Journal articles on the topic 'Channel Estimation'
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1
Qiao, Gang, Zeeshan Babar, Lu Ma, and Xue Li. "Cost Function based Soft Feedback Iterative Channel Estimation in OFDM Underwater Acoustic Communication." Infocommunications journal, no. 1 (2019): 29–37. http://dx.doi.org/10.36244/icj.2019.1.4.
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Underwater Acoustic (UWA) communication is mainly characterized by bandwidth limited complex UWA channels. Orthogonal Frequency Division Multiplexing (OFDM) solves the bandwidth problem and an efficient channel estimation scheme estimates the channel parameters. Iterative channel estimation refines the channel estimation by reducing the number of pilots and coupling the channel estimator with channel decoder. This paper proposes an iterative receiver for OFDM UWA communication, based on a novel cost function threshold driven soft decision feedback iterative channel technique. The receiver exploits orthogonal matching pursuit (OMP) channel estimation and low density parity check (LDPC) coding techniques after comparing different channel estimation and coding schemes. The performance of the proposed receiver is verified by simulations as well as sea experiments. Furthermore, the proposed iterative receiver is compared with other non-iterative and soft decision feedback iterative receivers.
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Nowak, Thorsten, and Andreas Eidloth. "Dynamic multipath mitigation applying unscented Kalman filters in local positioning systems." International Journal of Microwave and Wireless Technologies 3, no. 3 (March 25, 2011): 365–72. http://dx.doi.org/10.1017/s1759078711000274.
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Multipath propagation is still one of the major problems in local positioning systems today. Especially in indoor environments, the received signals are disturbed by blockages and reflections. This can lead to a large bias in the user's time-of-arrival (TOA) value. Thus multipath is the most dominant error source for positioning. In order to improve the positioning performance in multipath environments, recent multipath mitigation algorithms based upon the concept of sequential Bayesian estimation are used. The presented approach tries to overcome the multipath problem by estimating the channel dynamics, using unscented Kalman filters (UKF). Simulations on artificial and measured channels from indoor as well as outdoor environments show the profit of the proposed estimator model. Furthermore, the quality of channel estimation applying the UKF and the channel sounding capabilities of the estimator are shown.
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Wang, Yanyan, and Yingsong Li. "Sparse Multipath Channel Estimation Using Norm Combination Constrained Set-Membership NLMS Algorithms." Wireless Communications and Mobile Computing 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8140702.
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A norm combination penalized set-membership NLMS algorithm with l0 and l1 independently constrained, which is denoted as l0 and l1 independently constrained set-membership (SM) NLMS (L0L1SM-NLMS) algorithm, is presented for sparse adaptive multipath channel estimations. The L0L1SM-NLMS algorithm with fast convergence and small estimation error is implemented by independently exerting penalties on the channel coefficients via controlling the large group and small group channel coefficients which are implemented by l0 and l1 norm constraints, respectively. Additionally, a further improved L0L1SM-NLMS algorithm denoted as reweighted L0L1SM-NLMS (RL0L1SM-NLMS) algorithm is presented via integrating a reweighting factor into our L0L1SM-NLMS algorithm to properly adjust the zero-attracting capabilities. Our developed RL0L1SM-NLMS algorithm provides a better estimation behavior than the presented L0L1SM-NLMS algorithm for implementing an estimation on sparse channels. The estimation performance of the L0L1SM-NLMS and RL0L1SM-NLMS algorithms is obtained for estimating sparse channels. The achieved simulation results show that our L0L1SM- and RL0L1SM-NLMS algorithms are superior to the traditional LMS, NLMS, SM-NLMS, ZA-LMS, RZA-LMS, and ZA-, RZA-, ZASM-, and RZASM-NLMS algorithms in terms of the convergence speed and steady-state performance.
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He, Ruixuan, Xiaoran Liu, Kai Mei, Guangwei Gong, Jun Xiong, and Jibo Wei. "Iterative Joint Estimation Procedure of Channel and PDP for OFDM Systems." Entropy 24, no. 11 (November 15, 2022): 1664. http://dx.doi.org/10.3390/e24111664.
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The power-delay profile (PDP) estimation of wireless channels is an important step to generate a channel correlation matrix for channel linear minimum mean square error (LMMSE) estimation. Estimated channel frequency response can be used to obtain time dispersion characteristics that can be exploited by adaptive orthogonal frequency division multiplexing (OFDM) systems. In this paper, a joint estimator for PDP and LMMSE channel estimation is proposed. For LMMSE channel estimation, we apply a candidate set of frequency-domain channel correlation functions (CCF) and select the one that best matches the current channel to construct the channel correlation matrix. The initial candidate set is generated based on the traditional CCF calculation method for different scenarios. Then, the result of channel estimation is used as an input for the PDP estimation whereas the estimated PDP is further used to update the candidate channel correlation matrix. The enhancement of LMMSE channel estimation and PDP estimation can be achieved by the iterative joint estimation procedure. Analysis and simulation results show that in different communication scenarios, the PDP estimation error of the proposed method can approach the Cramér–Rao lower bound (CRLB) after a finite number of iterations. Moreover, the mean square error of channel estimation is close to the performance of accurate PDP-assisted LMMSE.
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Hussein, Walaa, Kamil Audah, N. K. Noordin, Habib Kraiem, Aymen Flah, Mohd Fadlee, and Alyani Ismail. "Least Square Estimation-Based Different Fast Fading Channel Models in MIMO-OFDM Systems." International Transactions on Electrical Energy Systems 2023 (August 29, 2023): 1–23. http://dx.doi.org/10.1155/2023/5547634.
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In cellular wireless communication systems, channel estimation (CE) plays a pivotal role as a crucial technique applied in orthogonal frequency division multiplexing (OFDM) modulation. CE utilizes a variety of methods, including decision-directed channel estimation, pilot-assisted channel estimation (PACE), and blind channel estimation. Among these options, PACE is widely favored for its remarkable stability and consistent superior performance. The idea of massive multiple-input multiple-output (MIMO) shows tremendous potential for the future of wireless communications. However, existing massive MIMO systems face challenges with their high computational complexity and intricate spatial structures, preventing efficient utilization of channel and sparsity features in these multiantenna systems. In communication channels, the signal received is often influenced by the characteristics of the channel and noise present at the receiver. To address this issue, an efficient dataset is utilized, employing the least square (LS) algorithm for minimization. OFDM is a commonly and widely used modulation method in communication systems utilized to specifically combat resonance fading in wireless channels. In wireless communication systems employing OFDM-MIMO, frequency selectivity and time-varying attributes due to multipath channels cause Intercarrier Interference (ICI) among symbols. Channel estimation is a vital aspect for mitigating the effects of fading channels. This investigation focuses on the application of a method examined in the study, which involves a block-type pilot symbol-assisted estimation technique for Rayleigh and Rician fading channel models. The research assesses the performance of the least square (LS) channel estimators in fast-fading channel models while employing various symbol mapping techniques focusing on bit error rate, throughput, and mean square error. The results indicate that the LS estimator exhibits excellent performance in Rayleigh and AWGN channels within the pedestrian A (PedA) model for both uplink and downlink scenarios. It outperforms the PedA model without channel estimation.
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Essai Ali, Mohamed Hassan, Ali R. Abdellah, Hany A. Atallah, Gehad Safwat Ahmed, Ammar Muthanna, and Andrey Koucheryavy. "Deep Learning Peephole LSTM Neural Network-Based Channel State Estimators for OFDM 5G and Beyond Networks." Mathematics 11, no. 15 (August 2, 2023): 3386. http://dx.doi.org/10.3390/math11153386.
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This study uses deep learning (DL) techniques for pilot-based channel estimation in orthogonal frequency division multiplexing (OFDM). Conventional channel estimators in pilot-symbol-aided OFDM systems suffer from performance degradation, especially in low signal-to-noise ratio (SNR) regions, due to noise amplification in the estimation process, intercarrier interference, a lack of primary channel data, and poor performance with few pilots, although they exhibit lower complexity and require implicit knowledge of the channel statistics. A new method for estimating channels using DL with peephole long short-term memory (peephole LSTM) is proposed. The proposed peephole LSTM-based channel state estimator is deployed online after offline training with generated datasets to track channel parameters, which enables robust recovery of transmitted data. A comparison is made between the proposed estimator and conventional LSTM and GRU-based channel state estimators using three different DL optimization techniques. Due to the outstanding learning and generalization properties of the DL-based peephole LSTM model, the suggested estimator significantly outperforms the conventional least square (LS) and minimum mean square error (MMSE) estimators, especially with a few pilots. The suggested estimator can be used without prior information on channel statistics. For this reason, it seems promising that the proposed estimator can be used to estimate the channel states of an OFDM communication system.
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Yang, Zhuo, Yiru Inoue, Jian Wan, and Lei Chen. "Channel Parameters Identification Based on IMM Algorithm for Variant Correlation Channel." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/137528.
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In wireless communication systems, correct knowledge of the correlation of a fading channel is essential for channel estimation. Both the reliability of the estimated channel impulse response (CIR) and the adjustment of an adaptive communication system need the accurate correlation information, which is difficult to identify especially when changing. By modeling the fading channel as a hybrid dynamic system, a channel estimation algorithm based on Interacting Multiple Model (IMM) is presented with the consideration of time-variant channel correlation. Applying the IMM algorithm, the proposed channel estimator can identify the channel correlation. With the accurate information of channel correlation, the proposed algorithm is capable of performing accurate estimation on the fading wireless channel with time-variant or time-invariant correlation. Our simulations demonstrate that the IMM based channel estimation algorithm has good performance in estimating CIR as well as in identifying the channel correlation.
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Kim, Tae-Kyoung, and Moonsik Min. "Reinforcement Learning-Aided Channel Estimator in Time-Varying MIMO Systems." Sensors 23, no. 12 (June 18, 2023): 5689. http://dx.doi.org/10.3390/s23125689.
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This paper proposes a reinforcement learning-aided channel estimator for time-varying multi-input multi-output systems. The basic concept of the proposed channel estimator is the selection of the detected data symbol in the data-aided channel estimation. To achieve the selection successfully, we first formulate an optimization problem to minimize the data-aided channel estimation error. However, in time-varying channels, the optimal solution is difficult to derive because of its computational complexity and the time-varying nature of the channel. To address these difficulties, we consider a sequential selection for the detected symbols and a refinement for the selected symbols. A Markov decision process is formulated for sequential selection, and a reinforcement learning algorithm that efficiently computes the optimal policy is proposed with state element refinement. Simulation results demonstrate that the proposed channel estimator outperforms conventional channel estimators by efficiently capturing the variation of the channels.
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Li, Jie, Fangjiong Chen, Songzuo Liu, Hua Yu, and Fei Ji. "Estimation of Overspread Underwater Acoustic Channel Based on Low-Rank Matrix Recovery." Sensors 19, no. 22 (November 15, 2019): 4976. http://dx.doi.org/10.3390/s19224976.
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In this paper, the estimation of overspread, i.e., doubly spread underwater acoustic (UWA) channels of strong dispersion is considered. We show that although the UWA channel dispersion causes the degeneration of channel sparsity, it leads to a low-rank structure especially when the channel delay-Doppler-spread function is separable in delay and Doppler domain. Therefore, we introduce the low-rank criterion to estimate the UWA channels, which can help to improve the estimation performance in the case of strong dispersion. The estimator is based on the discrete delay-Doppler-spread function representation of channel, and is formulated as a low-rank matrix recovery problem which can be solved by the singular value projection technique. Simulation examples are carried out to demonstrate the effectiveness of the proposed low-rank-based channel estimator.
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Nguyen-Duc, Quang, Lien Pham-Hong, Thang Nguyen-Manh, and Tra Luu-Thanh. "A Combined Algorithm of Kalman Estimator and Guard Interval Optimization for Mobile WiMAX." International Journal of Distributed Systems and Technologies 4, no. 1 (January 2013): 16–28. http://dx.doi.org/10.4018/jdst.2013010102.
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Mobile WiMAX (Worldwide Interoperability for Microwave Access) system has been recently applied widely in wireless communication systems. In this paper, the channel estimation algorithms were studied for the mobile WiMAX system. The comb-type pilot was used for channel estimation algorithms. The authors proposed an adaptive algorithm of channel estimation based on Kalman filter which had good performance in fading channels. Then, based on the result of channel estimation, we proposed an advanced algorithm of GI (Guard Interval) optimization. The results showed that the Kalman estimator combined with GI optimization algorithm showed the best performance in this paper. This algorithm was verified by computer simulation.
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Somasekhar, B., Ch Mohana Krishna, and Y. Murty. "Investigations on wavelet and Fourier transform based channel estimation in MIMO-OFDM system." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 228. http://dx.doi.org/10.14419/ijet.v7i2.21.12178.
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In this paper channel estimation methods for MIMO-OFDM system are investigated based on Fourier Transform and Wavelet Transform. The channel estimation algorithm based on Discrete Fourier Transform (DFT) cause energy leakage in multipath channel with non-sample-spaced time delays. Discrete Cosine Transform (DCT) based channel estimator can mitigate the drawback of Discrete Fourier Transform based channel estimator, when the non-sample spaced path delays are available in multipath fading channels. Wavelet based systems provide better spectral efficiency because of no cyclic prefix requirement, with narrow side lobes and also exhibit improved BER performance. Simulation results reveal that the DWT based transform outperforms the conventional DFT and DCT based channel estimator in terms of bit error rate and mean square error.
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Nguyễn, Thành, Mai Thanh Hai, Nguyen Huu Tho, Nguyen Thi Thao, and Nguyen Tat Nam. "Expectation maximization channel estimation for nonlinear OFDM systems." Journal of Military Science and Technology, no. 81 (August 26, 2022): 31–43. http://dx.doi.org/10.54939/1859-1043.j.mst.81.2022.31-43.
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This paper introduces the expectation maximization (EM)-based channel estimation for the high power amplifier (HPA)-incurred nonlinear orthogonal frequency division multiplexing (OFDM) systems based on the linearization using extended Bussgang decomposition. Analyses and numerical simulations show that the proposed algorithm only requires reasonable computation complexity with relatively small number of iterations while vastly improves the estimation performance compared to the other conventional estimation methods such as least square error (LSE) or minimum mean square error (MMSE) applied to such system. Moreover, the EM-LSE estimator could give almost the same performance as the EM-MMSE counterpart while does not require channel statistics, forming a robust estimator for both fading and nonlinear channels with reduced computation complexity. This makes the estimator more applicable.
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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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Lv, Chenglei, Qiushi Sun, Huifang Chen, and Lei Xie. "Doppler and Channel Estimation Using Superimposed Linear Frequency Modulation Preamble Signal for Underwater Acoustic Communication." Journal of Marine Science and Engineering 12, no. 2 (February 16, 2024): 338. http://dx.doi.org/10.3390/jmse12020338.
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Due to the relative motion between transmitters and receivers and the multipath characteristic of wideband underwater acoustic channels, Doppler and channel estimations are of great significance for an underwater acoustic (UWA) communication system. In this paper, a preamble signal based on superimposed linear frequency modulation (LFM) signals is first designed. Based on the designed preamble signal, a real-time Doppler factor estimation algorithm is proposed. The relative correlation peak shift of two LFM signals in the designed preamble signal is utilized to estimate the Doppler factor. Moreover, an enhanced channel estimation algorithm, the correlation-peak-search-based improved orthogonal matching pursuit (CPS-IOMP) algorithm, is also proposed. In the CPS-IOMP algorithm, the excellent autocorrelation characteristic of the designed preamble signal is used to estimate the channel sparsity and multipath delays, which are utilized to construct the simplified dictionary matrix. The simulation and sea trial data analysis results validated the designed preamble, the proposed Doppler estimation algorithm, and the channel estimation algorithm. The performance of the proposed Doppler factor estimation is better than that of the block estimation algorithm. Compared with the original OMP algorithm with known channel sparsity, the proposed CPS-IOMP algorithm achieves a similar estimation accuracy with a smaller computational complexity, as well as requiring no prior knowledge about the channel sparsity.
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Zope, Rajendrakumar Govinda, and Balasaheb Shrirangrao Agarkar. "Channel Estimation for DS-CDMA Rake Receiver using Sparse Recovery Approach." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 1 (May 30, 2022): 41–46. http://dx.doi.org/10.35940/ijrte.f6841.0511122.
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In the literature of direct-sequence code-division multiple-access (DS-CDMA), for issues related to channel estimation, some exclusive architectures have been proposed, which are characterized by known channel noise statistics and noise observation. But in reality, the channel parameters are frequently assessed utilizing training sequences that lead to difficulty in obtaining the channel noise statistics. Channel estimation quality has been proved to play an important role in the performance of rake receiver. This paper addresses the issues of optimizing DS–CDMA rake receiver channel estimation equipped with an Iterative least square sparse recovery (IL2SR) channel estimator. Moreover, the ambient noises corrupt the signal received and multiple-access interference further aggravates it. Because of this observation noises become hard to acquire. Hence this paper proposes as an iterative least square structure for channel estimation algorithm in rake receiver employed in DS-CDMA communication systems. Further, examination of blind channel estimation problem for rake-based DS-CDMA communication framework having multi-path fading channels with time variation is also attempted. The validity of the proposed techniques has been verified through results obtained from simulation for different channel parameters and spreading codes. Further exploration has been carried out with execution of the IL2SR with Rake receiver in DS-CDMA framework for multi-path fading channels. It is found that better performance is obtained with this framework under various channels with different spreading codes. The proposed system is compared with Kalman based techniques and it was found that DS-CDMA framework under additive white gaussian noise (AWGN)channel with IL2SR receiver reveals better outcomes in terms of bit error rate (BER). Also, there has been improvement in video quality while using the proposed IL2SR receiver with increase in the values of ratio of signal to noise ratio.
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Rajat Gupta. "Advanced Smart Channel Estimation Scheme for MIMO OSTBC Systems Based Wireless Communication." Journal of Electrical Systems 20, no. 7s (May 4, 2024): 1853–58. http://dx.doi.org/10.52783/jes.3876.
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Labeling diversity is used in an orthogonal space-time block coded (OSTBC) scheme to improve wireless connection reliability without reducing spectral efficiency. Compared to the conventional STBC system, it achieves improved link dependability. The purpose of this work is to provide a blind wireless channel estimator that is bandwidth-efficient for the OSTBC system. Methods for channel estimation, such as least-squares (LS) & minimum mean square error (MMSE) methods typically use the channel bandwidth inefficiently. The receiver noise variance and prior broadcast pilot symbols knowledge & statistics information of channel are required for LS & MMSE channel estimating algorithms to accurately estimate the channel. An neural network machine learning (NNML) channel estimation with transmitter end power-share is suggested in order to make blind channel estimator simpler for the OSBC-based MIMO transmission & to lessen amount of bandwidth requirement for estimation of channel. By using mathematical modeling equivalent to noise power, we determine the ideal transmit fraction of power that reduces bandwidth consumption due to channel estimate. It is demonstrated that the blind NN-ML nased channel estimation with transmitter power-share uses 20% of the bandwidth of the MMSE & LS wireless channel estimators in order to achieve the OSTBC system's low bit error rate (BER) in the case of M-PSK modulation.
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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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Zhou, Yuhang, Xiaoli Huo, Zhiqun Gu, Jiawei Zhang, Yi Ding, Rentao Gu, and Yuefeng Ji. "Self-Attention Mechanism-Based Multi-Channel QoT Estimation in Optical Networks." Photonics 10, no. 1 (January 6, 2023): 63. http://dx.doi.org/10.3390/photonics10010063.
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It is essential to estimate the quality of transmission (QoT) of lightpaths before their establishment for efficient planning and operation of optical networks. Due to the nonlinear effect of fibers, the deployed lightpaths influence the QoT of each other; thus, multi-channel QoT estimation is necessary, which provides complete QoT information for network optimization. Moreover, the different interfering channels have different effects on the channel under test. However, the existing artificial-neural-network-based multi-channel QoT estimators (ANN-QoT-E) neglect the different effects of the interfering channels in their input layer, which affects their estimation accuracy severely. In this paper, we propose a self-attention mechanism-based multi-channel QoT estimator (SA-QoT-E) to improve the estimation accuracy of the ANN-QoT-E. In the SA-QoT-E, the input features are designed as a sequence of feature vectors of channels that route the same path, and the self-attention mechanism dynamically assigns weights to the feature vectors of interfering channels according to their effects on the channel under test. Moreover, a hyperparameter search method is used to optimize the SA-QoT-E. The simulation results show that, compared with the ANN-QoT-E, our proposed SA-QoT-E achieves higher estimation accuracy, and can be directly applied to the network wavelength expansion scenarios without retraining.
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Sangirov, Gulomjon, Yong Qing Fu, and Ye Fang. "A MMSE Channel Estimation Method in QC-LDPC Coded OFDM Systems." Advanced Materials Research 989-994 (July 2014): 3759–62. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3759.
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An orthogonal frequency division multiplexing (OFDM) is one of the effective techniques used in wireless communications. In OFDM systems, channel impairments due to multipath dispersive wireless channels can cause deep fades in wireless channels. The OFDM receiver also requires an accurate and computationally efficient channel state information when coherent detection is involved. Therefore, it needs a good robust estimation method of the channel in wireless communication for OFDM systems. And one of these channel estimation methods is minimum mean square error (MMSE) channel estimation. MMSE channel estimation one most used method in OFDM systems. In this work we enhanced robustness of MMSE channel estimation by using it in base of quasi-cyclic low density parity check (QC-LDPC) coded OFDM system.
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Chen, You Gan, Xiao Mei Xu, and Lan Zhang. "Rate Compatible LDPC Codes Design for Shallow Water Acoustic Communications." Applied Mechanics and Materials 198-199 (September 2012): 1609–14. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1609.
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The fast temporal variations shallow water acoustic (SWA) channels need the temporal variations channel coding scheme at the acceptable cost. Hence, flexible channel coding rate, adjusted according to different ocean channel characteristics, should be desired in the design of practical error control SWA communication system. In this paper, we propose rate-compatible LDPC (RC-LDPC) codes to improve the communication system reliability for SWA communications. The proposed SWA system adopting RC-LDPC codes consists of three important preprocessing: channel state information (CSI) estimator, signal-to-noise ratio (SNR) estimator and RC-LDPC pattern. For the estimation error, we define and derive the sensitivity to imperfect CSI and imperfect SNR. Then the design of RC-LDPC codes and SWA channel profile are described. Furthermore, the RC-LDPC performance pattern is given and sensitivity to estimation error of CSI and SNR are analyzed via simulation. It is shown that RC-LDPC codes have good performances with wide range of rates in SWA channels. Finally, coding rate distributions of RC-LDPC codes in different SNR at BER below 10-4 for SWA channel are investigated.
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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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Li, Yingsong, Zhan Jin, and Yanyan Wang. "Adaptive Channel Estimation Based on an Improved Norm-Constrained Set-Membership Normalized Least Mean Square Algorithm." Wireless Communications and Mobile Computing 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/8056126.
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An improved norm-constrained set-membership normalized least mean square (INCSM-NLMS) algorithm is proposed for adaptive sparse channel estimation (ASCE). The proposed INCSM-NLMS algorithm is implemented by incorporating an lp-norm penalty into the cost function of the traditional set-membership normalized least mean square (SM-NLMS) algorithm, which is also denoted as lp-norm penalized SM-NLMS (LPSM-NLMS) algorithm. The derivation of the proposed LPSM-NLMS algorithm is given theoretically, resulting in a zero attractor in its iteration. By using this proposed zero attractor, the convergence speed is effectively accelerated and the channel estimation steady-state error is also observably reduced in comparison with the existing popular SM-NLMS algorithms for estimating exact sparse multipath channels. The estimation behaviors are investigated via a typical sparse wireless multipath channel, a typical network echo channel, and an acoustic channel. The computer simulation results show that the proposed LPSM-NLMS algorithm is better than those corresponding sparse SM-NLMS and traditional SM-NLMS algorithms when the channels are exactly sparse.
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Murali Krishna, P. V., and T. V. Ramana. "Millimeter Wave MIMO out Door Channel Estimation and Precoding." Journal of Physics: Conference Series 2070, no. 1 (November 1, 2021): 012143. http://dx.doi.org/10.1088/1742-6596/2070/1/012143.
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Abstract Millimeter wave (mm Wave) communications is one of the technologies for 5G cellular systems. In the mm Wave communication, there is a lot of path loss can be reduced by Precoding. The channel state information (CSI) should be known at the transmitting station, in the design of precoding matrices and to get good accuracy in estimating sparse channels a Compressive sensing (CS) based recovery algorithms was used. Not only for good accuracy the algorithm is also used for mm Wave channel estimation for exploiting the mm Wave channel’s sparse in multi-path construction. Hence, in this paper, for mm Wave outdoor channel estimation, the CS recovery methods orthogonal matching pursuit (OMP) and compressive sampling matching pursuit (CoSaMP) are used. The singular value decomposition (SVD) precoding is developed using the estimated channel. By (MSE) mean square error and spectral efficiency which were the performance metrics in channel estimation and precoding were done by using MATLAB simulations to get the efficacy of the OMP and CoSaMP algorithm.
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N, Prabakaran, Sivakannan S, Bennilo Fernandes J, and Thirugnanam G. "Channel estimation for long term evolution downlink receiver system performance." International Journal of Engineering & Technology 7, no. 1.3 (December 31, 2017): 82. http://dx.doi.org/10.14419/ijet.v7i1.3.9272.
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3GPP LTE is the advancement of the UMTS because of ever‐increasing requests for brilliant interactive media administrations as indicated by client's desires. Since downlink is dependably an essential factor in scope and limit viewpoints, extraordinary consideration has been given in choosing innovations for LTE downlink collector. In this paper, LTE-downlink receiver framework exhibitions of Channel Response estimations are tried and assessed utilizing FDD transmission plans. An insertion calculation is utilized to acquire all Channel Response estimations. In this model direct addition calculations utilized as a part of recurrence and image area. The high information rates and the high limit can be accomplished by utilizing the upsides of the two advances. These advances have been chosen for LTE downlink receiver. Pilot‐assisted channel estimation is a strategy in which known signs, called pilots, are transmitted alongside information to get channel learning for legitimate deciphering of got signals. This paper goes for channel estimation for LTE downlink receiver framework. The execution of the framework recreated in various remote channel models that comprises of AWGN, RAW AWGN and Veh A channels. The execution of the framework is assessed utilizing distinctive regulations, for example, QPSK, 16 QAM and 64 QAM. Execution of these calculations has been measured as far as Bit Error Rate (BER) Vs Eb/No.
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Huang, Sheng Bo, Yu Cang Wen, Wen Ye, and Tong Liang Fan. "A Simplified Time-Domain Channel Estimation Approach for OFDM System." Applied Mechanics and Materials 738-739 (March 2015): 1111–14. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.1111.
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OFDM usually incorporates pilot tones in the frequency domain (FD) or training symbols in the time-domain (TD) to facilitate channel estimation algorithms. TD channel estimation becomes more attractive in quasi-static channels because channel estimation scheme will optimize the spectral efficiency by re-using the training symbols designated for FD channel estimation. A channel estimation method based on time domain averaging algorithm is proposed. Due to the principle of centralized energy in time domain, the effective channel impulse response length can be detected by setting of threshold for the estimated channel impulse response length. Computer simulation demonstrates the performance of the proposed algorithms in terms of bit error rate performance.
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Wang, Zhigang, Zihao Wang, Fusheng Zhu, Zezhou Luo, Fang Li, and Haopeng Liu. "Joint Doppler Shift and Channel Estimation for High-Speed Railway Wireless Communications in Tunnel Scenarios." Wireless Communications and Mobile Computing 2022 (January 31, 2022): 1–6. http://dx.doi.org/10.1155/2022/6804412.
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In this work, we study the problem of Doppler shift and channel estimation for wireless communication systems on high-speed railways (HSRs). We focus on tunnel scenario, one of the classical scenarios of HSRs. We first build up the mathematical system model, design a joint Doppler shift and channel estimator, and compare its performance with the typical Moose algorithm. We show that our estimator outperforms the Moose algorithm in Doppler estimation. Besides, since wireless channels in tunnel scenarios often contain several or multiple taps, we suggest an adaptive frame structure to improve transmission efficiency. Simulations are then provided to corroborate our proposed studies.
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Jiang, Yin Ping, and Tao Zhang. "On Improved Noise Variance Estimation for Aeronautical Communications." Applied Mechanics and Materials 336-338 (July 2013): 1688–93. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1688.
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The L-band Digital Aeronautical Communication System Type-1 (L-DACS1) has been considered as the potential standard for future aeronautical communications. As an important parameter for L-DACS1, the noise variance can be estimated via the well known Second-Moment-Fourth-Moment (M2M4) blind algorithm. However, due to influence from aeronautical fading channels, performance degradation is encountered by conventional M2M4 algorithm in L-DACS1. In this paper, channel responses obtained from L-DACS1 pilot symbols were carried out to compensate the estimation degradation brought by channel fading, which lead to an improved M2M4 algorithm for L-DACS1. It is shown through simulations that the proposed approach outperforms conventional M2M4 estimator in terms of estimation error for L-DACS1 in aeronautical fading channels.
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Khalid, Muhammad, Abid Muhammad Khan, Muhammad Rauf, Muhammad Taha Jilani, and Sheraz Afzal. "FPGA-Based Time-Domain Channel Estimation in Gaussian Mixture Model." Mathematical Problems in Engineering 2021 (May 3, 2021): 1–12. http://dx.doi.org/10.1155/2021/5596301.
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The performance of time-domain channel estimation deteriorates due to the presence of Gaussian mixture model (GMM) noise, which results in high mean squared error (MSE) as a challenging issue. The performance of the estimator further decreases when the complexity of the estimator is high due to the high convergence rate. In this paper, an optimized channel estimation method is proposed with low complexity and high accuracy in the GMM environment. In this channel estimation, an improved Gauss-Seidel iterative method is utilized with a minimum number of iterations. The convergence rate of the Gauss-Seidel method is improved by estimating an appropriate initial guess value when no guard bands are used in the orthogonal frequency-division multiplexing (OFDM) symbol. Simulation results provide an acceptable MSE for GMM environments, up to the probability of 5% impulsive noise component. This paper also presents the design and implementation of the proposed estimator in the NEXYS-2 FPGA platform that provides resources allocation, reconfigurability, schematic, and the timing diagram for detailed insight.
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Shailender, Shelej Khera, Sajjan Singh, and Jyoti. "Estimation of Channel for Millimeter-Wave Hybrid Massive MIMO Systems using Orthogonal Matching Pursuit (OMP)." Journal of Physics: Conference Series 2327, no. 1 (August 1, 2022): 012040. http://dx.doi.org/10.1088/1742-6596/2327/1/012040.
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Abstract In a millimeter-wave (mm-Wave) large MIMO system, hybrid precoding is a critical component for lowering radio-frequency hardware costs as compared to the traditional full-digital precoding strategy. Knowledge of channels is required for hybrid precoding. Though, estimation of the channel is problematic for the mm-Wave system because of the usage of a massive array of antenna and hybrid architecture with analog precoding. Due to the extremely directed nature of wireless propagation, wireless channels have spatial sparsity. In this paper, we exploit this spatial sparsity nature of wireless channels to develop orthogonal matching pursuit technique-based estimation of channels for hybrid millimeter-wave (mm-Wave) wireless systems. Genie (Ideal) channel estimation is also performed in which we presume that actual angle of departures (AoD) and angle of arrivals (AoA) are known to us. Finally, the simulation results reveal that suggested OMP algorithm-based channel estimation has a significant advantage over conventional approaches like least-squares channel estimation.
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Asyhari, A. Taufiq, Tobias Koch, and Albert Guillén i Fàbregas. "Nearest Neighbor Decoding and Pilot-Aided Channel Estimation for Fading Channels." Entropy 22, no. 9 (August 31, 2020): 971. http://dx.doi.org/10.3390/e22090971.
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We study the information rates of noncoherent, stationary, Gaussian, and multiple-input multiple-output (MIMO) flat-fading channels that are achievable with nearest neighbor decoding and pilot-aided channel estimation. In particular, we investigate the behavior of these achievable rates in the limit as the signal-to-noise ratio (SNR) tends to infinity by analyzing the capacity pre-log, which is defined as the limiting ratio of the capacity to the logarithm of the SNR as the SNR tends to infinity. We demonstrate that a scheme estimating the channel using pilot symbols and detecting the message using nearest neighbor decoding (while assuming that the channel estimation is perfect) essentially achieves the capacity pre-log of noncoherent multiple-input single-output flat-fading channels, and it essentially achieves the best so far known lower bound on the capacity pre-log of noncoherent MIMO flat-fading channels. Extending the analysis to fading multiple-access channels reveals interesting relationships between the number of antennas and Doppler bandwidth in the comparative performance of joint transmission and time division multiple-access.
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Wang, Xiaoyu, Xiaohua Wang, Rongkun Jiang, Weijiang Wang, Qu Chen, and Xinghua Wang. "Channel Modelling and Estimation for Shallow Underwater Acoustic OFDM Communication via Simulation Platform." Applied Sciences 9, no. 3 (January 28, 2019): 447. http://dx.doi.org/10.3390/app9030447.
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The performance of underwater acoustic (UWA) communication is heavily dependent on channel estimation, which is predominantly researched by simulating UWA channels modelled in complex and dynamic underwater environments. In UWA channels modelling, the measurement-based approach provides an accurate method. However, acquirement of environment data and simulation processes are scenario-specific and thus not cost-effective. To overcome such restraints, this article proposes a comprehensive simulation platform that combines UWA channel modelling with orthogonal frequency division multiplexing (OFDM) channel estimation, allowing users to model UWA channels for different ocean environments and simulate channel estimation with configurable input parameters. Based on the simulation platform, three independent
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Georgoulakis, Kristina. "Blind Estimation of Linear and Nonlinear Sparse Channels." Journal of Telecommunications and Information Technology, no. 1 (March 30, 2013): 65–71. http://dx.doi.org/10.26636/jtit.2013.1.1203.
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This paper presents a Clustering Based Blind Channel Estimator for a special case of sparse channels – the zero pad channels. The proposed algorithm uses an unsupervised clustering technique for the estimation of data clusters. Clusters labelling is performed by a Hidden Markov Model of the observation sequence appropriately modified to exploit channel sparsity. The algorithm achieves a substantial complexity reduction compared to the fully evaluated technique. The proposed algorithm is used in conjunction with a Parallel Trellis Viterbi Algorithm for data detection and simulation results show that the overall scheme exhibits the reduced complexity benefits without performance reduction.
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Jiang, Ting, Maozhong Song, Xiaorong Zhu, and Xu Liu. "Channel Estimation for Broadband Millimeter Wave MIMO Systems Based on High-Order PARALIND Model." Wireless Communications and Mobile Computing 2021 (November 23, 2021): 1–12. http://dx.doi.org/10.1155/2021/6408442.
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Channel state information (CSI) is important to improve the performance of wireless transmission. However, the problems of high propagation path loss, multipath, and frequency selective fading make it difficult to obtain the CSI in broadband millimeter-wave (mmWave) system. Based on the inherent multidimensional structure of mmWave multipath channels and the correlation between channel dimensions, mmWave multiple input multiple output (MIMO) channels are modelled as high-order parallel profiles with linear dependence (PARALIND) model in this paper, and a new PARALIND-based channel estimation algorithm is proposed for broadband mmWave system. Due to the structural property of PARALIND model, the proposed algorithm firstly separates the multipath channels of different scatterers by PARALIND decomposition and then estimates the channel parameters from the factor matrices decomposed from the model based on their structures. Meanwhile, the performance of mmWave channel estimation is analysed theoretically. A necessary condition for channel parameter estimation is given based on the uniqueness principle of PARALIND model. Simulation results show that the proposed algorithm performs better than traditional compressive sensing-based channel estimation algorithms.
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An, XuDong, Lei Zhao, Han Wu, and QinJuan Zhang. "Channel estimation algorithm based on attention mechanism." Journal of Physics: Conference Series 2290, no. 1 (June 1, 2022): 012112. http://dx.doi.org/10.1088/1742-6596/2290/1/012112.
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Abstract As the key to wireless communication, channel estimation has become a hot research topic in recent years. In this paper, we propose a deep learning method based on the channel estimation of inverse convolutional network and expanded convolutional network to address the problems that the performance of traditional channel estimation algorithms in orthogonal frequency division multiplexing (OFDM) systems can hardly meet the communication requirements of complex scenarios and are greatly affected by noise. The method constructs a lightweight deconvolutional network using the correlation of channels, and achieves channel interpolation and estimation step by step with a few layers of deconvolutional operations, which achieves channel estimation with low complexity. To improve the estimation performance, an expanded convolutional network is further constructed to suppress the channel noise and improve the accuracy of channel estimation. The simulation results show that the channel estimation can be performed at different signal levels. The simulation results show that the proposed deep learning method based on deconvolution and dilation convolution has lower estimation error and lower complexity than the traditional methods under different signal-to-noise ratios (SNR).
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Chang, Rui, Chaowei Yuan, and Jianhe Du. "PARAFAC-Based Multiuser Channel Parameter Estimation for MmWave Massive MIMO Systems over Frequency Selective Fading Channels." Electronics 10, no. 23 (November 30, 2021): 2983. http://dx.doi.org/10.3390/electronics10232983.
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Channel estimation is crucial in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, especially with a few training sequences. To solve the problem of uplink channel estimation in mmWave massive MIMO systems, a PARAFAC-based algorithm is proposed for joint estimation of multiuser channels. The orthogonal frequency divisional multiplexing (OFDM) technique is exploited to combat the frequency selective fading channels. In this paper, the received signal at the base station (BS) is formulated as a third-order parallel factor (PARAFAC) tensor, and then a low-complexity algorithm is designed for fast estimation of the factor matrices related to channel parameters, thus leading to joint estimation of multiuser channel parameters via one-dimensional search. Moreover, the Cramér–Rao Bound (CRB) results for multiuser channel parameters are derived for evaluation. Theorical analysis and numerical results reveal that the algorithm performs well with a few training sequences. Compared with existing algorithms, the proposed algorithm has clear advantages both in estimation accuracy and computational complexity.
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Zhang, Yuzhi, Shumin Zhang, Yang Wang, Qingyuan Liu, and Xiangxiang Li. "Model-Driven Deep-Learning-Based Underwater Acoustic OTFS Channel Estimation." Journal of Marine Science and Engineering 11, no. 8 (August 1, 2023): 1537. http://dx.doi.org/10.3390/jmse11081537.
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Accurate channel estimation is the fundamental requirement for recovering underwater acoustic orthogonal time–frequency space (OTFS) modulation signals. As the Doppler effect in the underwater acoustic channel is much more severe than that in the radio channel, the channel information usually cannot strictly meet the compressed sensing sparsity assumption in the orthogonal matching pursuit channel estimation algorithm. This deviation ultimately leads to a degradation in system performance. This paper proposes a novel approach for OTFS channel estimation in underwater acoustic communications, utilizing a model-driven deep learning technique. Our method incorporates a residual neural network into the OTFS channel estimation process. Specifically, the orthogonal matching pursuit algorithm and denoising convolutional neural network (DnCNN) collaborate to perform channel estimation. The cascaded DnCNN denoises the preliminary channel estimation results generated by the orthogonal matching pursuit algorithm for more accurate OTFS channel estimation results. The use of a lightweight DnCNN network with a single residual block reduces computational complexity while still preserving the accuracy of the neural network. Through extensive evaluations conducted on simulated and experimental underwater acoustic channels, the outcomes demonstrate that our proposed method outperforms traditional threshold-based and orthogonal matching pursuit channel estimation techniques, achieves superior accuracy in channel estimation, and significantly reduces the system’s bit error rate.
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Lu, Wei, Yongliang Wang, Xiaoqiao Wen, Shixin Peng, and Liang Zhong. "Downlink Channel Estimation in Massive Multiple-Input Multiple-Output with Correlated Sparsity by Overcomplete Dictionary and Bayesian Inference." Electronics 8, no. 5 (April 28, 2019): 473. http://dx.doi.org/10.3390/electronics8050473.
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We exploited the temporal correlation of channels in the angular domain for the downlink channel estimation in a massive multiple-input multiple-output (MIMO) system. Based on the slow time-varying channel supports in the angular domain, we combined the channel support information of the downlink angular channel in the previous timeslot into the channel estimation in the current timeslot. A downlink channel estimation method based on variational Bayesian inference (VBI) and overcomplete dictionary was proposed, in which the support prior information of the previous timeslot was merged into the VBI for the channel estimation in the current timeslot. Meanwhile the VBI was discussed for a complex value in our system model, and the structural sparsity was utilized in the Bayesian inference. The Bayesian Cramér–Rao bound for the channel estimation mean square error (MSE) was also given out. Compared with other algorithms, the proposed algorithm with overcomplete dictionary achieved a better performance in terms of channel estimation MSE in simulations.
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WANG, Dong-yu, Tao DUAN, and Yong-jian ZHANG. "OFDM channel estimation with dispersive fading channels." Journal of China Universities of Posts and Telecommunications 19 (June 2012): 75–86. http://dx.doi.org/10.1016/s1005-8885(11)60465-2.
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Wang, Liang, Peiyue Qiao, Junyan Liang, Tong Chen, Xinjie Wang, and Guang Yang. "Accurate Channel Estimation and Adaptive Underwater Acoustic Communications Based on Gaussian Likelihood and Constellation Aggregation." Sensors 22, no. 6 (March 10, 2022): 2142. http://dx.doi.org/10.3390/s22062142.
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Achieving accurate channel estimation and adaptive communications with moving transceivers is challenging due to rapid changes in the underwater acoustic channels. We achieve an accurate channel estimation of fast time-varying underwater acoustic channels by using the superimposed training scheme with a powerful channel estimation algorithm and turbo equalization, where the training sequence and the symbol sequence are linearly superimposed. To realize this, we develop a ‘global’ channel estimation algorithm based on Gaussian likelihood, where the channel correlation between (among) the segments is fully exploited by using the product of the Gaussian probability-density functions of the segments, thereby realizing an ideal channel estimation of each segment. Moreover, the Gaussian-likelihood-based channel estimation is embedded in turbo equalization, where the information exchange between the equalizer and the decoder is carried out in an iterative manner to achieve an accurate channel estimation of each segment. In addition, an adaptive communication algorithm based on constellation aggregation is proposed to resist the severe fast time-varying multipath interference and environmental noise, where the encoding rate is automatically determined for reliable underwater acoustic communications according to the constellation aggregation degree of equalization results. Field experiments with moving transceivers (the communication distance was approximately 5.5 km) were carried out in the Yellow Sea in 2021, and the experimental results verify the effectiveness of the two proposed algorithms.
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Wang, Diya, Yonglin Zhang, Yupeng Tai, Lixin Wu, Haibin Wang, Jun Wang, Wenyu Luo, Fabrice Meriaudeau, and Fan Yang. "Cluster-aware channel estimation with deep learning method in deep-water acoustic communications." Journal of the Acoustical Society of America 154, no. 3 (September 1, 2023): 1757–69. http://dx.doi.org/10.1121/10.0020861.
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In underwater acoustic (UWA) communications, channels often exhibit a clustered-sparse structure, wherein most of the channel impulse responses are near zero, and only a small number of nonzero taps assemble to form clusters. Several algorithms have used the time-domain sparse characteristic of UWA channels to reduce the complexity of channel estimation and improve the accuracy. Employing the clustered structure to enhance channel estimation performance provides another promising research direction. In this work, a deep learning-based channel estimation method for UWA orthogonal frequency division multiplexing (OFDM) systems is proposed that leverages the clustered structure information. First, a cluster detection model based on convolutional neural networks is introduced to detect the cluster of UWA channels. This method outperforms the traditional Page test algorithm with better accuracy and robustness, particularly in low signal-to-noise ratio conditions. Based on the cluster detection model, a cluster-aware distributed compressed sensing channel estimation method is proposed, which reduces the noise-induced errors by exploiting the joint sparsity between adjacent OFDM symbols and limiting the search space of channel delay spread. Numerical simulation and sea trial results are provided to illustrate the superior performance of the proposed approach in comparison with existing sparse UWA channel estimation methods.
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Li, Lin, Xiao Han, and Wei Ge. "Iterative Signal Detection and Channel Estimation with Superimposed Training Sequences for Underwater Acoustic Information Transmission in Time-Varying Environments." Remote Sensing 16, no. 7 (March 29, 2024): 1209. http://dx.doi.org/10.3390/rs16071209.
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Underwater signal processing is primarily based on sound waves because of the unique properties of water. However, the slow speed and limited bandwidth of sound introduce numerous challenges, including pronounced time-varying characteristics and significant multipath effects. This paper explores a channel estimation method utilizing superimposed training sequences. Compared with conventional schemes, this method offers higher spectral efficiency and better adaptability to time-varying channels owing to its temporal traversal. To ensure success in this scheme, it is crucial to obtain time-varying channel estimation and data detection at low SNRs given that superimposed training sequences consume power resources. To achieve this goal, we initially employ coarse channel estimation utilizing superimposed training sequences. Subsequently, we employ approximate message passing algorithms based on the estimated channels for data detection, followed by iterative channel estimation and equalization based on estimated symbols. We devise an approximate message passing channel estimation method grounded on a Gaussian mixture model and refine its hyperparameters through the expectation maximization algorithm. Then, we refine the channel information based on time correlation by employing an autoregressive hidden Markov model. Lastly, we perform numerical simulations of communication systems by utilizing a time-varying channel toolbox to simulate time-varying channels, and we validate the feasibility of the proposed communication system using experimental field data.
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Longoria-Gandara, Omar, Ramon Parra-Michel, Roberto Carrasco-Alvarez, and Eduardo Romero-Aguirre. "Iterative MIMO Detection and Channel Estimation Using Joint Superimposed and Pilot-Aided Training." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3723862.
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This paper presents a novel iterative detection and channel estimation scheme that combines the effort of superimposed training (ST) and pilot-aided training (PAT) for multiple-input multiple-output (MIMO) flat fading channels. The proposed method, hereafter known as joint mean removal ST and PAT (MRST-PAT), implements an iterative detection and channel estimation that achieves the performance of data-dependent ST (DDST) algorithm, with the difference that the data arithmetic cyclic mean is estimated and removed from data at the receiver’s end. It is demonstrated that this iterative and cooperative detection and channel estimator algorithm surpasses the effects of data detection identifiability condition that DDST has shown when higher orders of modulation are used. Theoretical performance of the MRST-PAT scheme is provided and corroborated by numerical simulations. In addition, the performance comparison between the proposed method and different MIMO channel estimation techniques is analyzed. The joint effort between ST and PAT shows that MRST-PAT is a solid candidate in communications systems for multiamplitude constellations in Rayleigh fading channels, while achieving high-throughput data rates with manageable complexity and bit-error rate (BER) as a figure of merit.
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Cho, Yong-Ho. "Fast Sparse Bayesian Learning-Based Channel Estimation for Underwater Acoustic OFDM Systems." Applied Sciences 12, no. 19 (October 10, 2022): 10175. http://dx.doi.org/10.3390/app121910175.
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Harsh underwater channels and energy constraints are the two critical issues of underwater acoustic (UWA) communications. To achieve a high channel estimation performance under a severe underwater channel, sparse Bayesian learning (SBL)-based channel estimation was adopted for UWA orthogonal frequency division multiplexing (OFDM) systems. Accurate channel estimation can guarantee the successful reception of transmitted data and reduce retransmission occurrences, thereby, leading to energy-efficient communications. However, SBL-based algorithms have improved performances in iterative ways, which require high power consumption. In this paper, a fast SBL algorithm based on a weighted learning rule for hyperparameters is proposed for channel estimation in a UWA-OFDM system. It was shown via numerical analysis that the proposed weighted learning rule enables fast convergence and more accurate channel estimation simultaneously. Simulation results confirm that the proposed algorithm achieves higher accuracy in channel estimation with much fewer iteration numbers in comparison to conventional SBL-based methods for a time-varying UWA channel.
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Conlon, Lorcán O., Ping Koy Lam, and Syed M. Assad. "Multiparameter Estimation with Two-Qubit Probes in Noisy Channels." Entropy 25, no. 8 (July 26, 2023): 1122. http://dx.doi.org/10.3390/e25081122.
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This work compares the performance of single- and two-qubit probes for estimating several phase rotations simultaneously under the action of different noisy channels. We compute the quantum limits for this simultaneous estimation using collective and individual measurements by evaluating the Holevo and Nagaoka–Hayashi Cramér-Rao bounds, respectively. Several quantum noise channels are considered, namely the decohering channel, the amplitude damping channel, and the phase damping channel. For each channel, we find the optimal single- and two-qubit probes. Where possible we demonstrate an explicit measurement strategy that saturates the appropriate bound and we investigate how closely the Holevo bound can be approached through collective measurements on multiple copies of the same probe. We find that under the action of the considered channels, two-qubit probes show enhanced parameter estimation capabilities over single-qubit probes for almost all non-identity channels, i.e., the achievable precision with a single-qubit probe degrades faster with increasing exposure to the noisy environment than that of the two-qubit probe. However, in sufficiently noisy channels, we show that it is possible for single-qubit probes to outperform maximally entangled two-qubit probes. This work shows that, in order to reach the ultimate precision limits allowed by quantum mechanics, entanglement is required in both the state preparation and state measurement stages. It is hoped the tutorial-esque nature of this paper will make it easily accessible.
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Kong, Chuiyuan, Hao Wang, and Junming Chang. "Compressed Sensing Channel Estimation Algorithm Combined with Wavelet Denoising." Journal of Physics: Conference Series 2637, no. 1 (November 1, 2023): 012039. http://dx.doi.org/10.1088/1742-6596/2637/1/012039.
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Abstract In wireless multipath channels, there is sparsity, in which traditional channel estimation algorithms do not take advantage of and ignore the impact of noise. An effective estimation algorithm combining compressed sensing and wavelet de-noising is proposed. Before channel estimation, the pilot signal received by the receiver is de-noised by the wavelet de-noising method to obtain the de-noised measurement vector, and then the original signal is recovered by the compressed sensing technology for sparse channel estimation. This algorithm breaks away from the limitation of traditional algorithms that require predictive sparsity to achieve sparsity adaptive signal reconstruction. Through simulation analysis, it has been proven that the fusion wavelet denoising algorithm can improve the estimation performance, reduce estimation errors, and be more suitable for sparse channel estimation by denoising the signal during channel estimation.
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Zhou, Xiao, Chengyou Wang, Ruiguang Tang, and Mingtong Zhang. "Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems." Applied Sciences 8, no. 9 (September 10, 2018): 1607. http://dx.doi.org/10.3390/app8091607.
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Channel estimation is an important module for improving the performance of the orthogonal frequency division multiplexing (OFDM) system. The pilot-based least square (LS) algorithm can improve the channel estimation accuracy and the symbol error rate (SER) performance of the communication system. In pilot-based channel estimation, a certain number of pilots are inserted at fixed intervals between OFDM symbols to estimate the initial channel information, and channel estimation results can be obtained by one-dimensional linear interpolation. The minimum mean square error (MMSE) and linear minimum mean square error (LMMSE) algorithms involve the inverse operation of the channel matrix. If the number of subcarriers increases, the dimension of the matrix becomes large. Therefore, the inverse operation is more complex. To overcome the disadvantages of the conventional channel estimation methods, this paper proposes a novel OFDM channel estimation method based on statistical frames and the confidence level. The noise variance in the estimated channel impulse response (CIR) can be largely reduced under statistical frames and the confidence level; therefore, it reduces the computational complexity and improves the accuracy of channel estimation. Simulation results verify the effectiveness of the proposed channel estimation method based on the confidence level in time-varying dynamic wireless channels.
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Wang, Song, and Jonathan H. Manton. "FFT/IFFT Based Blind SIMO Channel Identification." ECTI Transactions on Electrical Engineering, Electronics, and Communications 6, no. 2 (November 22, 2007): 158–63. http://dx.doi.org/10.37936/ecti-eec.200862.171784.
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This paper presents an FFT/IFFT based blind identification method for estimating the finite impulse response of single-input multiple-output channels driven by an unknown deterministic signal. The proposed algorithm successfully handles a very small size of received data, for which the existing blind channel estimation methods, including the subspace, cross-relation and shifted correlation algorithms, are known to be ineffective. Moreover, with no assumption of the precise knowledge of channel order, the proposed algorithm is capable of estimating channel parameters as well as detecting channel order. Simulations show that the proposed algorithm outper forms the existing methods in small sample size situations.
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Kim, Du Yong, and Moongu Jeon. "Robust Distributed Kalman Filter for Wireless Sensor Networks with Uncertain Communication Channels." Mathematical Problems in Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/238597.
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We address a state estimation problem over a large-scale sensor network with uncertain communication channel. Consensus protocol is usually used to adapt a large-scale sensor network. However, when certain parts of communication channels are broken down, the accuracy performance is seriously degraded. Specifically, outliers in the channel or temporal disconnection are avoided via proposed method for the practical implementation of the distributed estimation over large-scale sensor networks. We handle this practical challenge by using adaptive channel status estimator and robust L1-norm Kalman filter in design of the processor of the individual sensor node. Then, they are incorporated into the consensus algorithm in order to achieve the robust distributed state estimation. The robust property of the proposed algorithm enables the sensor network to selectively weight sensors of normal conditions so that the filter can be practically useful.
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Fan, Tong Liang, Min Jun Deng, and Hong Cheng Huang. "DFT-Based on Mahalanobis Distance Discriminant Analysis Method Channel Estimation Algorithm for OFDM Systems." Applied Mechanics and Materials 55-57 (May 2011): 472–77. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.472.
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An enhanced discrete Fourier transform DFT-based channel estimation for OFDM systems is proposed. Conventional DFT-based channel estimations improve the performance by suppressing time domain noise. However, they potentially require information on channel impulse responses and may also result in mean-square error (MSE) floor due to incorrect channel information such as channel delay spread. In order to overcome the disadvantage, our proposed channel estimation can improve the performance by deciding significant channel taps adaptively. Significant channel taps are detected on the basis of Mahalanobis distance discriminant analysis. Simulation results demonstrate that the proposed algorithm outperforms the conventional DFT-based estimation in terms of BER and MSE performance.
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Gaballa, Mohamed, Maysam Abbod, and Ammar Aldallal. "Investigating the Combination of Deep Learning for Channel Estimation and Power Optimization in a Non-Orthogonal Multiple Access System." Sensors 22, no. 10 (May 11, 2022): 3666. http://dx.doi.org/10.3390/s22103666.
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In a non-orthogonal multiple access (NOMA) system, the successive interference cancellation (SIC) procedure is typically employed at the receiver side, where several user’s signals are decoded in a subsequent manner. Fading channels may disperse the transmitted signal and originate dependencies among its samples, which may affect the channel estimation procedure and consequently affect the SIC process and signal detection accuracy. In this work, the impact of Deep Neural Network (DNN) in explicitly estimating the channel coefficients for each user in NOMA cell is investigated in both Rayleigh and Rician fading channels. The proposed approach integrates the Long Short-Term Memory (LSTM) network into the NOMA system where this LSTM network is utilized to predict the channel coefficients. DNN is trained using different channel statistics and then utilized to predict the desired channel parameters that will be exploited by the receiver to retrieve the original data. Furthermore, this work examines how the channel estimation based on Deep Learning (DL) and power optimization scheme are jointly utilized for multiuser (MU) recognition in downlink Power Domain Non-Orthogonal Multiple Access (PD-NOMA) system. Power factors are optimized with a view to maximize the sum rate of the users on the basis of entire power transmitted and Quality of service (QoS) constraints. An investigation for the optimization problem is given where Lagrange function and Karush–Kuhn–Tucker (KKT) optimality conditions are applied to deduce the optimum power coefficients. Simulation results for different metrics, such as bit error rate (BER), sum rate, outage probability and individual user capacity, have proved the superiority of the proposed DL-based channel estimation over conventional NOMA approach. Additionally, the performance of optimized power scheme and fixed power scheme are evaluated when DL-based channel estimation is implemented.