Nansai Hu

Bio: Nansai Hu is an academic researcher from Stevens Institute of Technology. The author has contributed to research in topics: Cognitive radio & Radio access network. The author has an hindex of 5, co-authored 7 publications receiving 81 citations.

Most Active Band (MAB) Attack and Countermeasures in a Cognitive Radio Network

Abstract: This paper investigates a type of attacks on a cognitive radio (CR) network, most active band (MAB) attack, where an attacker or a malicious CR node senses/determines the most active band within a multi-band CR network and targets this band through a denial of service (DoS) attack. We propose a countermeasure strategy, coordinated concealment strategy (CCS), to counter the MAB attack. Our results show that CCS significantly outperforms CR's inherent capability of signal/interference avoidance under a MAB attack. We also introduce power control in CCS to further improve the countermeasure performance in terms of the percentage of survival nodes.

Identification of legacy radios in a cognitive radio network using a radio frequency fingerprinting based method

Abstract: Cognitive radio (CR) networks provide an open architecture for effectively utilizing communication resources through flexible opportunistic spectrum access methods. To successfully realize its benefits and minimize the misuses of a CR network, distinguishing radio/user classes (legacy radios/users versus secondary radios/users) and individual radio/user terminals (within one class/type) is a critical and challenging task in CR network operation. In this paper, we propose a radio frequency fingerprinting (RFF) based approach combined with machine learning algorithms to differentiate radio/user classes and terminals. In our experiments, the proposed method is implemented for distinguishing radio class (MOTOROLA walkie talkies (as legacy radios) versus Universal Software Radio Peripheral (USRP) (as secondary radios)) and distinguishing individual radio terminals within one radio class. The experimental results demonstrate that the proposed method is very effective in differentiating radio types and radio terminals.

An integrated incremental self-organizing map and hierarchical neural network approach for cognitive radio learning

Abstract: In this paper, an incremental self-organizing map integrated with hierarchical neural network (ISOM-HNN) is proposed as an efficient approach for signal classification in cognitive radio networks. This approach can effectively detect unknown radio signals in the uncertain communication environment. The adaptability of ISOM can improve the real-time learning performance, which provides the advantage of using this approach for on-line learning and control of cognitive radios in many real-world application scenarios. Furthermore, we propose to integrate the ISOM with the hierarchical neural network (HNN) to improve the learning and prediction accuracy. Detailed learning algorithm and simulation results are presented in this work to demonstrate the effectiveness of this approach.

Analysis of Cooperative TDMA in Rayleigh Fading Channels

Abstract: The time-division-multiple-access-based (TDMA) protocol has been widely utilized as a reliable media access control (MAC) mechanism. By allocating each user a dedicated time slot, a given TDMA user transmits its packets in its exclusively assigned time slots, whereas other users are in idle mode (not transmitting). However, in this paper, a cooperative TDMA (C-TDMA) method is investigated, which enables cooperative transmissions among multiple TDMA users to improve the probability of success of packet transmissions. Specifically, a TDMA user not only transmits packets in its assigned slots but monitors/overhears other users' packets in other time slots as well. It will then be able to assist the other users, if needed, to retransmit their failed packets through cooperative diversity. For performance evaluations, expressions of three metrics, i.e., network throughput, packet-dropping rate, and average packet delay, are derived, considering a Rayleigh fading channel. Both theoretical and simulation results are presented to demonstrate that the throughput, dropping rate, and delay performance are all improved significantly with the use of C-TDMA.

Classification and Control of Cognitive Radios Using Hierarchical Neural Network

Abstract: This paper proposes a method to protect the communication band through machine learning in cognitive networks. A machine learning cognitive radio (MLCR) extracts features from the signal waveforms received from various radios. A machine learning radio user (MLRU) assigns the states, i.e., unauthorized/authorized, and the associated actions, i.e., interfering/no interfering, to each waveform. The MLCR learns through a proposed hierarchical neural network to classify the signal states based on their features. The {signal, action} pairs are stored in the knowledge base and can be retrieved by MLCR automatically based on its prediction of the signal state related to the presented signal waveform. A case study of protecting the band of a legacy radio using our proposed method is provided to validate the effectiveness of this work.

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.).

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

Advances on Security Threats and Countermeasures for Cognitive Radio Networks: A Survey

Abstract: Cognitive radio (CR) is regarded as an emerging technology, which equips wireless devices with the capability to adapt their operating parameters on the fly based on the radio environment, to utilize the scarce radio frequency spectrum in an efficient and opportunistic manner. However, due to the increasingly pervasive existence of smart wireless devices in cognitive radio networks (CRNs), CR systems are vulnerable to numerous security threats that affect the overall performance. There have been many significant advances on security threats and countermeasures in CRNs in the past few years. Our main goal in this paper is to present the state-of-the-art research results and approaches proposed for CRN security to protect both unlicensed secondary users and licensed primary users. Specifically, we present the recent advances on security threats/attacks and countermeasures in CRNs focusing more on the physical layer by categorizing them in terms of their types, their existence in the CR cycle, network protocol layers (exploited during their activities and defense strategies), and game theoretic approaches. The recent important attacks and countermeasures in CRNs are also summarized in the form of tables. We also present recommendations that can be followed while implementing countermeasures to enhance CRN security. With this paper, readers can have a more thorough understanding of CRN security attacks and countermeasures, as well as research trends in this area.

Kernel-Based Learning for Statistical Signal Processing in Cognitive Radio Networks: Theoretical Foundations, Example Applications, and Future Directions

Abstract: Kernel-based learning (KBL) methods have recently become prevalent in many engineering applications, notably in signal processing and communications. The increased interest is mainly driven by the practical need of being able to develop efficient nonlinear algorithms, which can obtain significant performance improvements over their linear counterparts at the price of generally higher computational complexity. In this article, an overview of applying various KBL methods to statistical signal processing-related open issues in cognitive radio networks (CRNs) is presented. It is demonstrated that KBL methods provide a powerful set of tools for CRNs and enable rigorous formulation and effective solutions to both long-standing and emerging design problems.