Robust Beamforming for Cognitive and Cooperative Wireless Networks

Ph.DDOCTOR OF PHILOSOPH

Convex Optimisation for Communication Systems

In this thesis new robust methods for the efficient sharing of the radio spectrum for underlay cognitive radio (CR) systems are developed. These methods provide robustness against uncertainties in the channel state information (CSI) that is available to the cognitive radios. A stochastic approach is taken and the robust spectrum sharing methods are formulated as convex optimisation problems. Three efficient spectrum sharing methods; power control, cooperative beamforming and conventional beamforming are studied in detail. The CR power control problem is formulated as a sum rate maximisation problem and transformed into a convex optimisation problem. A robust power control method under the assumption of partial CSI is developed and also transformed into a convex optimisation problem. A novel method of detecting and removing infeasible constraints from the power allocation problem is presented that results in considerably improved performance. The performance of the proposed methods in Rayleigh fading channels is analysed by simulations. The concept of cooperative beamforming for spectrum sharing is applied to an underlay CR relay network. Distributed single antenna relay nodes are utilised to form a virtual antenna array that provides increased gains in capacity through cooperative beamforming. It is shown that the cooperative beamforming problems can be transformed into convex optimisation problems. New robust cooperative beamformers under the assumption of partial and imperfect CSI are developed and also transformed into convex optimisation problems. The performance of the proposed methods in Rayleigh fading channels is analysed by simulations. Conventional beamforming to allow efficient spectrum sharing in an underlay CR system is studied. The beamforming problems are formulated and transformed into convex optimisation problems. New robust beamformers under the assumption of partial and imperfect CSI are developed and also transformed into convex optimisation problems. The performance of the proposed methods in Rayleigh fading channels is analysed by simulations

Collaborative Estimation in Distributed Sensor Networks

Networks of smart ultra-portable devices are already indispensable in our lives, augmenting our senses and connecting our lives through real time processing and communication of sensory (e.g., audio, video, location) inputs. Though usually hidden from the user\u27s sight, the engineering of these devices involves fierce tradeoffs between energy availability (battery sizes impact portability) and signal processing / communication capability (which impacts the smartness of the devices). The goal of this dissertation is to provide a fundamental understanding and characterization of these tradeoffs in the context of a sensor network, where the goal is to estimate a common signal by coordinating a multitude of battery-powered sensor nodes. Most of the research so far has been based on two key assumptions -- distributed processing and temporal independence -- that lend analytical tractability to the problem but otherwise are often found lacking in practice. This dissertation introduces novel techniques to relax these assumptions -- leading to vastly efficient energy usage in typical networks (up to 20% savings) and new insights on the quality of inference. For example, the phenomenon of sensor drift is ubiquitous in applications such as air-quality monitoring, oceanography and bridge monitoring, where calibration is often difficult and costly. This dissertation provides an analytical framework linking the state of calibration to the overall uncertainty of the inferred parameters. In distributed estimation, sensor nodes locally process their observed data and send the resulting messages to a sink, which combines the received messages to produce a final estimate of the unknown parameter. In this dissertation, this problem is generalized and called collaborative estimation , where some sensors can potentially have access to the observations from neighboring sensors and use that information to enhance the quality of their messages sent to the sink, while using the same (or lower) energy resources. This is motivated by the fact that inter-sensor communication may be possible if sensors are geographically close. As demonstrated in this dissertation, collaborative estimation is particularly effective in energy-skewed and information-skewed networks, where some nodes may have larger batteries than others and similarly some nodes may be more informative (less noisy) compared to others. Since the node with the largest battery is not necessarily also the most informative, the proposed inter-sensor collaboration provides a natural framework to route the relevant information from low-energy-high-quality nodes to high-energy-low-quality nodes in a manner that enhances the overall power-distortion tradeoff. This dissertation also analyzes how time-correlated measurement noise affects the uncertainties of inferred parameters. Imperfections such as baseline drift in sensors result in a time-correlated additive component in the measurement noise. Though some models of drift have been reported in the literature earlier, none of the studies have considered the effect of drifting sensors on an estimation application. In this dissertation, approximate measures of estimation accuracy (Cramer-Rao bounds) are derived as a function of physical properties of sensors -- namely the drift strength, correlation (Markov) factor and the time-elapsed since last calibration. For stationary drift (Markov factor less than one), it is demonstrated that the first order effect of drift is asymptotically equivalent to scaling the measurement noise by an appropriate factor. When the drift is non-stationary (Markov factor equal to one), it is established that the constant part of a signal can only be estimated inconsistently (with non-zero asymptotic variance). The results help quantify the notions that measurements taken sooner after calibration result in more accurate inference

Mathematical optimisation and signal processing techniques in wireless relay networks

With the growth of wireless networks such as sensor networks and mesh networks, the challenges of sustaining higher data rates and coverage, coupled with requirement for high quality of services, need to be addressed. The use of spatial diversity proves to be an attractive option due to its ability to significantly enhance network performance without additional bandwidth or transmission power. This thesis proposes the use of cooperative wireless relays to improvise spatial diversity in wireless sensor networks and wireless mesh networks. Cooperation in this context implies that the signals are exchanged between relays for optimal performance. The network gains realised using the proposed cooperative relays for signal forwarding are significantly large, advocating the utilisation of cooperation amongst relays. The work begins with proposing a minimum mean square error (MMSE) based relaying strategy that provides improvement in bit error rate. A simplified algorithm has been developed to calculate the roots of a polynomial equation. Following this work, a novel signal forwarding technique based on convex optimisation techniques is proposed which attains specific quality of services for end users with minimal transmission power at the relays. Quantisation of signals passed between relays has been considered in the optimisation framework. Finally, a reduced complexity scheme together with a more realistic algorithm incorporating per relay node power constraints is proposed. This optimisation framework is extended to a cognitive radio environment where relays in a secondary network forward signals without causing harmful interferences to primary network users.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Optimising multiple antenna techniques for physical layer security

Wireless communications offer data transmission services anywhere and anytime, but with the inevitable cost of introducing major security vulnerabilities. Indeed, an eavesdropper can overhear a message conveyed over the open insecure wireless media putting at risk the confidentiality of the wireless users. Currently, the way to partially prevent eavesdropping attacks is by ciphering the information between the authorised parties through complex cryptographic algorithms. Cryptography operates in the upper layers of the communication model, bit it does not address the security problem where the attack is suffered: at the transmission level. In this context, physical layer security has emerged as a promising framework to prevent eavesdropping attacks at the transmission level. Physical layer security is based on information-theoretic concepts and exploits the randomness and the uniqueness of the wireless channel. In this context, this thesis presents signal processing techniques to secure wireless networks at the physical layer by optimising the use of multiple-antennas. A masked transmission strategy is used to steer the confidential information towards the intended receiver, and, at the same time, broadcast an interfering signal to confuse unknown eavesdroppers. This thesis considers practical issues in multiple-antenna networks such as limited transmission resources and the lack of accurate information between the authorised transmission parties. The worst-case for the security, that occurs when a powerful eavesdropper takes advantage of any opportunity to put at risk the transmission confidentiality, is addressed. The techniques introduced improve the security by offering efficient and innovative transmission solutions to lock the communication at the physical layer. Notably, these transmission mechanisms strike a balance between confidentiality and quality to satisfy the practical requirements of modern wireless networks

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

Enable Reliable and Secure Data Transmission in Resource-Constrained Emerging Networks

The increasing deployment of wireless devices has connected humans and objects all around the world, benefiting our daily life and the entire society in many aspects. Achieving those connectivity motivates the emergence of different types of paradigms, such as cellular networks, large-scale Internet of Things (IoT), cognitive networks, etc. Among these networks, enabling reliable and secure data transmission requires various resources including spectrum, energy, and computational capability. However, these resources are usually limited in many scenarios, especially when the number of devices is considerably large, bringing catastrophic consequences to data transmission. For example, given the fact that most of IoT devices have limited computational abilities and inadequate security protocols, data transmission is vulnerable to various attacks such as eavesdropping and replay attacks, for which traditional security approaches are unable to address. On the other hand, in the cellular network, the ever-increasing data traffic has exacerbated the depletion of spectrum along with the energy consumption. As a result, mobile users experience significant congestion and delays when they request data from the cellular service provider, especially in many crowded areas. In this dissertation, we target on reliable and secure data transmission in resource-constrained emerging networks. The first two works investigate new security challenges in the current heterogeneous IoT environment, and then provide certain countermeasures for reliable data communication. To be specific, we identify a new physical-layer attack, the signal emulation attack, in the heterogeneous environment, such as smart home IoT. To defend against the attack, we propose two defense strategies with the help of a commonly found wireless device. In addition, to enable secure data transmission in large-scale IoT network, e.g., the industrial IoT, we apply the amply-and-forward cooperative communication to increase the secrecy capacity by incentivizing relay IoT devices. Besides security concerns in IoT network, we seek data traffic alleviation approaches to achieve reliable and energy-efficient data transmission for a group of users in the cellular network. The concept of mobile participation is introduced to assist data offloading from the base station to users in the group by leveraging the mobility of users and the social features among a group of users. Following with that, we deploy device-to-device data offloading within the group to achieve the energy efficiency at the user side while adapting to their increasing traffic demands. In the end, we consider a perpendicular topic - dynamic spectrum access (DSA) - to alleviate the spectrum scarcity issue in cognitive radio network, where the spectrum resource is limited to users. Specifically, we focus on the security concerns and further propose two physical-layer schemes to prevent spectrum misuse in DSA in both additive white Gaussian noise and fading environments

Transmission Design for Reconfigurable Intelligent Surface-Aided Wireless Systems

The performance benefits promised by Reconfigurable Intelligent Surface (RIS) are strongly dependent on the availability of highly accurate and up-to-date Channel State Information (CSI), which, however, is challenging to obtain. This thesis proposes efficient transceiver designs for a variety of CSI challenges such as worst channel condition in multicast systems, channel uncertainties caused by the presence of random blockages in millimeter wave systems, by the channel estimation error in downlink systems and by the presence of eavesdropper in security systems. First, a low-complexity transceiver design scheme in the multicast systems is proposed. In order to ensure the quality of service of the user with the worst channel condition, this thesis deploys an RIS to enhance signal coverage, and proposes two novel and efficient algorithms to jointly design the Base Station (BS) and RIS beamformings. The low-complexity algorithm with closed-form solutions is proved to have the same performance as the general second-order cone programming based algorithm. Second, novel fairness-oriented robust transceiver design schemes are proposed in RIS-aided millimeter wave systems. The channel uncertainty caused by the random blockages is analyzed, and the metric of maximum outage probability minimization is proposed. To address this problem, stochastic optimization techniques are adopted and closed-form solutions of the BS and RIS beamformings are then obtained. The proposed stochastic optimization algorithms are proved to converge to the set of stationary points. Third, a framework of robust transceiver design scheme is proposed to address the channel uncertainty caused by the cascaded BS-RIS-user channel estimation error. Two cascaded channel error models are analyzed, and the correspondingly two robust beamforming design problems are proposed. The optimization theory is used to address the complex non-convex optimization problems. The numerical results show that the proposed robust scheme can effectively resist channel uncertainty. Finally, robust transceiver design schemes are proposed in RIS-aided physical layer security systems. The schemes analyze the channel uncertainties caused by the eavesdropper who launches an active attack, and by the eavesdropper conducting passive eavesdropping. Numerical results show that the negative effect of the eavesdropper’s channel error is larger than that of the legitimate user

Large space structures and systems in the space station era: A bibliography with indexes

Bibliographies and abstracts are listed for 1219 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1, 1990 and December 31, 1990. The purpose is to provide helpful information to the researcher, manager, and designer in technology development and mission design according to system, interactive analysis and design, structural and thermal analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion, and solar power satellite systems