Transmitter Optimization Techniques for Physical Layer Security

Information security is one of the most critical issues in wireless networks as the signals transmitted through wireless medium are more vulnerable for interception. Although the existing conventional security techniques are proven to be safe, the broadcast nature of wireless communications introduces different challenges in terms of key exchange and distributions. As a result, information theoretic physical layer security has been proposed to complement the conventional security techniques for enhancing security in wireless transmissions. On the other hand, the rapid growth of data rates introduces different challenges on power limited mobile devices in terms of energy requirements. Recently, research work on wireless power transfer claimed that it has been considered as a potential technique to extend the battery lifetime of wireless networks. However, the algorithms developed based on the conventional optimization approaches often require iterative techniques, which poses challenges for real-time processing. To meet the demanding requirements of future ultra-low latency and reliable networks, neural network (NN) based approach can be employed to determine the resource allocations in wireless communications. This thesis developed different transmission strategies for secure transmission in wireless communications. Firstly, transmitter designs are focused in a multiple-input single-output simultaneous wireless information and power transfer system with unknown eavesdroppers. To improve the performance of physical layer security and the harvested energy, artificial noise is incorporated into the network to mask the secret information between the legitimate terminals. Then, different secrecy energy efficiency designs are considered for a MISO underlay cognitive radio network, in the presence of an energy harvesting receiver. In particular, these designs are developed with different channel state information assumptions at the transmitter. Finally, two different power allocation designs are investigated for a cognitive radio network to maximize the secrecy rate of the secondary receiver: conventional convex optimization framework and NN based algorithm

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Multiple-Antenna Systems: From Generic to Hardware-Informed Precoding Designs

5G-and-beyond communication systems are expected to be in a heterogeneous form of multiple-antenna cellular base stations (BSs) overlaid with small cells. The fully-digital BS structures can incur significant power consumption and hardware complexity. Moreover, the wireless BSs for small cells usually have strict size constraints, which incur additional hardware effects such as mutual coupling (MC). Consequently, the transmission techniques designed for future wireless communication systems should respect the hardware structures at the BSs. For this reason, in this thesis we extend generic downlink precoding to more advanced hardware-informed transmission techniques for a variety of BS structures. This thesis firstly extends the vector perturbation (VP) precoding to multiple-modulation scenarios, where existing VP-based techniques are sub-optimal. Subsequently, this thesis focuses on the downlink transmission designs for hardware effects in the form of MC, limited number of radio frequency (RF) chains, and low-precision digital-to-analog converters (DACs). For these scenarios, existing precoding techniques are either sub-optimal or not directly applicable due to the specific hardware constraints. In this context, this thesis first proposes analog-digital (AD) precoding methods for MC exploitation in compact single-user multiple-antenna systems with the concept of constructive interference, and further extends the idea of MC exploitation to multi-user scenarios with a joint optimisation on the precoding matrix and the mutual coupling effect. We further consider precoding for wireless BSs with a limited number of RF chains, in the form of compact parasitic antenna array as well as hybrid analog-digital structures designed for large-scale multiple-antenna systems. In addition, with a reformulation of the constructive interference, this thesis also considers the low-complexity precoding design for the use of low-resolution DACs for a massive-antenna array at the BSs. Analytical and numerical results reveal an improved performance of the proposed techniques compared to the state-of-the-art approaches, which validates the effectiveness of the introduced methods

Machine Learning Empowered Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surfaces (RISs) or known as intelligent reflecting surfaces (IRSs) have emerged as potential auxiliary equipment for future wireless networks, which attracts extensive research interest in their characteristics, applications, and potential. RIS is a panel surface equipped with a number of reflective elements, which can artificially modify the propagation environment of the electrogenic signals. Specifically, RISs have the ability to precisely adjust the propagation direction, amplitude, and phase-shift of the signals, providing users with a set of cascaded channels in addition to direct channels, and thereby improving the communication performances for users. Compared with other candidate technologies such as active relays, RIS has advantages in terms of flexible deployment, economical cost, and high energy efficiency. Thus, RISs have been considered a potential candidate technique for future wireless networks. In this thesis, a wireless network paradigm for the sixth generation (6G) wireless networks is proposed, where RISs are invoked to construct smart radio environments (SRE) to enhance communication performances for mobile users. In addition, beyond the conventional reselecting-only RIS, a novel model of RIS is originally proposed, namely, simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The STAR-RIS splits the incident signal into transmitted and reflected signals, making full utilization of them to generate $360^{\circ}$ coverage around the STAR-RIS panel, improving the coverage of the RIS. In order to fully exert the channel domination and beamforming ability of the RISs and STAR-RSIs to construct SREs, several machine learning algorithms, including deep learning (DL), deep reinforcement learning (DRL), and federated learning (FL) approaches are developed to optimize the communication performance in respect of sum data rate or energy efficiency for the RIS-assisted networks. Specifically, several problems are investigated including 1) the passive beamforming problem of the RIS with consideration of configuration overhead is resolved by a DL and a DRL algorithm, where the time overhead of configuration of RIS is successfully reduced by the machine learning algorithms. Consequently, the throughput during a time frame improved $95.2\%$ by invoking the proposed algorithms; 2) a novel framework of mobile RISs-enhanced indoor wireless networks is proposed, and a FL enhanced DRL algorithm is proposed for the deployment and beamforming optimization of the RIS. The average throughput of the indoor users severed by the mobile RIS is improved $15.1\%$ compared to the case of conventional fixed RIS; 3) A STAR-RIS assisted multi-user downlink multiple-input single-output (MISO) communication system is investigated, and a pair of hybrid reinforcement learning algorithms are proposed for the hybrid control of the transmitting and reflecting beamforming of the STAR-RIS, which ameliorate $7\%$ of the energy efficiency of the STAR-RIS assisted networks; 4) A tile-based low complexity beamforming approach is proposed for STAR-RISs, and the proposed tile-based beamforming approach is capable of achieving homogeneous data rate performance with element-based beamforming with appreciable lower complexity. By designing and operating the computer simulation, this thesis demonstrated 1) the performance gain in terms of sum data rate or energy efficiency by invoking the proposed RIS in the wireless communication networks; 2) the data rate or energy efficient performance gain of the proposed STAR-RIS compared to the existing reflecting-only RIS; 3) the effect of the proposed machine learning algorithms in terms of convergence rate, optimality, and complexity compared to the benchmarks of existing algorithms