825,330 research outputs found
Louis Chen lecture
No full textSide A. 1. Sibelius #1. 2. Louis Chen lecture -- Side B. 1. All L. Chen's lect.Live recording (lecture)Possibly reproduced from other commercial recording or radio broadcast (Pending for review) (Sibelius' piece)Electronic reproduction from Rulan Chao Pian Audio Cassette Collection.Performers, unknown.Spoken in Chinese and English
Blind joint maximum likelihood channel estimation and data detection for SIMO systems
No full textA blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of single-input multiple-output (SIMO) systems. The joint ML optimisation over channel and data is decomposed into an iterative optimisation loop. An efficient global optimisation algorithm called the repeated weighted boosting search is employed at the upper level to optimally identify the unknown SIMO channel model, and the Viterbi algorithm is used at the lower level to produce the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used to demonstrate the effectiveness of this joint ML optimisation scheme for blind adaptive SIMO systems
Replication Data for: Chen S, Christensen T, Ma L. Reputation Management and Administrative Reorganization: How Different Media Reputation Dimensions Matter for Agency Termination. Journal of Public Administration Research and Theory. 2022, https://doi.org/10.1093/jopart/muac028
No full textReplication Data for: Chen S, Christensen T, Ma L. Reputation Management and Administrative Reorganization: How Different Media Reputation Dimensions Matter for Agency Termination. Journal of Public Administration Research and Theory. 2022, https://doi.org/10.1093/jopart/muac02
Adaptive Minimum-BER Linear Multiuser Detection for DS-CDMA Signals in Multipath Channels
No full textThe problem of constructing adaptive minimum bit error rate (MBER) linear multiuser detectors is considered for direct-sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. Based on the approach of kernel density estimation for approximating the bit error rate (BER) from training data, a least mean squares (LMS) style stochastic gradient adaptive algorithm is developed for training linear multiuser detectors. Computer simulation is used to study the convergence speed and steady-state BER misadjustment of this adaptive MBER linear multiuser detector, and the results show that it outperforms an existing LMS-style adaptive MBER algorithm first presented at Globecom'98 by Yeh, Lopes and Barry
Adaptive Minimum Bit Error Rate Beamforming
No full textAn adaptive beamforming technique is proposed based on directly minimizing the bit error rate. It is demonstrated that this minimum bit error rate (MBER) approach utilizes the antenna array elements more intelligently, than the standard minimum mean square error (MMSE) approach. Consequently, MBER beamforming is capable of providing significant performance gains in terms of a reduced bit error rate over MMSE beamforming. A block-data adaptive implementation of the MBER beamforming solution is developed based on the Parzen window estimate of probability density function. Furthermore, a sample-by-sample adaptive implementation is considered, and a stochastic gradient algorithm, referred to as the least bit error rate, is derived. The proposed adaptive MBER beamforming technique provides an extension to the existing work (Mulgrew and Chen 2001, Chen et al 2001} for adaptive MBER equalization and multiuser detection
Original immunoblots Ouyang and Chen et al Developmental Cell
No full textUncropped immunoblotting images Ouyang and Chen Developmental Cell
Iterative Soft Interference Cancellation Aided Minimum Bit Error Rate Uplink Receiver Beamforming
No full textIterative multiuser receivers constitute an effective solution for transmission over Multiple Access Interference (MAI) infested channels, when invoking a combined multiuser detector and channel decoder. Most reduced-complexity methods in this area use the Complex-valued Minimum Mean Squared Error (CMMSE) Multiuser Detector (MUD). Since the desired output of BPSK systems is real-valued, minimizing the Mean Square Error (MSE) between the beamformer’s desired output and the real part of the beamformer output has the potential of significantly improving the attainable Bit Error Rate (BER) performance. We refer to this MMSE design as the Real-valued MMSE (RMMSE) receiver. In this paper, we explore a new Soft-Input Soft-Output (SISO) interference cancellation multiuser detection algorithm based on the novel Minimum BER (MBER) criterion. We demonstrate that the MBER turbo receiver outperforms both the CMMSE and the RMMSE algorithms, particularly in so-called ‘overloaded’ beamforming systems, where the number of receiver antennas is lower than the number of users supported
Reduced Complexity Single-Carrier Maximum-Likelihood Detection for Decision Feedback Assisted Space-Time Equalization
Full text linkA novel Decision-Feedback (DF) aided reduced complexity Maximum Likelihood (ML) Space-Time Equalizer (STE) designed for single-carrier multiple antenna assisted receivers is introduced. The proposed receiver structure is based on a recursive tree search, which is capable of achieving ML performance at a moderate computational cost and substantially outperforms the linear benchmarker based on the Minimum Mean-Squared Error (MMSE) criterion. Additionally a further complexity reduction scheme is proposed, which exploits the specific characteristics of both the wide-band channel and the proposed DF-STE
A Robust Nonlinear Beamforming Assisted Receiver for BPSK Signalling
No full textNonlinear beamforming designed for wireless communications is investigated. We derive the optimal nonlinear beamforming assisted receiver designed for binary phase shift keying (BPSK) signalling. It is shown that this optimal Bayesian beamformer significantly outperforms the classic linear minimum mean square error (LMMSE) beamformer at the expense of an increased complexity. Hence the achievable user capacity of the wireless system invoking the proposed beamformer is substantially enhanced. In particular, when the angular separation between the desired and interfering signals is below a certain threshold, a linear beamformer will fail while a nonlinear beamformer can still perform adequately. Blockadaptive implementation of the optimal Bayesian beamformer can be realized using a Radial Basis Function network based on the Relevance Vector Machine (RVM) for classification, and a recursive sample-by-sample adaptation is proposed based on an enhanced ?-means clustering aided recursive least squares algorithm
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