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

Downlink Beamforming in Small Cells with Scalar Information Exchange

Multi-input single-output (MISO) beamforming techniques are addressed in the downlink small cells environment, where a small cell base station (BS) has multiple antennas while a user has a single antenna. Two-types of extreme beamforming schemes at small cell base stations (BSs) are considered based on local channel state information (CSI) at transmitter: i) maximizing the desired channel gain and ii) minimizing the interference generated by each small cell base station (BS). Then, we propose a beamforming scheme that minimizes weighted generating-interference in pursuit of enhancing the achievable rate. In particular, an additional scalar information exchange is assumed between the BSs via a low-rate interface such as the X-2 interface in the 3GPP standards. By carefully choosing the weight coefficients based on the signal-to-interference-and-noise-ratios (SINRs), the scheme balances between these two philosophies - egoism and altruism. Simulation results show that there exists a trade-off between the amount of CSI knowledge and the achievable rate