Physical layer authentication for wireless communications

指導教員：姜　暁

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Securing NextG networks with physical-layer key generation: A survey

As the development of next-generation (NextG) communication networks continues, tremendous devices are accessing the network and the amount of information is exploding. However, with the increase of sensitive data that requires confidentiality to be transmitted and stored in the network, wireless network security risks are further amplified. Physical-layer key generation (PKG) has received extensive attention in security research due to its solid information-theoretic security proof, ease of implementation, and low cost. Nevertheless, the applications of PKG in the NextG networks are still in the preliminary exploration stage. Therefore, we survey existing research and discuss (1) the performance advantages of PKG compared to cryptography schemes, (2) the principles and processes of PKG, as well as research progresses in previous network environments, and (3) new application scenarios and development potential for PKG in NextG communication networks, particularly analyzing the effect and prospects of PKG in massive multiple-input multiple-output (MIMO), reconfigurable intelligent surfaces (RISs), artificial intelligence (AI) enabled networks, integrated space-air-ground network, and quantum communication. Moreover, we summarize open issues and provide new insights into the development trends of PKG in NextG networks

Unmanned Aircraft Systems in the Cyber Domain

Unmanned Aircraft Systems are an integral part of the US national critical infrastructure. The authors have endeavored to bring a breadth and quality of information to the reader that is unparalleled in the unclassified sphere. This textbook will fully immerse and engage the reader / student in the cyber-security considerations of this rapidly emerging technology that we know as unmanned aircraft systems (UAS). The first edition topics covered National Airspace (NAS) policy issues, information security (INFOSEC), UAS vulnerabilities in key systems (Sense and Avoid / SCADA), navigation and collision avoidance systems, stealth design, intelligence, surveillance and reconnaissance (ISR) platforms; weapons systems security; electronic warfare considerations; data-links, jamming, operational vulnerabilities and still-emerging political scenarios that affect US military / commercial decisions. This second edition discusses state-of-the-art technology issues facing US UAS designers. It focuses on counter unmanned aircraft systems (C-UAS) – especially research designed to mitigate and terminate threats by SWARMS. Topics include high-altitude platforms (HAPS) for wireless communications; C-UAS and large scale threats; acoustic countermeasures against SWARMS and building an Identify Friend or Foe (IFF) acoustic library; updates to the legal / regulatory landscape; UAS proliferation along the Chinese New Silk Road Sea / Land routes; and ethics in this new age of autonomous systems and artificial intelligence (AI).https://newprairiepress.org/ebooks/1027/thumbnail.jp

MIMO-based Friendly Jamming and Interference Management Techniques for Secure Wireless Communications

The ever-increasing growth of wireless systems has made them an essential part of our daily life. People rely heavily on wireless networks for communications and to conduct critical transactions from their mobile devices, including financial transactions, access to health records, etc. The proliferation of wireless communication devices opens the door for many security breaches, ranging from eavesdropping to jamming attacks. Such a disadvantage stems from the broadcast nature of wireless transmissions, which creates an exposed environment. In this dissertation, we focus on eavesdropping attacks. While cryptographic techniques can be used to thwart eavesdropping attacks and enable secure wireless communications, they are not sufficient to protect the lower-layer headers of a packet (i.e., PHY and MAC headers). Hence, even though the secret message is encrypted, these unencrypted headers can be exploited by an adversary to extract invaluable information and initiate malicious attacks (e.g., traffic classification). Physical-layer (PHY-layer) security has been introduced as a promising candidate to prevent attacks that exploit unencrypted lower layer headers. PHY-layer security techniques typically rely on injecting an intentional interference into the medium so as to confuse nearby eavesdroppers (Eve). Specifically, a legitimate transmit-receive (Alice-Bob) pair generates a bogus signal, namely friendly jamming (FJ), along with the information signal, to increase interference at Eve(s) but without affecting the legitimate receiver (Bob). Depending on which end of a legitimate link is responsible for generating the FJ signal, two types of FJ techniques exist: transmitter-based (TxFJ) and receiver-based (RxFJ). In this dissertation, we propose to advance the state-of-art in PHY-layer security by considering multi-link scenarios, including multi-user multiple-input multiple-output (MU-MIMO) and peer-to-peer (P2P) networks. Specifically, we consider a scenario where one or more external Eve(s) attempt to snoop on communications of various links. In such networks, transmission of one link may be interfered with neighboring links' transmissions. Thus, special care must be dedicated to handling interference. In our first contribution in this dissertation, we consider a P2P network tapped by external Eve(s) in which each Alice-Bob pair conceals its communications using TxFJ. TxFJ is realized at Alice side using MIMO precoding. The goal is to design the precoders for both information and TxFJ signals at all Alices so as to maximize a given utility (e.g., sum of communication rates) while preventing eavesdropping elsewhere. Because legitimate links do not cooperate with each other and there is no centralized authority to perform optimization, every link selfishly aims at maximizing its secrecy rate. Using non-cooperative game theory, we design a distributed method for maximizing the sum of secrecy rates. Under the exact knowledge of eavesdropping channels, we show that our distributed method has a comparable secrecy sum-rate to a centralized approach. In our next contribution, we focus on employing practical precoders in our design for a P2P network. Specifically, we employed a zero-forcing-based (ZF-based) precoder for the TxFJ of each Alice-Bob pair in a P2P network. We also assume that each link has a certain rate demand to be satisfied. In such a scenario, even though the non-cooperative game designed for this P2P network is shown to be convergent to its unique Nash Equilibrium (NE), there is still no guarantee that the resulting NE is Pareto-optimal. Hence, we propose a modified price-based game, in which each link is penalized for generating interference on other legitimate links. We show that the price-based game converges to the Pareto-optimal point of secrecy rate region. We then leverage mixed-strategy games to provide solutions that are robust to uncertainties in knowledge of eavesdropping channels. The proposed ZF-based design of precoders is also implemented on software-defined radios to assess its performance on a single link in real-world scenarios. In another contribution of this dissertation, we consider to further enhance the secrecy of each link in a P2P network by equipping each receiver with RxFJ. Hence, in addition to the power allocation between TxFJ and information signals, we optimize RxFJ power as well. We show that by using RxFJ at each Bob, we could leverage the well-established concept of concave games, which compared to non-convex games enjoy more simplified game-theoretic analysis. We derive sufficient conditions under which the game admits a unique NE. We also propose another version of our power control algorithm that can be implemented asynchronously, making it robust to transmission delays in the network. In our last contribution, we consider the downlink of a MU-MIMO network in the presence of an external Eve. No knowledge of Eve's location is assumed at the access point. The network is studied in underloaded and overloaded conditions. In an underloaded (overloaded) network, the number of antennas at the access point is larger (smaller) than the total number of downlink users' antennas. In the overloaded setting, traditional methods of creating TxFJ, such as ZF-based methods, are infeasible. We propose a linear precoding scheme that relaxes such infeasibility in overloaded MU-MIMO networks. In the worst-case scenario where Eve has knowledge of the channels between access point and downlink users, we show that our method imposes the most stringent condition on the number of antennas required at Eve to cancel out TxFJ signals. We also show that choosing the number of independent streams to be sent to downlink users has an important role in achieving a tradeoff between security, reliability, and the achievable rate

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC) and Ultra-Reliable and Low Latency Communications (URLLC), the mMTC brings the unique technical challenge of supporting a huge number of MTC devices in cellular networks, which is the main focus of this paper. The related challenges include Quality of Service (QoS) provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and Narrowband IoT (NB-IoT). Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenario along with the recent advances towards enhancing its learning performance and convergence. Finally, we discuss some open research challenges and promising future research directions

Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read