A Dual Sampling Communication Method in Wireless Networks.

PhD ThesisAs mobile wireless data traffic is increasing significantly, the development direction for wireless networks is focusing on very high data rates, extremely low latency, with a large number of connected devices and a reduction in energy usage. To satisfy the rapid rise in user and traffic capacity, raises challenges given the limited bandwidth resource. The main purpose for this research is to find ways to improve spectral efficiency, data transmission rate, and reduce latency. Simultaneous wireless transmissions happening in the same frequency band can help alleviate demand on transmission slots, with methods like network coding to support decoding at the end terminals. However, in general, signal asynchrony harms the transmission performance significantly. The main contribution of this research is the proposal of a Dual Sampling (DS) method, which aims to relieve the impact of signal asynchrony on simultaneous transmissions. The key concept behind the DS method is sampling twice within each symbol period to handle overlapping signals for successful decoding. Simulation results confirm that it manages to support simultaneous transmissions. Moreover, the DS method is implemented in both Information-Centric Networks (ICN) and Unmanned Aerial Vehicles (UAVs) aided wireless networks. Additionally, for ICN, a Cache Migration Protocol (CMP) is proposed to support simultaneous transmissions which reduces the transmission latency. While for UAV-aided wireless networks, by exploiting the DS method, simultaneous transmissions are supported resulting in better optimal max-min throughput along supported by suitableUAV flight trajectory planning. By demonstrating the performance gain in the application scenarios of ICN and UAV-aided wireless networks, the DS method can be regarded as an optional promising transmission mechanism when communicating with multiple users simultaneously

Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data

Complex system simulation has been playing an irreplaceable role in understanding, predicting, and controlling diverse complex systems. In the past few decades, the multi-scale simulation technique has drawn increasing attention for its remarkable ability to overcome the challenges of complex system simulation with unknown mechanisms and expensive computational costs. In this survey, we will systematically review the literature on multi-scale simulation of complex systems from the perspective of knowledge and data. Firstly, we will present background knowledge about simulating complex system simulation and the scales in complex systems. Then, we divide the main objectives of multi-scale modeling and simulation into five categories by considering scenarios with clear scale and scenarios with unclear scale, respectively. After summarizing the general methods for multi-scale simulation based on the clues of knowledge and data, we introduce the adopted methods to achieve different objectives. Finally, we introduce the applications of multi-scale simulation in typical matter systems and social systems