Mahesh K. Varanasi

Bio: Mahesh K. Varanasi is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: MIMO & Fading. The author has an hindex of 39, co-authored 286 publications receiving 8218 citations. Previous affiliations of Mahesh K. Varanasi include University of Colorado Colorado Springs & Rice University.

Multistage detection in asynchronous code-division multiple-access communications

Abstract: A multiuser detection strategy for coherent demodulation in an asynchronous code-division multiple-access system is proposed and analyzed. The resulting detectors process the sufficient statistics by means of a multistage algorithm based on a scheme for annihilating successive multiple-access interference. An efficient real-time implementation of the multistage algorithm with a fixed decoding delay is obtained and shown to require a computational complexity per symbol which is linear in the number of users K. Hence, the multistage detector contrasts with the optimum demodulator, which is based on a dynamic programming algorithm, has a variable decoding delay, and has a software complexity per symbol that is exponential in K. An exact expression is obtained and used to compute the probability of error is obtained for the two-stage detector, showing that the two-stage receiver is particularly well suited for near-far situations, approaching performance of single-user communications as the interfering signals become stronger. The near-far problem is therefore alleviated. Significant performance gains over the conventional receiver are obtained even for relatively high-bandwidth-efficiency situations. >

Near-optimum detection in synchronous code-division multiple-access systems

Abstract: Communication networks using code division multiple access (CDMA) include applications where several packets of information are transmitted synchronously and simultaneously over a common channel. Consideration is given to the problem of simultaneously demodulating every packet from such a transmission. A nonlinear detection scheme based on a linear complexity multistage multiple-access interference rejection algorithm is studied. A class of linear detectors is considered as constituting the first stage for the multistage detector. A bit-error probability comparison of the linear and multistage detectors is undertaken. It is shown that the multistage detectors are capable of achieving considerable improvements over the linear detectors, particularly in near-far situations, i.e., in the demodulation of weak signals in the presence of strong interfering signals. This problem has been of primary concern for currently operational CDMA systems. >

Performance Analysis of ZF and MMSE Equalizers for MIMO Systems: An In-Depth Study of the High SNR Regime

Abstract: This paper presents an in-depth analysis of the zero forcing (ZF) and minimum mean squared error (MMSE) equalizers applied to wireless multiinput multioutput (MIMO) systems with no fewer receive than transmit antennas. In spite of much prior work on this subject, we reveal several new and surprising analytical results in terms of output signal-to-noise ratio (SNR), uncoded error and outage probabilities, diversity-multiplexing (D-M) gain tradeoff and coding gain. Contrary to the common perception that ZF and MMSE are asymptotically equivalent at high SNR, we show that the output SNR of the MMSE equalizer (conditioned on the channel realization) is ρmmse = ρzf+η\ssrsnr, where ρzf is the output SNR of the ZF equalizer and that the gap η\ssrsnr is statistically independent of ρzf and is a nondecreasing function of input SNR. Furthermore, as \ssr snr\ura ∞, η\ssrsnr converges with probability one to a scaled F random variable. It is also shown that at the output of the MMSE equalizer, the interference-to-noise ratio (INR) is tightly upper bounded by [(η\ssrsnr)/(ρzf)]. Using the decomposition of the output SNR of MMSE, we can approximate its uncoded error, as well as outage probabilities through a numerical integral which accurately reflects the respective SNR gains of the MMSE equalizer relative to its ZF counterpart. The e-outage capacities of the two equalizers, however, coincide in the asymptotically high SNR regime. We also provide the solution to a long-standing open problem: applying optimal detection ordering does not improve the D-M tradeoff of the vertical Bell Labs layered Space-Time (V-BLAST) architecture. It is shown that optimal ordering yields a SNR gain of 10log10N dB in the ZF-V-BLAST architecture (where N is the number of transmit antennas) whereas for the MMSE-V-BLAST architecture, the SNR gain due to ordered detection is even better and significantly so.

Optimum decision feedback multiuser equalization with successive decoding achieves the total capacity of the Gaussian multiple-access channel

Abstract: The complex Gaussian multiple-access channel (GMAC) Y_=AX_+N_ is considered. The transmitters send information independently with a power constraint so that X_ has a product distribution with E[|X/sub k/|/sup 2/]/spl les/P/sub k/. It is known that multiuser codes exist that will achieve any rate-tuple in the capacity region of the GMAC provided that an optimum joint decoder is used. However, little progress has been made in multiuser coding, and moreover, optimum joint decoding would be too complex. We restrict the decoder to be of a successive decoding type with equalization. Such a decoder is parameterized by feedforward and feedback equalization vectors. The optimum successive decoder (OSD) is obtained by maximizing the mutual information for each user over those vectors. The key result of this paper is that the OSD achieves the total capacity of the GMAC at any vertex of the capacity region. With the OSD, each transmitter can use a single-user code independently of the other users. The complexity of equalization is also only linear in the number of users. For the conventional GMAC, Y=/spl Sigma//sub i=1//sup M/ X/sub i/+N, where A is a row vector with unit elements, the OSD is degenerate and involves no equalization. It reduces to the well-known successive decoder for that channel as described by Wynter (1974) and Cover (1975). Furthermore, for the particular case of the uncoded channel, the OSD reduces to a new decision feedback multiuser detector. This detector is optimum in the sense that it maximizes signal-to-interference ratio for each user.

Parametric generalized Gaussian density estimation

Abstract: The primary objective of this paper is to compare the large‐sample as well as the small‐sample properties of different methods for estimating the parameters of a three‐parameter generalized Gaussian distribution. Three estimators, namely, the moment method (MM), the maximum‐likelihood (ML), and the moment/Newton‐step (MNS) estimators, are considered. The applicability of general asymptotic optimality results of the efficient ML and MNS estimation techniques is studied in the generalized Gaussian context. The asymptotic normal distributions of the estimators are obtained. The asymptotic relative superiority of the ML estimator or its variant, the MNS estimator, over the moment method is studied in terms of asymptotic relative efficiency. Based on this study, it is concluded that deviations from normality in the underlying distribution of the data necessitate the use of the efficient ML or MNS methods. In the small‐sample case, a detailed comparative study of the estimators is made possible by extensive Monte Carlo simulations. From this study, it is concluded that the maximum‐likelihood method is found to be significantly superior for heavy‐tailed distributions. In a region of the parameter space corresponding to the vicinity of the Gaussian distribution, the moment method compares well with the other methods. Further, the MNS estimator is shown to perform best for light‐tailed distributions. The simulation results are shown to lend support to analytically derived asymptotic results for each of the methods.

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

Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels

Abstract: Multiple antennas can be used for increasing the amount of diversity or the number of degrees of freedom in wireless communication systems. We propose the point of view that both types of gains can be simultaneously obtained for a given multiple-antenna channel, but there is a fundamental tradeoff between how much of each any coding scheme can get. For the richly scattered Rayleigh-fading channel, we give a simple characterization of the optimal tradeoff curve and use it to evaluate the performance of existing multiple antenna schemes.

Iterative (turbo) soft interference cancellation and decoding for coded CDMA

Abstract: The presence of both multiple-access interference (MAI) and intersymbol interference (ISI) constitutes a major impediment to reliable communications in multipath code-division multiple-access (CDMA) channels. In this paper, an iterative receiver structure is proposed for decoding multiuser information data in a convolutionally coded asynchronous multipath DS-CDMA system. The receiver performs two successive soft-output decisions, achieved by a soft-input soft-output (SISO) multiuser detector and a bank of single-user SISO channel decoders, through an iterative process. At each iteration, extrinsic information is extracted from detection and decoding stages and is then used as a priori information in the next iteration, just as in turbo decoding. Given the multipath CDMA channel model, a direct implementation of a sliding-window SISO multiuser detector has a prohibitive computational complexity. A low-complexity SISO multiuser detector is developed based on a novel nonlinear interference suppression technique, which makes use of both soft interference cancellation and instantaneous linear minimum mean-square error filtering. The properties of such a nonlinear interference suppressor are examined, and an efficient recursive implementation is derived. Simulation results demonstrate that the proposed low complexity iterative receiver structure for interference suppression and decoding offers significant performance gain over the traditional noniterative receiver structure. Moreover, at high signal-to-noise ratio, the detrimental effects of MAI and ISI in the channel can almost be completely overcome by iterative processing, and single-user performance can be approached.