Kaigui Bian

Bio: Kaigui Bian is an academic researcher from Peking University. The author has contributed to research in topics: Cognitive radio & Air quality index. The author has an hindex of 31, co-authored 215 publications receiving 4359 citations. Previous affiliations of Kaigui Bian include Virginia Tech & University of Virginia.

Robust Distributed Spectrum Sensing in Cognitive Radio Networks

Abstract: Distributed spectrum sensing (DSS) enables a Cognitive Radio (CR) network to reliably detect licensed users and avoid causing interference to licensed communications. The data fusion technique is a key component of DSS. We discuss the Byzantine failure problem in the context of data fusion, which may be caused by either malfunctioning sensing terminals or Spectrum Sensing Data Falsification (SSDF) attacks. In either case, incorrect spectrum sensing data will be reported to a data collector which can lead to the distortion of data fusion outputs. We investigate various data fusion techniques, focusing on their robustness against Byzantine failures. In contrast to existing data fusion techniques that use a fixed number of samples, we propose a new technique that uses a variable number of samples. The proposed technique, which we call Weighted Sequential Probability Ratio Test (WSPRT), introduces a reputation-based mechanism to the Sequential Probability Ratio Test (SPRT). We evaluate WSPRT by comparing it with a variety of data fusion techniques under various network operating conditions. Our simulation results indicate that WSPRT is the most robust against the Byzantine failure problem among the data fusion techniques that were considered.

Joint Trajectory and Power Optimization for UAV Relay Networks

Abstract: In this letter, we consider an unmanned aerial vehicle (UAV) relay network, where the UAV works as an amplify-and-forward relay. We optimize the trajectory of UAV, the transmit power of UAV, and the mobile device by minimizing the outage probability of this relay network. The analytical expression of outage probability is derived first. A closed-form low-complexity solution with joint trajectory design and power control is proposed to solve this non-convex problem. Simulation results show that the outage probability of the proposed solution is significantly lower than that of the fixed power relay and circle trajectory for the UAV relay.

Jigsaw: indoor floor plan reconstruction via mobile crowdsensing

Abstract: The lack of floor plans is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and time-consuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this paper, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes and shapes. Our experiments on 3 stories of 2 large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about 1~2m and 5~9°, while the hallway connectivity is 100% correct.

An Overview of Dynamic Spectrum Sharing: Ongoing Initiatives, Challenges, and a Roadmap for Future Research

Abstract: We are in the midst of a major paradigm shift in how we manage radio spectrum. This paradigm shift is necessitated by the growth of wireless services of all types and the demand pressure imposed on limited spectrum resources under legacy management regimes. The shift is feasible because of advances in radio and networking technologies that make it possible to share spectrum dynamically in all possible dimensions—i.e., across frequencies, time, location, users, uses, and networks. Realizing the full potential of this shift to Dynamic Spectrum Sharing will require the co-evolution of wireless technologies, markets, and regulatory policies; a process which is occurring on a global scale. This paper provides a current overview of major technological and regulatory reforms that are leading the way toward a global paradigm shift to more flexible, dynamic, market-based ways to manage and share radio spectrum resources. We focus on current efforts to implement database-driven approaches for managing the shared co-existence of users with heterogeneous access and interference protection rights, and discuss open research challenges.

A quorum-based framework for establishing control channels in dynamic spectrum access networks

Abstract: Establishing a control channel for medium access control is a challenging problem in multi-channel and dynamic spectrum access (DSA) networks. In the design of multi-channel MAC protocols, the use of channel (or frequency) hopping techniques (a.k.a. parallel rendezvous) have been proposed to avoid the bottleneck of a single control channel. In DSA networks, the dynamic and opportunistic use of the available spectrum requires that the radios are able to "rendezvous" -- i.e., find each other to establish a link. The use of a dedicated global control channel simplifies the rendezvous process but may not be feasible in many opportunistic spectrum sharing scenarios due to the dynamically changing availability of all the channels, including the control channel. To address this problem, researchers have proposed the use of channel hopping protocols for enabling rendezvous in DSA networks.This paper presents a systematic approach, based on quorum systems, for designing and analyzing channel hopping protocols for the purpose of control channel establishment. The proposed approach, called Quorum-based Channel Hopping (QCH) system, can be used for implementing rendezvous protocols in DSA networks that are robust against link breakage caused by the appearance of incumbent user signals. We describe two optimal QCH systems under the assumption of global clock synchronization: the first system is optimal in the sense that it minimizes the time-to-rendezvous between any two channel hopping sequences; the second system is optimal in the sense that it guarantees the even distribution of the rendezvous points in terms of both time and channel, thus solving the \emph{rendezvous convergence} problem. We also propose an asynchronous QCH system that does not require global clock synchronization. Our analytical and simulation results show that the channel hopping schemes designed using our framework outperform existing schemes under various network conditions.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Evolution and the Theory of Games

Abstract: In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Advances in cognitive radio networks: A survey

Abstract: With the rapid deployment of new wireless devices and applications, the last decade has witnessed a growing demand for wireless radio spectrum. However, the fixed spectrum assignment policy becomes a bottleneck for more efficient spectrum utilization, under which a great portion of the licensed spectrum is severely under-utilized. The inefficient usage of the limited spectrum resources urges the spectrum regulatory bodies to review their policy and start to seek for innovative communication technology that can exploit the wireless spectrum in a more intelligent and flexible way. The concept of cognitive radio is proposed to address the issue of spectrum efficiency and has been receiving an increasing attention in recent years, since it equips wireless users the capability to optimally adapt their operating parameters according to the interactions with the surrounding radio environment. There have been many significant developments in the past few years on cognitive radios. This paper surveys recent advances in research related to cognitive radios. The fundamentals of cognitive radio technology, architecture of a cognitive radio network and its applications are first introduced. The existing works in spectrum sensing are reviewed, and important issues in dynamic spectrum allocation and sharing are investigated in detail.