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Journal articles on the topic 'Channel estimation errors'

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Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

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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2

Larsson, E. G. "Diversity and Channel Estimation Errors." IEEE Transactions on Communications 52, no. 2 (February 2004): 205–8. http://dx.doi.org/10.1109/tcomm.2003.822731.

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3

Kyritsi, P., R. A. Valenzuela, and D. C. Cox. "Channel and capacity estimation errors." IEEE Communications Letters 6, no. 12 (December 2002): 517–19. http://dx.doi.org/10.1109/lcomm.2002.806446.

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4

ur Rehman, Junaid, and Hyundong Shin. "Entanglement-Free Parameter Estimation of Generalized Pauli Channels." Quantum 5 (July 1, 2021): 490. http://dx.doi.org/10.22331/q-2021-07-01-490.

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We propose a parameter estimation protocol for generalized Pauli channels acting on d-dimensional Hilbert space. The salient features of the proposed method include product probe states and measurements, the number of measurement configurations linear in d, minimal post-processing, and the scaling of the mean square error comparable to that of the entanglement-based parameter estimation scheme for generalized Pauli channels. We also show that while measuring generalized Pauli operators the errors caused by the Pauli noise can be modeled as measurement errors. This makes it possible to utilize the measurement error mitigation framework to mitigate the errors caused by the generalized Pauli channels. We use this result to mitigate noise on the probe states and recover the scaling of the noiseless probes, except with a noise strength-dependent constant factor. This method of modeling Pauli channel as measurement noise can also be of independent interest in other NISQ tasks, e.g., state tomography problems, variational quantum algorithms, and other channel estimation problems where Pauli measurements have the central role.

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5

Gao, Heli, Jie Chen, Shaun Quegan, Wei Yang, and Chunsheng Li. "Parameter Estimation and Error Calibration for Multi-Channel Beam-Steering SAR Systems." Remote Sensing 11, no. 12 (June 14, 2019): 1415. http://dx.doi.org/10.3390/rs11121415.

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Multi-channel beam-steering synthetic aperture radar (multi-channel BS-SAR) can achieve high resolution and wide-swath observations by combining beam-steering technology and azimuth multi-channel technology. Various imaging algorithms have been proposed for multi-channel BS-SAR but the associated parameter estimation and error calibration have received little attention. This paper focuses on errors in the main parameters in multi-channel BS-SAR (the derotation rate and constant Doppler centroid) and phase inconsistency errors. These errors can significantly reduce image quality by causing coarser resolution, radiometric degradation, and appearance of ghost targets. Accurate derotation rate estimation is important to remove the spectrum aliasing caused by beam steering, and spectrum reconstruction for multi-channel sampling requires an accurate estimate of the constant Doppler centroid and phase inconsistency errors. The time shift and scaling effect of the derotation error on the azimuth spectrum are analyzed in this paper. A method to estimate the derotation rate is presented, based on time shifting, and integrated with estimation of the constant Doppler centroid. Since the Doppler histories of azimuth targets are space-variant in multi-channel BS-SAR, the conventional estimation methods of phase inconsistency errors do not work, and we present a novel method based on minimum entropy to estimate and correct these errors. Simulations validate the proposed error estimation methods.

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6

Kim, Jung-Bin, and In-Ho Lee. "Outage Analysis of Partial Relay Selection Schemes with Feedback Delay and Channel Estimation Errors in Nonidentical Rayleigh Fading Channels." International Journal of Antennas and Propagation 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/1862391.

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This paper investigates the impact of the channel estimation error and outdated channel state information (CSI) on the outage performances of partial relay selection (PRS) and efficient partial relay selection (EPRS). Considering imperfect channel estimation and outdated CSI with decode-and-forward (DF) relaying strategy, closed-form expressions for exact outage probabilities and asymptotic outage probabilities for PRS and EPRS are provided assuming independent and nonidentically distributed Rayleigh fading channels. Numerical investigations verify the analytical expression for outage probability and show how much performance is degraded by the channel estimation errors and the feedback delay that causes the outdated CSI.

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7

Chen, Xiao Min, Xiao Dan Yu, Wei Tan, and Xiang Bin Yu. "Iterative Detection Scheme for Turbo-BLAST System with Adaptive Power Allocation in the Presence of Channel State Information Imperfection." Advanced Materials Research 646 (January 2013): 120–25. http://dx.doi.org/10.4028/www.scientific.net/amr.646.120.

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We propose an iterative detection scheme for Turbo-BLAST system with optimal power allocation in the presence of channel state information imperfection. The proposed scheme uses the channel estimation matrix for detection and treats the interference caused by channel estimation errors and additive white Gaussian noise as equivalent noise where the channel estimation matrix and the statistical characteristic of channel estimation errors are necessitated. Simulation results show the proposed algorithm is effective to improve bit error rate (BER) performance through iterative detection for modified Turbo-BLAST system with optimal power allocation in the presence of imperfect channel state information.

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8

Rim, Minjoong, and Chung Kang. "Uplink Non-Orthogonal Multiple Access with Channel Estimation Errors for Internet of Things Applications." Sensors 19, no. 4 (February 21, 2019): 912. http://dx.doi.org/10.3390/s19040912.

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One of the key requirements for next generation wireless or cellular communication systems is to efficiently support a large number of connections for Internet of Things (IoT) applications, and uplink non-orthogonal multiple access (NOMA) schemes can be used for this purpose. In uplink NOMA systems, pilot symbols, as well as data symbols can be superimposed onto shared resources. The error rate performance can be severely degraded due to channel estimation errors, especially when the number of superimposed packets is large. In this paper, we discuss uplink NOMA schemes with channel estimation errors, assuming that quadrature phase shift keying (QPSK) modulation is used. When pilot signals are superimposed onto the shared resources and a large number of devices perform random accesses concurrently to a single resource of the base station, the channels might not be accurately estimated even in high SNR environments. In this paper, we propose an uplink NOMA scheme, which can alleviate the performance degradation due to channel estimation errors.

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9

Costa, Bruno Felipe, and Taufik Abrão. "MIMO Precoding for Correlated Fading Channels." Journal of Circuits, Systems and Computers 25, no. 05 (February 25, 2016): 1650041. http://dx.doi.org/10.1142/s0218126616500419.

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This contribution proposes a precoder-decoder design aiming to improve the performance of multiple-input–multiple-output (MIMO) detectors under correlated fading channels. The MIMO detection principle namely minimum mean squared error (MMSE) detector is analyzed under such channel condition. The proposed approach deploys the channel state information (CSI) aiming to estimate the level of spatial correlation channel, namely normalized correlation index [Formula: see text] and uses this information to improve the MIMO system performance. Furthermore, the impact of the [Formula: see text] estimation errors on the performance, as well the performance degradation for different levels of correlation have been analyzed and compared with the classical MMSE-MIMO detector operating under uncorrelated channels and perfect channel estimation.

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10

Narasimhan, R. "Error Propagation Analysis of V-BLAST With Channel-Estimation Errors." IEEE Transactions on Communications 53, no. 1 (January 2005): 27–31. http://dx.doi.org/10.1109/tcomm.2004.840670.

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11

Zhang, Li, Tao Cui, and Xianda Zhang. "Distributed estimation for sensor networks with channel estimation errors." Tsinghua Science and Technology 16, no. 3 (June 2011): 300–307. http://dx.doi.org/10.1016/s1007-0214(11)70044-x.

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12

Piantanida, Pablo, Gerald Matz, and Pierre Duhamel. "Outage Behavior of Discrete Memoryless Channels Under Channel Estimation Errors." IEEE Transactions on Information Theory 55, no. 9 (September 2009): 4221–39. http://dx.doi.org/10.1109/tit.2009.2025574.

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13

Zhong, Liang, Li Huang, and Zhengmin Kong. "Channel Rectification and Signal Estimation Based on EIV Model in Massive MIMO System." Mathematical Problems in Engineering 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/8107825.

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The performance of Massive MIMO is severely limited by channel estimation error, which is caused by pilot contamination and channel aging. In this paper, we propose an estimation algorithm based on the weighted total least-squares method with errors-in-variables (EIV) model to alleviate the influence of pilot contamination and channel aging. Then, a channel rectification method has been investigated to diminish the inaccuracy of channel estimation. Comparing with the traditional methods, it not only helps to make the signal estimation more accurate, but also provides opportunities to correct the channel model with estimation error and update the aged channel statement information. Simulations are provided to verify the efficacy of this method.

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14

Al Rabee, Faeik T., and Richard D. Gitlin. "Uplink Power-Domain Non-Orthogonal Multiple Access (NOMA)." International Journal of Interdisciplinary Telecommunications and Networking 12, no. 4 (October 2020): 65–73. http://dx.doi.org/10.4018/ijitn.2020100105.

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Non-orthogonal multiple access (NOMA) has been proposed as a promising multiple access (MA) technique in order to meet the requirements for fifth generation (5G) communications and to enhance the performance in internet of things (IoT) networks by enabling massive connectivity, high throughput, and low latency. This paper investigates the bit error rate (BER) performance of two-user uplink power-domain NOMA with a successive interference cancellation (SIC) receiver and taking into account channel estimation errors. The analysis considers two scenarios: perfect (ideal) channel estimation and a channel with estimation errors for various modulations schemes, BPSK, QPSK, and 16-QAM. The simulation results show that, as expected, increasing of the modulation level increases the SIC receiver BER. For example, at a signal-to-noise ratio (SNR) of 5 dB for perfect channel estimation and QPSK modulation, the user that is detected first has a BER of 0.005 compared to 0.14 for the user that is detected with the aid of the SIC receiver. Similarly, the BER of QPSK, assuming 0.25 channel estimation error of user 1, is equal to 0.06 at SNR = 15 dB compared to 0.017 for perfect estimation.

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15

Il'ina, Irina, and Yana Sutem'eva. "MODELING OF ERRORS IN THE MEASUREMENT CHANNELS OF THE AUTOMATED PROCESS CONTROL SYSTEM IN THE LABVIEW ENVIRONMENT." Modern Technologies and Scientific and Technological Progress 1, no. 1 (May 17, 2021): 121–22. http://dx.doi.org/10.36629/2686-9896-2021-1-1-121-122.

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Questions of estimation of errors of measuring channels of automated process control systems are investigated. A method for studying measurement channel errors based on their mathematical modeling in the visual programming environment LabVIEW is proposed

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16

Liu, Xia, and Marek E. Bialkowski. "Optimization of Training Signal Transmission for Estimating MIMO Channel under Antenna Mutual Coupling Conditions." International Journal of Antennas and Propagation 2010 (2010): 1–10. http://dx.doi.org/10.1155/2010/213576.

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This paper reports investigations on the effect of antenna mutual coupling on performance of training-based Multiple-Input Multiple-Output (MIMO) channel estimation. The influence of mutual coupling is assessed for two training-based channel estimation methods, Scaled Least Square (SLS) and Minimum Mean Square Error (MMSE). It is shown that the accuracy of MIMO channel estimation is governed by the sum of eigenvalues of channel correlation matrix which in turn is influenced by the mutual coupling in transmitting and receiving array antennas. A water-filling-based procedure is proposed to optimize the training signal transmission to minimize the MIMO channel estimation errors.

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17

Zhao, Zheng, Weiming Tian, Yunkai Deng, Cheng Hu, and Tao Zeng. "Calibration Method of Array Errors for Wideband MIMO Imaging Radar Based on Multiple Prominent Targets." Remote Sensing 13, no. 15 (July 30, 2021): 2997. http://dx.doi.org/10.3390/rs13152997.

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Wideband multiple-input-multiple-output (MIMO) imaging radar can achieve high-resolution imaging with a specific multi-antenna structure. However, its imaging performance is severely affected by the array errors, including the inter-channel errors and the position errors of all the transmitting and receiving elements (TEs/REs). Conventional calibration methods are suitable for the narrow-band signal model, and cannot separate the element position errors from the array errors. This paper proposes a method for estimating and compensating the array errors of wideband MIMO imaging radar based on multiple prominent targets. Firstly, a high-precision target position estimation method is proposed to acquire the prominent targets’ positions without other equipment. Secondly, the inter-channel amplitude and delay errors are estimated by solving an equation-constrained least square problem. After this, the element position errors are estimated with the genetic algorithm to eliminate the spatial-variant error phase. Finally, the feasibility and correctness of this method are validated with both simulated and experimental datasets.

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18

Kurt, T., and H. Deliç. "Transmitter diversity for robustness to channel estimation errors." Electronics Letters 41, no. 5 (2005): 263. http://dx.doi.org/10.1049/el:20047510.

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19

Crozier, S. N., D. D. Falconer, and S. A. Mahmoud. "Least sum of squared errors (lsse) channel estimation." IEE Proceedings F Radar and Signal Processing 138, no. 4 (1991): 371. http://dx.doi.org/10.1049/ip-f-2.1991.0048.

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20

Jeon, Jeongho, and Anthony Ephremides. "Channel-Aware Random Access in the Presence of Channel Estimation Errors." IEEE Transactions on Control of Network Systems 4, no. 3 (September 2017): 439–50. http://dx.doi.org/10.1109/tcns.2015.2512320.

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21

LIU, BIN, DAVID J. HILL, and GUANRONG CHEN. "SYNCHRONIZATION ERRORS AND UNIFORM SYNCHRONIZATION WITH AN ERROR BOUND FOR CHAOTIC SYSTEMS." International Journal of Bifurcation and Chaos 18, no. 11 (November 2008): 3341–54. http://dx.doi.org/10.1142/s021812740802241x.

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This paper investigates the problem of estimating synchronization errors and its application to uniform synchronization with an error bound for the general master-slave chaos synchronization scheme via feedback control, which is subjected to disturbances by unknown but bounded channel noise. Based on the Lyapunov function and nonlinear parametric variation techniques, estimation formulae for synchronization errors are derived. It is possible to synchronize two master-slave chaotic systems with a relatively small error bound, even in the case with unknown but bounded noisy disturbances. After the theoretical analysis, some representative examples and their numerical simulations are given for illustration.

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22

Ma, Y., R. Schober, and S. Pasupathy. "Effect of Channel Estimation Errors on MRC Diversity in Rician Fading Channels." IEEE Transactions on Vehicular Technology 54, no. 6 (November 2005): 2137–42. http://dx.doi.org/10.1109/tvt.2005.853454.

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23

Sun, Huiyuan, Naoki Murata, Jihui Zhang, Tetsu Magariyachi, Prasanga N. Samarasinghe, Shigetoshi Hayashi, Thushara D. Abhayapala, and Tetsunori Itabashi. "Secondary channel estimation in spatial active noise control systems using a single moving higher order microphone." Journal of the Acoustical Society of America 151, no. 3 (March 2022): 1922–31. http://dx.doi.org/10.1121/10.0009837.

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Spatial active noise control (ANC) systems focus on minimizing unwanted acoustic noise over continuous spatial regions by generating anti-noise fields with secondary loudspeakers. Conventionally, error microphones are necessary inside the region to measure the channels from the secondary loudspeakers to the error microphones and record the residual sound field during the noise control. These error microphones highly limit the implementation of spatial ANC systems because of their impractical geometry and obstruction to the users from accessing the region. Recent advances, such as virtual sensing, focus on ANC with microphones placed away from the region. While these techniques relax the usage of error microphones during the noise control, an error microphone array remains necessary during the secondary channel estimation. In this paper, we propose a method to estimate secondary channels without using an error microphone array. Instead, a moving higher order microphone is applied to obtain the secondary channels from the secondary loudspeakers to the region of interest, which includes all desired error microphone locations. By simulation, we show that the proposed method is robust against various measuring errors introduced by the movement of the microphone and is suitable for the secondary channel estimation in spatial ANC systems.

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24

De Nardis, Luca, Jocelyn Fiorina, Guido Carlo Ferrante, and Maria-Gabriella Di Benedetto. "Robustness of Time Reversal versus All-Rake Transceivers in Multiple Access Channels." Wireless Communications and Mobile Computing 2018 (June 11, 2018): 1–16. http://dx.doi.org/10.1155/2018/7548926.

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Time reversal (TR) is an effective solution in both single user and multiuser communications for moving complexity from the receiver to the transmitter, in comparison to traditional postfiltering based on Rake receivers. Imperfect channel estimation may, however, affect pre- versus postfiltering schemes in a different way; this paper analyzes the robustness of time reversal versus All-Rake (AR) transceivers, in multiple access communications, with respect to channel estimation errors. Two performance indicators are adopted in the analysis: symbol error probability and spectral efficiency. Analytic expressions for both indicators are derived and used as the basis for simulation-based performance evaluation. Results show that while TR leads to slight performance advantage over AR when channel estimation is accurate, its performance is severely degraded by large channel estimation errors, indicating a clear advantage for AR receivers in this case, in particular when extremely short impulsive waveforms are adopted. Results however also show a stronger non-Gaussianity of interference in the TR case suggesting that the adoption of a receiver structure adapted to non-Gaussian interference might tilt the balance towards TR.

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25

Guo, Zheng, and Aijun Song. "Impact of channel fluctuations on channel estimation errors in underwater acoustic communications." Journal of the Acoustical Society of America 146, no. 4 (October 2019): 2763. http://dx.doi.org/10.1121/1.5136567.

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26

Wu, Jwo-Yuh, and Wen-Ming Lee. "Optimal Linear Channel Prediction for LTE-A Uplink Under Channel Estimation Errors." IEEE Transactions on Vehicular Technology 62, no. 8 (October 2013): 4135–42. http://dx.doi.org/10.1109/tvt.2013.2257909.

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27

He, Biao, and Xiangyun Zhou. "Secure On-Off Transmission Design With Channel Estimation Errors." IEEE Transactions on Information Forensics and Security 8, no. 12 (December 2013): 1923–36. http://dx.doi.org/10.1109/tifs.2013.2284754.

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28

Souto, Nuno, Rui Dinis, and Joao Carlos Silva. "Impact of Channel Estimation Errors on SC-FDE Systems." IEEE Transactions on Communications 62, no. 5 (May 2014): 1530–40. http://dx.doi.org/10.1109/tcomm.2013.121613.130046.

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29

Xiaodong Cai and G. B. Giannakis. "Adaptive PSAM accounting for channel estimation and prediction errors." IEEE Transactions on Wireless Communications 4, no. 1 (January 2005): 246–56. http://dx.doi.org/10.1109/twc.2004.840200.

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30

Yunfei Chen and N. C. Beaulieu. "SER of selection diversity MFSK with channel estimation errors." IEEE Transactions on Wireless Communications 5, no. 7 (July 2006): 1920–29. http://dx.doi.org/10.1109/twc.2006.1673103.

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31

Thian, Boon Sim, Hieu Duy Nguyen, and Sumei Sun. "Statistical Precoding for MIMO Systems With Channel Estimation Errors." IEEE Wireless Communications Letters 4, no. 1 (February 2015): 62–65. http://dx.doi.org/10.1109/lwc.2014.2368570.

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32

WANG, Lei, Yueming CAI, and Weiwei YANG. "Impact of Channel Estimation Errors in Cooperative Transmission over Nakagami-m Fading Channels." IEICE Transactions on Information and Systems E94-D, no. 2 (2011): 298–307. http://dx.doi.org/10.1587/transinf.e94.d.298.

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33

Kwon, Junhyeong, Shi-Jie Wen, Rita Fung, and Sanghyeon Baeg. "Temperature Estimation of HBM2 Channels with Tail Distribution of Retention Errors in FPGA-HBM2 Platform." Electronics 12, no. 1 (December 22, 2022): 32. http://dx.doi.org/10.3390/electronics12010032.

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High-bandwidth memory 2 (HBM2) vertically stacks multiple dynamic random-access memory (DRAM) dies to achieve a small form factor and high capacity. However, it is difficult to diagnose HBM2 issues owing to their structural complexity and 2.5D integration with heterogeneous chips. The effects of the temperature at the base logic die (TL), and the refresh interval at the stacked DRAM dies, were experimentally investigated by counting the dynamic retention errors in the eight channels in an HBM2. TL was indirectly controlled by the heatsink temperature (TS). The lognormal distribution represents the distribution of the cell counts with varying refresh times. All Z-magnitudes (multiples of the distribution standard deviation) over the various refresh cycle times (RCTs) up to 2.045 s in a single channel at TL of 70 °C appeared below 4.4, which means that the error bits belong to the tail distribution. The Z-differences in the eight channels were distinctively larger than the Z-differences of the same channels at a constant temperature, demonstrating that the temperature difference in the stacked dies resulted in larger Z-differences. The largest Z-difference was 0.091 for all the channels at an RCT of 1.406 s, which was approximately 4.82 times smaller than the Z-difference between the TL temperatures of 70 °C and 80 °C in a single channel. The Z-difference between the TL temperatures of 70 °C and 72 °C in a single channel was approximately the same as the Z-difference in all the channels at an RCT of 2.045 s.

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34

Liu, Li, Xiu Ping Zheng, and Li Jun Li. "Performance Analysis of WiMAX System Base on AMC Optimized Algorithm." Advanced Materials Research 532-533 (June 2012): 610–14. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.610.

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Aimed at WiMAX-OFDM system, an optimized algorithm for AMC was proposed from the angle of channel estimation. Simulation experiments were carried on in SUI channels which were typically slow fading, and influences of the traditional AMC technology and optimized AMC algorithm on performance of system were analyzed. Results show that the optimized AMC algorithm not only improves accuracy of channel estimation, but also reduces errors between the predicted signal-to-noise ratio and the actual SNR, which makes selected modulation and coding scheme more reasonable. Finally, the WiMAX system can obtain performance optimization.

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Spencer, Roy W., John R. Christy, William D. Braswell, and William B. Norris. "Estimation of Tropospheric Temperature Trends from MSU Channels 2 and 4." Journal of Atmospheric and Oceanic Technology 23, no. 3 (March 1, 2006): 417–23. http://dx.doi.org/10.1175/jtech1840.1.

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Abstract The problems inherent in the estimation of global tropospheric temperature trends from a combination of near-nadir Microwave Sounding Unit (MSU) channel-2 and -4 data are described. The authors show that insufficient overlap between those two channels’ weighting functions prevents a physical removal of the stratospheric influence on tropospheric channel 2 from the stratospheric channel 4. Instead, correlations between stratospheric and tropospheric temperature fluctuations based upon ancillary (e.g., radiosonde) information can be used to statistically estimate a correction for the stratospheric influence on MSU 2 from MSU 4. Fu et al. developed such a regression relationship from radiosonde data using the 850–300-hPa layer as the target predictand. There are large errors in the resulting fit of the two MSU channels to the tropospheric target layer, so the correlations from the ancillary data must be relied upon to provide a statistical minimization of the resulting errors. Such relationships depend upon the accuracy of the particular training dataset as well as the dataset time period and its global representativeness (i.e., temporal and spatial stationarity of the statistics). It is concluded that near-nadir MSU channels 2 and 4 cannot be combined to provide a tropospheric temperature measure without substantial uncertainty resulting from a necessary dependence on ancillary information regarding the vertical profile of temperature variations, which are, in general, not well known on a global basis.

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Sun, Ning, Jie Li, Debiao Zhang, Chenjun Hu, Xiaofei Peng, Jie Jiang, Shuai Wang, Zeyu Zhang, and Wentao Cui. "Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model." Applied Sciences 12, no. 12 (June 20, 2022): 6257. http://dx.doi.org/10.3390/app12126257.

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The performance of analog-to-digital converters (ADCs) has reached a bottleneck due to the limitations of the manufacturing process and testing environment. Time-interleaved ADC (TIADC) technology can increase the sampling rate without changing the resolution. However, channel mismatch severely degrades the dynamic performance of the TIADC system. For the channel mismatch problem of TIADC, most of the current solutions have preconditions, such as eliminating only some kind of error or increasing the complexity of the hardware. A few methods can estimate multiple errors without changing the hardware circuit. To improve the dynamic performance of the TIADC system, on the basis of an in-depth study of the channel mismatch error of TIADC, according to the system identification theory, an identification model is designed to characterize the frequency characteristics of TIADC. Using the system observation data, the transfer function parameters of the system are recursively estimated. By constructing and verifying the identification model of the TIADC system, and then through the frequency domain correction method, a digital compensation filter is established to complete the error correction of the system. The test results of the four-channel TIADC high-speed data acquisition system show that the actual input and output characteristics of the test system are consistent with the nature of the identification model. The four channels of the TIADC system are provided by four sub-channels of two AD9653 chips, and the highest sampling rate of a single channel is 125MSPS. For sinusoidal input signals from 20 MHz to 150 MHz, the sampling system can achieve a signal-to-noise ratio (SNR) above 56.8 dB and spurious free dynamic range (SFDR) above 69.7 dB. The dynamic performance of the sampling system is nearly equivalent to that of its sub-ADC; the feasibility of the model identification method and the effectiveness of error correction are verified in simulation and experiment.

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37

Chauhan, Sudakar Singh, and Sanjay Kumar. "Channel capacity of TAS/MRC system with adaptive transmission and channel estimation errors." Telecommunication Systems 63, no. 3 (December 15, 2015): 347–55. http://dx.doi.org/10.1007/s11235-015-0124-4.

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38

Abdul Majed, Mahmood Mohmmmed saeed, and Buthaina Mosa Omran. "Pilot Based Channel Estimation and Synchronization in OFDM System." Journal of Engineering 26, no. 6 (June 1, 2020): 50–59. http://dx.doi.org/10.31026/j.eng.2020.06.04.

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Channel estimation and synchronization are considered the most challenging issues in Orthogonal Frequency Division Multiplexing (OFDM) system. OFDM is highly affected by synchronization errors that cause reduction in subcarriers orthogonality, leading to significant performance degradation. The synchronization errors cause two issues: Symbol Time Offset (STO), which produces inter symbol interference (ISI) and Carrier Frequency Offset (CFO), which results in inter carrier interference (ICI). The aim of the research is to simulate Comb type pilot based channel estimation for OFDM system showing the effect of pilot numbers on the channel estimation performance and propose a modified estimation method for STO with less number of pilot to mitigate ISI effect. Channel estimation is simulated for different pilot number with 16QAM modulation in Ped-A Rayleigh channel. The STO effect assumed less than the cyclic prefix length to minimize ISI and maintain the orthogonality between subcarriers. The simulation results show that the high number of pilots are needed to achieve good channel estimation in case no synchronization used while similar results are achieved with smaller number of pilots if the propose modification on the synchronization method are used aligned with the channel estimation. The proposed system is evaluated in terms of BER and SNR.

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39

Li, Yuehui, Cong Liu, Guangshan Niu, Xiangdong Luo, Haocheng Ma, and Yiqiang Zhao. "Error Detection and Correction of Mismatch Errors in M-Channel TIADCs Based on Genetic Algorithm Optimization." Electronics 11, no. 15 (July 28, 2022): 2366. http://dx.doi.org/10.3390/electronics11152366.

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In order to achieve higher system performance, a digital calibration technique for the sub-channel mismatches of time-interleaved ADCs (TIADCs) is proposed in this paper. The sine-fit-based estimation algorithm is introduced to estimate the channel mismatches and a calibration algorithm is proposed to compensate for the mismatches. Subsequently, the genetic algorithm (GA) is firstly utilized to detect the mismatch errors of the outputs of sub-channels after frequency domain filtering. The detected offset error and gain error are then corrected by performing the calibration algorithm, and the time errors are corrected by fractional delay filters based on Farrow structure. The spurious-free dynamic range (SFDR) is enhanced from 19.69 dB to 108.12 dB, and the signal to noise and distortion ratio (SNDR) is enhanced from 16.02 dB to 97.63 dB. The proposed technique is further validated on the FPGA. Compared with existing calibration techniques, the proposed technique has the advantages of simple algorithm structure, low hardware resource consumption, and high calibration accuracy and can be applied to the calibration of high-resolution (18-bit) TIADCs for low-frequency inputs.

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Choi, Joo-Young, Han-Shin Jo, Cheol Mun, and Jong-Gwan Yook. "Preamble-Based Adaptive Channel Estimation for IEEE 802.11p." Sensors 19, no. 13 (July 5, 2019): 2971. http://dx.doi.org/10.3390/s19132971.

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Recently, research into autonomous driving and traffic safety has been drawing a great deal of attention. To realize autonomous driving and solve traffic safety problems, wireless access in vehicular environments (WAVE) technology has been developed, and IEEE 802.11p defines the physical (PHY) layer and medium access control (MAC) layer in the WAVE standard. However, the IEEE 802.11p frame structure, which has low pilot density, makes it difficult to predict the properties of wireless channels in a vehicular environment with high vehicle speeds; thus, the performance of the system is degraded in realistic vehicular environments. The motivation for this paper is to improve the channel estimation and tracking performance without changing the IEEE 802.11p frame structure. Therefore, we propose a channel estimation technique that can perform well over the entire SNR range of values by changing the method of channel estimation accordingly. The proposed scheme selectively uses two channel estimation schemes, each with outstanding performance for either high-SNR or low-SNR signals. To implement this, an adaptation algorithm based on a preamble is proposed. The preamble is a signal known to the transmitter–receiver, so that the receiver can obtain channel estimates without demapping errors, evaluating performance of the channel estimation schemes. Simulation results comparing the proposed method to other schemes demonstrate that the proposed scheme can selectively switch between the two schemes to improve overall performance.

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41

Liu, Shiming, Sihai Li, Qiangwen Fu, Yuanbo Tao, and Feng Wu. "A New MIMU/GNSS Ultra-Tightly Coupled Integration Architecture for Mitigating Abrupt Changes of Frequency Tracking Errors." Micromachines 11, no. 12 (December 16, 2020): 1117. http://dx.doi.org/10.3390/mi11121117.

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We present a new ultra-tightly coupled (UTC) integration architecture of a micro-electromechanical inertial measurement unit (MIMU) and global navigation satellite system (GNSS) to reduce the performance degradation caused by abrupt changes of frequency tracking errors. A large frequency error will lead to a decrease in the carrier-to-noise ratio (C/N0) estimate and an increase in the code discriminator estimation error. The disruptive effects of frequency errors on the estimation of C/N0 and on the code discriminator are quantitatively evaluated via theoretical analyses and Monte Carlo simulations. The new MIMU/GNSS UTC architecture introduces a large frequency error detector and a refined frequency processor based on a retuned frequency in each tracking channel. In addition, an adaptive channel prefilter with multiple fading factors is introduced as an alternate to the conventional prefilter. Numerical simulations based on a highly dynamic trajectory are used to assess performance. The simulation results show that when there is an abrupt step change in the frequency tracking error, the new UTC architecture can effectively suppress the divergence of navigation solutions and the loss of tracking lock, and can significantly reduce the deviation of the C/N0 estimation.

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42

Mayouche, Abderrahmane, Wallace A. Martins, Symeon Chatzinotas, and Bjorn Ottersten. "Data-Driven Precoded MIMO Detection Robust to Channel Estimation Errors." IEEE Open Journal of the Communications Society 2 (2021): 1144–57. http://dx.doi.org/10.1109/ojcoms.2021.3079643.

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43

Myers, Nitin Jonathan, and Arun Pachai Kannu. "Impact of Channel Estimation Errors on Single Stream MIMO Beamforming." IEEE Communications Letters 21, no. 6 (June 2017): 1345–48. http://dx.doi.org/10.1109/lcomm.2017.2669324.

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44

Zhou, Yulin, and Yunfei Chen. "Novel Energy-Harvesting AF Relaying Schemes With Channel Estimation Errors." IEEE Systems Journal 14, no. 1 (March 2020): 333–42. http://dx.doi.org/10.1109/jsyst.2019.2911037.

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45

Wen, Chao-Kai. "Performance Analysis of MIMO Cellular Network with Channel Estimation Errors." IEEE Transactions on Wireless Communications 9, no. 11 (November 2010): 3414–24. http://dx.doi.org/10.1109/twc.2010.092810.090966.

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Kim, Jung-Bin, Ji-Woong Choi, and John M. Cioffi. "Cooperative Distributed Beamforming With Outdated CSI and Channel Estimation Errors." IEEE Transactions on Communications 62, no. 12 (December 2014): 4269–80. http://dx.doi.org/10.1109/tcomm.2014.2364834.

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47

Lee, Kyungchun, and Joohwan Chun. "Symbol detection in V-BLAST architectures under channel estimation errors." IEEE Transactions on Wireless Communications 6, no. 2 (February 2007): 593–97. http://dx.doi.org/10.1109/twc.2006.05299.

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48

Modarresi, Mohammad, and Seyed Mohammad Sajad Sadough. "Improved MIMO Turbo-Trellis Coded Modulation Under Channel Estimation Errors." Arabian Journal for Science and Engineering 38, no. 9 (December 18, 2012): 2473–79. http://dx.doi.org/10.1007/s13369-012-0508-7.

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El-Keyi, A., and B. Champagne. "Collaborative Uplink Transmit Beamforming With Robustness Against Channel Estimation Errors." IEEE Transactions on Vehicular Technology 58, no. 1 (January 2009): 126–39. http://dx.doi.org/10.1109/tvt.2008.923668.

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Sadough, S. M. S., M. A. Khalighi, and P. Duhamel. "Improved Iterative MIMO Signal Detection Accounting for Channel-Estimation Errors." IEEE Transactions on Vehicular Technology 58, no. 7 (September 2009): 3154–67. http://dx.doi.org/10.1109/tvt.2009.2016103.

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