Software-defined Networking enabled Resource Management and Security Provisioning in 5G Heterogeneous Networks

Due to the explosive growth of mobile data traffic and the shortage of spectral resources, 5G networks are envisioned to have a densified heterogeneous network (HetNet) architecture, combining multiple radio access technologies (multi-RATs) into a single holistic network. The co-existing of multi-tier architectures bring new challenges, especially on resource management and security provisioning, due to the lack of common interface and consistent policy across HetNets. In this thesis, we aim to address the technical challenges of data traffic management, coordinated spectrum sharing and security provisioning in 5G HetNets through the introduction of a programmable management platform based on Software-defined networking (SDN). To address the spectrum shortage problem in cellular networks, cellular data traffic is efficiently offloaded to the Wi-Fi network, and the quality of service of user applications is guaranteed with the proposed delay tolerance based partial data offloading algorithm. A two-layered information collection is also applied to best load balancing decision-making. Numerical results show that the proposed schemes exploit an SDN controller\u27s global view of the HetNets and take optimized resource allocation decisions. To support growing vehicle-generated data traffic in 5G-vehicle ad hoc networks (VANET), SDN-enabled adaptive vehicle clustering algorithm is proposed based on the real-time road traffic condition collected from HetNet infrastructure. Traffic offloading is achieved within each cluster and dynamic beamformed transmission is also applied to improve trunk link communication quality. To further achieve a coordinated spectrum sharing across HetNets, an SDN enabled orchestrated spectrum sharing scheme that integrates participating HetNets into an amalgamated network through a common configuration interface and real-time information exchange is proposed. In order to effectively protect incumbent users, a real-time 3D interference map is developed to guide the spectrum access based on the SDN global view. MATLAB simulations confirm that average interference at incumbents is reduced as well as the average number of denied access. Moreover, to tackle the contradiction between more stringent latency requirement of 5G and the potential delay induced by frequent authentications in 5G small cells and HetNets, an SDN-enabled fast authentication scheme is proposed in this thesis to simplify authentication handover, through sharing of user-dependent secure context information (SCI) among related access points. The proposed SCI is a weighted combination of user-specific attributes, which provides unique fingerprint of the specific device without additional hardware and computation cost. Numerical results show that the proposed non-cryptographic authentication scheme achieves comparable security with traditional cryptographic algorithms, while reduces authentication complexity and latency especially when network load is high

Real time control in Linux

In this thesis, the approaches to achieving real time control under Linux operating platform are presented and four different real time control applications are discussed. The driver of the ADS 12 data acquisition card is programmed to enhance hardware supported by COMEDI through which the connection between computer and DAQ boards are built up. A simple project combining RTAI (Real Time Application Interface) with COMEDI is introduced together with the discussion of one SISO (Single-Input Single-Output) control project and two SIMO (Single-Inputs Multi- Output) control projects based on different controllers, and RTLab is selected to provide us with real time functionalities as it combines COMEDI with RTAI or RTLinx very well in Linux. Further more, to enhance the observability and maneuverability of RTLab, additional custom plugin graphic windows have also been made for every application in the project

Aggregated Feasible Region of Heterogeneous Demand-Side Flexible Resources---Part I: Theoretical Derivation of the Exact Model

In the first part of the two-part series, the model to describe the exact aggregated feasible region (AFR) of multiple types of demand-side resources is derived. Based on a discrete-time unified individual model of heterogeneous resources, the calculation of AFR is, in fact, a feasible region projection problem. Therefore, the Fourier-Motzkin Elimination (FME) method is used for derivation. By analyzing the redundancy of all possible constraints in the FME process, the mathematical expression and calculation method for the exact AFR is proposed. The number of constraints is linear with the number of resources and is exponential with the number of time intervals, respectively. The computational complexity has been dramatically simplified compared with the original FME. However, the number of constraints in the model is still exponential and cannot be simplified anymore. Hence, In Part II of this paper, several approximation methods are proposed and analyzed in detail.Comment: 10 page

Low-carbon developments in Northeast China: Evidence from cities

Cities are a major source of energy use and greenhouse gases emissions, as well as being at the core of the climate change mitigation. With the Revitalizing Old Industrial Base of Northeast China strategy, Northeast China has been a typical developing region with rapid industrialization and urbanization accompanied by substantial energy consumption and carbon emissions. Therefore, northeastern Chinese cities should play an important role in regional low-carbon developments. This study presents several improvements to previous method to improve the accuracy of the results. Using the modified method, for the first time, we compile carbon emission inventories for 30 cities in Northeast China based on fossil fuel combustion and industrial processes. The results indicate that Anshan emitted the most carbon emissions annually, followed by Benxi and the vice-provincial cities (including Changchun, Shenyang, Dalian and Harbin). In 2012, the total carbon emissions of the 30 cities amounted to 973.95 million tonnes, accounting for 9.71% and 2.75% of national and global carbon emissions, respectively. Most of the CO2 emissions of these cities were from the ‘nonmetal and metal industry’ and ‘energy production and supply’. Raw coal was the primary source of carbon emissions in Northeast China, and industrial processes also played a significant role in determining the carbon emissions. Additionally, both the average per capita carbon emissions and carbon emission intensity in the 30 cities were higher than the national levels. According to the differences in carbon emissions characteristics, we present several policy recommendations for carbon mitigation for northeastern Chinese cities. This study provides consistent and comparable spatial-temporal city-level emission database for further research on relationships between economic development and environmental protection in Northeast China. Simultaneously, this study provides practical reference values for other developing regions throughout the world to create low-carbon road maps

Towards Generalizable Reinforcement Learning for Trade Execution

Optimized trade execution is to sell (or buy) a given amount of assets in a given time with the lowest possible trading cost. Recently, reinforcement learning (RL) has been applied to optimized trade execution to learn smarter policies from market data. However, we find that many existing RL methods exhibit considerable overfitting which prevents them from real deployment. In this paper, we provide an extensive study on the overfitting problem in optimized trade execution. First, we model the optimized trade execution as offline RL with dynamic context (ORDC), where the context represents market variables that cannot be influenced by the trading policy and are collected in an offline manner. Under this framework, we derive the generalization bound and find that the overfitting issue is caused by large context space and limited context samples in the offline setting. Accordingly, we propose to learn compact representations for context to address the overfitting problem, either by leveraging prior knowledge or in an end-to-end manner. To evaluate our algorithms, we also implement a carefully designed simulator based on historical limit order book (LOB) data to provide a high-fidelity benchmark for different algorithms. Our experiments on the high-fidelity simulator demonstrate that our algorithms can effectively alleviate overfitting and achieve better performance.Comment: Accepted by IJCAI-2

Does a higher minimum wage accelerate labour division in agricultural production? Evidence from the main riceplanting area in China

Agricultural production outsourcing, a new means of agricultural production, can optimise the allocation of resources, reduce agricultural production costs, and improve agricultural productivity. However, farmers’ outsourcing behaviours are strongly interfered with by many factors such as economics, technology and institutions. Using a farmer-level data set from 2014 to 2018 in China, we examine the effects of the minimum wage increase on rice farmers’ production outsourcing behaviours. Our study relies on a Logit regression framework and uses the control function (C.F.) approach to address potential endogeneity concerns. Results show that the minimum wage increase significantly reduces the probability of farmers conducting production outsourcing. We also examine the heterogeneous effects of the minimum wage increase, and find that compared with other outsourcing services, the adverse effects on harvesting outsourcing are the strongest; the negative effects on production outsourcing are stronger for rice farmers with higher education. Our results provide new insights into understanding how labour regulation affects labour division in agricultural production