Learning based wireless communications with energy harvesting and robot vision systems

Department of Electrical EngineeringFrom self-driving cars to smartphones essential to our lives, many types of the electronic devices and computers handle intelligently our work. Thanks to the ???things??? that have become smarter, our lives have become more pleasant and faster, and literally easier. One of the big reasons we can live in such an environment is 'machine learning'. It is a technology that allows a machine to acquire new knowledge by learning through a huge amount of data, just like a person learns. Machine learning is one of the most important topics in many industries and researches these days. It is no exaggeration to say that machine learning is used in almost every field. Its application to (1) wireless communications and (2) computer vision based robotics are also essential. Learning based communication system has the following possibilities: (1) Unlike communication theory, real communication systems are non-linear. For this reason, deep learning-based communication systems may be more suitable for specific hardware configurations and channel optimization. (2) One of the great features of a communication system is that various signal processing functions (e.g., Coding, modulation, detection) are separated into several blocks. Rather than optimizing the performance of individual blocks, a machine learning-based end-to-end communication system can perform better. Because of these possibilities, machine learning is being applied to a wide range of communication systems such as heterogeneous access technology, cognitive radio, and resource allocation. In this dissertation, we propose a mathematical approach to the optimization problem of interference mitigation in a multi-cell network with and without energy harvesting. Also, we propose a recurrent neural network (RNN) based node selection algorithm for sensor networks with energy harvesting. Comparing the problem solving method of the former and the latter, the difference between the existing communication system and the learning based communication system can be clearly revealed. Computer vision based robotics is a study that extracts meaningful information from an image or video and applies the information to a robot. In particular, as a result of applying machine learning to this field, various robots, such as autonomous vehicles, unmanned courier robots, and smart home robots, are being developed. The more studies on robots equipped with cameras, the more convenient our lives, but on the contrary, they can invade our privacy. That is, it is a double-edged sword. In this dissertation, we propose a method to protect our privacy while utilizing other visual information well (i.e., Simultaneous localization and mapping (SLAM)) by detecting faces in extreme low resolution images.clos

무선 전력 통신 네트워크에서 합통신량 최대화 기반 자원 할당 기법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 이정우.With the explosive growth of smart devices equipped with wireless communication, there have been numerous challenges to untangle for supporting user demands in the next generation of communication networks such as Internet of Things networks. One of prime concerns is to overcome the finite lifespan of networks due to the limited battery capacity. Wireless power transfer (WPT) has been considered as a promising solution for providing self-sustainability to energy-constrained networks. WPT enables users to charge their batteries by collecting energy from a radio-frequency signal transmitted by a dedicated energy source. As a framework to the design of wireless networks with WPT, a wireless powered communication network (WPCN) consisting of a hybrid access-point (H-AP) and multiple users has emerged. A H-AP serves users in a WPCN as a base station as well as delivers energy to users as a dedicated energy source. In a WPCN, users charge their batteries by WPT via downlink, and use the energy for uplink transmission. Due to the scarcity of resources, an efficient design is crucial to exploit the system. To support this, I explore system design and resource allocation for WPCNs, especially in the perspective of throughput performance. In addition, I aim to mitigate severe rate disparity which originates from the doubly near-far problem, an inherent characteristic of a WPCN. To begin with, I discuss a cooperative WPCN, in which a user with good channel condition relays information of a user with bad channel condition to enhance user fairness. The sum-throughput is maximized in the considered network subject to a set of quality of service (QoS) requirements. By analyzing the optimal solution, the conditions under which the WPCN benefits from the cooperation are characterized. Based on the new findings, I propose a novel resource allocation algorithm for sum-throughput maximization, which is helpful to practical use of user cooperation. Secondly, I discuss a multi-antenna WPCN where non-orthogonal multiple access (NOMA) transmission is employed in the uplink. To address issues regarding adopting NOMA, user clustering exploiting the multi-antenna system is further applied so that the number of users in a single NOMA transmission is reduced. To deal with the difficulty of jointly optimizing cluster-specific beamforming and time/energy resources for sum-throughput maximization, the beamforming is determined first, and then the resources are optimized for given beamforming. A novel algorithm for cluster-specific beamforming design followed by the sum-throughput maximization algorithm is proposed. Lastly, I consider a WPCN assisted by intelligent reflecting surface (IRS) which has recently received significant attention for its potential of enhancing wireless communication experience. By employing an IRS in a WPCN,users harvest extra energy, and the signal strength of each user can be elevated. For the considered system model, beamforming at the IRS and resources are optimized to maximize sum-throughput. In particular, both NOMA and orthogonal multiple access are considered for uplink transmission, and the performance comparison between the two multiple access schemes are presented.무선 통신이 탑재된 스마트 기기의 폭발적인 성장으로, 사물 인터넷 네트워크와 같은 차세대 통신 네트워크에서 요구하는 성능을 충족하기 위하여 해결해야 할 여러 문제가 발생하였다. 주요 문제 중 하나는 기기의 한정된 배터리 용량으로 네트워크가 제한된 시간 동안에만 동작할 수 있는 것을 극복하는 것이다. 무선 전력 전송은 이와 같이 에너지가 제한된 네트워크에 자기 지속성을 부여할 수 있는 해결 방법으로 고려되고 있다. 사용자들은 무선 전력 전송을 통하여 전용 에너지원에 의해 전송되는 무선 주파수 신호로부터 에너지를 수집하고 배터리를 충전시킬 수 있다. 무선 전력 전송이 가능한 무선 네트워크를 설계하기 위한 체계로서 무선 전력 통신 네트워크(Wireless powered communication network, WPCN)가 제안되었다. WPCN은 기지국과 전용 에너지원의 역할을 같이 하는 hybrid access-point (H-AP)와 여러 사용자로 구성된다. WPCN에서 사용자들은 하향링크를 통하여 무선 전력 전송으로 배터리를 충전시키고, 상향링크를 통하여 해당 에너지로 정보를 전송한다. 이 때, 자원이 부족하므로 WPCN의 시스템을 활용하기 위해서는 효율적인 설계가 필수적이다. 이를 위하여, 본 논문에서는 WPCN을 위한 시스템 설계와 자원 할당에 대하여, 특히 통신량 관점에서 탐구하고자 한다. 또한, WPCN의 특징인 이중 근거리 문제에서 비롯된 높은 사용자간 전송 속도 격차를 완화하고자 한다. 우선, 협력 무선 전력 통신 네트워크에 대해 논의한다. 해당 네트워크에서는 채널 상태가 좋은 사용자가 그렇지 않은 사용자의 정보를 중계하여 사용자 공정성을 향상시킨다. 고려하는 시스템 모델에서 합통신량을 최대화하는 데 각 사용자의 서비스 품질(Quality of service, QoS)을 보장하도록 한다. 위 문제의 최적해를 분석하여, WPCN이 사용자 협력 기법을 통하여 이득을 얻는 조건을 밝히고, 이를 기반으로 사용자 협력 기법을 실용적으로 활용할 수 있는 합통신량 최대화를 위한 새로운 자원 할당 알고리즘을 제안한다. 다음으로, 상향링크에서 비직교 다중 접속(Non-orthogonal multiple access, NOMA)이 적용된 다중 안테나 WPCN에 대하여 논의한다. NOMA를 활용하는 것과 관련된 여러 문제를 해결하기 위하여, 다중 안테나 시스템을 이용한 사용자 클러스터링 기법이 추가로 적용되고, 이에 단일 NOMA 전송의 사용자 수가 감소한다. 합통신량 최대화를 위하여 클러스터별 빔형성과 시간 및 에너지 자원을 공동으로 최적화하는 것이 어렵기 때문에, 먼저 빔형성을 설계한 다음, 해당 빔형성이 적용된 네트워크에 대하여 자원을 최적화한다. 이에, 클러스터별 빔형성 설계와 합통신량 최대화를 위한 새로운 알고리즘을 제안한다. 마지막으로, 무선 통신의 성능을 향상시킬 후보 기술 중 하나인 지능형 반사 표면(Intelligent reflecting surface, IRS)이 도입된 WPCN을 고려한다. IRS를 도입함으로써, 사용자들은 추가로 에너지를 얻을 수 있으며 신호 세기를 높일 수 있다. 고려된 시스템 모델의 합통신량을 최대화하도록 IRS의 빔형성과 자원을 최적화한다. 특히, 상향링크를 위하여 NOMA와 직교 다중 접속이 고려되고, 두 다중 접속 기법간의 성능 비교가 이루어진다.1 Introduction 1 1.1 Related Work 3 1.1.1 Wireless Powered Communication Networks 3 1.1.2 A NOMA-Based WPCN 4 1.2 Contributions and Organization 6 1.3 Notation 8 2 Wireless Powered Communication Networks with User Cooperation 9 2.1 Introduction 10 2.2 System model 14 2.3 Problem Formulation 18 2.4 Optimal Solution of QoS Constrained Sum-Throughput Maximization 21 2.4.1 Case (I): Positive z1, z21 and z22 25 2.4.2 Case (II): Positive z1 and z22, and undefinable z21 35 2.5 QoS Constrained Sum-Throughput Maximization Algorithm 40 2.5.1 Proposed Algorithm 41 2.5.2 Computational Complexity Comparison 42 2.6 Sum-Throughput Maximization with Processing Cost 46 2.7 Simulation Results 48 2.8 Conclusion 53 3 NOMA-Based Wireless Powered Communication Networks with User Clustering 56 3.1 Introduction 57 3.1.1 Throughput Maximization in WPCN 58 3.1.2 User Clustering in NOMA 59 3.1.3 Motivation and Contribution 60 3.2 System model 62 3.3 Optimal Beamforming And Resource Allocation 66 3.3.1 Beamforming Design 67 3.3.2 Sum-Throughput Maximization 69 3.3.3 TDMA-based WPCN with Cluster-specific Beamforming 76 3.4 Simulation Results 79 3.5 Conclusion 87 4 IRS-Assisted Wireless Powered Communication Networks: Comparison of NOMA and OMA 89 4.1 Introduction 90 4.2 System Model 91 4.2.1 NOMA-based WPCN 92 4.2.2 OMA-based WPCN 94 4.3 Sum-Throughput Maximization 94 4.3.1 NOMA-based WPCN with throughput constraints 95 4.3.2 OMA-based WPCN with throughput constraints 98 4.4 Simulation Results 99 4.5 Conclusion 102 5 Conclusion 106 5.1 Summary 106 5.2 Future directions 107 Abstract (In Korean) 117 감사의 글 119Docto

Throughput Optimization for Massive MIMO Systems Powered by Wireless Energy Transfer

This paper studies a wireless-energy-transfer (WET) enabled massive multiple-input-multiple-output (MIMO) system (MM) consisting of a hybrid data-and-energy access point (H-AP) and multiple single-antenna users. In the WET-MM system, the H-AP is equipped with a large number $M$ of antennas and functions like a conventional AP in receiving data from users, but additionally supplies wireless power to the users. We consider frame-based transmissions. Each frame is divided into three phases: the uplink channel estimation (CE) phase, the downlink WET phase, as well as the uplink wireless information transmission (WIT) phase. Firstly, users use a fraction of the previously harvested energy to send pilots, while the H-AP estimates the uplink channels and obtains the downlink channels by exploiting channel reciprocity. Next, the H-AP utilizes the channel estimates just obtained to transfer wireless energy to all users in the downlink via energy beamforming. Finally, the users use a portion of the harvested energy to send data to the H-AP simultaneously in the uplink (reserving some harvested energy for sending pilots in the next frame). To optimize the throughput and ensure rate fairness, we consider the problem of maximizing the minimum rate among all users. In the large-$M$ regime, we obtain the asymptotically optimal solutions and some interesting insights for the optimal design of WET-MM system. We define a metric, namely, the massive MIMO degree-of-rate-gain (MM-DoRG), as the asymptotic UL rate normalized by $\log(M)$. We show that the proposed WET-MM system is optimal in terms of MM-DoRG, i.e., it achieves the same MM-DoRG as the case with ideal CE.Comment: 15 double-column pages, 6 figures, 1 table, to appear in IEEE JSAC in February 2015, special issue on wireless communications powered by energy harvesting and wireless energy transfe

Wireless-powered cooperative communications: protocol design, performance analysis and resource allocation

Radio frequency (RF) energy transfer technique has attracted much attention and has recently been regarded as a key enabling technique for wireless-powered communications. However, the high attenuation of RF energy transfer over distance has greatly limited the performance and applications of WPCNs in practical scenarios. To overcome this essential hurdle, in this thesis we propose to combat the propagation attenuation by incorporating cooperative communication techniques in WPCNs. This opens a new paradigm named wireless-powered cooperative communication and raises many new research opportunities with promising applications. In this thesis, we focus on the novel protocol design, performance analysis and resource allocation of wireless-powered cooperative communication networks (WPCCNs). We first propose a harvest-then-cooperate (HTC) protocol for WPCCNs, where the wireless-powered source and relay(s) harvest energy from the AP in the downlink (DL) and work cooperatively in the uplink (UL) for transmitting source information. The average throughput performance of the HTC protocol with two single relay selection schemes is analyzed. We then design two novel protocols and study the optimal resource allocation for another setup of WPCCNs with a hybrid relay that has a constant power supply. Besides cooperating with the source for UL information transmission, the hybrid relay also transmits RF energy concurrently with the AP during the DL energy transfer phase. Subsequently, we adopt the Stackelberg game to model the strategic interactions in power beacon (PB)-assisted WPCCNs, where PBs are deployed to provide wireless charging services to wireless-powered users via RF energy transfer and are installed by different operators with the AP. Finally, we develop a distributed power splitting framework using non-cooperative game theory for a large-scale WPCCN, where multiple source-destination pairs communicate through their dedicated wireless-powered relays