Qilian Liang

University of Texas at Arlington, Arlington, Texas, United States

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Publications (154)47.41 Total impact

  • Junjie Chen, Qilian Liang
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    ABSTRACT: In this paper, rate distortion performance of nested sampling and coprime sampling is studied. It is shown that with the increasing of distortion, the data rate decreases. With these two sparse sampling algorithms, the data rate is proved to be much less than that without sparse sampling. With the increasing of sampling spacings, the data rate decreases at certain distortion, which is because with more sparse sampling, less number of bits is required to represent the information. We also prove that with the same sampling pairs, the rate of nested sampling is less than that of coprime sampling at the same distortion. The reason is that nested sampling collects a little less number of samples than coprime sampling with the same length of data, which is a little sparser than coprime sampling.
    11/2014; 2014(1).
  • Zhuo Li, Qilian Liang, Xiuzhen Cheng
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    ABSTRACT: Considering the power saving potential of the emerging WiFi Direct technique, we evaluate the performance of WiFi Direct technique in Home Area Networks (HANs) for Smart Grid Communication from two aspects: power consumption and downlink outage performance. By modeling the traffic intensity and the number of working devices in a dynamic HAN as a Markov chain, the power consumption of the dynamic HAN with Power Saving Mechanism (PSM) and the conventional static HAN with Continuous Active Mode (CAM) are evaluated and compared. On the other hand, the probability density function (PDF) of the signal to interference and noise ratio (SINR) for the active user connected in the HAN is derived from the amplitude distribution property of the classical indoor Saleh-Valenzuela (S-V) channel. The numerical results show that WiFi Direct technique not only improves the power saving in the HAN for Smart Grid, but also enhances the reliability of HAN communications for Smart Grid.
    Ad Hoc Networks. 01/2014;
  • Ishrat Maherin, Qilian Liang
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    ABSTRACT: In this paper, we propose to apply information theory to Ultra wide band (UWB) radar sensor network (RSN) to detect target in foliage environment. Information theoretic algorithms such as Maximum entropy method (MEM) and mutual information are proven methods, that can be applied to data collected by various sensors. However, the complexity of the environment posses uncertainty in fusion center. Chernoff information provides the best error exponent of detection in Bayesian environment. In this paper, we consider the target detection as binary hypothesis testing and use Chernoff information as sensor selection criterion, which significantly reduce the processing load. Another strong information theoretic algorithm, method of types, is applicable to our MEM based target detection algorithm as entropy is dependent on the empirical distribution only. Method of types analyze the probability of a sequence based on empirical distribution. Based on this, we can find the bound on probability of detection. We also propose to use Relative entropy based processing in the fusion center based on method of types and Chernoff Stein Lemma. We study the required quantization level and number of nodes in gaining the best error exponent. The performance of the algorithms were evaluated, based on real world data.
    Physical Communication 01/2014;
  • Na Wu, Qilian Liang
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    ABSTRACT: This paper firstly introduces nested sampling and co-prime sampling, which were proposed recently, but have never been applied to real world target detection. We apply nested sampling and co-prime sampling to target detection in UWB radar sensor networks (RSN), based on a differential approach. The non-stationary UWB signal needs to be decomposed into several approximate wide sense stationary (WSS) signals so that nested sampling could be used in this situation. We also compare the performance of nested sampling and co-prime sampling against uniform under-sampling. The results show that in terms of good quality data and poor quality data, both nested sampling and co-prime sampling work better.
    Physical Communication 01/2014;
  • Ishrat Maherin, Qilian Liang
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    ABSTRACT: One of the biggest challenges in cognitive radio CR network is to maximise the throughput of the secondary user while keeping the interference to the primary under the interference threshold. Efficient spectrum sensing along with transmit power control can achieve this conflicting goal. In this paper, we propose a sensor network aided cognitive radio system which will reduce the missed detection and reduce the interference. We investigate the performance of various sensing and power allocation schemes for an OFDM-based cognitive radio system. We formulate an optimisation problem to design the optimal sensing time and transmit power. Mathematical analysis shows that interference between primary and secondary in OFDM-based system depends on spectral distance. Results show that distance dependent modified water filling DDMWF scheme can achieve the highest data rate for the cognitive radio based secondary user and then the optimal sensing time can be designed to maximise the throughput.
    International Journal of Sensor Networks. 07/2013; 13(4):234-240.
  • Junjie Chen, Qilian Liang, Jie Wang
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    ABSTRACT: Big data presents critical requirements for security in data collection and transmission of selected data through a communication network. This paper presents a new secure transmission for big data based on nested sparse sampling and coprime sampling. With nested sampling and coprime sampling, besides the advantage of higher spectrum efficiency, big data could also achieve higher power spectral density for binary frequency shift keying (BFSK) signal. When the sampling spacing pairs are big enough, the spectrum of BFSK signal performs like frequency hopping. This property has great advantage in the security of big data collection and transmission using FH/BFSK, as it could achieve low error probability. With the same multitone interfering signal added to FH/BFSK, the error probability becomes much lower using nested sampling and coprime sampling compared with the original FH/BFSK signal. This proves that both nested sampling and coprime sampling could be used in big data transmission to resist interference, while guaranteeing the transmission performance. Copyright © 2013 John Wiley & Sons, Ltd.
    Security and Communication Networks 05/2013; · 0.43 Impact Factor
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    ABSTRACT: Traditionally, jamming to the wireless system is a fatal threat to the security of home area networks (HANs), which impedes the two-way data transmission between electric devices and the smart meter, and thus deteriorates the reliability of the in-home communication of Smart Grid. On the basis of this consideration, this paper incorporates the power line system into the HAN and proposes a hybrid architecture of orthogonal frequency-division multiplexing-based wireless communication and power line communication for the Smart Grid security application. With this new solution, the channel diversity of the HAN is realized, and the communication reliability is still guaranteed even when the wireless channel suffers from jamming. Simulation results validate the feasibility of the proposed hybrid architecture, and furthermore, as a receiver diversity scheme, selection combining is preferred to maximum ratio combining. Copyright © 2013 John Wiley & Sons, Ltd.
    Security and Communication Networks 03/2013; · 0.43 Impact Factor
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    ABSTRACT: Security of data is an issue that is of significant interest. In this paper, we propose a new compressive sensing-based data encryption system that can represent the original signal with far fewer samples than the conventional Nyquist sampling-based system. Compressive sensing could also be treated as an encryption algorithm with good secrecy. As an application example, we apply it to sense-through-wall ultra-wideband (UWB) noise radar that requires enormous storage space and high security. Interestingly, a random Gaussian matrix is sufficient to capture the information of UWB noise radar signal; no knowledge of UWB signal is required in advance. Simulation results indicate only one-third of the original samples are needed to perfectly recover UWB noise radar signal, and compressive sensing provides good secrecy as an encryption algorithm. It is impossible to retrieve the original message without the entire sensing matrix. Copyright © 2013 John Wiley & Sons, Ltd.
    Security and Communication Networks 02/2013; · 0.43 Impact Factor
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    ABSTRACT: The smart grid system is composed of the power infrastructure and communication infrastructure and thus is characterized by the flow of electric power and information, respectively. Although there is no doubt that the wireless communication architecture will play a significant role in smart grid, the wireless network introduces additional vulnerabilities, given the scale of potential threats. Therefore, the physical layer security issue is of first priority in the study of smart grid and has already attracted substantial attention in the industry and academia. In this paper, we aimed to present a general overview of the physical layer security in wireless smart grid and cover the effective countermeasures proposed in the literature of smart grid to date. We first investigate the security challenges from malicious attacks. Specifically, two typical forms of malicious attack in smart grid, namely, jamming and bad data injecting, are studied. In addition, the related countermeasures against these malicious attacks are illustrated. Further, we analyze the state of the art of the privacy issues in smart grid. The private information and privacy concerns are introduced, and then the effective solutions to privacy security are provided. Finally, voltage regulation, a security topic that has been hardly studied in the wireless smart grid domain, is presented. We expect that the work presented here will advance the research on smart grid security. Copyright © 2013 John Wiley & Sons, Ltd.
    Security and Communication Networks 02/2013; · 0.43 Impact Factor
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    ABSTRACT: Compressive sensing provides a new approach to data acquisition and storage. In this paper, we derive some information theory bounds on the performance of noisy compressive sensing to calculate the data rate with particular distortion, which has significant meaning in data storage technique. We analyze the rate distortion performance of noisy compressive sensing under Mean Squared distortion and Hamming distortion, and give more accurate results. Besides, mathematical lower bounds of rate distortion function and theoretical minimal useful bit rates are provided for these two distortion for the first time. We also give a theoretical upper bound of the Mean Squared distortion of compressive sensing process. The relationships of bit rate per dimension R(D)/N and M, N, and M/N are given and plotted in this paper, and both theoretical analysis and numerical results show that compressive sensing uses less number of bits to represent the same information compared to conventional information acquisition and reconstruction techniques.
    Communications Workshops (ICC), 2013 IEEE International Conference on; 01/2013
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    ABSTRACT: The measurement of an emitter's position using electronic support passive sensors is termed passive localization and plays an important part both in electronic support and electronic attack. The emitting target could be in terrestrial or underwater environment. In this paper, we propose a time difference of arrival (TDoA) algorithm for passive localization in underwater and terrestrial environment. In terrestrial environment, it is assumed that a Rician flat fading model should be used because there exists line of sight. In underwater environment, we apply a modified UWB Saleh-Valenzuela (S-V) model to characterize the underwater acoustic fading channel. We propose the TDoA finding algorithm via estimating the delay of two correlated channels, and compare it with the existing approach. Simulations were conducted for terrestrial and underwater environment, and simulation results show that our TDoA algorithm performs much better than the cross-correlation-based TDoA algorithm with a lower level of magnitude in terms of average TDoA error and root-mean-square error (RMSE). Compared to the TDoA performance in terrestrial environment, the TDoA performance in underwater environment is much worse. This is because the underwater channel has clusters and rays, which introduces memory and uncertainties. For the two scenarios in underwater environment, the performance in rich scattering underwater environment is worse than that in less scattering underwater environment, because the latter has less clusters and rays, which would cause less uncertainties in TDoA.
    IEEE Transactions on Parallel and Distributed Systems 01/2013; 24(10):2100-2108. · 1.80 Impact Factor
  • Ji Wu, Qilian Liang, Baoju Zhang, Xiaorong Wu
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    ABSTRACT: Compressive sensing (CS) provides a framework for integrated sensing and compression of discrete-time signals that are sparse or compressible in a known basis or frame. In this paper, we apply adaptive compressive sensing to multiuser OFDM system in order to save the storage at the server due to high data transmission rate and a large number of supported users. A new standard compressive sensing equation is proposed which ensures it always works via exploiting the autoregressive property of the transmitted data. The subcarrier allocation information is estimated at the receiver which significantly reduces the system complexity and makes the proposed system more practical. Simulation results show the Yule-Walker equation with higher-order and more active users could improve the performance of recovery process.
    Communications Workshops (ICC), 2013 IEEE International Conference on; 01/2013
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    Qiong Wu, Qilian Liang
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    ABSTRACT: Estimating the spectrogram of non-stationary signal relates to many important applications in radar signal processing. In recent years, coprime sampling and array attract attention for their potential of sparse sensing with derivative to estimate autocorrelation coefficients with all lags, which could in turn calculate the power spectrum density. But this theoretical merit is based on the premise that the input signals are wide-sense stationary. In this article, we discuss how to implement coprime sampling for non-stationary signal, especially how to attain the benefits of coprime sampling meanwhile limiting the disadvantages due to lack of observations for estimations. Furthermore, we investigate the usage of coprime sampling for calculating ambiguity function of matched filter in radar system. We also examine the effect of it and conclude several useful guidelines of choosing configuration to conduct the sparse sensing while retain the detection quality.
    EURASIP Journal on Wireless Communications and Networking 01/2013; 2013(1). · 0.54 Impact Factor
  • Xiaoyang Li, Qilian Liang, Francis C.M. Lau
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    ABSTRACT: In this paper, a method for through-wall human detection based on the singular values decomposition of the measurement matrices is presented. After demonstrating the sparsity of the matrices using CLEAN algorithm, an SVD algorithm based on Lanczos process is applied to compute their singular values. We also analyze the singular values of matrices constructed by difference square techniques for different types of walls and compare our algorithm with a 2-D imaging approach proposed by researchers in Time Domain Company. Detection results show that our method performs well in gypsum wall, brick wall, and wooden door.
    Physical Communication 01/2013;
  • Zhuo Li, Qilian Liang
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    ABSTRACT: In this paper, multiuser selection scheme is employed in dynamic home area networks (HANs) for smart grid communications, to reduce the effects of fading at the receiver part of smart meter. The performance of multiuser selection scheme is evaluated in two cases: in the absence of interference and in the presence of multiuser interference (MUI). In the former case, the closed-form capacity outage probability as well as the numerical values of the bit error rate (BER) are obtained via the probability density function (PDF) of signal to noise ratio (SNR) that is derived from the amplitude distribution property of the classic indoor Saleh-Valenzuela (S-V) channel. In the latter case, the multiuser interference cancellation coefficient as a random variable is added for the calculation of signal to interference noise ratio (SINR). In addition, due to free space path loss and log-normal shadowing, the sum of MUI is treated as another log-normal-distributed random variable by exploiting central limit theorem. Numerical results show that the performance of multiuser selection scheme in HANs of smart grid is due to several factors, i.e., total number of devices in HANs, traffic intensity, modulation scheme, and multiuser interference cancellation coefficient, etc.
    IEEE Transactions on Smart Grid 01/2013; 4(1):13-20.
  • Xin Wang, Lijun Zhang, Amit Deokar, Qilian Liang
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    ABSTRACT: As a critical infrastructure element, smart grid is facing security and reliability challenges. In this paper, we have investigated the real-time communication between a smart meter and the central control system, a component that provides centralized monitoring and performs control operations for the power grid. To combat severe communication impairments induced by malicious attacks or link failures, we propose to implement space–time block coding over multiple antennas at the transmitter and the receiver. We first investigated the security challenges from malicious attacks, in which the malicious attacks of additive pattern were primarily addressed. Reliability challenges from link failure were then considered. Finally present were extensive numerical studies on multiple-link attacks, multiple-link failures and receiver with detection scenarios. Simulations showed that additive malicious attacks and link failures could be effectively mitigated in virtue of space–time block coding. Meanwhile, it is verified that more antennas will give more diversity gain and better performance. Copyright © 2012 John Wiley & Sons, Ltd.
    Security and Communication Networks 06/2012; · 0.43 Impact Factor
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    ABSTRACT: In this paper, we propose a new compressive sensing-based compression and recovery ultra-wideband (UWB) communication system. Compared with the conventional UWB system, we can jointly estimate the channel and compress the data, which can also simplify the design of hardware. No information about the transmitted signal is required in advance as long as the channel follows autoregressive process. As an application example, real-world UWB signal is collected and processed to evaluate the performance of our proposed system. The compression procedure is so simple that we just multiply random Gaussian or Bernoulli matrix with the original data to capture all the information we want. Simulation results show that the data could be perfectly recovered if the compression ratio does not exceed 2.5:1 when Bernoulli matrix is chosen as the sensing matrix. Copyright © 2012 John Wiley & Sons, Ltd.
    Wireless Communications and Mobile Computing 03/2012; · 0.86 Impact Factor
  • Lei Xu, Qilian Liang
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    ABSTRACT: Inspired by recent advances in multiple-input multiple-output (MIMO) radar, we apply orthogonal phase coded waveforms to MIMO radar system in order to gain better range resolution and target direction finding performance. We provide and investigate a generalized MIMO radar system model using orthogonal phase coded waveforms. In addition, we slightly modify the system model to improve the system performance. Accordingly, we propose the concept and the design methodology for a set of ternary phase coded waveforms that is the optimized punctured zero correlation zone (ZCZ) sequence-pair set (ZCZPS). We also study the MIMO radar ambiguity function of the system using phase coded waveforms, based on which we analyze the properties of our proposed phase coded waveforms which show that better range resolution could be achieved. In the end, we apply our proposed codes to the two MIMO radar system models and simulate their target direction finding performances. The simulation results show that the first MIMO radar system model could obtain ideal target direction finding performance when the number of transmit antennas is equal to the number of receive antennas. The second MIMO radar system model is more complicated but could improve the direction finding performance of the system.
    IEEE Transactions on Aerospace and Electronic Systems 01/2012; 48(3):2100-2113. · 1.30 Impact Factor
  • Junjie Chen, Qilian Liang, J. Paden, P. Gogineni
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    ABSTRACT: This work addresses the use of compressive sensing to compress real Synthetic Aperture Radar (SAR) raw data. Due to the low computational resources of the acquisition platforms and the steadily increasing resolution of SAR systems, huge amounts of data are collected and stored, which cannot generally be processed on board and must be transmitted to the ground to be processed and archived. Although compressive sensing (CS) has been proposed and studied by a lot of researchers, almost none of them touches the real application of it. While, in this paper, we test the sparsity of the real SAR raw data (obtained by University of Kansas in Greenland, 2010), compress it using compressive sensing, and then recover the original signal using several CS recovery algorithms (Basis Pursuit, Matching Pursuit and Orthogonal Matching Pursuit), and compare these methods' performance. Simulation results are presented to prove the successful application of CS to real SAR raw data. When proper sparsity matrix is chosen, the real SAR data could be transformed to sparse signal. Using our designed algorithm, the positions and the exact values of the SAR raw data can be almost perfectly recovered with a very low MSE at a compression ratio of 1/8. This is of great significance to help us perform further research in the applications of CS to real SAR raw data.
    Communications (ICC), 2012 IEEE International Conference on; 01/2012
  • Lei Xu, Qilian Liang, Xiaorong Wu, Baoju Zhang
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    ABSTRACT: Recently, there are a growing interest in the study of compressive sensing (CS). In this paper, we introduce CS to radar sensor network (RSN) within the pulse compression technique in order to efficiently compress, restore and then reconstruct the radar data. We employ a set of Stepped-Frequency waveforms as pulse compression codes for transmit sensors, and to use the same set of Stepped-Frequency (SF) waveforms as the sparse matrix for each receive sensor. We conclude that the signal samples along the time domain could be largely compressed so that they could be recovered by a small number of measurements which depend on the number of transmit sensors. In addition, we develop a Maximum Likelihood (ML) Algorithm for radio cross section (RCS) parameter estimation and provide the Cramer-Rao lower bound (CRLB) to validate the theoretical result. We also provide simulation results illustrating that the variance of RCS parameter estimation θ satisfies the CRLB and our ML estimator is an accurate estimator on the target RCS parameter.
    Globecom Workshops (GC Wkshps), 2012 IEEE; 01/2012

Publication Stats

565 Citations
47.41 Total Impact Points


  • 2002–2014
    • University of Texas at Arlington
      • Department of Electrical Engineering
      Arlington, Texas, United States
  • 2007–2011
    • George Washington University
      • Department of Computer Science
      Washington, D. C., DC, United States
    • Beijing University of Posts and Telecommunications
      • Department of Communication Engineering
      Peping, Beijing, China