Contributions to Resource Allocation in Cognitive Radio Networks

The continuous increase in the number of wireless devices and the huge demand for higher data rates have promoted the development of new wireless communications technologies with improved spectrum sharing features. Recently, the concept of cognitive radio (CR) has gained increased popularity for the efficient utilization of radio frequency (RF) spectrum. A CR is characterized as a communication system which is capable to learn the spectrum environment through sensing, and to adapt its signaling schemes for a better utilization of the radio frequency resources. Resource allocation, which involves scheduling of spectrum and power resources, represents a crucial problem for the performance of CR networks in terms of system throughput and bandwidth utilization. In this dissertation, we investigate resource allocation problems in a CR network by exploring a variety of optimization techniques. Specifically, in the first part of the dissertation, our goal is to maximize the total throughput of secondary users (SUs) in an orthogonal frequency division multiple access (OFDMA) CR network. In addition, the power of SUs is controlled to keep the interference introduced to primary users (PUs) under certain limits, which gives rise to a non-convex mixed integer non-linear programming (MINLP) optimization problem. It is illustrated that the original non-convex MINLP formulation admits a special structure and the optimal solution can be achieved efficiently using any standard convex optimization method under a general and practical assumption. In the second part of the dissertation, considering the imperfect sensing information, we study the joint spectrum sensing and resource allocation problem in a multi-channel-multi-user CR network. The average total throughput of SUs is maximized by jointly optimizing the sensing threshold and power allocation strategies. The problem is also formulated as a non-convex MINLP problem. By utilizing the continuous relaxation and convex optimization tools, the dimension of the non-convex MINLP problem is significantly reduced, which helps to reformulate the optimization problem without resorting to integer variables. A newly-developed optimization technique, referred to as the monotonic optimization, is then employed to obtain an optimal solution. Furthermore, a practical low-complexity spectrum sensing and resource allocation algorithm is proposed to reduce the computational cost

AI-enabled modeling and monitoring of data-rich advanced manufacturing systems

The infrastructure of cyber-physical systems (CPS) is based on a meta-concept of cybermanufacturing systems (CMS) that synchronizes the Industrial Internet of Things (IIoTs), Cloud Computing, Industrial Control Systems (ICSs), and Big Data analytics in manufacturing operations. Artificial Intelligence (AI) can be incorporated to make intelligent decisions in the day-to-day operations of CMS. Cyberattack spaces in AI-based cybermanufacturing operations pose significant challenges, including unauthorized modification of systems, loss of historical data, destructive malware, software malfunctioning, etc. However, a cybersecurity framework can be implemented to prevent unauthorized access, theft, damage, or other harmful attacks on electronic equipment, networks, and sensitive data. The five main cybersecurity framework steps are divided into procedures and countermeasure efforts, including identifying, protecting, detecting, responding, and recovering. Given the major challenges in AI-enabled cybermanufacturing systems, three research objectives are proposed in this dissertation by incorporating cybersecurity frameworks. The first research aims to detect the in-situ additive manufacturing (AM) process authentication problem using high-volume video streaming data. A side-channel monitoring approach based on an in-situ optical imaging system is established, and a tensor-based layer-wise texture descriptor is constructed to describe the observed printing path. Subsequently, multilinear principal component analysis (MPCA) is leveraged to reduce the dimension of the tensor-based texture descriptor, and low-dimensional features can be extracted for detecting attack-induced alterations. The second research work seeks to address the high-volume data stream problems in multi-channel sensor fusion for diverse bearing fault diagnosis. This second approach proposes a new multi-channel sensor fusion method by integrating acoustics and vibration signals with different sampling rates and limited training data. The frequency-domain tensor is decomposed by MPCA, resulting in low-dimensional process features for diverse bearing fault diagnosis by incorporating a Neural Network classifier. By linking the second proposed method, the third research endeavor is aligned to recovery systems of multi-channel sensing signals when a substantial amount of missing data exists due to sensor malfunction or transmission issues. This study has leveraged a fully Bayesian CANDECOMP/PARAFAC (FBCP) factorization method that enables to capture of multi-linear interaction (channels × signals) among latent factors of sensor signals and imputes missing entries based on observed signals