The Experts below are selected from a list of 14301 Experts worldwide ranked by ideXlab platform

Jiunn-tsair Chen - One of the best experts on this subject based on the ideXlab platform.

  • Joint Channel Parameter Estimation and Signal Detection for Downlink MIMO DS-CDMA Systems
    IEICE Transactions on Communications, 2005
    Co-Authors: Yung-yi Wang, Jiunn-tsair Chen, Ying Lu
    Abstract:

    SUMMARY This paper proposes two space-time joint Channel Parameter estimation and signal detection algorithms for downlink DS-CDMA systems with multiple-input-multiple-output (MIMO) wireless multipath fading Channels. The proposed algorithms initially use the space-time MUSIC to estimate the DOA-delays of the multipath Channel. Based on these estimated DOA-delays, a space-time Channel decoupler is developed to decompose the multipath downlink Channel into a set of independent parallel subChannels. The fading amplitudes of the multipath can then be estimated from the eigen space of the output of the space-time Channel decoupler. With these estimated Channel Parameters, signal detection is carried out by a maximal ratio combiner on a pathwise basis. Computer simulations show that the proposed algorithms outperform the conventional space-time RAKE receiver while having the similar performance compared with the space-time minimum mean square error receiver.

  • Constrained TST MUSIC for joint spatial-temporal Channel Parameter estimation
    2003 IEEE International Conference on Acoustics Speech and Signal Processing 2003. Proceedings. (ICASSP '03)., 2003
    Co-Authors: Wen-hsien Fang, Jiunn-tsair Chen
    Abstract:

    This paper presents an improved tree-structured multiple signal classification (MUSIC) algorithm to jointly estimate the directions of arrival (DOA) and propagation delays in a CDMA system. The proposed algorithm makes use of one spatial (S)-MUSIC and two temporal (T)-MUSIC algorithms alternatively to estimate the group delays and the DOA, respectively. In contrast to the previous version, a constrained temporal filtering process and a constrained spatial beamforming process are addressed, which try to minimize the filtered output power under a set of judicious chosen linear constraints. Such a constrained filtering approach can effectively partition the incoming rays and suppress the propagation error in the tree-structured estimation scheme, thus in turn enhancing the overall performance. Furthermore, the pairing of the estimated DOA and delays is also automatically determined. Compared with previous works, the new approach calls for low computational complexity but exhibits superior performance, as shown in the furnished simulations.

R. Geiger - One of the best experts on this subject based on the ideXlab platform.

  • Modeling of random Channel Parameter variations in MOS transistors
    ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196), 2001
    Co-Authors: R. Geiger
    Abstract:

    Widely used approaches to modeling random effects and extracting random Parameters in matching-critical circuits are based upon models derived under the widely accepted premise that distributed Parameter devices can be modeled with lumped Parameter models. In this paper, a new stochastic approach based upon a distributed Parameter model is presented that offers improvement in predicting the effects of random Parameter variations on device matching.

  • MOSGRAD-a tool for simulating the effects of systematic and random Channel Parameter variations
    ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196), 2001
    Co-Authors: R. Geiger
    Abstract:

    A CAD tool, MOSGRAD, that can be used to simulate the effects of distributed two-dimensional systematic and random variations in device Parameters on the performance of matching-critical circuits has been developed. In addition to applications for layouts with conventional rectangular transistors, this tool can predict the performance of nonconventional circuit structures in which multiple drain and/or source regions share a common Channel region as well as predict the performance of nonconventional layouts that may incorporate nonrectangular transistors or segmented transistors.

Wen-hsien Fang - One of the best experts on this subject based on the ideXlab platform.

  • Constrained TST MUSIC for joint spatial-temporal Channel Parameter estimation
    2003 IEEE International Conference on Acoustics Speech and Signal Processing 2003. Proceedings. (ICASSP '03)., 2003
    Co-Authors: Wen-hsien Fang, Jiunn-tsair Chen
    Abstract:

    This paper presents an improved tree-structured multiple signal classification (MUSIC) algorithm to jointly estimate the directions of arrival (DOA) and propagation delays in a CDMA system. The proposed algorithm makes use of one spatial (S)-MUSIC and two temporal (T)-MUSIC algorithms alternatively to estimate the group delays and the DOA, respectively. In contrast to the previous version, a constrained temporal filtering process and a constrained spatial beamforming process are addressed, which try to minimize the filtered output power under a set of judicious chosen linear constraints. Such a constrained filtering approach can effectively partition the incoming rays and suppress the propagation error in the tree-structured estimation scheme, thus in turn enhancing the overall performance. Furthermore, the pairing of the estimated DOA and delays is also automatically determined. Compared with previous works, the new approach calls for low computational complexity but exhibits superior performance, as shown in the furnished simulations.

  • Joint spatial-temporal Channel Parameter estimation using tree-structured MUSIC
    Vehicular Technology Conference. IEEE 55th Vehicular Technology Conference. VTC Spring 2002 (Cat. No.02CH37367), 2002
    Co-Authors: Wen-hsien Fang, Ming-lu Wu
    Abstract:

    We present a low complexity, yet high accuracy algorithm to jointly estimate the direction of arrival (DOAs) and propagation delays in the CDMA system. The proposed algorithm employs one spatial MUltiple SIgnal Classification (S-MUSIC) and two temporal (T)-MUSIC algorithms alternatively to estimate the group delays and the DOAs, respectively. Furthermore, a temporal filtering process and a spatial filtering process are also addressed to group the incoming rays so that the delays and the DOAs can be precisely estimated using the MUSIC algorithm. As such, the incoming rays are thus grouped, isolated, and estimated. Simulation results show that with such a tree-structured estimation scheme, the incoming rays can be resolved even with very close DOAs or delays.

Kah-seng Chung - One of the best experts on this subject based on the ideXlab platform.

  • Multipath Channel Parameter Estimation in Mobile Radio Environments Using Stationary Wavelet Transform
    2005 Asia-Pacific Conference on Communications, 2005
    Co-Authors: O.a. Aboaba, Kah-seng Chung
    Abstract:

    This paper investigates the potential application of the stationary wavelet transform as a low-computational complexity algorithm to jointly estimate the number and delays of impinging waves in mobile radio environments. It makes use of the built-in ability of wavelets at capturing the trends in non-stationary signals at different resolutions. The performance of the proposed scheme is assessed by means of Monte-Carlo simulations in an "eight-path" synthetic mobile radio Channel. Initial estimates of the number and delays of the impinging waves were first computed using discrete stationary wavelet transform. In particular, it is shown that the discrete stationary wavelet transform can be successfully used to improve resolution by more than a factor of ten. An amplitude estimation algorithm can then be subsequently applied to determine the amplitudes of the individual paths

Marius Pesavento - One of the best experts on this subject based on the ideXlab platform.

  • EUSIPCO - Multidimensional sparse recovery for MIMO Channel Parameter estimation
    2016 24th European Signal Processing Conference (EUSIPCO), 2016
    Co-Authors: Christian Steffens, Yang Yang, Marius Pesavento
    Abstract:

    Multipath propagation is a common phenomenon in wireless communication. Knowledge of propagation path Parameters such as complex Channel gain, propagation delay or angle-of-arrival provides valuable information on the user position and facilitates Channel response estimation. A major challenge in Channel Parameter estimation lies in its multidimensional nature, which leads to large-scale estimation problems which are difficult to solve. Current approaches of sparse recovery for multidimensional Parameter estimation aim at simultaneously estimating all Channel Parameters by solving one large-scale estimation problem. In contrast to that we propose a sparse recovery method which relies on decomposing the multidimensional problem into successive one-dimensional Parameter estimation problems, which are much easier to solve and less sensitive to off-grid effects, while providing proper Parameter pairing. Our proposed decomposition relies on convex optimization in terms of nuclear norm minimization and we present an efficient implementation in terms of the recently developed STELA algorithm.

  • Multidimensional sparse recovery for MIMO Channel Parameter estimation
    2016 24th European Signal Processing Conference (EUSIPCO), 2016
    Co-Authors: Christian Steffens, Yang Yang, Marius Pesavento
    Abstract:

    Multipath propagation is a common phenomenon in wireless communication. Knowledge of propagation path Parameters such as complex Channel gain, propagation delay or angle-of-arrival provides valuable information on the user position and facilitates Channel response estimation. A major challenge in Channel Parameter estimation lies in its multidimensional nature, which leads to large-scale estimation problems which are difficult to solve. Current approaches of sparse recovery for multidimensional Parameter estimation aim at simultaneously estimating all Channel Parameters by solving one large-scale estimation problem. In contrast to that we propose a sparse recovery method which relies on decomposing the multidimensional problem into successive one-dimensional Parameter estimation problems, which are much easier to solve and less sensitive to off-grid effects, while providing proper Parameter pairing. Our proposed decomposition relies on convex optimization in terms of nuclear norm minimization and we present an efficient implementation in terms of the recently developed STELA algorithm.