Resource allocation for wireless powered iot network doctor of philosophy major electronics engineering

Sum Rate Maximization for Multi-user Wireless Powered IoT Network with Non-linear Energy Harvester: Time and Power Allocation .... 12 2.3Joint Energy Harvesting Time and Power Allocation

Dissertation

Resource Allocation for Wireless

Powered IoT Network

무선전력 IoT 네트워크를 위한 자원할당 최적화 기법

Graduate School, Myongji University Department of Electronics Engineering

Nguyen Tien Tung

Dissertation Advisor Yong-Hwa Kim

February, 2021

Resource Allocation for Wireless

Powered IoT Network

Submitted in partial fulfillment of the requirements for

the Ph.D degree in Electronics Engineering

Resource Allocation for Wireless

Powered IoT Network

Graduate School, Myongji University Department of Electronics Engineering

Nguyen Tien Tung

We hereby recommend that the dissertation by the above candidate for the Ph.D degree in Electronics Engineering be

Acknowledgement

First and foremost, I would like to express my sincere gratitude to my advisor, Professor Yong-Hwa Kim, for guiding, supporting and valuable advice during my study I was very fortunate and happy to have such a great advisor and mentor for my Ph.D study I have obtained a great deal of experiences from my advisor

I would also like to extend my appreciation to the rest of my dissertation committee members, including Prof JungKuk Kim, Prof JaeMin Kim, Prof Jong-Ho Lee, Prof SeongWook Lee for their encouragement and valuable comments The valuable comments and feedbacks helped me to improve my dissertation

I would like to thank all Professors at the Department of Electronics Engineering, Myongji University who taught and helped me to complete this dissertation

I would also like to thank Dr Nguyen Van Dinh, Dr Pham Quoc Viet for their very kind support Special thanks to all my colleagues at the ICT Information Technology Convergence Technology for supporting and sharing everything

Last but not the least, I would like to dedicate this dissertation and show my deepest gratitude and appreciation to my family, my beloved wife, Nguyen Thi Nhu Quynh and my two lovely daughters, Nguyen Ngoc Dan Khue, Nguyen Ngoc Hoa Nhien

i

Table of Contents

List of Figures iv

List of Tables vi

Abstract vii

Chapter 1 Introduction 1

1.1 Background 1

1.2 Contribution 2

1.3Dissertation Outline 3

Chapter 2 Sum Rate Maximization for Multi-user Wireless Powered IoT Network with Non-linear Energy Harvester: Time and Power Allocation 4

2.1 Introduction 4

2.2Description of the System Model and Energy Harvesting Models 8

2.2.1 System Model 9

2.2.2 Energy Harvesting Model 10

2.2.3TDMA-enabled WPCN 11

2.2.4OFDMA-enable WPCN 12

2.3Joint Energy Harvesting Time and Power Allocation for TDMA-enable WPCN 15

2.4 Joint Energy Harvesting Time, Subcarrier Allocation and Power Allocation for

OFDMA-enabled WPCN 20

2.5 Simulation Results 25

2.6 Conclusion 38

Chapter 3 Resource Allocation for Energy Efficiency in OFDMA-Enabled WPCN 39 3.1 Introduction 39

3.2System Model and Power Consumption Model 42

3.2.1 System Model 42

3.2.2 Power Consumption Model 44

3.2.3 Problem Formulation 45

3.3 Solution to Energy Efficiency of OFDMA-enabled WPCN 46

3.4 Numerical Results 52

3.5Conclusion 56

Chapter 4 Resource Allocation for AF Relaying Wireless-Powered Networks with Nonlinear Energy Harvester 58

4.1 Introduction 58

4.2System Model and Problem Description 60

4.2.1 System Model 60

4.2.2 WET Phase 61

4.2.3 WIT Phase 62

4.3 Problem Formulation 63

iii

4.4 Proposed Solution 64

4.5 Numerical Results 71

4.6Conclusion 76

Chapter 5 Summary and Conclusions 77

References 79

Abstract in Korean 86

List of Figures

Fig 2.1 System model 9

Fig 2.2 (a) Frame structure of TDMA-enabled WPCN (b) Frame structure of OFDMA-enabled WPCN 11

Fig 2.3 Convergence behavior of the proposed algorithms 27

Fig 2.4 Average sum-rate versus the transmit power of the PB for different numbers of antennas at the AP 28

Fig 2.5 Average sum-rate versus the distance between the PB and AP 29

Fig 2.6 EH time versus the distance between the AP and PB 30

Fig 2 7 Performance comparison between the proposed algorithms and the equal time allocation (ETA) algorithm 31

Fig 2.8 Performance comparison between the proposed algorithms and the fixed EH time-based algorithm 32

Fig 2.9 Performance comparison between the proposed algorithms and the fixed EH time-based algorithm 33

Fig 2.10 EH time versus the transmit power of the PB 34

Fig 2.11 Average sum-rate versus the number of users 36

Fig 2.12 Average sum-rate versus the energy conversion efficiency 37

Fig 2.13 Average sum-rate versus the EH time 38

Fig 3.1 System model 43

Fig 3.2 Convergence of the proposed algorithm with different number of antennas at the AP 54

v

Fig 3.3 Energy efficiency for all schemes and EH durations of the proposed scheme versus PS's transmit power 55Fig 3.4 Energy efficiency versus number of users 56 Fig 4 1 Illustration of an RWPCN 61Fig 4.2 Convergence of the proposed algorithm with different numbers of antennas at the

BS and different power levels at the PS 72Fig 4.3 The e2e sum throughput of the system for different schemes versus the transmit power of the BS 73Fig 4 4 The e2e sum throughput versus the number of users 74Fig 4 5 End-to-end sum throughput versus the number of antennas at the BS 75

List of Tables

Table 2.1 Simulation parameters 26

Table 3.1 Simulation parameters 52

Table 3.2 Complexity analysis for different schemes 53

Table 4.1 Complexity analyses for different schemes 72

Table 4.2 Evaluation of user fairness issue 76

vii

Resource Allocation for Wireless Powered

Communication Networks

Nguyen Tien Tung

Department of Electronics Engineering Graduate School, Myongji University

Directed by Professor Kim Yong Hwa

Our first work aims to maximize the sum rate (SR) of a wireless powered communication network (WPCN), where an energy-constrained access point (AP) harvests energy from the radio-frequency signals transmitted by a power beacon (PB) for assisting user data transmission In the wireless information transfer (WIT) phase, AP employs the harvested energy to convey independent signals to multiple users through either time-division multiple access (TDMA) or orthogonal frequency-division multiple access (OFDMA) We jointly optimize the energy harvesting (EH) time and the AP power allocation, considering both the conventional linear and practical nonlinear EH models at the AP The optimization problems of both TDMA- and OFDMA-enabled WPCNs are formulated as nonconvex programs, which are challenging to solve globally To achieve an efficient optimal solution to the problem of TDMA-enabled WPCN, we first decompose the original nonconvex problem into three convex subproblems, and then propose a low-complexity iterative algorithm for its solution For the OFDMA-enabled WPCN, the problem belongs to a difficult class of mixed-integer nonconvex programming due to the

involvement of binary variables for subcarrier allocation To overcome this issue, we convert the problem to a quasi-convex problem and then employ a bisection search to obtain the optimal solution Simulation results are provided to confirm the benefit of jointly optimizing the EH time and the AP power allocation compared to baseline schemes The performance of the proposed TDMA-enabled WPCN is shown to be superior to that of the proposed OFDMA-enabled WPCN in terms of SR when the transmit power of PB and the number of antennas of AP are relatively large

The second work considers a wireless powered communication network (WPCN), where an energy-constrained device directly uses harvested energy from a power transfer source to transmit independent signals to multiple Internet of Thing (IoT) users using orthogonal frequency division multiple access (OFDMA) Our goal is to maximize the system energy efficiency (EE) by jointly optimizing the duration of energy harvesting (EH), subcarrier and power allocation The formulated problem is a mixed integer nonlinear programming (MINLP) problem due to the presence of binary assignment variables, and thus it is very challenging to solve it directly By leveraging Dinkelbach method, a very efficient iterative algorithm with closed-form solutions in each iteration is developed, where its convergence is guaranteed Numerical results show that the proposed algorithm obtains a fast convergence and outperforms baseline algorithms Notably, they also reveal that the power source should transmit its maximum allowable power to obtain the optimal EE performance

The third work considers a relay-based wireless-powered communication network to assist wireless communication between a source and multiple users In particular, the relay adopts a nonlinear energy model to harvest energy from a power beacon and subsequently uses it for information transmission over time division multiple access Aiming at the

ix

maximization of end-to-end (e2e) sum throughput, we formulate a novel optimization problem that jointly optimizes the power and time fraction for energy and information transmission For a simple yet efficient solution for the nonconvex problem, we first convert it to a more computationally tractable problem and then develop an iterative algorithm, in which closed-form solutions are obtained at each iteration The effectiveness

of our proposed approach is verified and demonstrated through simulation results Moreover, the results reveal that the source should transmit with its maximum allowable power budget to obtain the optimal e2e sum throughput

Keyword

Amplify-and-forward relay, nonconvex optimization, nonlinear energy harvester, IoT, Energy-efficiency, Wireless powered communication networks, TDMA, OFDMA, resource allocation, subcarrier assignment

Chapter 1 Introduction

1.1 Background

With the exponential growth in the number of smart devices, it is predicted that there will

be 28 billion connected devices in the globe by 2021 [1] These devices are identified as Internet of Things (IoT) devices that perform tasks in a wide range of applications including industrial automation, smart home, connected vehicles, environmental monitoring, home intelligent machine, etc [2] However, from another point of view, the demand of spectrum usage as well as energy consumption has increased dramatically Therefore, spectrum utilization and energy consumption management for any wireless communication network become the most critical issues that need to be extensive studied [3]

Moreover, because of the rapidly increase in energy costs and the tremendous carbon footprints of existing systems, an environment-friendly solution, which is radio frequency (RF) energy harvesting technology, for both maintain the network lifetime and enhance the communication quality is proposed [4] Exploiting the characteristics of RF signals that carry both information and power, energy-constrained devices can be charged without replacing their batteries regularly This technology has an alternative role for maintaining wireless communication networks, especially wireless IoT networks, deployed in the places with poor or unstable fixed power sources, while reducing costs or danger and

-2-

increasing convenience [5] Relying on the technology, two main research directions, such

as simultaneous wireless information and power transfer (SWIPT) and wireless powered communication network (WPCN) has been widely investigated For SWIPT-based network, the two functions, i.e., information and energy transfer, are implemented in the same source For WPCN-based network, the information transfer and energy transfer are separately deployed at the different sources

In addition, the performance of EH-based networks needs to be carefully evaluated in terms of resource usage, which is scarce when the number of devices soars Resource allocation, e.g., sum-rate, energy efficiency for EH-based networks is critical and has become a hot topic in recent years However, resource management in EH-based networks

is completely different from convention systems without EH and more challenge in designing solutions Because apart from the critical and popular issues related to multiple access, spectrum usage, coverage expansion, or energy efficiency have extensively studied

in conventional non-EH systems The time allocation for the EH and the information transmission durations arising in the EH-based networks should also be considered to design

1.2 Contributions

This dissertation has contributed to address spectral and energy efficiency issues in the field of IoT WPCNs We develop efficient solutions for resource allocation the such networks The key contributions are summarized as follows:

• Sum rate maximization problem of a multi-users WPCN adopting time-division multiple access (TDMA) and orthogonal frequency-division multiple access

(OFDMA) is investigated This work is the first attempt to design a solution by jointly optimizing the energy harvesting (EH) time and power allocation in transmission phase The contributions have published in [6]

• Energy efficiency maximization of a multi-users WPCN adopting orthogonal frequency-division multiple access (OFDMA) is considered This work proposes

an efficient iterative algorithm with closed-form solutions in each iteration to address this issue by leveraging Dinkelbach method The contributions of this works have appeared in [7]

• Sum throughput maximization of a relay-based WPCN to assist wireless communication between a source and multiple users is evaluated An efficient algorithm is proposed to determine the solution by jointly optimizing the power and time fraction for energy and information transmission The results of this work have published in [8]

1.3 Dissertation Outline

The rest of the thesis is organized as follows In chapter 2, we study the sum rate maximization for multi-user wireless powered IoT network with non-linear energy harvester In chapter 3, we turn our attention to the resource allocation for energy efficiency in OFDMA-enable WPCN In chapter 4, we investigate the resource allocation for AF relaying wireless powered networks with non-linear energy harvester In chapter 5,

a conclusion of this thesis is provided and some directions of research are drawn in the future

Energy harvesting (EH) technology has received considerable attention to power IoT devices and improve energy efficiency [16]–[24] IoT devices in wireless networks are powered by harvesting ambient energy such as solar, wind, thermoelectric, and electromechanical [16], [17] However, EH technologies using natural sources have

limitations in specific environments owing to the irregular and unforeseeable nature of ambient sources

Radio frequency wireless power transfer (RF-WPT) is an EH technology where the nodes of wireless networks collect energy from RF signals and convert this energy into electricity [18]–[26] For RF-WPT, two cutting-edge EH techniques, have been studied extensively, i.e., simultaneous wireless information and power transfer (SWIPT) [18]–[21], [25], [27] and wireless powered communication network (WPCN)[2] , [22]–[24], [26], [28], [29] For SWIPT, a hybrid access point (HAP) transfers both energy and information

to the users simultaneously The EH receivers in SWIPT-based networks use time switching (TS) and/or power splitting (PS) protocol(s) to harvest energy and signal processing the same signal For a WPCN, wireless energy transfer (WET) and wireless information transfer (WIT) are completely separate Unlike SWIPT, energy-constrained devices in a WPCN adopt a "harvest-then-transmit" protocol to power from dedicated wireless energy transmitter(s) [2], [22]–[24]

To meet a dramatic growth of demand for wireless data traffic, the sum-rate (SR) or spectral efficiency (SE) maximization will certainly be considered as an important metric and have been widely investigated for resource management due to the trade-off between WET and WIT in WPCNs A global optimal solution is proposed to maximize the throughput of a WPCN by jointly designing an energy beamforming approach for downlink WET and time allocation for uplink WIT [2] In a WPCN, the HAP transfers energy to users using WET in the downlink and receives information from users using WIT in the uplink sequentially [22], [23], [26] The duration of downlink WET and uplink WIT are optimized using time division multiple access (TDMA) to maximize the throughput [22] The sum-rate maximization problem of a cognitive radio WPCN is

-6-

investigated based on joint power control for downlink WET and time allocation among secondary users in uplink WIT [23] By jointly optimizing subcarrier scheduling and power allocation in the uplink WIT, an iterative algorithm is proposed to maximize the sum-rate of a full duplex orthogonal frequency division multiple access (OFDMA)-based WPCN [26] In addition, only power beacons (PBs) are used as dedicated wireless energy sources of the energy-constrained devices in downlink WET without communication of the network [2], [24] The coverage probability has been investigated for a PB-based WPCN [24], where transmitters harvest energy from a PB in WET and then communicate with their corresponding receivers in WIT

So far, the above existing works [2], [22]–[24], [26] have only focused on downlink WET and uplink WIT in WPCNs However, some practical scenarios located in toxic environments, underground, tunnels, remote locations, or in disaster areas [30] involve energy-constrained nodes that transmit information to IoT devices under a limited power constraint and these environments increase the difficulty of replacing batteries or deploying a fixed power supply Therefore, it is very desirable for the transmitter to harvest energy from the RF signals of a dedicated wireless transmitter to communicate with the IoT devices Moreover, the energy-constrained node needs to allocate its transmit power for information transmission to serve multiple devices in downlink WIT which has been not addressed in the previous works

In this chapter, we propose a novel PB-based WPCN comprised of one single antenna

PB, one energy-constrained multiple antenna access point (AP), and K  single antenna 1IoT devices, as shown in Fig 2.1 The AP harvests energy from the RF signals transmitted

by the PB to assist the downlink communication between the AP and IoT devices The

goal is to maximize the total SR of the network by jointly optimizing time allocation and transmit power allocation (TPA) at the AP using TDMA and OFDMA

In short, the major contributions of this paper are summarized as follows:

• We first propose a WPCN in which the AP receives the RF energy from the PB and then delivers information to the IoT devices on the downlink In the proposed WPCN, AP adopts a “harvest-then-transmit” protocol powered by the

PB in the WET phase and allocates transmit power to convey information to IoT devices in the WIT phase using TDMA and OFDMA Contrary to the RF-WPT in [18]–[26], a practical model of EH circuit is taken into account by considering a non-linear EH model with respect to the power received at the AP [31]–[33]

• For TDMA and OFDMA, we formulate the SR maximization problems by jointly optimizing the EH time and transmit power Both the resulting problems are nonconvex programs, which are troublesome to solve For TDMA-based problem, toward an efficient solution we first decompose the original nonconvex optimization problem into three convex sub-problems including the

EH duration for the AP, time allocation for transferring information from the

AP to multiple IoT devices, and TPA for the AP We then develop a complexity iterative algorithm for its solution For OFDMA-based WPCN, a mixed-integer programming (MIP) problem is transformed into a quasi-convex problem due to the involvement of binary variables for subcarrier allocation, and a bisection search is then employed to obtain the optimal solution

low-• Finally, we provide extensive numerical results to confirm that our proposed algorithms are efficient in terms of the SR In particular, we compare the SR of

-8-

the proposed method with that of baseline schemes, i.e., a fixed EH time [21], [25] and equal time allocation (ETA) [22], [23] In addition, simulation results also reveal that the TDMA-enabled WPCN outperforms the OFDMA enabled WPCN when the transmit power of the PB and the number of AP antennas are relatively large

This chapter is organized as follows Section 2.2 presents the system model, energy harvesting model and problem formulations for TDMA and OFDMA Section 2.3 and 2.4 investigate the SR maximization problems for the proposed TDMA-enabled WPCN and OFDMA-enabled WPCN, respectively In Section 2.5, the numerical results are presented

in order to demonstrate the effectiveness of the proposed solutions Finally, Section 2.6 concludes this paper

2.2 Description of the System Model and Energy Harvesting Models

In this section, we first describe the system model with multiple IoT users and introduce the EH model with conventional linear and the practical non-linear energy harvesters Then, the SR maximization problems for TDMA-enabled and OFDMA-enabled WCPN are formulated

2.2.1 System Model

As shown in Fig 2.1, we consider a WPCN consisting of one single-antenna power beacon PB and one energy-constrained AP equipped with N T antennas, and K 1 single-antenna IoT users A total transmission time block, denoted by T max, is divided into two phases such as the WET phase from the PB to AP and the WIT phase from the AP to IoT users Without loss of generality, T max is normalized to one, i.e., T max =1 The frameworks for the TDMA- and OFDMA-enabled schemes are depicted in Fig 2.2(a) and (b), respectively It is assumed that channel state information (CSIs) are perfectly known at the

AP and by all users [22], [34]

Fig 2.1 System model

-10-

2.2.2 Energy Harvesting Model

In IoT networks, all transceivers including AP should be low-cost and low-power devices equipped with very limited battery capacity As a result, it is of practical interest

to adopt the ``harvest-then-transmit'' protocol [22] at the energy-constrained AP to harvest energy from the PB during the WET phase and then convey information to K users during the WIT phase Let 0(0,1) be the time fraction spent during WET The total power

received at the AP is P Ein =PB hp 2, where PB is the transmit power at the PB, N T 1

1 exp(ab)

 =+ (2.3)

2.2.3 TDMA-enabled WPCN

As illustrated in Fig 2.2 (a), the AP uses energy harvested from the first phase to consecutively transmit signals to K IoT users during the WIT phase in the remaining

0

1− time fraction at the same frequency band, B, by using TDMA transmission

Fig 2.2 (a) Frame structure of TDMA-enabled WPCN (b) Frame structure of enabled WPCN

OFDMA-For simplicity, the maximum ratio transmission (MRT) technique is used to beamform the signal users [34] The transmit power of the AP allocated for information transmission

to the k-th user during  is denoted by k p k The channel vector from the AP to k th user is

k k

-12-

Our goal is to maximize the total SR of K IoT users subject to the time fraction and power

constraints By jointly optimizing  and 0 p k, the optimization problem of interest is stated

as follows:

( )    

0

TDMA , ,

0

0 1

P1 : max s.t

C2 : 1,C3 : 0,

k k sum p

k K k k

k K

k k k

subcarriers (SCs) in the same duration (1−0) of the WIT phase To eliminate interference, each SC is allocated to each user We introduce a binary SC assignment variable x k n, ,