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Browsing by Author "Yang, Liuqing, advisor"

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    Cooperative sensing for target estimation and target localization
    (Colorado State University. Libraries, 2011) Zhang, Wenshu, author; Yang, Liuqing, advisor; Pezeshki, Ali, committee member; Luo, J. Rockey, committee member; Wang, Haonan, committee member
    As a novel sensing scheme, cooperative sensing has drawn great interests in recent years. By utilizing the concept of "cooperation", which incorporates communications and information exchanges among multiple sensing devices, e.g. radar transceivers in radar systems, sensor nodes in wireless sensor networks, or mobile handsets in cellular systems, the sensing capability can achieve significant improvement compared to the conventional noncooperative mode in many aspects. For example, cooperative target estimation is inspired by the benefits of MIMO in communications, where multiple transmit and/or receive antennas can increase the diversity to combat channel fading for enhanced transmission reliability and increase the degrees of freedom for improved data rate. On the other hand, cooperative target localization is able to dramatically increase localization performance in terms of both accuracy and coverage. From the perspective of cooperative target estimation, in this dissertation, we optimize waveforms from multiple cooperative transmitters to facilitate better target estimation in the presence of colored noise. We introduce the normalized MSE (NMSE) minimizing criterion for radar waveform designs. Not only is it more meaningful for parameter estimation problems, but it also exhibits more similar behaviors with the MI criterion than its MMSE counterpart. We also study the robust designs for both the probing waveforms at the transmitter and the estimator at the receiver to address one type of a priori information uncertainties, i.e., in-band target and noise PSD uncertainties. The relationship between MI and MSEs is further investigated through analysis of the sensitivity of the optimum design to the out-band PSD uncertainties as known as the overestimation error. From the perspective of cooperative target localization, in this dissertation, we study the two phases that comprise a localization process, i.e., the distance measurement phase and the location update phase. In the first distance measurement phase, thanks to UWB signals' many desirable features including high delay resolution and obstacle penetration capabilities, we adopt UWB technology for TOA estimation, and then translate the TOA estimate into distance given light propagation speed. We develop a practical data-aided ML timing algorithm and obtain its optimum training sequence. Based on this optimum sequence, the original ML algorithm can be simplified without affecting its optimality. In the second location update phase, we investigate secure cooperative target localization in the presence of malicious attacks, which consists of a fundamental issue in localization problems. We explicitly incorporate anchors' misplacements into distance measurement model and explore the pairwise sparse nature of the misplacements. We formulate the secure localization problem as an ℓ1-regularized least squares (LS) problem and establish the pairwise sparsity upper bound which defines the largest possible number of identifiable malicious anchors. Particularly, it is demonstrated that, with target cooperation, the capability of secure localization is improved in terms of misplacement estimation and target location estimation accuracy compared to the single target case.
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    Data mining and spatiotemporal analysis of modern mobile data
    (Colorado State University. Libraries, 2019) Fang, Luoyang, author; Yang, Liuqing, advisor; Jayasumana, Anura P., committee member; Luo, Jie, committee member; Wang, Haonan, committee member
    Modern mobile network technologies and smartphones have successfully penetrated nearly every aspect of human life due to the increasing number of mobile applications and services. Massive mobile data generated by mobile networks with timestamp and location information have been frequently collected. Mobile data analytics has gained remarkable attention from various research communities and industries, since it can broadly reveal the human spatiotemporal mobility patterns from the individual level to an aggregated one. In this dissertation, two types of spatiotemporal modeling with respect to human mobility behaviors are considered, namely the individual modeling and aggregated modeling. As for individual spatiotemporal modeling, location privacy is studied in terms of user identifiability between two mobile datasets, merely based on their spatiotemporal traces from the perspective of a privacy adversary. The success of user identification then hinges upon the effective distance measures via user spatiotemporal behavior profiling. However, user identification methods depending on a single semantic distance measure almost always lead to a large portion of false matches. To improve user identification performance, we propose a scalable multi-feature ensemble matching framework that integrates multiple explored spatiotemporal models. On the other hand, the aggregated spatiotemporal modeling is investigated for network and traffic management in cellular networks. Traffic demand forecasting problem across the entire mobile network is first studied, which is considered as the aggregated behavior of network users. The success of demand forecasting relies on effective modeling of both the spatial and temporal dependencies of the per-cell demand time series. However, the main challenge of the spatial relevancy modeling in the per-cell demand forecasting is the uneven spatial distribution of cells in a network. In this work, a dependency graph is proposed to model the spatial relevancy without compromising the spatial granularity. Accordingly, the spatial and temporal models, graph convolutional and recurrent neural networks, are adopted to forecast the per-cell traffic demands. In addition to demand forecasting, a per-cell idle time window (ITW) prediction application is further studied for predictive network management based on subscribers' aggregated spatiotemporal behaviors. First, the ITW prediction is formulated into a regression problem with an ITW presence confidence index that facilitates direct ITW detection and estimation. To predict the ITW, a deep-learning-based ITW prediction model is proposed, consisting of a representation learning network and an output network. The representation learning network is aimed to learn patterns from the recent history of demand and mobility, while the output network is designed to generate the ITW predicts with the learned representation and exogenous periodic as inputs. Upon this paradigm, a temporal graph convolutional network (TGCN) implementing the representation learning network is also proposed to capture the graph-based spatiotemporal input features effectively.
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    Power system data classification and prediction by functional data analysis
    (Colorado State University. Libraries, 2021) Sun, Hongfei, author; Yang, Liuqing, advisor; Luo, Jie, advisor; Zhang, Hongming, committee member; Duan, Dongliang, committee member; Wang, Haonan, committee member
    The last couple of decades have witnessed the development of our electric power grid. The growing population size and increasing consumerism have increased the load demand and brought more pressure on the grid. Meanwhile, new elements are being introduced to the power grid, such as various forms of renewable energy resources, electric vehicles, and so on, which need to be monitored constantly and managed adequately. In addition, the allocation of the various resources in the power systems is now conducted in a much more dynamic manner than ever. All these new dimensions have driven the development of the traditional grid into the smart grid and call for new methodologies in system design, operation, and control. This dissertation focuses on modeling power systems with data-driven approaches, with applications in power system cyber-attack detection and recovery, and large-scale, long-term load characterization. Firstly, the modeling of the spatial-temporal relationship among the quantities across the entire power systems is provided with applications to cyber-attack detection and data recovery. Then, the non-conforming load classification approaches based on Functional Principle Component Analysis (FPCA) will be introduced. This work is the first effort towards such loads due to the recently growing penetration of Distributed Energy Resources (DER) users. Lastly, we will introduce the regional high-resolution medium-term load forecasting approach. In order to satisfy the new purpose of load forecasting, serving for real-time applications, our approach can provide higher resolution than existing long-term load forecasting and longer leading time than the existing short-term load forecasting time-series load curve. Based on the presented case studies and simulation results, we provided the corresponding suggestions to the present industrial power system.
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    Reliable and energy-efficient cooperative OFDM communications over underwater acoustic channels
    (Colorado State University. Libraries, 2015) Cheng, Xilin, author; Yang, Liuqing, advisor; Azimi-Sadjadi, Mahmood R., committee member; Luo, J. Rockey, committee member; Wang, Haonan, committee member
    Underwater acoustic sensor networks (UWASN) have been attracting growing research interests in recent decades due to various promising applications. Underwater acoustic communications (UAC), which adopts acoustic waves as the information carrier, is one of the key communication techniques to realize UWASN. However, UAC is very challenging due to low carrier frequency, distance-dependent bandwidth, large delay spread, long and variable propagation delay, and doubly-selective fading. In this research, we will consider cooperative communications to improve the reliability and energy efficiency of dual-hop UAC. OFDM is adopted as the physical-layer transmission technique. First, we will examine power allocation issues. Two transmission scenarios are considered, namely short-range transmission and medium-long range transmission. For the former scenario, an adaptive system is developed based on instantaneous channel state information (CSI); for the latter scenario, an selective relaying protocol is designed based on statistical CSI. Secondly, we will focus on the decomposed fountain codes design to enable reliable communications with higher energy efficiency. Finally, to improve the packet transmission reliability, data repetition within one or two consecutive OFDM symbols is implemented according to the mirror-mapping rules. Theoretical analyses and simulation results demonstrate that the reliability and energy efficiency of dual-hop UAC can be substantially improved using the aforementioned techniques.
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    Sensing, communications and monitoring for the smart grid
    (Colorado State University. Libraries, 2012) Duan, Dongliang, author; Yang, Liuqing, advisor; Scharf, Louis L., committee member; Luo, Jie, committee member; Song, Rui, committee member
    With the increasing concern for environmental factors, reliability, and quality of service, power grids in many countries are undergoing revolution towards a more distributed and flexible "smart grid." In the development of the envisioned smart grid, situational awareness takes a fundamental role for a number of crucial advanced operations, such as power flow scheduling, dynamic pricing, energy management, wide area control, wide area protection etc. To fulfill the mission of situational awareness across various entities in the grid, more advanced sensing, communications and monitoring techniques need to be introduced to the existing power grid. In this research, we will first address the issue of battery power efficiency (BPE) in a wireless sensor network (WSN) which is essential for the sensing system lifetime. We show that the BPE can be improved either by selecting a more battery-power-efficient modulation format or by developing a cooperative communications scheme. Then, to transmit the sensed data over the scarse wireless bandwidth, we adopt cognitive radio as a possible solution. To enable the cognitive radio communication, we aim at improving both the reliability and efficiency of the overall system via cooperative spectrum sensing. With these fundamental communication capabilities available for the sensed data, we then investigate wide area power grid monitoring based on synchronized measurements from newly developed devices such as phasor measurement units (PMUs), mode meters and so on. In addition, an optimal fusion technique is studied as a good foundation for detection in wireless sensor networks, with application to event detection in the power grid.
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    Simultaneous wireless information and power transfer (SWIPT) in cooperative networks
    (Colorado State University. Libraries, 2019) Wang, Dexin, author; Yang, Liuqing, advisor; Chong, Edwin K. P., committee member; Luo, Jie, committee member; Wang, Haonan, committee member
    In recent years, the capacity and charging speed of batteries have become the bottleneck of mobile communications systems. Energy harvesting (EH) is regarded as a promising technology to significantly extend the lifetime of battery-powered devices. Among many EH technologies, simultaneous wireless information and power transfer (SWIPT) proposes to harvest part of the energy carried by the wireless communication signals. In particular, SWIPT has been successfully applied to energy-constrained relays that are mainly or exclusively powered by the energy harvested from the received signals. These relays are known as EH relays, which attract significant attention in both the academia and the industry. In this research, we investigate the performance of SWIPT-based EH cooperative networks and the optimization problems therein. Due to hardware limitations, the energy harvesting circuit cannot decode the signal directly. Power splitting (PS) is a popular and effective solution to this problem. Therefore, we focus on PS based SWIPT in this research. First, different from existing work that employs time-switching (TS) based SWIPT, we propose to employ PS based SWIPT for a truly full-duplex (FD) EH relay network, where the information reception and transmission take place simultaneously at the relay all the time. This more thorough exploitation of the FD feature consequently leads to a significant capacity improvement compared with existing alternatives in the literature. Secondly, when multiple relays are available in the network, we explore the relay selection (RS) and network beamforming techniques in EH relay networks. Assuming orthogonal bandwidth allocation, both single relay selection (SRS) and general relay selection (GRS) without the limit on the number of cooperating relays are investigated and the corresponding RS methods are proposed. We will show that our proposed heuristic GRS methods outperform the SRS methods and achieve very similar performance compared with the optimal RS method achieved by exhaustive search but with dramatically reduced complexity. Under the shared bandwidth assumption, network beamforming among EH relays is investigated. We propose a joint PS factor optimization method based on semidefinite relaxation. Simulations show that network beamforming achieves the best performance among all other cooperative techniques. Finally, we study the problem of power allocation and PS factor optimization for SWIPT over doubly-selective wireless channels. In contrast to existing work in the literature, we take the channel variation in both time and frequency domains into consideration and jointly optimize the power allocation and the PS factors. The objective is to maximize the achievable data rate with constraints on the delivered energy in a time window. Since the problem is difficult to solve directly due to its nonconvexity, we proposed a two-step approach, named joint power allocation and splitting (JoPAS), to solve the problem along the time and frequency dimensions sequentially. Simulations show significantly improved performance compared with the existing dynamic power splitting scheme. A suboptimal heuristic algorithm, named decoupled power allocation and splitting (DePAS), is also proposed with significantly reduced computational complexity and simulations demonstrate its near-optimum performance.
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    Spectrum efficiency for future wireless communications
    (Colorado State University. Libraries, 2015) Yu, Bo, author; Yang, Liuqing, advisor; Luo, Jie, committee member; Morton, Yu, committee member; Wang, Haonan, committee member
    Spectrum efficiency has long been at the center of wireless communication research, development, and operation. Today, it is even more so with the explosive popularity of mobile internet, social networks, and smart phones that are more powerful than our desktops not long ago. As a result, there is an urgent need to further improve the spectrum efficiency in order to provide higher wireless data capacity. To respond to this demand, the 3rd Generation Partnership Project (3GPP) standardized the radio interface specifications for the next generation mobile communications system, called Long Term Evolution (LTE), in Release 8 specifications in 2008. Then the development continued and an enhanced LTE radio interface called LTE-Advanced (LTE-A) was standardized in Release 10 specifications in 2011. In order to ensure the sustainability of 3GPP radio access technologies over the coming decade, 3GPP standardization will need to continue identifying and providing new solutions that can respond to the future challenges. In this research, we investigate the potential technologies for further spectrum efficiency enhancement in the future steps of the standardization. One key direction is the further enhancement of local area technologies, which play a more and more important role in complementing the wide area networks. Specifically, we investigate two promising techniques for spectrum efficiency improvement in a macro-assisted small cell architecture, called the Phantom cell, which is proposed by DOCOMO. One is the possibility of dynamic allocation of subframes to uplink (UL) or downlink (DL) in time-division duplexing (TDD), called `Dynamic TDD'. The other is the more dynamic and flexible 3-dimensional (3D) beamforming which is facilitated by the adoption of active antenna systems (AAS) in BSs. In addition, full-duplex transmission and cooperative communication are two promising techniques known to enhance the spectrum efficiency of wireless communications. We focus on applying full-duplex in cooperative relaying networks and investigating the optimal resource allocation (both power and relay location) for full-duplex decode-and-forward (DF) relaying systems for spectrum efficiency enhancement.