Joint Secure Communication and Radar Beamforming: A Secrecy-Estimation Rate-Based Design

This paper considers transmit beamforming in dual-function radar-communication (DFRC) system, where a DFRC transmitter simultaneously communicates with a communication user and detects a malicious target with the same waveform. Since the waveform is embedded with information, the information is risked to be intercepted by the target. To address this problem, physical-layer security technique is exploited. By using secrecy rate and estimation rate as performance measure for communication and radar, respectively, three secrecy rate maximization (SRM) problems are formulated, including the SRM with and without artificial noise (AN), and robust SRM. For the SRM beamforming, we prove that the optimal beamformer can be computed in closed form. For the AN-aided SRM, by leveraging alternating optimization similar closed-form solution is obtained for the beamformer and the AN covariance matrix. Finally, the imperfect CSI of the target is also considered under the premise of a moment-based random phase-error model on the direction of arrival at the target. Simulation results demonstrate the efficacy and robustness of the proposed designs.Comment: 14 page

Learning-based Intelligent Surface Configuration, User Selection, Channel Allocation, and Modulation Adaptation for Jamming-resisting Multiuser OFDMA Systems

Reconfigurable intelligent surfaces (RISs) can potentially combat jamming attacks by diffusing jamming signals. This paper jointly optimizes user selection, channel allocation, modulation-coding, and RIS configuration in a multiuser OFDMA system under a jamming attack. This problem is non-trivial and has never been addressed, because of its mixed-integer programming nature and difficulties in acquiring channel state information (CSI) involving the RIS and jammer. We propose a new deep reinforcement learning (DRL)-based approach, which learns only through changes in the received data rates of the users to reject the jamming signals and maximize the sum rate of the system. The key idea is that we decouple the discrete selection of users, channels, and modulation-coding from the continuous RIS configuration, hence facilitating the RIS configuration with the latest twin delayed deep deterministic policy gradient (TD3) model. Another important aspect is that we show a winner-takes-all strategy is almost surely optimal for selecting the users, channels, and modulation-coding, given a learned RIS configuration. Simulations show that the new approach converges fast to fulfill the benefit of the RIS, due to its substantially small state and action spaces. Without the need of the CSI, the approach is promising and offers practical value.Comment: accepted by IEEE TCOM in Jan. 202

Scheduling space-to-ground optical communication under cloud cover uncertainty

Any reliable model for scheduling optical space-to-ground communication must factor in cloud cover conditions due to attenuation of the laser beam by water droplets in the clouds. In this article, we provide two alternative models of uncertainty for cloud cover predictions: a robust optimization model with a polyhedral uncertainty set and a distributionally robust optimization model with a moment-based ambiguity set. We computationally analyze their performance over a realistic communication system with one satellite and a network of ground stations located in the U.K. The models are solved to schedule satellite operations for six months utilizing cloud cover predictions from official weather forecasts. We found that the presented formulations with the treatment of uncertainty outperform in the long-term models, in which uncertainty is ignored. Both treatments of uncertainty exhibit similar performance. Nonetheless, the novel variant with the polyhedral uncertainty set is considerably faster to solve

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

A vision-based optical character recognition system for real-time identification of tractors in a port container terminal

Automation has been seen as a promising solution to increase the productivity of modern sea port container terminals. The potential of increase in throughput, work efficiency and reduction of labor cost have lured stick holders to strive for the introduction of automation in the overall terminal operation. A specific container handling process that is readily amenable to automation is the deployment and control of gantry cranes in the container yard of a container terminal where typical operations of truck identification, loading and unloading containers, and job management are primarily performed manually in a typical terminal. To facilitate the overall automation of the gantry crane operation, we devised an approach for the real-time identification of tractors through the recognition of the corresponding number plates that are located on top of the tractor cabin. With this crucial piece of information, remote or automated yard operations can then be performed. A machine vision-based system is introduced whereby these number plates are read and identified in real-time while the tractors are operating in the terminal. In this paper, we present the design and implementation of the system and highlight the major difficulties encountered including the recognition of character information printed on the number plates due to poor image integrity. Working solutions are proposed to address these problems which are incorporated in the overall identification system.postprin

Job shop scheduling with artificial immune systems

The job shop scheduling is complex due to the dynamic environment. When the information of the jobs and machines are pre-defined and no unexpected events occur, the job shop is static. However, the real scheduling environment is always dynamic due to the constantly changing information and different uncertainties. This study discusses this complex job shop scheduling environment, and applies the AIS theory and switching strategy that changes the sequencing approach to the dispatching approach by taking into account the system status to solve this problem. AIS is a biological inspired computational paradigm that simulates the mechanisms of the biological immune system. Therefore, AIS presents appealing features of immune system that make AIS unique from other evolutionary intelligent algorithm, such as self-learning, long-lasting memory, cross reactive response, discrimination of self from non-self, fault tolerance, and strong adaptability to the environment. These features of AIS are successfully used in this study to solve the job shop scheduling problem. When the job shop environment is static, sequencing approach based on the clonal selection theory and immune network theory of AIS is applied. This approach achieves great performance, especially for small size problems in terms of computation time. The feature of long-lasting memory is demonstrated to be able to accelerate the convergence rate of the algorithm and reduce the computation time. When some unexpected events occasionally arrive at the job shop and disrupt the static environment, an extended deterministic dendritic cell algorithm (DCA) based on the DCA theory of AIS is proposed to arrange the rescheduling process to balance the efficiency and stability of the system. When the disturbances continuously occur, such as the continuous jobs arrival, the sequencing approach is changed to the dispatching approach that involves the priority dispatching rules (PDRs). The immune network theory of AIS is applied to propose an idiotypic network model of PDRs to arrange the application of various dispatching rules. The experiments show that the proposed network model presents strong adaptability to the dynamic job shop scheduling environment.postprin