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Journal articles on the topic 'Multiple systems estimation'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Swapna, Sonti. "Channel Estimation for MIMO Systems." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 201–4. http://dx.doi.org/10.22214/ijraset.2022.39776.

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Abstract: A combination of multiple-input multiple-output (MIMO) systems and orthogonal frequency division multiplexing (OFDM) technologies can be employed in modern wireless communication systems to achieve high data rates and improved spectrum efficiency. For multiple input multiple output (MIMO) systems, this paper provides a Rayleigh fading channel estimation technique based on pilot carriers. The channel is estimated using traditional Least Square (LS) and Minimum Mean Square (MMSE) estimation techniques. The MIMO-OFDM system's performance is measured using the Bit Error Rate (BER) and Mean Square Error (MSE) levels. Keywords: MIMO, MMSE, Channel estimation, BER, OFDM

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2

González-Coma, José P., Pedro Suárez-Casal, Paula M. Castro, and Luis Castedo. "FDD Channel Estimation Via Covariance Estimation in Wideband Massive MIMO Systems." Sensors 20, no. 3 (February 10, 2020): 930. http://dx.doi.org/10.3390/s20030930.

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A method for channel estimation in wideband massive Multiple-Input Multiple-Output systems using hybrid digital analog architectures is developed. The proposed method is useful for Frequency-Division Duplex at either sub-6 GHz or millimeter wave frequency bands and takes into account the beam squint effect caused by the large bandwidth of the signals. To circumvent the estimation of large channel vectors, the posed algorithm relies on the slow time variation of the channel spatial covariance matrix, thus allowing for the utilization of very short training sequences. This is possibledue to the exploitation of the channel structure. After identifying the channel covariance matrix, the channel is estimated on the basis of the recovered information. To that end, we propose a novel method that relies on estimating the tap delays and the gains as sociated with each path. As a consequence, the proposed channel estimator achieves low computational complexity and significantly reduces the training overhead. Moreover, our numerical simulations show better performance results compared to the minimum mean-squared error solution.

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Napolitano, A., and M. Tanda. "Blind parameter estimation in multiple-access systems." IEEE Transactions on Communications 49, no. 4 (April 2001): 688–98. http://dx.doi.org/10.1109/26.917775.

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4

Robinson, P. M. "Multiple local whittle estimation in stationary systems." Annals of Statistics 36, no. 5 (October 2008): 2508–30. http://dx.doi.org/10.1214/07-aos545.

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5

Billings, S. A., and A. K. Swain. "Reconstruction of multiple-input multiple-output non-linear differential equation models from the generalized frequency response function matrix." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 214, no. 1 (February 1, 2000): 35–52. http://dx.doi.org/10.1243/0959651001540500.

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A new algorithm is introduced to identify differential equation models for linear and non-linear multiple-input multiple-output systems from frequency response data using a weighted complex orthogonal estimator. The estimation procedure is progressive beginning with the estimation of the linear terms and then sequentially adding higher-order non-linear terms to build up the model. Simulated examples are included to demonstrate the performance of the new algorithm.

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Zhang, Zhe, Chunyu Wang, and Wenhu Qin. "Semantically Synchronizing Multiple-Camera Systems with Human Pose Estimation." Sensors 21, no. 7 (April 2, 2021): 2464. http://dx.doi.org/10.3390/s21072464.

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Multiple-camera systems can expand coverage and mitigate occlusion problems. However, temporal synchronization remains a problem for budget cameras and capture devices. We propose an out-of-the-box framework to temporally synchronize multiple cameras using semantic human pose estimation from the videos. Human pose predictions are obtained with an out-of-the-shelf pose estimator for each camera. Our method firstly calibrates each pair of cameras by minimizing an energy function related to epipolar distances. We also propose a simple yet effective multiple-person association algorithm across cameras and a score-regularized energy function for improved performance. Secondly, we integrate the synchronized camera pairs into a graph and derive the optimal temporal displacement configuration for the multiple-camera system. We evaluate our method on four public benchmark datasets and demonstrate robust sub-frame synchronization accuracy on all of them.

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Bird, Sheila M., and Ruth King. "Multiple Systems Estimation (or Capture-Recapture Estimation) to Inform Public Policy." Annual Review of Statistics and Its Application 5, no. 1 (March 7, 2018): 95–118. http://dx.doi.org/10.1146/annurev-statistics-031017-100641.

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8

Khan, Imran, Mohammad Zafar, Majid Ashraf, and Sunghwan Kim. "Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems." Electronics 7, no. 12 (December 3, 2018): 382. http://dx.doi.org/10.3390/electronics7120382.

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Traditional channel estimation algorithms such as minimum mean square error (MMSE) are widely used in massive multiple-input multiple-output (MIMO) systems, but require a matrix inversion operation and an enormous amount of computations, which result in high computational complexity and make them impractical to implement. To overcome the matrix inversion problem, we propose a computationally efficient hybrid steepest descent Gauss–Seidel (SDGS) joint detection, which directly estimates the user’s transmitted symbol vector, and can quickly converge to obtain an ideal estimation value with a few simple iterations. Moreover, signal detection performance was further improved by utilizing the bit log-likelihood ratio (LLR) for soft channel decoding. Simulation results showed that the proposed algorithm had better channel estimation performance, which improved the signal detection by 31.68% while the complexity was reduced by 45.72%, compared with the existing algorithms.

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9

Rao, Zhushi, Qinzhong Shi, and Ichiro Hagiwara. "Optimal Estimation of Dynamic Loads for Multiple-Input System." Journal of Vibration and Acoustics 121, no. 3 (July 1, 1999): 397–401. http://dx.doi.org/10.1115/1.2893993.

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An optimal method is developed to estimate the dynamic loads for systems subjected to multiple inputs. The method focuses on minimizing the ensemble mean square error of the estimation. First, the inverse system analysis technique is employed to establish the error estimation equation. Then, by applying the noncausal Wiener filtering theory, the optimal estimator of dynamic loads is derived out. Numerical simulation work demonstrates that the method is of a good ability in suppressing the influence of measurement noises on estimation accuracy. Meanwhile, the simulating calculation of load estimation by a conventional method is also performed and the comparison of both results shows that the method proposed in this paper is rather effective and practicable for dynamic load estimation.

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10

Wang, Yan, and Joanne Thandrayen. "MULTIPLE-RECORD SYSTEMS ESTIMATION USING LATENT CLASS MODELS." Australian & New Zealand Journal of Statistics 51, no. 1 (March 2009): 101–11. http://dx.doi.org/10.1111/j.1467-842x.2008.00531.x.

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11

Shah-Mansouri, Vahid, and Vincent W. S. Wong. "Cardinality Estimation in RFID Systems with Multiple Readers." IEEE Transactions on Wireless Communications 10, no. 5 (May 2011): 1458–69. http://dx.doi.org/10.1109/twc.2011.030411.100390.

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12

Carbonelli, Cecilia, and Urbashi Mitra. "Clustered Channel Estimation for UWB Multiple Antenna Systems." IEEE Transactions on Wireless Communications 6, no. 3 (March 2007): 970–81. http://dx.doi.org/10.1109/twc.2007.05275.

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13

Chen, Yung-Fang, Yen-Hsien Lee, and Po-Ting Hwang. "Adaptive Channel Estimation for Multiple Antenna OFDM Systems." Wireless Personal Communications 59, no. 2 (January 21, 2010): 331–43. http://dx.doi.org/10.1007/s11277-010-9920-z.

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14

Banerjee, A., Y. Arkun, B. Ogunnaike, and R. Pearson. "Estimation of nonlinear systems using linear multiple models." AIChE Journal 43, no. 5 (May 1997): 1204–26. http://dx.doi.org/10.1002/aic.690430511.

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15

Hopkins, M. A., and H. F. VanLandingham. "Optimal Nonlinear Estimation of Linear Stochastic Systems." Journal of Dynamic Systems, Measurement, and Control 116, no. 3 (September 1, 1994): 529–36. http://dx.doi.org/10.1115/1.2899248.

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This paper presents a new nonlinear method of simultaneous parameter and state estimation called pseudo-linear identification (PLID), for stochastic linear time-invariant discrete-time systems. No assumptions are required about pole or zero locations; nor about relative degree, except that the system transfer function must be strictly proper. Under standard gaussian assumptions, for completely controllable and observable systems, it is proved that PLID is the minimum mean-square-error estimator of the states and model parameters, conditioned on the input and output measurements. It is also proved, given persistent excitation, that the parameter estimates converge a.e. to the true parameter values. All results have been extended to the multiple-input, multiple-output case, but the single-input, single-output case is presented here to simplify notation.

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16

Jóhannsson, Óli D., and Thomas Mejer Hansen. "Estimation using multiple-point statistics." Computers & Geosciences 156 (November 2021): 104894. http://dx.doi.org/10.1016/j.cageo.2021.104894.

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17

Heitger, Dan L. "Estimating Activity Costs: How the Provision of Accurate Historical Activity Data from a Biased Cost System Can Improve Individuals' Cost Estimation Accuracy." Behavioral Research in Accounting 19, no. 1 (January 1, 2007): 133–59. http://dx.doi.org/10.2308/bria.2007.19.1.133.

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An integral component of effective cost control and performance evaluation is the ability to accurately estimate relationships between activities and overhead costs (i.e., activity costs). Individuals using a single cost pool system often have to rely on memory of historical activity data when estimating activity costs. If individuals' recall of data is representative of the historical data, then reliance on memory should not be detrimental to cost estimation accuracy. However, individuals often possess incorrect initial beliefs about activity costs. These incorrect beliefs are expected to serve as an anchor from which individuals make insufficient adjustments when estimating activity costs based on memory of historical activity data. Multiple cost pool systems frequently provide biased standard rates; however, such systems also provide accurate historical activity data when individuals estimate costs. I extend prior accounting research by experimentally examining whether a multiple cost pool system's provision of accurate historical activity data improves activity cost estimation for individuals with incorrect cost beliefs even when the cost system also provides biased standard rates. The main contribution of the study is its finding that the multiple cost pool system's provision of historical activity data improves individuals' adjustments from their incorrect initial cost beliefs when estimating activity costs, thereby increasing their estimation accuracy. The results suggest that this improved adjustment from incorrect initial cost beliefs occurs because the provision of historical activity data improves individuals' recognition of how wrong their initial cost beliefs were in reality. This result is achieved even though the cost system provides biased standard rates. The ability of flawed cost systems to improve individuals' activity cost estimation in other such ways has received little research attention and is important because of its potential for improving managerial decision making.

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18

Jeon, Heegyun, Sungmin Aum, Hyungbo Shim, and Yongsoon Eun. "Resilient State Estimation for Control Systems Using Multiple Observers and Median Operation." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/3750264.

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This paper addresses the problem of state estimation for linear dynamic systems that is resilient against malicious attacks on sensors. By “resiliency” we mean the capability of correctly estimating the state despite external attacks. We propose a state estimation with a bank of observers combined through median operations and show that the proposed method is resilient in the sense that estimated states asymptotically converge to the true state despite attacks on sensors. In addition, the effect of sensor noise and process disturbance is also considered. For bounded sensor noise and process disturbance, the proposed method eliminates the effect of attack and achieves state estimation error within a bound proportional to those of sensor noise and disturbance. While existing methods are computationally heavy because online solution of nonconvex optimization is needed, the proposed approach is computationally efficient by using median operation in the place of the optimization. It should be pointed out that the proposed method requires the system states being observable with every sensor, which is not a necessary condition for the existing methods. From resilient system design point of view, however, this fact may not be critical because sensors can be chosen for resiliency in the design stage. The gained computational efficiency helps real-time implementation in practice.

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19

Almradi, Ahmed M., and Sohail A. Dianat. "NDA SNR and CRLB Estimation Over MISO with STBC Channels." International Journal of Business Data Communications and Networking 8, no. 4 (October 2012): 1–16. http://dx.doi.org/10.4018/jbdcn.2012100101.

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This paper discusses the problem of Non Data Aided (NDA) Signal to Noise Ratio (SNR) estimation of Binary Phase Shift keying (BPSK) modulated signals using the Expectation Maximization (EM) Algorithm. In addition, the Cramer-Rao Lower Bounds (CRLB) for the estimation of Data Aided (DA) and Non Data Aided (NDA) Signal to Noise Ratio (SNR) estimation is derived. Multiple Input Single Output (MISO) channels with Space Time Block Codes (STBC) is used. The EM algorithm is a method that finds the Maximum Likelihood (ML) solution iteratively when there are unobserved (hidden or missing) data. Extension of the proposed approach to other types of linearly modulated signals in estimating SNR is straight forward. The performance of the estimator is assessed using the NDA CRLBs. Alamouti coding technique is used in this paper with two transmit antennas and one receive antenna. The authors’ assumption is that the received signal is corrupted by additive white Gaussian noise (AWGN) with unknown variance, and scaled by fixed unknown complex channel gain. Monte Carlo simulations are used to show that the proposed estimator offers a substantial improvement over the conventional Single Input Single Output (SISO) NDA SNR estimator due to the use of the statistical dependences in space and time. Moreover, the proposed NDA SNR estimator works close to the NDA SNR estimator over Single Input Multiple Output (SIMO) channels.

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20

Chen, Tengpeng, He Ren, and Gehan A. J. Amaratunga. "Interval State Estimation in Active Distribution Systems Considering Multiple Uncertainties." Sensors 21, no. 14 (July 6, 2021): 4644. http://dx.doi.org/10.3390/s21144644.

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Distribution system state estimation (DSSE) plays a significant role for the system operation management and control. Due to the multiple uncertainties caused by the non-Gaussian measurement noise, inaccurate line parameters, stochastic power outputs of distributed generations (DG), and plug-in electric vehicles (EV) in distribution systems, the existing interval state estimation (ISE) approaches for DSSE provide fairly conservative estimation results. In this paper, a new ISE model is proposed for distribution systems where the multiple uncertainties mentioned above are well considered and accurately established. Moreover, a modified Krawczyk-operator (MKO) in conjunction with interval constraint-propagation (ICP) algorithm is proposed to solve the ISE problem and efficiently provides better estimation results with less conservativeness. Simulation results carried out on the IEEE 33-bus, 69-bus, and 123-bus distribution systems show that the our proposed algorithm can provide tighter upper and lower bounds of state estimation results than the existing approaches such as the ICP, Krawczyk-Moore ICP(KM-ICP), Hansen, and MKO.

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21

Sanchez-Fernandez, M., M. de-Prado-Cumplido, J. Arenas-Garcia, and F. Perez-Cruz. "SVM Multiregression for Nonlinear Channel Estimation in Multiple-Input Multiple-Output Systems." IEEE Transactions on Signal Processing 52, no. 8 (August 2004): 2298–307. http://dx.doi.org/10.1109/tsp.2004.831028.

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22

Sireesha, Polireddi. "Channel Estimation for Massive MIMO Systems." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 137–38. http://dx.doi.org/10.22214/ijraset.2022.39775.

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Abstract: In MIMO millimeter-wave (mmWave) systems, while the hybrid digital/analog precoding structure provides the ability to increase the reach rate, it also faces the challenge of reducing the channel time limit due to the large number of horns on both sides of the Tx / Rx. . In this paper, channel measurement is done by searching with multiple beams, and a new hierarchical multi-beam search system is proposed, using a pre-designed analog codebook. Performance tests show that, compared to a highperformance system, the proposed system not only achieves a high level of success in getting multiple beams under normal system settings but also significantly reduces channel estimation time Keywords: Massive MIMO, Channel Estimation, precoding

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23

Wang, Wei, and Chunyan Han. "H∞Channel Estimation for DS-CDMA Systems: A Partial Difference Equation Approach." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/307342.

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In the communications literature, a number of different algorithms have been proposed for channel estimation problems with the statistics of the channel noise and observation noise exactly known. In practical systems, however, the channel parameters are often estimated using training sequences which lead to the statistics of the channel noise difficult to obtain. Moreover, the received signals are corrupted not only by the ambient noises but also by multiple-access interferences, so the statistics of observation noises is also difficult to obtain. In this paper, we will investigate theH∞channel estimation problem for direct-sequence code-division multiple-access (DS-CDMA) communication systems with time-varying multipath fading channels. The channel estimator is designed by applying a partial difference equation approach together with the innovation analysis theory. This method can give a sufficient and necessary condition for the existence of anH∞channel estimator.

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24

Bucy, R. S. "Geometry and multiple direction estimation." Information Sciences 57-58 (September 1991): 145–58. http://dx.doi.org/10.1016/0020-0255(91)90074-5.

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25

Lum, Kristian, Megan Emily Price, and David Banks. "Applications of Multiple Systems Estimation in Human Rights Research." American Statistician 67, no. 4 (November 2013): 191–200. http://dx.doi.org/10.1080/00031305.2013.821093.

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26

Tan, Yizhi, Jingrong Zhou, and Jiayin Qin. "Novel Channel Estimation for Non-orthogonal Multiple Access Systems." IEEE Signal Processing Letters 23, no. 12 (December 2016): 1781–85. http://dx.doi.org/10.1109/lsp.2016.2617897.

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27

Sun, Shuli, Lihua Xie, Wendong Xiao, and Yeng Chai Soh. "Optimal linear estimation for systems with multiple packet dropouts." Automatica 44, no. 5 (May 2008): 1333–42. http://dx.doi.org/10.1016/j.automatica.2007.09.023.

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28

Häring, L., and A. Czylwik. "Synchronization in MIMO OFDM systems." Advances in Radio Science 2 (May 27, 2005): 147–53. http://dx.doi.org/10.5194/ars-2-147-2004.

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Abstract. In this paper, an overview of carrier frequency offset (CFO) estimation algorithms for Orthogonal Frequency Division Multiplexing (OFDM) systems is presented. It is well-known that multicarrier systems suffer from their high sensitivity to mismatches of transmitter and receiver oscillator frequencies. The performance degrades since the CFO destroys the orthogonality of the subcarriers. Hence, extensive research has been done on the estimation and correction of the CFO in Single-Input Single-Output (SISO) systems. Mainly, the proposed algorithms can be categorized into data-aided and blind techniques. Several estimation techniques have been extended to the Single-Input Multiple- Output (SIMO) case where multiple receive antennas can be utilized to gain diversity. However, less attention has been paid on synchronization in the attractive Multiple-Input Multiple-Output (MIMO) case which is topic of tremendous interest in current research. The present paper concentrates on aspects of this new scenario. Starting with algorithms for SISO and SIMO, this contribution reviews briefly proposed carrier frequency synchronization techniques which could be implemented in forthcoming MIMO systems.

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29

Westwick, David T., Eric A. Pohlmeyer, Sara A. Solla, Lee E. Miller, and Eric J. Perreault. "Identification of Multiple-Input Systems with Highly Coupled Inputs: Application to EMG Prediction from Multiple Intracortical Electrodes." Neural Computation 18, no. 2 (February 1, 2006): 329–55. http://dx.doi.org/10.1162/089976606775093855.

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A robust identification algorithm has been developed for linear, time-invariant, multiple-input single-output systems, with an emphasis on how this algorithm can be used to estimate the dynamic relationship between a set of neural recordings and related physiological signals. The identification algorithm provides a decomposition of the system output such that each component is uniquely attributable to a specific input signal, and then reduces the complexity of the estimation problem by discarding those input signals that are deemed to be insignificant. Numerical difficulties due to limited input bandwidth and correlations among the inputs are addressed using a robust estimation technique based on singular value decomposition. The algorithm has been evaluated on both simulated and experimental data. The latter involved estimating the relationship between up to 40 simultaneously recorded motor cortical signals and peripheral electromyograms (EMGs) from four upper limb muscles in a freely moving primate.The algorithm performed well in both cases:it provided reliable estimates of the system output and significantly reduced the number of inputs needed for output prediction. For example, although physiological recordings from up to 40 different neuronal signals were available, the input selection algorithm reduced this to 10 neuronal signals that made signicant contributions to the recorded EMGs.

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Yan, Hui, and Zhong Pei Zhang. "A Low-Complexity Algorithm of Joint Multiple Frequency Offsets and Channels Estimation in Cooperative Relay Systems." Advanced Materials Research 748 (August 2013): 1046–50. http://dx.doi.org/10.4028/www.scientific.net/amr.748.1046.

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A low complexity joint estimator of multiple CFOs and Channels is presented in cooperative relay systems. The new algorithm first utilizes correlation-based frequency estimator to get CFOs initial estimation, and then serial interference cancellation based on correlation properties of training sequence in different relays is done to obtain the initial channel estimation. Moreover, a parallel iteration scheme with interference cancellation is proposed to reduce time complexity of the traditional serial iteration. In the overall process, matrix inversion is avoided. Thus, the complexity of the proposed algorithm in both time and computation is much less than the existing algorithms. In last, simulation results verify the iterative algorithm achieves a good performance in Decode and Forward (DF) relay system.

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31

Shimizu, Chihiro, Koji Karato, and Kiyohiko Nishimura. "Nonlinearity of housing price structure." International Journal of Housing Markets and Analysis 7, no. 4 (September 30, 2014): 459–88. http://dx.doi.org/10.1108/ijhma-10-2013-0055.

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Purpose – The purpose of this article, starting from linear regression, was to estimate a switching regression model, nonparametric model and generalized additive model as a semi-parametric model, perform function estimation with multiple nonlinear estimation methods and conduct comparative analysis of their predictive accuracy. The theoretical importance of estimating hedonic functions using a nonlinear function form has been pointed out in ample previous research (e.g. Heckman et al. (2010). Design/methodology/approach – The distinctive features of this study include not only our estimation of multiple nonlinear model function forms but also the method of verifying predictive accuracy. Using out-of-sample testing, we predicted and verified predictive accuracy by performing random sampling 500 times without replacement for 9,682 data items (the same number used in model estimation), based on data for the years before and after the year used for model estimation. Findings – As a result of estimating multiple models, we believe that when it comes to hedonic function estimation, nonlinear models are superior based on the strength of predictive accuracy viewed in statistical terms and on graphic comparisons. However, when we examined predictive accuracy using out-of-sample testing, we found that the predictive accuracy was inferior to linear models for all nonlinear models. Research limitations/implications – In terms of the reason why the predictive accuracy was inferior, it is possible that there was an overfitting in the function estimation. Because this research was conducted for a specific period of time, it needs to be developed by expanding it to multiple periods over which the market fluctuates dynamically and conducting further analysis. Practical implications – Many studies compare predictive accuracy by separating the estimation model and verification model using data at the same point in time. However, when attempting practical application for auto-appraisal systems and the like, it is necessary to estimate a model using past data and make predictions with respect to current transactions. It is possible to apply this study to auto-appraisal systems. Social implications – It is recognized that housing price fluctuations caused by the subprime crisis had a massive impact on the financial system. The findings of this study are expected to serve as a tool for measuring housing price fluctuation risks in the financial system. Originality/value – While the importance of nonlinear estimation when estimating hedonic functions has been pointed out in theoretical terms, there is a noticeable lag when it comes to testing based on actual data. Given this, we believe that our verification of nonlinear estimation’s validity using multiple nonlinear models is significant not just from an academic perspective – it may also have practical applications.

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Ye Wu, Xu Zhu, and A. K. Nandi. "Soft-Input Turbo Channel Estimation for Single-Carrier Multiple-Input–Multiple-Output Systems." IEEE Transactions on Vehicular Technology 58, no. 7 (September 2009): 3867–73. http://dx.doi.org/10.1109/tvt.2009.2016974.

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Ro, Jae-Hyun, Woon-Sang Lee, Hyun-Sun Hwang, Duckdong Hwang, Young-Hwan You, and Hyoung-Kyu Song. "An Efficient Estimation of the Number of Optimal Iterations for GS Pre-coding in Downlink Massive MIMO Systems." Applied Sciences 10, no. 23 (December 6, 2020): 8735. http://dx.doi.org/10.3390/app10238735.

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This paper proposes an estimation scheme of the number iterations for optimal Gauss–Seidel (GS) pre-coding in the downlink massive multiple input multiple output (MIMO) systems for the first time. The number of iterations in GS pre-coding is one of the key parameters and should be estimated accurately prior to signal transmission in the downlink systems. For efficient estimation without presentations of the closed-form solution for the GS pre-coding symbols, the proposed estimation scheme uses the relative method which calculates the normalized Euclidean distance (NED) between consecutive GS solutions by using the property of the monotonic decrease function of the GS solutions. Additionally, an efficient initial solution for the GS pre-coding is proposed as a two term Neumann series (NS) based on the stair matrix for improving the accuracy of estimation and accelerating the convergence rate of the GS solution. The evaluated estimation performances verify high accuracy in the downlink massive MIMO systems even in low loading factors. In addition, an additional complexity for estimating the number of the optimal iterations is nearly negligible.

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Shatti, Ahmed Hussein, and Ehab Abdul Razzaq Hussein. "Massive multiple-input multiple-output channel estimation under hardware and channel impairments." Bulletin of Electrical Engineering and Informatics 12, no. 1 (February 1, 2023): 215–23. http://dx.doi.org/10.11591/eei.v12i1.4025.

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Hardware problems are the most detrimental issues to channel estimates in wireless communication systems. Because of the enormous number of antennas at the base station (BS) in cellular massive multiple-input multiple-output (MIMO) systems and because one radio frequency (RF) chain per antenna is required, hardware impairments in such systems will be quite severe. Many research publications have used a quality-cost tradeoff to adjust for RF unit hardware issues. In this study, we have taken a different approach by reducing the error floor caused by impairments in the predicted channels. Here are two steps to remedy the problem. In phase 1, a single active user channel in a single cell was calculated statistically rather than parametrically. In phase 2, a convex optimization approach was used to regularize the estimated channel in phase 1 to reduce error and provide a robust channel estimate. The results of our proposed procedure are measured by the normalized minimum mean squared error (NMSE) versus a range from the effective signal-to-noise ratio, and it shows a significant reduction (nearly one order of magnitude) in the error floor as compared with the conventional one, especially at high signal-to-noise ratio (SNR) in the range of (20 dB-30 dB). Simulation results were extracted in MATLAB R2020a.

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Kure, Taiki, Haruka Danil Tsuchiya, Yusuke Kameda, Hiroki Yamamoto, Daisuke Kodaira, and Junji Kondoh. "Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation." Energies 15, no. 8 (April 13, 2022): 2855. http://dx.doi.org/10.3390/en15082855.

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The power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical approaches for various time ranges. A short-term (30 min ahead) forecasting method had been proposed previously for multiple PV systems using motion estimation. This method forecasts the short time ahead PV power generation by estimating the motion between two geographical images of the distributed PV power systems. In this method, the parameter λ, which relates the smoothness of the resulting motion vector field and affects the accuracy of the forecasting, is important. This study focuses on the parameter λ and evaluates the effect of changing this parameter on forecasting accuracy. In the periods with drastic power output changes, the forecasting was conducted on 101 PV systems. The results indicate that the absolute mean error of the proposed method with the best parameter is 10.3%, whereas that of the persistence forecasting method is 23.7%. Therefore, the proposed method is effective in forecasting periods when PV output changes drastically within a short time interval.

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Kim, Tae-Kyoung, and Moonsik Min. "A Low-Complexity Algorithm for a Reinforcement Learning-Based Channel Estimator for MIMO Systems." Sensors 22, no. 12 (June 9, 2022): 4379. http://dx.doi.org/10.3390/s22124379.

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This paper proposes a low-complexity algorithm for a reinforcement learning-based channel estimator for multiple-input multiple-output systems. The proposed channel estimator utilizes detected symbols to reduce the channel estimation error. However, the detected data symbols may include errors at the receiver owing to the characteristics of the wireless channels. Thus, the detected data symbols are selectively used as additional pilot symbols. To this end, a Markov decision process (MDP) problem is defined to optimize the selection of the detected data symbols. Subsequently, a reinforcement learning algorithm is developed to solve the MDP problem with computational efficiency. The developed algorithm derives the optimal policy in a closed form by introducing backup samples and data subblocks, to reduce latency and complexity. Simulations are conducted, and the results show that the proposed channel estimator significantly reduces the minimum-mean square error of the channel estimates, thus improving the block error rate compared to the conventional channel estimation.

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Alaghbari, Khaled Abdulaziz, Lim Heng Siong, and Alan W. C. Tan. "Robust correntropy ICA based blind channel estimation for MIMO-OFDM systems." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 34, no. 3 (May 5, 2015): 962–78. http://dx.doi.org/10.1108/compel-08-2014-0199.

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Purpose – The purpose of this paper is to propose a robust correntropy assisted blind channel estimator for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) for improved channel gains estimation and channel ordering and sign ambiguities resolution in non-Gaussian noise channel. Design/methodology/approach – The correntropy independent component analysis with L1-norm cost function is used for blind channel estimation. Then a correntropy-based method is formulated to resolve the sign and order ambiguities of the channel estimates. Findings – Simulation study on Gaussian noise scenario shows that the proposed method achieves almost the same performance as the conventional L2-norm based method. However, in non-Gaussian noise scenarios performance of the proposed method significantly outperforms the conventional and other popular estimators in terms of mean square error (MSE). To solve the ordering and sign ambiguities problems, an auto-correntropy-based method is proposed and compared with the extended cross-correlation-based method. Simulation study shows improved performance of the proposed method in terms of MSE. Originality/value – This paper presents for the first time, a correntropy-based blind channel estimator for MIMO-OFDM as well as simulated comparison results with traditional correlation-based methods in non-Gaussian noise environment.

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Rahman, Md Jahidur, and Jiaxin Yang. "Multiple-Observation-Based Robust Channel and Doppler Estimation in High Mobility Applications." International Journal of Vehicular Technology 2013 (May 13, 2013): 1–8. http://dx.doi.org/10.1155/2013/472154.

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Channel estimation is a challenging task, especially in high mobility applications due to the rapid variation of the propagation environment. This paper presents a new technique that exploits past channel impulse responses (CIRs) in order to trace and compensate Doppler frequency in mobile applications, enabling robust estimation of time-varying channel. Based on the fact that channel taps at different time instants can be fitted with a sinusoidal wave, a joint estimator is proposed to estimate the channel parameters. Therefore, the efficiency of the channel estimation can be improved and stringent delay requirements for the communication systems can also be satisfied. Simulation results show that system performance in terms of bit error rate (BER) is significantly improved with the proposed algorithm.

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Salau, N. P. G., J. O. Trierweiler, and A. R. Secchi. "State estimation of chemical engineering systems tending to multiple solutions." Brazilian Journal of Chemical Engineering 31, no. 3 (September 2014): 771–85. http://dx.doi.org/10.1590/0104-6632.20140313s00002625.

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Sengupta, C., J. R. Cavallaro, and B. Aazhang. "On multipath channel estimation for CDMA systems using multiple sensors." IEEE Transactions on Communications 49, no. 3 (March 2001): 543–53. http://dx.doi.org/10.1109/26.911461.

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Liu, Andong, Li Yu, and Wen-an Zhang. "Moving horizon estimation for networked systems with multiple packet dropouts." Journal of Process Control 22, no. 9 (October 2012): 1593–608. http://dx.doi.org/10.1016/j.jprocont.2012.06.012.

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He, Zhiqiang, Jiaolong Yang, Xiaodan Wang, Yang Liu, and Yue Rong. "Channel Estimation of MIMO Relay Systems With Multiple Relay Nodes." IEEE Access 5 (2017): 27649–58. http://dx.doi.org/10.1109/access.2017.2775202.

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Ye Li. "Simplified channel estimation for OFDM systems with multiple transmit antennas." IEEE Transactions on Wireless Communications 1, no. 1 (2002): 67–75. http://dx.doi.org/10.1109/7693.975446.

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Pourabbas, Elaheh, and Arie Shoshani. "Efficient estimation of joint queries from multiple OLAP databases." ACM Transactions on Database Systems 32, no. 1 (March 2007): 2. http://dx.doi.org/10.1145/1206049.1206051.

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Asif, Rao Muhammad, Mustafa Shakir, Ateeq Ur Rehman, Muhammad Shafiq, Rehan Ali Khan, and Wali Ullah Khan. "Performance Evaluation of Spectral Efficiency for Uplink and Downlink Multi-Cell Massive MIMO Systems." Journal of Sensors 2022 (June 30, 2022): 1–12. http://dx.doi.org/10.1155/2022/7205687.

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Massive multiple-input and multiple-output (MIMO) systems have become the most persuasive technology for 5G as it increased the energy efficiency gigantically as compared to other wireless communication systems. Being the most vibrant research technology in the communication sector, this research work is based on the optimal model development of energy-efficient massive MIMO systems. The proposed model is a realistic model that augmented the spectral efficiency (SE) of massive MIMO systems where a multi-cell model scenario is considered. Channel estimation is carried out at the base stations (BSs) based on uplink (UL) transmission while the minimum mean-squared error (MMSE), Element-wise MMSE, and Least-square (LS) estimators are used for the estimation. We analyze the achievable SE of the UL based on the MMSE channel estimator with different receive combining schemes. Moreover, the downlink (DL) transmission model is also modelled with different precoding schemes by taking the same vectors used in combining schemes. The simulation results show a significant improvement in spectral efficiency by developing UL and DL transmission models and also realized that the average sum of SE per cell can be improved by optimized MMSE channel estimation, installing multiple BS antennas, and serving multiple UEs per cell. The findings of this work specify that the massive MIMO system can be developed by optimizing the channel estimation for the augmentation of SE in UL and DL transmissions. Conclusively, it can be summarized that some complex computations of MMSE channel estimators can enhance the average sum of SE per cell as per the results verified in this model.

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Lonkeng, Alban Doumtsop, and Jie Zhuang. "Two-Dimensional DOA Estimation Using Arbitrary Arrays for Massive MIMO Systems." International Journal of Antennas and Propagation 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6794920.

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With the quick advancement of wireless communication networks, the need for massive multiple-input-multiple-output (MIMO) to offer adequate network capacity has turned out to be apparent. As a portion of array signal processing, direction-of-arrival (DOA) estimation is of indispensable significance to acquire directional data of sources and to empower the 3D beamforming. In this paper, the performance of DOA estimation for massive MIMO systems is analyzed and compared using a low-complexity algorithm. To be exact, the 2D Fourier domain line search (FDLS) MUSIC algorithm is studied to mutually estimate elevation and azimuth angle, and arbitrary array geometry is utilized to represent massive MIMO systems. To avoid the computational burden in estimating the data covariance matrix and its eigenvalue decomposition (EVD) due to the large-scale sensors involved in massive MIMO systems, the reduced-dimension data matrix is applied on the signals received by the array. The performance is examined and contrasted with the 2D MUSIC algorithm for different types of antenna configuration. Finally, the array resolution is selected to investigate the performance of elevation and azimuth estimation. The effectiveness and advantage of the proposed technique have been proven by detailed simulations for different types of MIMO array configuration.

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Minh, Vu Trieu, Nitin Afzulpurkar, and W. M. Wan Muhamad. "Fault Detection and Control of Process Systems." Mathematical Problems in Engineering 2007 (2007): 1–20. http://dx.doi.org/10.1155/2007/80321.

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This paper develops a stochastic hybrid model-based control system that can determine online the optimal control actions, detect faults quickly in the control process, and reconfigure the controller accordingly using interacting multiple-model (IMM) estimator and generalized predictive control (GPC) algorithm. A fault detection and control system consists of two main parts: the first is the fault detector and the second is the controller reconfiguration. This work deals with three main challenging issues: design of fault model set, estimation of stochastic hybrid multiple models, and stochastic model predictive control of hybrid multiple models. For the first issue, we propose a simple scheme for designing faults for discrete and continuous random variables. For the second issue, we consider and select a fast and reliable fault detection system applied to the stochastic hybrid system. Finally, we develop a stochastic GPC algorithm for hybrid multiple-models controller reconfiguration with soft switching signals based on weighted probabilities. Simulations for the proposed system are illustrated and analyzed.

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Nasseri. "Iterative Channel Estimation Algorithm in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing Systems." Journal of Computer Science 6, no. 2 (February 1, 2010): 224–28. http://dx.doi.org/10.3844/jcssp.2010.224.228.

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Yao, Bobin, Wenjie Wang, and Qinye Yin. "DOD and DOA Estimation in Bistatic Non-Uniform Multiple-Input Multiple-Output Radar Systems." IEEE Communications Letters 16, no. 11 (November 2012): 1796–99. http://dx.doi.org/10.1109/lcomm.2012.091212.121605.

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Wan, F., W. P. Zhu, and M. N. S. Swamy. "Channel estimation of pulse-shaped multiple-input multiple-output orthogonal frequency division multiplexing systems." IET Communications 4, no. 17 (2010): 2104. http://dx.doi.org/10.1049/iet-com.2010.0102.

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