Rician MIMO Channel- and Jamming-Aware Decision Fusion

In this manuscript we study channel-aware decision fusion (DF) in a wireless sensor network (WSN) where: (i) the sensors transmit their decisions simultaneously for spectral efficiency purposes and the DF center (DFC) is equipped with multiple antennas; (ii) each sensor-DFC channel is described via a Rician model. As opposed to the existing literature, in order to account for stringent energy constraints in the WSN, only statistical channel information is assumed for the non-line-of sight (scattered) fading terms. For such a scenario, sub-optimal fusion rules are developed in order to deal with the exponential complexity of the likelihood ratio test (LRT) and impractical (complete) system knowledge. Furthermore, the considered model is extended to the case of (partially unknown) jamming-originated interference. Then the obtained fusion rules are modified with the use of composite hypothesis testing framework and generalized LRT. Coincidence and statistical equivalence among them are also investigated under some relevant simplified scenarios. Numerical results compare the proposed rules and highlight their jammingsuppression capability.Comment: Accepted in IEEE Transactions on Signal Processing 201

Physical-Layer Security in Cognitive Radio Networks

The fifth-generation (5G) communications and beyond are expected to serve a huge number of devices and services. However, due to the fixed spectrum allocation policies, the need for cognitive radio networks (CRNs) has increased accordingly. CRNs have been proposed as a promising approach to address the problem of under-utilization and scarcity of the spectrum. In CRNs, secondary users (SUs) access the licensed spectrum of the primary users (PUs) using underlay, overlay, or interweave paradigms. SUs can access the spectrum band simultaneously with the PUs in underlay access mode provided that the SUs’ transmission power does not cause interference to the PUs’ communication. In this case, SUs should keep monitoring the interference level that the PU receiver can tolerate and adjust the transmission power accordingly. However, varying the transmission power may lead to some threats to the privacy of the information transfer of CRNs. Therefore, securing data transmission in an underlay CRN is a challenge that should be addressed. Physical-layer security (PLS) has recently emerged as a reliable method to protect the confidentiality of the SUs’ transmission against attacks, especially for the underlay model with no need for sharing security keys. Indeed, PLS has the advantage of safeguarding the data transmission without the necessity of adding enormous additional resources, specifically when there are massively connected devices. Apart from the energy consumed by the various functions carried out by SUs, enhancing security consumes additional energy. Therefore, energy harvesting (EH) is adopted in our work to achieve both; energy efficiency and spectral efficiency. EH is a significant breakthrough for green communication, allowing the network nodes to reap energy from multiple sources to lengthen battery life. The energy from various sources, such as solar, wind, vibration, and radio frequency (RF) signals, can be obtained through the process of EH. This accumulated energy can be stored to be used for various processes, such as improving the users’ privacy and prolonging the energy-constrained devices’ battery life. In this thesis, for the purpose of realistic modelling of signal transmission, we explicitly assume scenarios involving moving vehicles or nodes in networks that are densely surrounded by obstacles. Hence, we begin our investigations by studying the link performance under the impact of cascaded κ−μ fading channels. Moreover, using the approach of PLS, we address the privacy of several three-node wiretap system models, in which there are two legitimate devices communicating under the threat of eavesdroppers. We begin by a three-node wiretap system model operating over cascaded κ − μ fading channels and under worst-case assumptions. Moreover, assuming cascaded κ − μ distributions for all the links, we investigate the impact of these cascade levels, as well as the impact of multiple antennas employed at the eavesdropper on security. Additionally, the PLS is examined for two distinct eavesdropping scenarios: colluding and non-colluding eavesdroppers. Throughout the thesis, PLS is mainly evaluated through the secrecy outage probability (SOP), the probability of non-zero secrecy capacity (Pnzcr ), and the intercept probability (Pint). Considering an underlay CRN operating over cascaded Rayleigh fading channel, with the presence of an eavesdropper, we explore the PLS for SUs in the network. This study is then extended to investigate the PLS of SUs in an underlay single-input-multiple-output (SIMO) CRN over cascaded κ-μ general fading channels with the presence of a multi-antenna eavesdropper. The impact of the constraint over the transmission power of the SU transmitter due to the underlay access mode is investigated. In addition, the effects of multiple antennas and cascade levels over security are well-explored. In the second part of our thesis, we propose an underlay CRN, in which an SU transmitter communicates with an SU destination over cascaded κ-μ channels. The confidentiality of the shared information between SUs is threatened by an eavesdropper. Our major objective is to achieve a secured network, while at the same time improving the energy and spectrum efficiencies with practical modeling for signals’ propagation. Hence, we presume that the SU destination harvests energy from the SU transmitter. The harvested energy is used to produce jamming signals to be transmitted to mislead the eavesdropper. In this scenario, a comparison is made between an energy-harvesting eavesdropper and a non-energy harvesting one. Additionally, we present another scenario in which cooperative jamming is utilized as one of the means to boost security. In this system model, the users are assumed to communicate over cascaded Rayleigh channels. Moreover, two scenarios for the tapping capabilities of the eavesdroppers are presented; colluding and non-colluding eavesdroppers. This study is then extended for the case of non-colluding eavesdroppers, operating over cascaded κ-μ channels. Finally, we investigate the reliability of the SUs and PUs while accessing the licensed bands using the overlay mode, while enhancing the energy efficiency via EH techniques. Hence, we assume that multiple SUs are randomly distributed, in which one of the SUs is selected to harvest energy from the PUs’ messages. Then, utilizing the gathered energy, this SU combines its own messages with the amplified PUs messages and forwards them to the destinations. Furthermore, we develop two optimization problems with the potential of maximizing the secondary users’ rate and the sum rate of both networks

Neural-network-aided automatic modulation classification

Automatic modulation classification (AMC) is a pattern matching problem which significantly impacts divers telecommunication systems, with significant applications in military and civilian contexts alike. Although its appearance in the literature is far from novel, recent developments in machine learning technologies have triggered an increased interest in this area of research. In the first part of this thesis, an AMC system is studied where, in addition to the typical point-to-point setup of one receiver and one transmitter, a second transmitter is also present, which is considered an interfering device. A convolutional neural network (CNN) is used for classification. In addition to studying the effect of interference strength, we propose a modification attempting to leverage some of the debilitating results of interference, and also study the effect of signal quantisation upon classification performance. Consequently, we assess a cooperative setting of AMC, namely one where the receiver features multiple antennas, and receives different versions of the same signal from the single-antenna transmitter. Through the combination of data from different antennas, it is evidenced that this cooperative approach leads to notable performance improvements over the established baseline. Finally, the cooperative scenario is expanded to a more complicated setting, where a realistic geographic distribution of four receiving nodes is modelled, and furthermore, the decision-making mechanism with regard to the identity of a signal resides in a fusion centre independent of the receivers, connected to them over finite-bandwidth backhaul links. In addition to the common concerns over classification accuracy and inference time, data reduction methods of various types (including “trained” lossy compression) are implemented with the objective of minimising the data load placed upon the backhaul links.Open Acces

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

Physical Layer Security in Integrated Sensing and Communication Systems

The development of integrated sensing and communication (ISAC) systems has been spurred by the growing congestion of the wireless spectrum. The ISAC system detects targets and communicates with downlink cellular users simultaneously. Uniquely for such scenarios, radar targets are regarded as potential eavesdroppers which might surveil the information sent from the base station (BS) to communication users (CUs) via the radar probing signal. To address this issue, we propose security solutions for ISAC systems to prevent confidential information from being intercepted by radar targets. In this thesis, we firstly present a beamformer design algorithm assisted by artificial noise (AN), which aims to minimize the signal-to-noise ratio (SNR) at the target while ensuring the quality of service (QoS) of legitimate receivers. Furthermore, to reduce the power consumed by AN, we apply the directional modulation (DM) approach to exploit constructive interference (CI). In this case, the optimization problem is designed to maximize the SINR of the target reflected echoes with CI constraints for each CU, while constraining the received symbols at the target in the destructive region. Apart from the separate functionalities of radar and communication systems above, we investigate sensing-aided physical layer security (PLS), where the ISAC BS first emits an omnidirectional waveform to search for and estimate target directions. Then, we formulate a weighted optimization problem to simultaneously maximize the secrecy rate and minimize the Cram\'er-Rao bound (CRB) with the aid of the AN, designing a beampattern with a wide main beam covering all possible angles of targets. The main beam width of the next iteration depends on the optimal CRB. In this way, the sensing and security functionalities provide mutual benefits, resulting in the improvement of mutual performances with every iteration of the optimization, until convergence. Overall, numerical results show the effectiveness of the ISAC security designs through the deployment of AN-aided secrecy rate maximization and CI techniques. The sensing-assisted PLS scheme offers a new approach for obtaining channel information of eavesdroppers, which is treated as a limitation of conventional PLS studies. This design gains mutual benefits in both single and multi-target scenarios