Smart Sensing and Performance Analysis for Cognitive Radio Networks

Static spectrum access policy has resulted in spectrum scarcity as well as low spectrum utility in today\u27s wireless communications. To utilize the limited spectrum more efficiently, cognitive radio networks have been considered a promising paradigm for future network. Due to the unique features of cognitive radio technology, cognitive radio networks not only raise new challenges, but also bring several fundamental problems back to the focus of researchers. So far, a number of problems in cognitive radio networks have remained unsolved over the past decade. The work presented in this dissertation attempts to fill some of the gaps in the research area of cognitive radio networks. It focuses primarily on spectrum sensing and performance analysis in two architectures: a single cognitive radio network and multiple co-existing cognitive radio networks. Firstly, a single cognitive radio network with one primary user is considered. A weighted cooperative spectrum sensing framework is designed, to increase the spectrum sensing accuracy. After studying the architecture of a single cognitive radio network, attention is shifted to co-existing multiple cognitive radio networks. The weakness of the conventional two-state sensing model is pointed out in this architecture. To solve the problem, a smart sensing model which consists of three states is designed. Accordingly, a method for a two-stage detection procedure is developed to accurately detect each state of the three. In the first stage, energy detection is employed to identify whether a channel is idle or occupied. If the channel is occupied, received signal is further analyzed at the second stage to determine whether the signal originates from a primary user or an secondary user. For the second stage, a statistical model is developed, which is used for distance estimation. The false alarm and miss detection probabilities for the spectrum sensing technology are theoretically analyzed. Then, how to use smart sensing, coupled with a designed media access control protocol, to achieve fairness among multiple CRNs is thoroughly investigated. The media access control protocol fully takes the PU activity into account. Afterwards, the significant performance metrics including throughput and fairness are carefully studied. In terms of fairness, the fairness dynamics from a micro-level to macro-level is evaluated among secondary users from multiple cognitive radio networks. The fundamental distinctions between the two-state model and the three-state sensing model are also addressed. Lastly, the delay performance of a cognitive radio network supporting heterogeneous traffic is examined. Various delay requirements over the packets from secondary users are fully considered. Specifically, the packets from secondary users are classified into either delay-sensitive packets or delay-insensitive packets. Moreover, a novel relative priority strategy is designed between these two types of traffic by proposing a transmission window strategy. The delay performance of both a single-primary user scenario and a multiple-primary user scenario is thoroughly investigated by employing queueing theory

DTER: Schedule Optimal RF Energy Request and Harvest for Internet of Things

We propose a new energy harvesting strategy that uses a dedicated energy source (ES) to optimally replenish energy for radio frequency (RF) energy harvesting powered Internet of Things. Specifically, we develop a two-step dual tunnel energy requesting (DTER) strategy that minimizes the energy consumption on both the energy harvesting device and the ES. Besides the causality and capacity constraints that are investigated in the existing approaches, DTER also takes into account the overhead issue and the nonlinear charge characteristics of an energy storage component to make the proposed strategy practical. Both offline and online scenarios are considered in the second step of DTER. To solve the nonlinear optimization problem of the offline scenario, we convert the design of offline optimal energy requesting problem into a classic shortest path problem and thus a global optimal solution can be obtained through dynamic programming (DP) algorithms. The online suboptimal transmission strategy is developed as well. Simulation study verifies that the online strategy can achieve almost the same energy efficiency as the global optimal solution in the long term

RF Energy Harvesting Wireless Communication: RF Environment, Device Hardware and Practical Issues

Radio frequency (RF) based wireless power transfer provides an attractive solution to extend the lifetime of power-constrained wireless sensor networks. Through harvesting RF energy from surrounding environments or dedicated energy sources, low-power wireless devices can be self-sustaining and environment-friendly. These features make the RF energy harvesting wireless communication (RF-EHWC) technique attractive to a wide range of applications. The objective of this article is to investigate the latest research activities on the practical RF-EHWC design. The distribution of RF energy in the real environment, the hardware design of RF-EHWC devices and the practical issues in the implementation of RF-EHWC networks are discussed. At the end of this article, we introduce several interesting applications that exploit the RF-EHWC technology to provide smart healthcare services for animals, wirelessly charge the wearable devices, and implement 5G-assisted RF-EHWC

A Comparison of Deep Learning Algorithms on Image Data for Detecting Floodwater on Roadways

Object detection and segmentation algorithms evolved significantly in the last decade. Simultaneous object detection and segmentation paved the way for real-time applications such as autonomous driving. Detection and segmentation of (partially) flooded roadways are essential inputs for vehicle routing and traffic management systems. This paper proposes an automatic floodwater detection and segmentation method utilizing the Mask Region-Based Convolutional Neural Networks (Mask-R-CNN) and Generative Adversarial Networks (GAN) algorithms. To train the model, manually labeled images with urban, suburban, and natural settings are used. The performances of the algorithms are assessed in accurately detecting the floodwater captured in images. The results show that the proposed Mask-R-CNN-based floodwater detection and segmentation outperform previous studies, whereas the GAN-based model has a straightforward implementation compared to other models

Security Hardening of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks

Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the security threats and mitigation for AI-powered applications in NextG networks have not been investigated deeply in academia and industry due to being new and more complicated. This paper focuses on an AI-powered IRS implementation in NextG networks along with its vulnerability against adversarial machine learning attacks. This paper also proposes the defensive distillation mitigation method to defend and improve the robustness of the AI-powered IRS model, i.e., reduce the vulnerability. The results indicate that the defensive distillation mitigation method can significantly improve the robustness of AI-powered models and their performance under an adversarial attack

Security Hardening of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks

Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the security threats and mitigation for AI-powered applications in NextG networks have not been investigated deeply in academia and industry due to being new and more complicated. This paper focuses on an AI-powered IRS implementation in NextG networks along with its vulnerability against adversarial machine learning attacks. This paper also proposes the defensive distillation mitigation method to defend and improve the robustness of the AI-powered IRS model, i.e., reduce the vulnerability. The results indicate that the defensive distillation mitigation method can significantly improve the robustness of AI-powered models and their performance under an adversarial attack.publishedVersio

A game-theoretic resource allocation approach for intercell device-to-device communications in cellular networks

Device-to-Device (D2D) communication is a recently emerged disruptive technology for enhancing the performance of current cellular systems. To successfully implement D2D communications underlaying cellular networks, resource allocation to D2D links is a critical issue, which is far from trivial due to the mutual interference between D2D users and cellular users. Most of the existing resource allocation research for D2D communications has primarily focused on the intracell scenario while leaving the intercell settings not considered. In this paper, we investigate the resource allocation issue for intercell scenarios where a D2D link is located in the overlapping area of two neighboring cells. Specifically, We present three intercell D2D scenarios regarding the resource allocation problem. To address the problem, we develop a repeated game model under these scenarios. Distinct from existing works, we characterize the communication infrastructure, namely Base Stations (BSs), as players competing resource allocation quota from D2D demand, and we define the utility of each player as the payoff from both cellular and D2D communications using radio resources. We also propose a resource allocation algorithm and protocol based on the Nash equilibrium derivations. Numerical results indicate that the developed model not only significantly enhances the system performance including sum rate and sum rate gain, but also sheds lights on resource configurations for intercell D2D scenarios

Loss of p53 Attenuates the Contribution of IL-6 Deletion on Suppressed Tumor Progression and Extended Survival in Kras-Driven Murine Lung Cancer

Interleukin-6 (IL-6) is involved in lung cancer tumorigenesis, tumor progression, metastasis, and drug resistance. Previous studies show that blockade of IL-6 signaling can inhibit tumor growth and increase drug sensitivity in mouse models. Clinical trials in non-small cell lung cancer (NSCLC) reveal that IL-6 targeted therapy relieves NSCLC-related anemia and cachexia, although other clinical effects require further study. We crossed IL-6-/- mice with KrasG12D mutant mice, which develop lung tumors after activation of mutant KrasG12D, to investigate whether IL-6 inhibition contributes to tumor progression and survival time in vivo. KrasG12D; IL-6-/- mice exhibited increased tumorigenesis, but slower tumor growth and longer survival, than KrasG12D mice. Further, in order to investigate whether IL-6 deletion contributes to suppression of lung cancer metastasis, we generated KrasG12D; p53flox/flox; IL-6-/- mice, which developed lung cancer with a trend for reduced metastases and longer survival than KrasG12D; p53flox/flox mice. Tumors from KrasG12D; IL-6-/- mice showed increased expression of TNFα and decreased expression of CCL-19, CCL-20 and phosphorylated STAT3 (pSTAT3) than KrasG12D mice; however, these changes were not present between tumors from KrasG12D; p53flox/flox; IL-6-/- and KrasG12D; p53flox/flox mice. Upregulation of pSTAT3 and phosphorylated AKT (pAKT) were observed in KrasG12D tumors with p53 deletion. Taken together, these results indicate that IL-6 deletion accelerates tumorigenesis but delays tumor progression and prolongs survival time in a Kras-driven mouse model of lung cancer. However, these effects can be attenuated by p53 deletion