Interference Mitigation in Multi-Hop Wireless Networks with Advanced Physical-Layer Techniques

In my dissertation, we focus on the wireless network coexistence problem with advanced physical-layer techniques. For the first part, we study the problem of Wireless Body Area Networks (WBAN)s coexisting with cross-technology interference (CTI). WBANs face the RF cross-technology interference (CTI) from non-protocol-compliant wireless devices. Werst experimentally characterize the adverse effect on BAN caused by the CTI sources. Then we formulate a joint routing and power control (JRPC) problem, which aims at minimizing energy consumption while satisfying node reachability and delay constraints. We reformulate our problem into a mixed integer linear programing problem (MILP) and then derive the optimal results. A practical JRPC protocol is then proposed. For the second part, we study the coexistence of heterogeneous multi-hop networks with wireless MIMO. We propose a new paradigm, called cooperative interference mitigation (CIM), which makes it possible for disparate networks to cooperatively mitigate the interference to/from each other to enhance everyone\u27s performance. We establish two tractable models to characterize the CIM behaviors of both networks by using full IC (FIC) and receiver-side IC (RIC) only. We propose two bi-criteria optimization problems aiming at maximizing both networks\u27 throughput, while cooperatively canceling the interference between them based on our two models. In the third and fourth parts, we study the coexistence problem with MIMO from a different point of view: the incentive of cooperation. We propose a novel two-round game framework, based on which we derive two networks\u27 equilibrium strategies and the corresponding closed-form utilities. We then extend our game-theoretical analysis to a general multi-hop case, specifically the coexistence problem between primary network and multi-hop secondary network in the cognitive radio networks domain. In the final part, we study the benefits brought by reconfigurable antennas (RA). We systematically exploit the pattern diversity and fast reconfigurability of RAs to enhance the throughput of MWNs. Werst propose a novel link-layer model that captures the dynamic relations between antenna pattern, link coverage and interference. Based on our model, a throughput optimization framework is proposed by jointly considering pattern selection and link scheduling, which is formulated as a mixed integer non-linear programming problem

Changes of dependency structure in East Asia from 1990 to 2000:Analysis by intermediate input according to sector

This paper attempts to interpret the situation surrounding the development of regional economy integration in East Asia by examining the degree of self-dependency and dependency on foreign countries in this region by using the International Input-Output (IIO) approach. We show that the economic interdependency in East Asia grew stronger from 1990 to 2000, with a strong upturn of the interdependency on China and Korea, and a downturn of dependency on Japan in this region. Moreover, our analysis suggests that dependency on foreign countries is increasing and self-dependency is decreasing. ASEAN4 was largely dependent on Japan, China and Korea, whereas Japan, China and Korea were largely dependent on other countries.From the fall of self-dependency in the heavy industries sectors and the decrease of dependency in sectors like the iron and steel, we can know that the economic effect of ASEAN4, China, Korea and Japan is seeping into other regions. Therefore, strong efforts should be made to strengthen the economic cooperation in this region in the future

CAPIA: Cloud Assisted Privacy-Preserving Image Annotation

Using public cloud for image storage has become a prevalent trend with the rapidly increasing number of pictures generated by various devices. For example, today\u27s most smartphones and tablets synchronize photo albums with cloud storage platforms. However, as many images contain sensitive information, such as personal identities and financial data, it is concerning to upload images to cloud storage. To eliminate such privacy concerns in cloud storage while keeping decent data management and search features, a spectrum of keywords-based searchable encryption (SE) schemes have been proposed in the past decade. Unfortunately, there is a fundamental gap remains open for their support of images, i.e., appropriate keywords need to be extracted for images before applying SE schemes to them. On one hand, it is obviously impractical for smartphone users to manually annotate their images. On the other hand, although cloud storage services now offer image annotation services, they rely on access to users\u27 unencrypted images. To fulfill this gap and open the first path from SE schemes to images, this paper proposes a cloud assisted privacy-preserving automatic image annotation scheme, namely CAPIA. CAPIA enables cloud storage users to automatically assign keywords to their images by leveraging the power of cloud computing. Meanwhile, CAPIA prevents the cloud from learning the content of images and their keywords. Thorough analysis is carried out to demonstrate the security of CAPIA. A prototype implementation over the well-known IAPR TC-12 dataset further validates the efficiency and accuracy of CAPIA

Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning

Our work aims at efficiently leveraging ambiguous demonstrations for the training of a reinforcement learning (RL) agent. An ambiguous demonstration can usually be interpreted in multiple ways, which severely hinders the RL-Agent from learning stably and efficiently. Since an optimal demonstration may also suffer from being ambiguous, previous works that combine RL and learning from demonstration (RLfD works) may not work well. Inspired by how humans handle such situations, we propose to use self-explanation (an agent generates explanations for itself) to recognize valuable high-level relational features as an interpretation of why a successful trajectory is successful. This way, the agent can provide some guidance for its RL learning. Our main contribution is to propose the Self-Explanation for RL from Demonstrations (SERLfD) framework, which can overcome the limitations of traditional RLfD works. Our experimental results show that an RLfD model can be improved by using our SERLfD framework in terms of training stability and performance