Optimizing Energy Efficiency for Supporting Near-Cloud Access Region of UAV-Based NOMA Networks in IoT Systems

Tran, Huu Q.; Phan, Ca V.; Vien, Quoc-Tuan

doi:https://doi.org/10.1155/2021/4345622

Wireless Communications and Mobile Computing

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Abstract Introduction System Model Conclusion Appendix Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2021 | Article ID 4345622 | https://doi.org/10.1155/2021/4345622

Optimizing Energy Efficiency for Supporting Near-Cloud Access Region of UAV-Based NOMA Networks in IoT Systems

Huu Q. Tran,^1,2Ca V. Phan,¹and Quoc-Tuan Vien³

Academic Editor: L. J. García Villalba

Received13 May 2021

Accepted15 Sept 2021

Published06 Oct 2021

Abstract

Nonorthogonal multiple access (NOMA) and unmanned aerial vehicle (UAV) are two promising technologies for the wireless fifth generation (5G) networks and beyond. On the one hand, UAVs can be deployed as flying base stations to build line-of-sight (LoS) communication links to two ground users (GUs) and to improve the performance of conventional terrestrial cellular networks. On the other hand, NOMA enables the share of an orthogonal resource to multiple users simultaneously, thus improving the spectral efficiency and supporting massive connectivities. This paper presents two protocols, namely, cloud-based central station- (CCS-) based power-splitting protocol (PSR) and time-switching protocol (TSR), for simultaneous wireless information and power transmission (SWIPT) at UAV employed in power domain NOMA-based multitier heterogeneous cloud radio access network (H-CRAN) of Internet of Things (IoT) system. The system model with types of UAVs and two users in which the CCS manages the entire H-CRAN and operates as a central unit in the cloud is proposed in our work. Closed-form expressions of throughput and energy efficiency (EE) for UAVs are derived. In particular, the EE is determined for the impacts of power allocation at CCS, various UAV types, and channel environment. The simulation results show that the performance for CCS-based PSR outperforms that for CCS-based TSR for the impacts of power allocation at the CCS. On the contrary, the TSR protocol has a higher EE than the PSR in the cases of the impact of various UAV types and channel environment. The analytic results match Monte Carlo simulations.

1. Introduction

Nonorthogonal multiple access (NOMA) has been recognized as one of the emerging technologies for the fifth generation (5G) network and beyond in the last decade [1–5]. Compared to conventional orthogonal multiple access (OMA), NOMA exhibits benefits such as low latency, high spectral efficiency (SE), high energy efficiency (EE), and fairness among users [6, 7]. Moreover, a huge number of devices are depicted that they will communicate with each other via wireless Internet connection in the future [8]. These provide a platform for the occurrence of the Internet of Things (IoT) concepts. The IoT concept has been defined by several study groups [9]. In IoT networks, the devices can be machines, sensors, smart phones, or any devices with wireless connection, thereby supporting massive object communication [10]. The key components which can realize the IoT concept in reality are sensor nodes [11, 12]. H-CRAN [13] is a new architecture which can enable users to utilize diverse services with low-cost operation, wide coverage, increased network architecture flexibility, and superior SE and EE by the employment of cloud computing and virtualization techniques [7, 14, 15]. H-CRAN is combined by a heterogeneous cellular network (HCN) [16] and a cloud radio access network (C-RAN) [17] and thus obtains the benefits of HCN and C-RAN. The baseband unit (BBU) pool is the main subsystem of H-CRAN architecture. Instead of utilizing the distributed processing at the base stations (BSs) like in the HCN, the BBU pool exploits a centralized signal processing mechanism to reduce the manufacturing and operating cost [18, 19]. H-CRANs have drawn as a promising new technology and architecture in both industry and academia as well as platform of IoT. Therefore, H-CRAN has been considered the most important access method in the field of IoT [19]. In [20], a novel scheme for allocating the resource based on content sensing was proposed in 5G H-CRAN.

Normally, the nodes constrained by limited power want to prolong their lifetime need to harvest energy from other sources such as power grid, mechanical vibration, wind energy, solar energy, or radio frequency (RF) energy. The RF energy harvesting is one of the techniques which can be exploited in 5G networks [21]. These networks receive energy carried by RF signals and then convert to direct current (DC) energy for consuming and dedicating information transmission [22]. In RF EH relaying communication system, one of the relaying nodes is selected by the source to forward its information to the destination node. To perform this process, the relaying node harvests the energy from the RF-transmitted signal of the source to power up themselves [11]. By employing simultaneous wireless information and power transfer (SWIPT) mechanism, the relaying node not only harvests the energy but also receives the transmitted information from the RF signal of a source [21]. Thus, the SWIPT can be a potential technique to boost the EE and reliability in relay networks [23]. To harvest energy and process information at the relaying node, some protocols were proposed such as power-splitting-based relaying (PSR) and time-switching-based relaying (TSR) protocols along with decode-and-forward (DF) or amplify-and-forward (AF) mechanisms [3, 24]. In PSR, the EH and information processing (IP) are performed during the first phase while forwarding the information at the remaining time block. Otherwise, the TSR protocol divides the block time into three slots in which the EH occupies the first time block, the IP is in the second time slot, and then the information forwarding is in the last time slot of the block time. The combination of the power domain NOMA and SWIPT-based relaying communication was considered by many researchers [3, 25, 26]. Two critical techniques employed in NOMA include successive interference cancellation (SIC) and superposition coding [2]. The power allocation principle has more power for far user and less power for close user [3]. The relay node selection with the best conditions from the source helps obtain an optimized performance in the system [27–29]. In [30], a prioritization-based buffer-aided relay selection scheme which can seamlessly combine the NOMA and OMA transmission in the relay network was proposed. The proposed scheme considerably improved the data throughput at both low and high signal-to-noise ratio (SNR) regions. Thus, this scheme is attractive for cooperative NOMA in the IoT. In [31], two weighted-max-min and max-weighted-harmonic-mean optimal relay selection schemes were proposed for cooperative NOMA with fixed and adaptive power allocations at the relays. In [32], two relay selection algorithms with broadcasting, namely, buffer-aided (BA)-NOMA and BA-NOMA/OMA, were proposed for power-domain NOMA and hybrid NOMA/OMA. The simulation results demonstrated that the outage probability, average throughput, and average delay were improved.

1.1. Motivation and Contribution

In this paper, we propose a new system model along with two simultaneous EH and IP protocols based on PSR and TSR for relaying node in cooperative SWIPT NOMA-based H-CRAN of IoT network. We also propose an iterative algorithm to solve optimization issue for cloud-edge of downlink H-CRAN. Closed-form expressions of the performance metric in terms of throughput and EE are derived.

The main contributions of this paper are summarized as follows: (i)A novel system model is proposed in this work which consists of one cloud-based central station (CCS) and types of unmanned aerial vehicle (UAV) and operates based on cooperative nonorthogonal multiple access (C-NOMA) scheme(ii)An employment of two SWIPT-based EH and IP protocols, namely, CCS-based PSR and CCS-based TSR, is exploited at the relaying node in this model(iii)Closed-form expressions of throughput and EE are derived for the SWIPT NOMA system model(iv)Impacts of power allocation, UAV types, and channel environment are investigated to realize the change of performance metric in the SWIPT NOMA H-CRAN(v)The simulation results show that the EE of PSR is higher than that of TSR under different power allocation conditions at CCS. In contrast, the TSR protocol has a superior EE than PSR protocol under the impacts of UAV types and channel environment

1.2. Related Works

In [13], we proposed energy-efficient NOMA for wireless downlink in a multitier heterogeneous cellular network coordinated by a CCS, namely, H-CRAN. The proposed NOMA allocates different powers to different BS types depending on their relative distances to the CCS and the channel quality of the wireless links to enhance the spectrum efficiency and achievable throughput. Moreover, we investigated the employment of EH- and DF-based NOMA in a SWIPT system. Two PSR and TSR protocols are considered. In PSR and TSR protocols, the energy-constrained relay node uses apportion of the received power for EH, while the remaining energy is for IP [3–5]. In [33], a subchannel assignment and power allocation in multitier 5G H-CRANs were investigated to improve the system throughput. In [34], a power allocation was proposed for the wireless downlink in the H-CRAN. The EE of the practical NOMA-based H-CRAN was analyzed. This proposed scheme obtains a four times higher EE over the frequency division multiple access scheme. In [35], the EE and SE can be considerably enhanced by employing H-CRANs. In [36], a remote radio head selection algorithm along with a cross-layer EE-based resource allocation scheme was proposed to enhance the EE of the users in power domain NOMA H-CRAN to maximize the EE of the elastic users. In [37], the EE of an H-CRAN with several green remote radio heads powered by energy harvesting (EH) modules was studied. The maximum EE of the system was achieved by solving the optimization problem, namely, the mixed integer nonlinear programming problem. The optimization problem was solved by the mesh adaptive direct search algorithm, and thus, the higher EE was obtained. The complexity and grid power consumption of the optimization problem is low.

In this paper, we combine our research works on H-CRAN, EH, IP, and DF-based using PSR and TSR protocols in a SWIPT C-NOMA system.

1.3. Organization

The rest of the paper is organized as follows. Section 2 presents the detail of the proposed system model and assumptions. Section 3 analyzes the performance parameters including throughput and energy efficiency of the system. Section 4 discusses the simulation results. Finally, Section 5 gives the main conclusions.

2. System Model

In the system model, to manage the whole H-CRAN, a CCS working as a central unit in the cloud is utilized. The distance between and CCS is . All are assumed to connect to the CCS using wireless backhaul links with perfectly synchronous signals.

Table 1 lists the definition of the parameters used in the model and through the paper.

The CCS transmits the signal to users using multiple access points. Each is equipped with a single antenna and operates in half duplex (HD) mode, where .

In our work, the best UAV selection case among UAVs is considered. Moreover, all UAVs are provided via wireless energy from the CCS instead of the conventional powers such as grid power and solar energy. The channels from the CCS to and from to two users and are flat Rayleigh block fading.

As shown in Figure 1, the shadowing impact and path loss of are less severe than ; the relation between and satisfies .

2.1. Two EH Protocols at

At the , we consider two EH mechanisms including CCS-based PSR and CCS-based TSR protocols at the .

2.1.1. The CCS-Based PSR Protocol for the Energy Harvesting at

Figure 2 describes a diagram illustration of CCS-based PSR scheme for harvesting energy at in the block time of . The received signal power at is indicated by . It is assumed that the CCS sends the information to in the half-block of , while the information is transmitted from to two users and in the remaining time of .

The transmitted signal at the CCS is given by the following:

We assume that uses the harvested energy to forward the signal to and . The power of transmitting-receiving circuit of is negligible.

We can briefly describe the operation of the system as follows. Each communication block occupies two time slots. All blocks are normalized to unit. In the first slot time, the CCS transmits the superposed signal, i.e., . The expression of satisfies to 1, and without loss of generality, it is assumed that . Applying superposition signal coding at the CCS as shown in the cooperative NOMA diagram [27], the observed signal at is given by the following:

Based on the power-splitting architecture in [28] (Figure 3(b)), by employing the CCS-based PSR protocol, divides the received energy into (i) harvested energy and (ii) energy for processing the information. The harvested energy at can be computed by the following:

The total harvested energy at can be expressed by the following:

Assuming that the EH at each is equal, the power of for CCS-based PSR protocol can be determined from Equation (3) as follows:

It is assumed that values at as well as at UAVs are equal. For simplicity, is named the EH efficiency. depends on the energy conversion process from RF signal to DC in the receiver at .

2.1.2. The CCS-Based TSR Protocol for the Energy Harvesting at

Figure 3 illustrates the CCS-based TSR protocol of EH system. The block diagram for EH and information receiver in TSR protocol is based on [28] (Figure 2(b)). In the total time block , is utilized for EH while is for forwarding the information. In the , the first is dedicated for transmitting data from the CCS to and the remaining is for forwarding data from to user . The harvested energy at is given by the following:

The total harvested energy at can be expressed by the following:

Therefore, the power of for CCS-based TSR protocol can be determined from Equation (6) as follows:

In downlink power domain NOMA, SIC mechanism is exploited to decode the received signals at receivers, while superposition coding is applied to the code of the transmitted signals at transmitters. Thus, the SIC process is only considered at UAVs to achieve the best data forwarding as well as to allocate a higher power to and in our work. For instance, at , the best UAV first decodes symbol by treating symbol as a noise and then performs SIC process to achieve signal. Therefore, the signal-to-interference-plus-noise ratio (SINR) for symbol and SNR for symbol are given by the following:

where represents the transmit SNR. It is noted from Figure 1 that processes signals and during the first time slot; then, the selected UAV sends the signal to two users and during the second time slot. Thus, the received signal at is combined by , and noise and is given by the following: where and is the channel gain between the selected UAV and .

From Equation (11), the SINR at is determined by applying SIC, i.e., decodes while treating as a noise, as follows: where denotes the transmitted SNR at . Similarly, since both and are in , it is necessary for SIC to decode its own symbol . To perform SIC, decodes symbol by treating symbol as noise according to their priority power level and cancels using SIC to obtain symbol . Therefore, the SINR for at is given by the following:

The SNR for at decoded by its own is given by the following:

3. Performance Analysis

3.1. Throughput of the System

From Equation (2), for EH, the achievable throughput in bits/s at can be given by the following: where denotes the EH coefficient for CCS-based PSR and CCS-based TSR protocols and is expressed by the following:

The total throughput for EH in NOMA can be given by the following:

For IP, the achievable throughput in bits/s at is expressed by the following: where represents the channel bandwidth and denotes the IP coefficient for CCS-based PSR and CCS-based TSR protocols and is given by the following

The total throughput for IP in NOMA is given by the following:

3.2. The Consumed Power Model

For the wireless downlink, the total consumed power includes UAVs, CCS, and backhaul powers.

3.2.1. The Consumed Power at a

In a realistic cellular network, the consumed power of a UAV consists of the signal processing power at power amplifier (PA), transceivers, RF, and base band (BB) unit. Besides, the power attenuation caused by DC power, main source (MS), cooling, and the noneffectiveness of PA needs to be considered.

, , and represent the radiated output power at an antenna element, RF power, and BB power of a -th type UAV, respectively. The consumed power of a -th type UAV can be given by the following: where is the sequence number of transmitting/receiving, is the PA efficiency, is the interforwarding loss, is the loss of the DC-DC power, is the loss of MS, and is the cooling loss at the -th type UAV.

3.2.2. The Backhauling Power

For downlink from the CCS to a UAV, the consumed power caused by wireless backhaul consists of the downlink interface power of wireless switching as well as general switching at the CCS. represents the backhauling power for downlink from the CCS to a -th type UAV. Assuming that downlink interfaces and switching at UAV utilize the same type, is expressed by the following: where is the number of interfaces for each switching, is the maximum consumed power of the switching as all interfaces are in use, is the power for one interface in the general switching, is the flow-through switching node, and is the maximization of the access flow of the switching at the -th type UAV which can process. Here, is a critical component which effects on the consumed power for the general connection board of the switching.

In general, the total consumed power for H-CRAN downlink is given by the following:

3.2.3. Power Allocation for Wireless Downlink in H-CRAN

For simplicity, it is assumed that the power of interference at UAVs of a cell type is equal, i.e., . We consider the power allocation for UAVs in the -th cell type and represent the total transmitted power for UAVs in the -th cell type and denote the total transmitted power at CCS for this -th UAV type.

Without loss of generality, it is assumed that , where denotes the normalized channel gain of the link from CCS to over the noise power. can be expressed by the following:

The power allocated at UAVs in -th type cells satifies . We denote as a ratio of power allocation for and . It means that

According to recusive rule, Equation (25) is computed by the following:

Similarly, the power allocation for , is computed by the following:

At CCS, the total transmitted power for UAV in the -th cell type can be obtained by the following:

Therefore, the power for can be given by the following:

Plugging Equation (29) into Equation (26), the power for each UAV, i.e., , is computed by the following:

3.3. Efficiency Analysis and Optimization for Cloud-Edge in H-CRAN Downlink

3.3.1. Energy Efficiency of NOMA System

and denote the total throughput in bits/s and the EE in bits/J, respectively. The EE is defined as a ratio of the total throughput over the total consumed power in the entire network. It can be given by the following: where is calculated by using Equation (23).

For NOMA H-CRAN downlink, the total EE is obtained by the following:

Proof. See Appendix.☐☐

3.3.2. Optimization Problem for Supporting Near-Cloud Access Region Simultaneously for the Best UAV Selection and Transmitted EH to User

As presented in Equation (33), the efficiency of NOMA is affected by the quantity of cells of the different types. In reality, a huge number of cells causes low EE and significantly reduced throughput at cloud access region. For simplicity, it is assumed that the cell number, i.e., , in H-CRAN is known and is constant. The aim of optimization issue is to find the maximum UAV of each cell type which this cell can be supported through constraints of the minimum throughput requirements at edge-cloud and the available limited power at the CCS. In the group of UAVs, it is only the best UAV which is selected to serve user . This selection is based on the best channel selection from many downlink signal channels from UAVs. The optimization problem for UAVs of -th cell type, , can be formulated by Equations (34) and (35) where is the maximum power allocated for UAVs of the -th cell type, is the edge-cloud threshold throughput, and LHS of the constraint (Equation (35)) corresponding with the edge-cloud throughput, i.e., , , is given by Equation (18) with . For simplicity, let denote the maximum number of the -th UAV type. To find , it can be performed by using the repeat algorithm as presented in a brief algorithm 1. The energy efficiency can be correspondingly determined by Equation (33) where .

4. Simulation Results

4.1. Simulation Parameters

The simulation parameters for evaluation scenarios of the SWIPT-based NOMA H-CRAN model are listed in Table 2.

4.2. The Performance for NOMA in Downlink H-CRAN

4.2.1. The Impacts of Power Allocation at CCS

First, we analyze the impacts of power allocation at the CCS on the performance of the proposed NOMA system in downlink H-CRAN. Figures 4–6 plot the EE of NOMA versus UAV number and different power levels at the CCS. Specifically, the power of 3 CCSs, i.e., , is simulated in urban cellular network model with . The distance from UAV to the CCS is within range of from 100 m to 8 km with a step of 200 m. Its corresponding channel gain is from 20 to 0 dB, while the attenuation factor is 0.5. Based on , it is observed from Figure 4 that the EE for and , and CCS-based PSR protocol is the most superior at 5409 (bits/J), while this value for CCS-based TSR protocol is 979 (bits/J) at . Similarly, the EE for and , and CCS-based PSR protocol is the highest at 3468 (bits/J), while this value for CCS-based TSR is 591 (bits/J) at . In the case of , the highest value of the EE for CCS-based PSR at is 1844 (bits/J), while the highest value of the EE for CCS-based TSR at is 309 (bits/J). This implies that the EE of CCS-based PSR is higher than that of CCS-based TSR. Similarly, based on and (), Figures 5 and 6 show that the CCS-based PSR protocol achieves a higher EE than the CCS-based TSR.

4.2.2. The Impacts of Types

Figures 7–9 describe the dependence of EE of NOMA in downlink H-CRAN on UAV types. Specifically, three types of UAVs such as macro-UAV, RRH, and micro-UAV are considered. The exploitation of mentioned UAVs shows the best EE performance. The exploitation of micro-UAV can achieve the highest EE performance for large networks, while this EE can be obtained for any UAV types for small networks. It is due to the fact that there is a difference in consuming power at the different types of UAVs. This agrees with the impacts of the types as well as the number of UAVs on the performance of H-CRAN. It is observed from Figure 7 that the highest EE value of CCS-based PSR for -based micro-UAV is 1352 (bits/J) at , while this value for CCS-based TSR is 1373 (bits/J) at . In the case of RRH UAV, while the EE for CCS-based PSR achieves the highest value of 1133 (bits/J) at , that for CCS-based TSR obtains the highest value of 1152 (bits/J) at . Finally, in the case of macro-UAV, the highest EE for CCS-based PSR is 1025 (bits/J) at , and the highest EE for CCS-based TSR is 1152 (bits/J) at . This implies that the CCS-based TSR has a higher EE than CCS-based PSR. Similarly, based on and (), Figures 5 and 7 show that the CCS-based TSR protocol achieves a higher EE than the CCS-based PSR.

4.2.3. The Impacts of Channel Environment

Figure 10 illustrates the EE with a maximum number of UAVs versus the threshold edge-cloud throughput in which urban and shadowed urban environments are considered. From the figure, one can see that the maximum number of UAUs in both environments reduces as the edge-cloud throughput increases. Besides, Figure 10 also shows that the urban model can support two times UAVs, and its EE is higher than that for the shadowed urban model. Besides, in the case of , the highest EE for CCS-based PSR is 889 (bits/J) at and the highest EE for CCS-based TSR is 912 (bits/J) at . In the case of , the CCS-based PSR achieves the best EE of 1861 (bits/J) at , while the CCS-based TSR achieves the best EE of 1882 (bits/J) at . In general, it can be concluded that the CCS-based TSR has a better EE than CCS-based PSR.

5. Conclusion

Two CCS-based PSR and CCS-based TSR protocols for EH and IP in cooperative SWIPT H-CRAN NOMA systems applied in IoT networks were presented in this paper. The closed-form expressions of throughput and the EE for UAVs were derived. The numerical simulation results show that the CCS-based PSR protocol achieved a higher EE as compared to CCS-based TSR protocol under the impacts of the power allocation at CCS. Specifically, the max EE of the NOMA system () for CCS-based PSR is higher than the CCS-based TSR protocol about 3.4 times, 3.5 times, and 3.9 times versus UAV number with , , and , respectively. Moreover, the CCS-based PSR protocol achieved a lower EE than the CCS-based TSR for the impact of UAV types and the impact of the channel environment. Specifically, the max EE of the NOMA system () for CCS-based PSR is lower than the CCS-based TSR protocol about 1.265 times, 1.229 times, and 1.205 times versus UAV number with micro-UAVs, RRHs, macro-UAVs, respectively. The analytic results matched the simulation results. For future work, we can develop the system using multiple antennas at two users and to enhance the performance of the system.

Appendix

The EE for EH is determined by substituting Equations (17) and (23) into Equation (32) and is obtained by the following:

The EE for IP is determined by substituting Equations (20) and (23) into Equation (32) and is obtained by the following:

Adding (36) and (37) and Equation (33) can be obtained.

The proof is completed.

Data Availability

The data used to support the findings of this study are included in the paper.

Conflicts of Interest

The authors declare there is no conflict of interest in this manuscript.

Authors’ Contributions

Huu Q. Tran is responsible for the conceptualization, methodology, software, formal analysis, and investigation. Huu Q. Tran is responsible for the data curation and writing the original draft preparation. Huu Q. Tran, Ca V. Phan, and Quoc-Tuan Vien are responsible for the validation and resources. Huu Q. Tran, Ca V. Phan, and Quoc-Tuan Vien are responsible for the writing, reviewing, and editing. Ca V. Phan and Quoc-Tuan Vien are responsible for the supervision.

Acknowledgments

This study was self-funded by the authors.

References

S. T. Ooi, R. Ngah, and M. H. Azmi, “Full-duplex user-centric communication using non-orthogonal multiple access,” TELKOMNIKA Telecommunication Comput Electron and Control, vol. 17, no. 5, pp. 2169–2178, 2019.
View at: Publisher Site | Google Scholar
S. R. Islam, N. Avazov, O. A. Dobre, and K. S. Kwak, “Power-domain non-orthogonal multiple access (NOMA) in 5G systems: potentials and challenges,” IEEE Communications Surveys Tutorials, vol. 19, no. 2, pp. 721–742, 2017.
View at: Publisher Site | Google Scholar
H. Q. Tran, T. T. Nguyen, C. V. Phan, and Q. T. Vien, “Power-splitting relaying protocol for wireless energy harvesting and information processing in NOMA systems,” IET Communications, vol. 13, no. 14, pp. 2132–2140, 2019.
View at: Publisher Site | Google Scholar
H. Q. Tran, C. V. Phan, and Q. T. Vien, “Power splitting versus time switching based cooperative relaying protocols for SWIPT in NOMA systems,” Physical Communication, vol. 41, article 101098, 2020.
View at: Publisher Site | Google Scholar
H. Q. Tran, C. V. Phan, and Q. T. Vien, “Performance analysis of power-splitting relaying protocol in SWIPT based cooperative NOMA systems,” EURASIP Journal on Wireless Communications and Networking, vol. 2021, no. 1, Article ID 1981, 2021.
View at: Publisher Site | Google Scholar
L. P. Liyn, H. Ghani, F. N. Roslim et al., “Ant-colony and nature-inspired heuristic models for NOMA systems: a review,” Telkomnika, vol. 18, no. 4, pp. 1754–1761, 2020.
View at: Publisher Site | Google Scholar
M. Vaezi, Z. Ding, and H. V. Poor, Multiple access techniques for 5G wireless networks and beyond, Springer, Cham, 2019.
View at: Publisher Site
L. Ericsson, More Than 50 Billion Connected Devices, vol. 14, White Paper, 2011.
O. Elijah, T. A. Rahman, I. Orikumhi, C. Y. Leow, and M. H. D. N. Hindia, “An overview of Internet of Things,” Telkomnika, vol. 18, no. 5, pp. 2320–2327, 2020.
View at: Publisher Site | Google Scholar
F. G. Abdulkadhim, Z. Yi, M. Khalid, and S. A. Waheeb, “A survey on the applications of IoT: an investigation into existing environments, present challenges and future opportunities,” Telkomnika, vol. 18, no. 3, p. 1447, 2020.
View at: Publisher Site | Google Scholar
A. Rauniyar, P. Engelstad, and O. N. Østerb, “RF energy harvesting and information transmission based on power splitting and NOMA for IoT relay systems,” in 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA), pp. 1–8, Cambridge, MA, USA, 2018.
View at: Publisher Site | Google Scholar
J. M. Corchado, J. Bajo, D. I. Tapia, and A. Abraham, “Using heterogeneous wireless sensor networks in a telemonitoring system for healthcare,” IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 2, pp. 234–240, 2010.
View at: Publisher Site | Google Scholar
H. Q. Tran, P. Q. Truong, C. V. Phan, and Q. Vien, “On the energy efficiency of NOMA for wireless backhaul in multi-tier heterogeneous CRAN,” in 2017 International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom), pp. 229–234, Da Nang, Vietnam, 2017.
View at: Google Scholar
M. Peng, Y. Li, Z. Zhao, and C. Wang, “System architecture and key technologies for 5G heterogeneous cloud radio access networks,” Network, vol. 29, no. 2, pp. 6–14, 2015.
View at: Publisher Site | Google Scholar
X. Wang, C. Cavdar, L. Wang et al., “Joint allocation of radio and optical resources in virtualized cloud RAN with CoMP,” in IEEE Global Communications Conference (GLOBECOM), pp. 1–6, Washington, DC, USA, 2016.
View at: Publisher Site | Google Scholar
S. Chinnadurai and D. Yoon, “Energy efficient MIMO-NOMA HCN with IoT for wireless communication systems,” in 2018 International Conference on Information and Communication Technology Convergence (ICTC), pp. 856–859, Jeju, Korea (South), 2018.
View at: Publisher Site | Google Scholar
M. F. Hossain, A. U. Mahin, T. Debnath, F. B. Mosharrof, and K. Z. Islam, “Recent research in cloud radio access network (C-RAN) for 5G cellular systems - a survey,” Journal of Network and Computer Applications, vol. 139, pp. 31–48, 2019.
View at: Publisher Site | Google Scholar
M. Peng, K. Zhang, J. Jiang, J. Wang, and W. Wang, “Energy-efficient resource assignment and power allocation in heterogeneous cloud radio access networks,” IEEE Transactions on Vehicular Technology, vol. 64, no. 11, pp. 5275–5287, 2015.
View at: Publisher Site | Google Scholar
X. Zhang, M. Jia, X. Gu, and Q. Guo, “An energy efficient resource allocation scheme based on cloud-computing in H-CRAN,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4968–4976, 2019.
View at: Publisher Site | Google Scholar
J. Yang, J. Luo, F. Lin, and J. Wang, “Content-sensing based resource allocation for delay-sensitive VR video uploading in 5G H-CRAN,” Sensors, vol. 19, no. 3, p. 697, 2019.
View at: Publisher Site | Google Scholar
T. N. Nguyen, M. Tran, P. Van-Duc, N. Hoang-Nam, and N. Thanh-Long, “Outage probability analysis of EH relay-assisted non-orthogonal multiple access (NOMA) systems over block rayleigh fading channel,” International Journal of Electrical and Computer Engineering, vol. 9, no. 5, pp. 3607–3614, 2019.
View at: Publisher Site | Google Scholar
L. R. Varshney, “Transporting information and energy simultaneously,” in 2008 IEEE International Symposium on Information Theory, pp. 1612–1616, Toronto, ON, Canada, 2008.
View at: Publisher Site | Google Scholar
Z. Sun, L. Yang, J. Yuan, and D. W. K. Ng, “Physical-layer network coding based decoding scheme for random access,” IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp. 3550–3564, 2019.
View at: Publisher Site | Google Scholar
A. A. Nasir, X. Zhou, S. Durrani, and R. A. Kennedy, “Relaying protocols for wireless energy harvesting and information processing,” IEEE Transactions on Wireless Communications, vol. 12, no. 7, pp. 3622–3636, 2013.
View at: Publisher Site | Google Scholar
N. T. Do, D. B. da Costa, T. Q. Duong, and B. An, “A BNBF user selection scheme for NOMA-based cooperative relaying systems with SWIPT,” IEEE Communications Letters, vol. 21, no. 3, pp. 664–667, 2017.
View at: Publisher Site | Google Scholar
Y. Yuan, Y. Xu, Z. Yang, P. Xu, and Z. Ding, “Energy efficiency optimization in full-duplex user-aided cooperative SWIPT NOMA systems,” IEEE Transactions on Communications, vol. 67, no. 8, pp. 5753–5767, 2019.
View at: Publisher Site | Google Scholar
Z. Ding, H. Dai, and H. V. Poor, “Relay selection for cooperative NOMA,” IEEE Wireless Communications Letters, vol. 5, no. 4, pp. 416–419, 2016.
View at: Publisher Site | Google Scholar
Z. Yang, Z. Ding, Y. Wu, and P. Fan, “Novel relay selection strategies for cooperative NOMA,” IEEE Transactions on Vehicular Technology, vol. 66, no. 11, pp. 10114–10123, 2017.
View at: Publisher Site | Google Scholar
H. Lei, Z. Yang, K. H. Park et al., “Secrecy outage analysis for cooperative NOMA systems with relay selection schemes,” IEEE Transactions on Communications, vol. 67, no. 9, pp. 6282–6298, 2019.
View at: Publisher Site | Google Scholar
M. Alkhawatrah, Y. Gong, G. Chen, S. Lambotharan, and J. A. Chambers, “Buffer-aided relay selection for cooperative NOMA in the Internet of Things,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5722–5731, 2019.
View at: Publisher Site | Google Scholar
P. Xu, Z. Yang, Z. Ding, and Z. Zhang, “Optimal relay selection schemes for cooperative NOMA,” IEEE Transactions on Vehicular Technology, vol. 67, no. 8, pp. 7851–7855, 2018.
View at: Publisher Site | Google Scholar
N. Nomikos, T. Charalambous, D. Vouyioukas, G. K. Karagiannidis, and R. Wichman, “Hybrid NOMA/OMA with buffer-aided relay selection in cooperative networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 13, no. 3, pp. 524–537, 2019.
View at: Publisher Site | Google Scholar
S. Ali, A. Ahmad, R. Iqbal, S. Saleem, and T. Umer, “Joint RRH-association, sub-channel assignment and power allocation in multi-tier 5G C-RANs,” IEEE Access, vol. 6, pp. 34393–34402, 2018.
View at: Publisher Site | Google Scholar
Q. T. Vien, T. A. le, B. Barn, and C. V. Phan, “Optimising energy efficiency of non-orthogonal multiple access for wireless backhaul in heterogeneous cloud radio access network,” IET Communications, vol. 10, no. 18, pp. 2516–2524, 2016.
View at: Publisher Site | Google Scholar
R. Q. Hu and Yi Qian, “An energy efficient and spectrum efficient wireless heterogeneous network framework for 5G systems,” IEEE Communications Magazine, vol. 52, no. 5, pp. 94–101, 2014.
View at: Publisher Site | Google Scholar
A. Mokdad, P. Azmi, N. Mokari, M. Moltafet, and M. Ghaffari-Miab, “Cross-layer energy efficient resource allocation in PD-NOMA based H-CRANs: implementation via GPU,” IEEE Transactions on Mobile Computing, vol. 18, no. 6, pp. 1246–1259, 2019.
View at: Publisher Site | Google Scholar
N. A. Chughtai, M. Ali, S. Qaisar, M. Imran, M. Naeem, and F. Qamar, “Energy efficient resource allocation for energy harvesting aided H-CRAN,” IEEE Access, vol. 6, pp. 43990–44001, 2018.
View at: Publisher Site | Google Scholar

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Copyright © 2021 Huu Q. Tran et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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