Tight probablisitic MSE constrained multiuser MISO transceiver design under channel uncertainty

A novel optimization method is proposed to solve the probabilistic mean square error (MSE) constrained multiuser multiple-input single-output (MU-MISO) transceiver design problem. Since the probabilistic MSE constraints cannot be expressed in closed-form under Gaussian channel uncertainty, existing probabilistic transceiver design methods rely on probability inequality approximations, resulting in conservative MSE outage realizations. In this paper, based on local structure of the feasible set in the probabilistic MSE constrained transceiver design problem, a set squeezing procedure is proposed to realize tight MSE outage control. Simulation results show that the MSE outage can be realized tightly, which results in significantly reduced transmit power compared to the existing inequality based probabilistic transceiver design.published_or_final_versio

Tight Probabilistic SINR Constrained Beamforming Under Channel Uncertainties

In downlink multi-user beamforming, a single bases- tation is serving a number of users simultaneously. However, energy intended for one user may leak to other unintended users, causing interference. With signal-to-interference-plus-noise ratio (SINR) being one of the most crucial quality metrics to users, beamforming design with SINR guarantee has always been an important research topic. However, when the channel state information is not accurate, the SINR requirements become probabilistic constraints, which unfortunately are not tractable analytically for general uncertainty distribution. Therefore, ex- isting probabilistic beamforming methods focus on the relatively simple Gaussian and uniform channel uncertainties, and mainly rely on probability inequality based approximated solutions, resulting in conservative SINR outage realizations. In this paper, based on the local structure of the feasible set in the probabilistic beamforming problem, a systematic method is proposed to realize tight SINR outage control for a large class of channel uncertainty distributions. With channel estimation and quantization errors as examples, simulation results show that the SINR outage can be re- alized tightly, which results in reduced transmit power compared to the existing inequality based probabilistic beamformers.published_or_final_versio

QoS constrained robust MIMO transceiver design under unknown interference

We study the robust transceiver optimization in multiple-input multiple-output (MIMO) systems aiming at minimizing transmit power under probabilistic quality-of-service (QoS) requirements. Owing to the unknown distributed interference, the channel estimation error can be arbitrary distributed. Under this situation, the QoS requirements should account for the worst-case channel estimation error distribution. While directly finding the worst-case distribution is challenging, two methods are proposed to solve the robust transceiver design problem. One is based on the Chebyshev inequality, the other is based on a novel duality method. Simulation results show that the QoS requirement is satisfied by both proposed algorithms. Furthermore, among the two proposed methods, the duality method shows a superior performance in transmit power, while the Chebyshev method demonstrates a lower computational complexity. © 2012 IEEE.published_or_final_versio

Chiral Spin Liquid in a Frustrated Anisotropic Kagome Heisenberg Model

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Semi-blind CFO, channel estimation and data detection for ofdm systems over doubly selective channels

Proceedings of the IEEE International Symposium on Circuits and Systems, 2010, p. 1887-1890Semi-blind joint CFO, channel estimation and data detection for OFDM systems over doubly selective channels (DSCs) is investigated in this work. A joint iterative algorithm is developed based on the maximum a posteriori expectation-maximization (MAP-EM) algorithm. In addition, a novel algorithm is also proposed to obtain the initial estimates of CFO and channels. Simulation results show that the performance of the proposed CFO and channel estimators approaches to that of the estimators with full training at high SNRs. Moreover, after convergence, the performance of data detection is close to the ideal case with perfect CFO and channel state information. ©2010 IEEE.published_or_final_versionThe IEEE International Symposium on Circuits and Systems (ISCAS), Paris, France, 30 May-2 June 2010. In Proceedings of ISCAS, 2010, p. 1887-189

IQ imbalance compensation: A semi-blind method for OFDM systems in fast fading channels

Proceedings of the IEEE Asia-Pacific Conference on Circuits and Systems, 2010, p. 362-365In this paper, an orthogonal frequency division multiplexing (OFDM) system operating in a fast fading environment modeled by a doubly selective channel (DSC) is considered. The paper first reformulates a commonly adopted system model using the generalized complex exponential basis expansion technique. The resulting model enables the IQ imbalance and DSC to be estimated in the time domain with a small number of scattered pilots within an OFDM symbol. A joint estimation and compensation scheme is then proposed which compensates all the inter-carrier interference terms. Simulation results show that the proposed compensation method achieves better symbol error rate performance than previous proposed methods. © 2010 IEEE.published_or_final_versionThe IEEE Asia Pacific Conference on Circuits and Systems (APCCAS'2010), Kuala Lumpur, Malaysia, 6-9 December 2010. in Proceedings of APCCAS'2010, 2010, p. 362-36

Semiblind iterative data detection for OFDM systems with CFO and doubly selective channels

Data detection for OFDM systems over unknown doubly selective channels (DSCs) and carrier frequency offset (CFO) is investigated. A semiblind iterative detection algorithm is developed based on the expectation-maximization (EM) algorithm. It iteratively estimates the CFO, channel and recovers the unknown data using only limited number of pilot subcarriers in one OFDM symbol. In addition, efficient initial CFO and channel estimates are also derived based on approximated maximum likelihood (ML) and minimum mean square error (MMSE) criteria respectively. Simulation results show that the proposed data detection algorithm converges in a few iterations and moreover, its performance is close to the ideal case with perfect CFO and channel state information. © 2010 IEEE.published_or_final_versio

Joint channel estimation and data detection for OFDM systems over doubly selective channels

In this paper, a joint channel estimation and data detection algorithm is proposed for OFDM systems under doubly selective channels (DSCs). After representing the DSC using Karhunen-Loève basis expansion model (K-L BEM), the proposed algorithm is developed based on the expectationmaximization (EM) algorithm. Basically, it is an iterative algorithm including two steps at each iteration. In the first step, the unknown coefficients in K-L BEM are first integrated out to obtain a function which only depends on data, and meanwhile, a maximum a posteriori (MAP) channel estimator is obtained. In the second step, data are directly detected by a novel approach based on the function obtained in the first step. Moreover, a Bayesian Cramer-Rao Lower Bound (BCRB) which is valid for any channel estimator is also derived to evaluate the performance of the proposed channel estimator. The effectiveness of the proposed algorithm is finally corroborated by simulation results. ©2009 IEEE.published_or_final_versionThe 20th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2009), Tokyo, Japan. 13-16 September 2009. In Proceedings of the 20th PIMRC, 2009, p. 446-45

Superimposed training-based channel estimation and data detection for OFDM amplify-and-forward cooperative systems under high mobility

In this paper, joint channel estimation and data detection in orthogonal frequency division multiplexing (OFDM) amplify-and-forward (AF) cooperative systems under high mobility is investigated. Unlike previous works on cooperative systems in which a number of subcarriers are solely occupied by pilots, partial data-dependent superimposed training (PDDST) is considered here, thus preserving the spectral efficiency. First, a closed-form channel estimator is developed based on the least squares (LS) method with Tikhonov regularization and a corresponding data detection algorithm is proposed using the linear minimum mean square error (LMMSE) criterion. In the derived channel estimator, the unknown data is treated as part of the noise and the resulting data detection may not meet the required performance. To address this issue, an iterative method based on the variational inference approach is derived to improve performance. Simulation results show that the data detection performance of the proposed iterative algorithm initialized by the LMMSE data detector is close to the ideal case with perfect channel state information. © 2006 IEEE.published_or_final_versio

Learning-based Ensemble Average Propagator Estimation

By capturing the anisotropic water diffusion in tissue, diffusion magnetic resonance imaging (dMRI) provides a unique tool for noninvasively probing the tissue microstructure and orientation in the human brain. The diffusion profile can be described by the ensemble average propagator (EAP), which is inferred from observed diffusion signals. However, accurate EAP estimation using the number of diffusion gradients that is clinically practical can be challenging. In this work, we propose a deep learning algorithm for EAP estimation, which is named learning-based ensemble average propagator estimation (LEAPE). The EAP is commonly represented by a basis and its associated coefficients, and here we choose the SHORE basis and design a deep network to estimate the coefficients. The network comprises two cascaded components. The first component is a multiple layer perceptron (MLP) that simultaneously predicts the unknown coefficients. However, typical training loss functions, such as mean squared errors, may not properly represent the geometry of the possibly non-Euclidean space of the coefficients, which in particular causes problems for the extraction of directional information from the EAP. Therefore, to regularize the training, in the second component we compute an auxiliary output of approximated fiber orientation (FO) errors with the aid of a second MLP that is trained separately. We performed experiments using dMRI data that resemble clinically achievable $q$-space sampling, and observed promising results compared with the conventional EAP estimation method.Comment: Accepted by MICCAI 201