their

approach in detail, to gain the overall comprehension of

their proposed algorithm Thus, the following figures entails

their investigated problem formulation of the scenario

Figure 1: Summery of Problem Formulation of [3]

As observed from Figure 2, at each node of the network, the expressions of the transmitted signals and the receive signals are shown The first transmission takes place from

PT, transmitting signals to both PR and ST in which is referred as the first transmission phase, the power allocated for each subcarrier transmission from PT is determined by the Water Filling Algorithm in order to agree with the constraints of the sum of powers at PT The signals received

at PR is identified as the information decoding rate and power transmitted on the subcarriers from both ST and PT The subsets of subcarriers are transmitted from PT to the secondary station ST the same way is transmitted to PR and are denoted as (K), and this is referred to as the First transmission phase In the second transmission phase, the node ST is retransmitting the signals to PR using the Amplify and Forward (AF) protocol The Relay station ST receives the subcarriers from PT, and they are divided into

N information subcarriers, thus ST is retransmitting the information decoding subcarriers to PR and the remaining subcarrier is used for Energy harvesting at PR The terminal

ST is pairing with the subcarriers from the first transmission resulting in a cooperative relaying for the transmission The received signal at PR is combined from both transmissions This is done by using the Maximum Ratio Combiner (MCR), which is a method used to determine the resulted signal from combining diversity of signals [3]

The next step in determining the trend of this research is

to investigate the Optimized Solution of this scenario, specifically stating, what was the approach in apprehending this problem The investigation has led to producing the following figure which elaborates the optimization of the parameters, these parameters will be used in the simulation for this research:

Figure 3 Parameters Optimisation depiction from [3]

According to [3] Equation (1) is the Non- Convex

optimization, where the authors are trying to maximize the

Information decoding rate at PR as in the following:

Where ET=Energy Threshold

Ps, k=Power Allocated to the Kth Subcarrier at ST

Ks=Subsets of ST subcarrier

Ώs = Sets of K subcarrier of the Secondary Transmission

kp = Subsets of PT subcarrier

Hence it is essential for them to find optimum

values of Ps, k and Kp when the Rp is Maximum Hence,

they have divided the equation into two conditions which

are:

 The energy threshold ET is less than or equal to the

Harvested Energy E

 The Second Condition is restricting the Total

Transmission power of ST (Ps) to be greater than

the sum of the transmitted subcarriers for each

subset of Ks , where Ks denotes the subsets of

Relay Station ST

The method in this research will follow the proposed

iteration algorithm in the literature by [3], where they have

divided the solution into two conditions as mentioned

before The first optimization is to maximize the lagrangian

value Kp and Ps,k which is essentially will maximize the

instnanatouse information decoding rate Rp The higher the

Kp the more subsets of data rates are transmitted, and the

higher the Ps,k , the higher transmission power per

subcarrier of the Relay station ST, thus delivering more data

rate with higher transmission power to the Primary Receiver

PR

Equation (2) of the Lagrangian values in [3] are expressed

as the following:

It is observed that the Lagrangian equation is dependent

of the variables βs and βt These values represent the constraints of transmitted power and Energy harvesting respectively The variables of βs and βt are Equation (3,4) expressed as the following [3]:

Where the βt value depend on the Energy harvesting threshold ET as well as Ps,k As for βs depends on the total transmitted power of the Relay station Ps and the power allocated for each subset Ps,k In the proposed optimization, these values are combined to∆β which is optimized to solve for values of Kp and Ps,k for each iteration of the algorithm The first optimization is that for a given ∆β and a constant value of Kp , Ps,k is computed in accordance with the Karush-Kuhn-Tucker (KKT) Condition The second optimization of Kp is by using mathematical derivations of the lagrangian equation (11) with respect to Ps,k as elaborated in [3] Finally, the expressions of both Kp and Ps,k are solved by the authors

If the simulation is optimized for the Kpwhich are the subsets of PT through the Relay stations, then the remaining Subsets Ks are considered the Energy harvesting Subsets at the Primary receiver PR, this is only true for Relaying condition As for direct transmission the number of subcarrier K are transmitted without partitioning of information decoding or EH It I deduced that the total subcarriers K at PR consists of both Kp and Ks, hence solving for Kp would essentially allow to solve for Ks which are the remaining subsets of Energy harvesting subcarriers from ST to PR This is confirmed with the expression of Energy harvesting Equation (5) from the literature [3] It can be seen that, the energy harvesting is computed for all values of Ks according to the following equation:

Where the Energy harvested at PR is dependent of the transmission power received, and the number of subsets Ks

in that transmission Whenever the Relay transmission takes place at ST, both Ks and Kp are pairing with each other, hence performing Relaying of EH and information transfer subcarriers to the PR

The implementation of the simulation will be depicted from the iteration algorithm proposed by the authors, and in order to ensure the legitimacy of the simulation behavior by

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developing cases where different parameters are utilized

There are values which are considered missing due to the

confidentiality of the literature These values are the

constraints of Transmitted Power and Energy harvested

which are essential to optimize for and

Nevertheless, these values are predicted and utilized in the

simulation based on the error marginality and the logical

estimation of these values Further, cases of testing

conditions are developed for the simulation based on these

values to monitor the behavior of the simulation and the

ensuring its feasibility

B Problem Solution

Interpreting the mentioned optimization above will

results in the creation of simulation in accordance to the

literature The following figure is a functional block

diagram of the simulation elaborating the behavior of the

simulation:

Figure 4 Simulation structure

The proposed simulation is divided into five main

functions along with a main initialization function operating

concurrently to compute the essential values for plotting the

results The following table summarizes these functions and

their corresponding purposes:

TABLE 1 FUNCTIONS OF PROPOSED SIMULATION

Main Program launcher

 Declaring the parameters required for the simulation

 Bandwidth

 Subcarriers

 Transmission powers

 Channel gains

 Noise

Water Filling Algorithm

 Computing the total allocated power for each subcarrier at PR

 The value of Power at

PT

Function computing

 Function to compute instantaneous information decoding rate at

PR per subcarriers K

 The value of instantaneou

s information decoding rate

Function computing

 Function to compute the overall instantaneous information decoding rate at

PR

 The value of instantaneou

s information decoding rate

Path Gain Function  Function to

generate the Path Gain

 The value of the Path Gain

Function Computing

 Function to compute the Optimal subset

of Subcarriers at

ST and PR

 The value of the Optimal subsets of Subcarriers

Main Loop function

 Execute functions

 Execute Main Literature Iteration

 Declaring essential parameters such

as the path gains, Transmitted Power and Threshold Energy

 Generating Graphs

 Generate essential outputs from executed functions such as the Water Filling Algorithm, and Values

of and

 Generate Graphical representatio

ns

Once the simulation files are created, as mentioned before, an attempt to manipulate the parameters which are missing, and once similar results are obtained first The values of and are initialized within the Main Loop function in which the iteration takes place They are estimated in order to obtain similar results to the allocated literature which will further be correlated to the literature in [3] It is critical that similar results must be obtained as a primary step, before declaring that the simulation feasible Note that there are functions called within function, as in the function to compute the overall , the values of are required within the function computing Similar approach is done determining the optimum value of , again the simulation structure is designed in accordance to the optimization steps from the allocated literature

IV.IMPLEMENTATION AND RESULTS

In order to achieve the required similar results to that of

the authors, the implementation will be divided into cases

where first, a fixed value of while converges closer to

, since is constraint of transmitted power of ST and

could affect the transmission of EH and information

decoding rate subcarriers The second case is, a fixed value

of while converges away from The last case is,

increasing the value of while and are relatively

closer and apart from each other and that the last

cases proposed is the manipulation of the Minimum Energy

harvested Threshold while other parameters are close

and far apart from each other In order to get logical and

visible results, the total transmitted power from PT is

minimized relatively to the transmitted power from ST,

since the observation of results are from PR side, hence it is

essential to observe the power received by PR without PT

transmission intrusions The following table entails the

parameters utilized in the mentioned cases:

TABLE 2 SIMULATION PARAMETERS

Cases \ parameters

converging to 0.0551 0.0215 5

converging away from 0.25 0.003 5

higher Values of , While

and are relatively closer

0.0551 0.0215 55

higher Values of , While

and are relatively apart

0.025 0.003 55

Other parameters are examined from [3] presumptions, and

implemented into the simulation which are, The Rayleigh

Fading channels are considered along with random

generation of gaussian noise values with variance of

for each channel path between all three nodes The

bandwidth considered for the system is and the

number of subcarriers allocated to convey the information

and the Energy harvesting subcarrier are total of 8

subcarriers Finally, the path gains are calculated as part of

the transmission power of each transmission as well as for

each subsets of K These parameters are utilized with a

combination of the following three cases:

 Figure 5: For converging to : Substituting

values in the Simulation with a fixed value of the

 Figure 6: For converging away from :

Substituting values in the Simulation with a fixed value

 Figure 7: For higher Values of , While and

are relatively closer

 Figure 8: For higher Values of , While and

are relatively apart

As demonstrated, Figure 5 shows the Number of

subcarriers allocated for Energy harvesting and information

decoding rate subcarrier on the left-hand side, where it is

observed that the number of EH subcarriers are more than

the information decoding subcarriers While on the

right-hand side, the information decoding rate at PR vs an

incremental of total transmitted power from ST is displayed

the figure on the RHS graphically represents different fixed value of the Energy Threshold utilized in the simulation

Figure 6 demonstrates the number of information

decoding rate subcarrier allocated for the ST transmission

on the LHS, where the transmission is meant for information decoding only and the EH subcarriers are suppressed On the RHS the information decoding rate at PR vs the total transmitted power from ST is displayed when three fixed values of Energy threshold are utilized The figure shows a slight increase in the information decoding rate

Figure 7 shows the number of subcarriers with input

parameters same as in figure 5, except that the energy threshold values are increase on the RHS, in which there are more power allocated for the Energy harvesting subcarriers

On the LHS the different higher values of Energy threshold are utilized with the same simulation parameters as figure 5 displaying the same behavior except with more dispersion for higher

Another interesting result obtained where there are higher values of Energy threshold is utilized, in which both and are far apart In this case, it is expected to transmit information decoding subcarriers with Energy harvesting

subcarriers and this is demonstrated in Figure 8 on the LHS

The curve of the information decoding rate at PR vs the transmitted power of ST relatively satisfies the situation where there is a raise in the information decoding rate then a gradually deceasing curve of data rates as more transmission power is used from ST

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Figure 2 Simulation result when converging

1111100

Figure 3 Simulation Results when converging away from

Figure 4 Simulation Results for Higher Values of

Figure 5 Simulation Results for Higher Values of

V.DISCUSSIONS

For a cooperative relay network utilizing normal

circumstances where the network is optimized for

maximizing the information decoding rates at PR as the

authors [3] have already proven This is demonstrated in

Figure 6, where an incremented transmission of power from

ST vs the information decoding rate at PR is displayed It

can be seen the allocated number of subcarriers for data

transmission is (six), while subcarrier for EH is two only

The reason is, for this solution the value of is optimized

due to the fact, that the constraint of the transmission power

at ST is considerable than the constraint of Energy

harvesting at ST In this case is very low, since

controls how much power is transmitted to PR, it is essential

to keep it at higher values than at all times If is

optimized according to the KKT condition from the

literature, hence, more information decoding rate subcarrier

is allocated as which is elaborated in Figure 6, showing

that = {2,3,4,5,6,8,}, while = {1,7} as Energy

harvesting subsets, hence the total of eight subcarriers K In

this simulation, they are essentially the same data arranged

according to the iteration from the literature The power

allocated for each subset is computed from Equation (6) as

the following [3]:

Where : is the path gain of the transmission from ST

to PR Therefore, minimising and increasing will

essentially optimise the for data transmission for each

subset of

In Figure 6 on the RHS, the information decoding rate

at PR is slightly increasing with increasing the transmission

power of ST, in which is maintained a steady throughput

hence the transmission is optimized for information transfer

In Figure 6, the Power allocated for EH transmission

percentage is of the total transmission power

As for EH transmission in which is demonstrated by

Figure 5 LHS, the value of is assigned closer to the value

of , hence increasing the constraint of Energy harvesting and further increasing the chances of transmitting more EH subcarriers from ST to PR In this case the value of is optimized to be minimized, hence maximizing the values of where and the rest subsets are

It is observed from Figure 5 on the RHS, as more EH subcarrier are transmitted, the data rates transmission is decreasing as more power is transmitted for different values of , this is because the subcarriers are meant for EH rather than information decoding at the receiving side PR Further, as more power is transmitted from ST, a steady state data rates are maintained, this indicates that the three subcarriers of still conveying information at a constant rate for the transmission In Figure

5, the Power allocated for EH transmission percentage is

of the total transmission power

Another factor in which is affecting the EH transmission which is the value of the minimum Energy Harvesting threshold Figure 7 demonstrate that increasing the

value of would allow for more subcarriers to be changed into EH subcarriers however in this case, it is maximising the total EH power per subcarrier in the simulation This is also confirmed with the literature [3], in which they have stated that as increases more EH subcarriers are transmitted, hence less information decoding subcarriers are transmitted and there will be a decrease in the throughput as more powers are transmitted Therefore, in Figure 7 to the RHS the data rates at PR is decreased as the transmission power from ST is increased As higher power is transmitted the data rates is maintained at a steady value, due to the fact that there are still information decoding subcarriers allocated for the transmission

Lastly, Figure 8 LHS demonstrates the results obtained

when value is larger than , which entails that the majority of transmission in meant for information decoding subcarriers however, increasing the minimum Energy harvesting thresholds value would allow for more subcarrier

to be changed into EH subcarriers The results were a

- 201 -

information decoding and EH subcarriers On the RHS of

Figure 8, it can be seen that there is an increase of the

information decoding rate at lower transmission power of

ST, indicating that the power is allocated for information

decoding subcarrier initially As more power is transmitted,

the rest of higher power is allocated for energy harvesting

indicating a decline in the information decoding rate

Further the data rates are maintained as steady values as

even higher power is transmitted from ST

To conclude the observations, the key factors deciding

the behavior of the proposed cooperative network to

whether have Energy harvesting or Information decoding

optimization, are the values of the constraint of total

transmitted power of ST and the Energy harvesting

constraint as well as the Energy harvesting Threshold

.The more value converges to value, the more

Energy harvesting subcarrier will be allocated for

transmission from ST This is only applied in the Relay

cooperative network in which when the Relay occurs

wherein the subsets of are pairing with subsets of

The factor deciding how many types of subsets are allocated

for and are , and If the network is optimised

for information decoding rates, then > , and this is

determined by the iteration algorithm proposed by the

literature [3], where and is optimized Furthermore, if

the network is optimized for Energy harvesting transmission

then should be very small value less than 1

Finally increasing the values of the minimum Energy

harvesting Threshold will increase the chances of EH

subcarrier transmitted from ST as is dependent on in

Equation (13) from the literature

This paper presents the simulation and the analysis

carried out in implementing an Energy harvesting technique

which is the SWIPT, utilized in a cooperative Relay network

consisting of three nodes, and OFDM frequency access

scheme in a 5G wireless channel spectrum By depicting the

literature into the research paper, a simulation is designed,

and the parameters are initialized to the simulated scenario,

it was found that there are parameters in which decides the

behaviour of the cooperative network These parameters are

the initial values of constraints of total transmitted power

from ST , the constraints of the Harvested Energy ,

and the minimum Harvested Energy threshold

Changing these parameters would result in optimising the

cooperative network for whether to have maximum

information decoding transmission or maximum Energy

harvesting transmission Thus, achieving the main objective

of the research which were the optimisation of EH

transmission in a cooperative network by allocating the

appropriate parameter of the simulation to optimise for EH

transmission The method of EH transmission in a relay

network can be beneficial in terms with telecommunication

deployment scenarios, as an example for this, one Relay

station that is stationary is transmitting EH subcarrier to UE

terminal which is moving in a restricted geographical area,

and the UE may require longer operating hours with limited

data transmission Another advantage is that the EH

transmission could aid minimizing the number of recharging

of UE terminals as the EH transmission is operating

indefinitely from any base station There are other

telecommunication scenarios in which the EH transmission

could be beneficial That is why this research could be considered as a starting point to conduct another research with different aspects of cellular communication One recommendation is to use the heretofore residual research to re-simulate the NOMA frequency access scheme in which different power allocation is utilized for UEs or Micro base stations Another recommended research is to utilize the Sparse code multiple access (SCMA) which is considered for 5G data multiplexing, in a combination of filtered-OFDM and Energy harvesting subcarrier allocation to estimate the efficiency of the current 5G capacity with EH transmission To sum up, all the Energy harvesting research aims for is to replenish the centralized power source of UEs from the free signals in travelling in the air , also minimize the dependency of centralized power sources, cut the operation cost of UEs , and this will essentially allow for more greener cellular communication

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