Context Awareness and Intelligence in Cognitive Radio Networks: Design and Applications

<p>CR technology, which is the next-generation wireless communication system, improves the utilization of the overall radio spectrum through dynamic adaptation to local spectrum availability. In CR networks, unlicensed or Secondary Users (SUs) may operate in underutilized spectrum (called white spaces) owned by the licensed or Primary Users (PUs) conditional upon PUs encountering acceptably low interference levels. Ideally, the PUs are oblivious to the presence of the SUs. Context awareness enables an SU to sense and observe its operating environment, which is complex and dynamic in nature; while intelligence enables the SU to learn knowledge, which can be acquired through observing the consequences of its prior action, about its operating environment so that it carries out the appropriate action to achieve optimum network performance in an efficient manner without following a strict and static predefined set of policies. Traditionally, without the application of intelligence, each wireless host adheres to a strict and static predefined set of policies, which may not be optimum in many kinds of operating environment. With the application of intelligence, the knowledge changes in line with the dynamic operating environment. This thesis investigates the application of an artificial intelligence approach called reinforcement learning to achieve context awareness and intelligence in order to enable the SUs to sense and utilize the high quality white spaces. To date, the research focus of the CR research community has been primarily on the physical layer of the open system interconnection model. The research into the data link layer is still in its infancy, and our research work focusing on this layer has been pioneering in this field and has attacted considerable international interest. There are four major outcomes in this thesis. Firstly, various types of multi-channel medium access control protocols are reviewed, followed by discussion of their merits and demerits. The purpose is to show the additional functionalities and challenges that each multi-channel medium access control protocol has to offer and address in order to operate in CR networks. Secondly, a novel cross-layer based quality of service architecture called C2net for CR networks is proposed to provide service prioritization and tackle the issues associated with CR networks. Thirdly, reinforcement learning is applied to pursue context awareness and intelligence in both centralized and distributed CR networks. Analysis and simulation results show that reinforcement learning is a promising mechanism to achieve context awareness and intelligence. Fourthly, the versatile reinforcement learning approach is applied in various schemes for performance enhancement in CR networks.</p>

Context Awareness and Intelligence in Cognitive Radio Networks: Design and Applications

<p>CR technology, which is the next-generation wireless communication system, improves the utilization of the overall radio spectrum through dynamic adaptation to local spectrum availability. In CR networks, unlicensed or Secondary Users (SUs) may operate in underutilized spectrum (called white spaces) owned by the licensed or Primary Users (PUs) conditional upon PUs encountering acceptably low interference levels. Ideally, the PUs are oblivious to the presence of the SUs. Context awareness enables an SU to sense and observe its operating environment, which is complex and dynamic in nature; while intelligence enables the SU to learn knowledge, which can be acquired through observing the consequences of its prior action, about its operating environment so that it carries out the appropriate action to achieve optimum network performance in an efficient manner without following a strict and static predefined set of policies. Traditionally, without the application of intelligence, each wireless host adheres to a strict and static predefined set of policies, which may not be optimum in many kinds of operating environment. With the application of intelligence, the knowledge changes in line with the dynamic operating environment. This thesis investigates the application of an artificial intelligence approach called reinforcement learning to achieve context awareness and intelligence in order to enable the SUs to sense and utilize the high quality white spaces. To date, the research focus of the CR research community has been primarily on the physical layer of the open system interconnection model. The research into the data link layer is still in its infancy, and our research work focusing on this layer has been pioneering in this field and has attacted considerable international interest. There are four major outcomes in this thesis. Firstly, various types of multi-channel medium access control protocols are reviewed, followed by discussion of their merits and demerits. The purpose is to show the additional functionalities and challenges that each multi-channel medium access control protocol has to offer and address in order to operate in CR networks. Secondly, a novel cross-layer based quality of service architecture called C2net for CR networks is proposed to provide service prioritization and tackle the issues associated with CR networks. Thirdly, reinforcement learning is applied to pursue context awareness and intelligence in both centralized and distributed CR networks. Analysis and simulation results show that reinforcement learning is a promising mechanism to achieve context awareness and intelligence. Fourthly, the versatile reinforcement learning approach is applied in various schemes for performance enhancement in CR networks.</p>

Instance-Aware Coherent Video Style Transfer for Chinese Ink Wash Painting

Recent researches have made remarkable achievements in fast video style transfer based on western paintings. However, due to the inherent different drawing techniques and aesthetic expressions of Chinese ink wash painting, existing methods either achieve poor temporal consistency or fail to transfer the key freehand brushstroke characteristics of Chinese ink wash painting. In this paper, we present a novel video style transfer framework for Chinese ink wash paintings. The two key ideas are a multi-frame fusion for temporal coherence and an instance-aware style transfer. The frame reordering and stylization based on reference frame fusion are proposed to improve temporal consistency. Meanwhile, the proposed method is able to adaptively leave the white spaces in the background and to select proper scales to extract features and depict the foreground subject by leveraging instance segmentation. Experimental results demonstrate the superiority of the proposed method over state-of-the-art style transfer methods in terms of both temporal coherence and visual quality. Our project website is available at https://oblivioussy.github.io/InkVideo/.

Annihilation as Segregation Philosophy: White Spaces of Law/Power/Desire

In this article, I investigate the spatial dimensions of the law and their relationships with desire and power. Annihilation, in my view, presents conceptions of white spaces of Law/Power/Desire that are threatened by interracial relationships associated with nightmare spaces of difference. I examine strategies of how space is conceived of and controlled in this white supremacist mindset and how categories of bodies that move through areas to form relationships are controlled. In particular, I expose how the segregation philosophy of the film relies on the control of white women and the prohibition of their connection with black men.

LPWAN in the TV White Spaces

Low-Power Wide-Area Network (LPWAN) is an enabling Internet-of-Things technology that supports long-range, low-power, and low-cost connectivity to numerous devices. To avoid the crowd in the limited ISM band (where most LPWANs operate) and cost of licensed band, the recently proposed Sensor Network over White Spaces (SNOW) is a promising LPWAN platform that operates over the TV white spaces. As it is a very recent technology and is still in its infancy, the current SNOW implementation uses the Universal Software Radio Peripheral devices as LPWAN nodes, which has high costs (≈$750 USD per device) and large form-factors, hindering its applicability in practical deployment. In this article, we implement SNOW using low-cost, low form-factor, low-power, and widely available commercial off-the-shelf (COTS) devices to enable its practical and large-scale deployment. Our choice of the COTS device (TI CC13x0: CC1310 or CC1350) consequently brings down the cost and form-factor of a SNOW node by 25× and 10×, respectively. Such implementation of SNOW on the CC13x0 devices, however, faces a number of challenges to enable link reliability and communication range. Our implementation addresses these challenges by handling peak-to-average power ratio problem, channel state information estimation, carrier frequency offset estimation, and near-far power problem. Our deployment in the city of Detroit, Michigan, demonstrates that CC13x0-based SNOW can achieve uplink and downlink throughputs of 11.2 and 4.8 kbps per node, respectively, over a distance of 1 km. Also, the overall throughput in the uplink increases linearly with the increase in the number of SNOW nodes.