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 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 Sinh Cong Lam Faculty of Electronics and Telecommunications Vietnam National University Hanoi,Vietnam SinhCong.Lam@student.uts.edu.au 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 978-1-7281-3841-1/19/$31.00 ©2019 IEEE - 192 - Authorized licensed use limited to: University of Canberra Downloaded on June 06,2020 at 05:15:44 UTC from IEEE Xplore Restrictions apply 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 - 193 - Authorized licensed use limited to: University of Canberra Downloaded on June 06,2020 at 05:15:44 UTC from IEEE Xplore Restrictions apply 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 timeswitching (TS) and the power-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-Symbolinterference (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 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 mmWave 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 - 194 - Authorized licensed use limited to: University of Canberra Downloaded on June 06,2020 at 05:15:44 UTC from IEEE Xplore Restrictions apply 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 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: - 195 - Authorized licensed use limited to: University of Canberra Downloaded on June 06,2020 at 05:15:44 UTC from IEEE Xplore Restrictions apply 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]: Figure 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 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 - 196 - Authorized licensed use limited to: University of Canberra Downloaded on June 06,2020 at 05:15:44 UTC from IEEE Xplore Restrictions apply 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 TABLE FUNCTIONS OF PROPOSED SIMULATION Function Purpose Declaring the parameters required for the simulation Computing the total allocated power for each subcarrier at PR Function to compute instantaneous information decoding rate at PR per subcarriers K Function to compute the overall instantaneous information decoding rate at PR Function to generate the Path Gain The value of Power at PT The value of instantaneou s information decoding rate The value of instantaneou s information decoding rate Function to compute the Optimal subset of Subcarriers at ST and PR The value of the Path Gain The value of the Optimal subsets of Subcarriers 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 Main Program launcher Water Filling Algorithm 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: Function computing Function computing Path Gain Function Function Computing Main Loop function Figure 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: Resulting value Bandwidth Subcarriers Transmission powers Channel gains Noise 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 - 197 - Authorized licensed use limited to: University of Canberra Downloaded on June 06,2020 at 05:15:44 UTC from IEEE Xplore Restrictions apply 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 SIMULATION PARAMETERS Cases \ parameters 0.0551 0.0215 converging away from 0.25 0.003 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 converging to the figure on the RHS graphically represents different fixed value of the Energy Threshold utilized in the simulation Figure 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 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 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 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 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 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 Energy Threshold Figure 6: For converging away from : Substituting values in the Simulation with a fixed value of the Energy Threshold Figure 7: For higher Values of are relatively closer Figure 8: For higher Values of are relatively apart , While and , While and As demonstrated, Figure 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 righthand side, the information decoding rate at PR vs an incremental of total transmitted power from ST is displayed - 198 - Authorized licensed use limited to: University of Canberra Downloaded on June 06,2020 at 05:15:44 UTC from IEEE Xplore Restrictions apply Figure Simulation result when converging 1111100 Figure Simulation Results when converging away from Figure Simulation Results for Higher Values of - 199 - Authorized licensed use limited to: University of Canberra Downloaded on June 06,2020 at 05:15:44 UTC from IEEE Xplore Restrictions apply Figure 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 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 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 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 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 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 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 combination of and for both - 200 - Authorized licensed use limited to: University of Canberra Downloaded on June 06,2020 at 05:15:44 UTC from IEEE Xplore Restrictions apply 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 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 VI.CONCLUSION 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 filteredOFDM 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 REFERENCES [1] S Buzzi, I Chih-Lin, T E Klein, H V Poor, C Yang, and A J I J o S A i C Zappone, "A survey of energy-efficient techniques for 5G networks and challenges ahead," vol 34, no 4, pp 697-709, 2016 [2] T C R a D I S C m b t P O o t E Union, "Why the EU is betting big on 5G," no vol 15, ISSN 1977-4036, 2015 [3] Z Na et al., "Subcarrier allocation based simultaneous wireless information and power transfer algorithm in 5G cooperative OFDM communication systems," vol 29, pp 164-170, 2018 [4] R Q Hu and Z Zhang, "Dynamic power splitting between information and power transfer in non-orthogonal multiple access," in 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP), 2017: IEEE, pp 1-7 [5] Y Alsaba, C Y Leow, and S K A J I A Rahim, "Full-duplex cooperative non-orthogonal multiple access with beamforming and energy harvesting," vol 6, pp 19726-19738, 2018 [6] J Xue et al., "Transceiver design of optimum wirelessly powered fullduplex MIMO IoT devices," vol 66, no 5, pp 1955-1969, 2018 [7] T X Tran, W Wang, S Luo, and K C J I T o V T Teh, "Nonlinear Energy Harvesting for Millimeter Wave Networks With Large-Scale Antennas," vol 67, no 10, pp 9488-9498, 2018 [8] B C Chung, K Lee, and D.-H J I S J Cho, "Proportional fair energy-efficient resource allocation in energy-harvesting-based wireless networks," vol 12, no 3, pp 2106-2116, 2016 [9] D W K Ng, E S Lo, and R Schober, "Energy-efficient resource allocation in multiuser OFDM systems with wireless information and power transfer," in 2013 IEEE Wireless communications and networking conference (WCNC), 2013: IEEE, pp 3823-3828 [10] T A Khan and R W Heath, "Analyzing wireless power transfer in millimeter wave networks with human blockages," in MILCOM 2017-2017 IEEE Military Communications Conference (MILCOM), 2017: IEEE, pp 115-120 [11] G N Kamga and S J I T o C Aïssa, "Wireless power transfer in mmWave massive MIMO systems with/without rain attenuation," vol 67, no 1, pp 176-189, 2018 [12] F Yuan, S Jin, K.-K Wong, J Zhao, and H J I A Zhu, "Wireless information and power transfer design for energy cooperation distributed antenna systems," vol 5, pp 8094-8105, 2017 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 - 201 - Authorized licensed use limited to: University of Canberra Downloaded on June 06,2020 at 05:15:44 UTC from IEEE Xplore Restrictions apply ... 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... 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. .. 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,