Pooi Yuen Kam

Bio: Pooi Yuen Kam is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Fading & Rayleigh fading. The author has an hindex of 26, co-authored 301 publications receiving 2833 citations. Previous affiliations of Pooi Yuen Kam include National University of Singapore & Huazhong University of Science and Technology.

Backscatter-NOMA: A Symbiotic System of Cellular and Internet-of-Things Networks

Abstract: Non-orthogonal multiple access (NOMA) is envisioned as a key technology to enhance the spectrum efficiency for 5G cellular networks. Meanwhile, ambient backscatter communication is a promising solution to the Internet of Things (IoT), due to its high spectrum efficiency and power efficiency. In this paper, we are interested in a symbiotic system of cellular and IoT networks and propose a backscatter-NOMA system, which incorporates a downlink NOMA system with a backscatter device (BD). In the proposed system, the base station (BS) transmits information to two cellular users according to the NOMA protocol, while a BD transmits its information over the BS signals to one cellular user using the passive radio technology. In particular, if the BS only serves the cellular user that decodes BD information, the backscatter-NOMA system turns into a symbiotic radio (SR) system. We derive the expressions of the outage probabilities and the ergodic rates and analyze the corresponding diversity orders and slopes for both backscatter-NOMA and SR systems. Finally, we provide the numerical results to verify the theoretical analysis and demonstrate the interrelationship between the cellular networks and the IoT networks.

Bit error probabilities MDPSK over the nonselective Rayleigh fading channel with diversity reception

Abstract: Data transmission using M-ary differential phase shift keying (MDPSK) over the nonselective Rayleigh fading channel with diversity reception is considered. While previous studies on error probability mostly assume no fading fluctuation, the author considers, exclusively, the case in which the fading process fluctuates from one symbol interval to the next. Exact bit error probability results for 2, 4, and 8 DPSK as well as tight upper bounds are derived. Some applications of the results are discussed. >

Decision-Aided Carrier Phase Estimation for Coherent Optical Communications

Abstract: We analytically studied the block length effect (BLE) of decision-aided maximum likelihood (DA ML) carrier phase estimation in coherent optical phase-modulated systems. The results agree well with the trends found using extensive Monte Carlo simulations. In order to eliminate the BLE and accurately recover the carrier phase, an adaptive decision-aided (DA) receiver is proposed that does not require knowledge of the statistical characteristics of the carrier phase, or any parameter to be preset. The simulation results show that using the adaptive DA receiver, the maximum tolerance ratio of the linewidth per laser to symbol rate (?vT) at a bit error rate (BER) = 10-4 has been increased to 2.5 × 10-4, 4.1 × 10-5, and 9.5 × 10-6, respectively, for quadrature-, 8- and 16-phase-shift keying formats. The ratio (?vT) of the adaptive DA receiver in 16 quadrature amplitude modulation (QAM) is decreased to 2 × 10-5 due to the constellation penalty from 2.5 × 10-5 by using DA ML with optimum memory length, though it consistently performs well without optimizing any parameters as in DA ML. The phase error variance of the adaptive DA receiver is also analytically investigated. In addition, an analog-to-digital converter with bit resolution higher than 4 bits is shown to be sufficient to implement our adaptive DA receiver.

Maximum Likelihood Carrier Phase Recovery for Linear Suppressed-Carrier Digital Data Modulations

Abstract: The problem of ML estimation of the Phase of a general data-modulated carrier is considered. The shortcomings of current iterative approaches to the problem are pointed out, and the correct conceptual approach is proposed. The true ML estimator is then obtained and found to be nonimplementable. However, by specializing to limits of high and low SNR, the general ML estimator is shown to reduce to implementable DA and NDA ML estimators, respectively. The DA receiver's performance in terms of phase tracking and symbol error probability can be analyzed, and even the effects of past decision errors on current system performance can be assessed. For circular signal constellations, the DA receiver has a simple and totally linear structure which is easy to implement. The NDA ML estimator is shown to be equivalent to the common carrier loops. Our emphasis here on explicit computation of the ML phase estimate from the past received signal leads to detection strategies which do not require a carrier loop and a VCO for coherent detection.

MAP/ML Estimation of the Frequency and Phase of a Single Sinusoid in Noise

Abstract: The problem of estimating the frequency and carrier phase of a single sinusoid observed in additive, white, Gaussian noise is addressed. Much of the work in the literature considers maximum likelihood (ML) estimation. However, the ML estimator given by the location of the peak of a periodogram in the frequency domain shown in D.C. Rife and R. R. Boorstyn, "Single-tone parameter estimation from discrete-time observations," IEEE Transactions on Information Theory, vol. IT-20, pp. 591-598, Sep. 1974, has a very high computational complexity. This paper derives the explicit structure of the ML estimator for data processing in the time domain, assuming only reasonably high signal-to-noise ratio (SNR). The result of this approximate ML estimator shows that both the phase and the magnitude of the noisy signal samples are utilized in the estimator, and the phase data alone as assumed in S. A. Tretter, "Estimating the frequency of a noisy sinusoid by linear regression", IEEE Transactions on Information Theory, vol. IT-31, pp. 832-835, Nov. 1985 and S. Kay, "A fast and accurate single frequency estimator", IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 39, pp. 1203-1205, May 1991, is not a sufficient statistic. The sample-by-sample iterative processing nature of the estimator enables us to propose a novel, recursive phase-unwrapping algorithm that allows the estimator to be implemented efficiently. To facilitate the performance analysis, a new, linearized observation model for the instantaneous signal phase that is more accurate than that of S. A. Tretter, "Estimating the frequency of a noisy sinusoid by linear regression", IEEE Transactions on Information Theory, vol. IT-31, pp. 832-835, Nov. 1985 and of S. Kay, "A fast and accurate single frequency estimator", vol. 39, pp. 1203-1205, May 1991, is proposed. This new model explains physically why the phase data are weighted by the magnitude information in the ML estimator. Moreover, by incorporating a priori knowledge via the a priori probability density function of the unknown frequency and the carrier phase, the explicit structure of the approximate maximum a posteriori probability (MAP) estimator is derived, and the Bayesian Cramer-Rao lower bound (BCRLB) on the mean-square error (mse) is obtained. Our analysis shows that the mse performance of the MAP estimator can approach the BCRLB very closely

Wireless communications

Abstract: Alhussein Abouzeid Rensselaer Polytechnic Institute Raviraj Adve University of Toronto Dharma Agrawal University of Cincinnati Walid Ahmed Tyco M/A-COM Sonia Aissa University of Quebec, INRSEMT Huseyin Arslan University of South Florida Nallanathan Arumugam National University of Singapore Saewoong Bahk Seoul National University Claus Bauer Dolby Laboratories Brahim Bensaou Hong Kong University of Science and Technology Rick Blum Lehigh University Michael Buehrer Virginia Tech Antonio Capone Politecnico di Milano Javier Gómez Castellanos National University of Mexico Claude Castelluccia INRIA Henry Chan The Hong Kong Polytechnic University Ajit Chaturvedi Indian Institute of Technology Kanpur Jyh-Cheng Chen National Tsing Hua University Yong Huat Chew Institute for Infocomm Research Tricia Chigan Michigan Tech Dong-Ho Cho Korea Advanced Institute of Science and Tech. Jinho Choi University of New South Wales Carlos Cordeiro Philips Research USA Laurie Cuthbert Queen Mary University of London Arek Dadej University of South Australia Sajal Das University of Texas at Arlington Franco Davoli DIST University of Genoa Xiaodai Dong, University of Alberta Hassan El-sallabi Helsinki University of Technology Ozgur Ercetin Sabanci University Elza Erkip Polytechnic University Romano Fantacci University of Florence Frank Fitzek Aalborg University Mario Freire University of Beira Interior Vincent Gaudet University of Alberta Jairo Gutierrez University of Auckland Michael Hadjitheodosiou University of Maryland Zhu Han University of Maryland College Park Christian Hartmann Technische Universitat Munchen Hossam Hassanein Queen's University Soong Boon Hee Nanyang Technological University Paul Ho Simon Fraser University Antonio Iera University "Mediterranea" of Reggio Calabria Markku Juntti University of Oulu Stefan Kaiser DoCoMo Euro-Labs Nei Kato Tohoku University Dongkyun Kim Kyungpook National University Ryuji Kohno Yokohama National University Bhaskar Krishnamachari University of Southern California Giridhar Krishnamurthy Indian Institute of Technology Madras Lutz Lampe University of British Columbia Bjorn Landfeldt The University of Sydney Peter Langendoerfer IHP Microelectronics Technologies Eddie Law Ryerson University in Toronto

Sequential Monte Carlo methods in practice

Abstract: Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practitioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance.

Table Of Integrals Series And Products

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Principles of Physical Layer Security in Multiuser Wireless Networks: A Survey

Abstract: This paper provides a comprehensive review of the domain of physical layer security in multiuser wireless networks. The essential premise of physical layer security is to enable the exchange of confidential messages over a wireless medium in the presence of unauthorized eavesdroppers, without relying on higher-layer encryption. This can be achieved primarily in two ways: without the need for a secret key by intelligently designing transmit coding strategies, or by exploiting the wireless communication medium to develop secret keys over public channels. The survey begins with an overview of the foundations dating back to the pioneering work of Shannon and Wyner on information-theoretic security. We then describe the evolution of secure transmission strategies from point-to-point channels to multiple-antenna systems, followed by generalizations to multiuser broadcast, multiple-access, interference, and relay networks. Secret-key generation and establishment protocols based on physical layer mechanisms are subsequently covered. Approaches for secrecy based on channel coding design are then examined, along with a description of inter-disciplinary approaches based on game theory and stochastic geometry. The associated problem of physical layer message authentication is also briefly introduced. The survey concludes with observations on potential research directions in this area.