LS Channel Estimation Research Articles

Deep Residual Learning with Attention Mechanism for OFDM Channel Estimation

In this paper, we apply deep learning to the channel estimation problem of OFDM. Precisely speaking, to reduce the influence of noise on LS channel estimation, we design a channel estimation model based on attention mechanism and residual network. AttRNet-Conv and AttRNet-FC use attentional mechanism to change the weight of the feature to suppress noise. Compared with other channel estimation algorithms based on deep learning, AttRNet-Conv and AttRNet-FC avoid damaging channel information. The simulation results show that AttRNet-Conv and AttRNet-FC are superior to existing channel estimation algorithms based on deep learning in both high and low pilot frequency conditions. Even at low SNR, the estimation accuracy is higher than that of LMMSE. Experimental results show thatAttRNet-Conv and AttRNet-FC has strong robustness. In addition, we find that AttRNet-Conv is more suitable for channel estimation at low SNR in EPA, and the AttRNet-FC model is used in other cases.

Numerical Evaluation of the MU-MIMO Beamforming Performance with Channel Estimation

This paper presents the numerical evaluation of the ZF beamforming algorithm and DFT precoding using the LS channel estimation in the multiuser multiantenna (MU-MIMO) downlink system. The sum rate performance depending on a number of users are presented. The arising inter user correlation degrades the sum rate (spectral efficiency) performance of multiuser MIMO system especially in scenarios where the number of users is larger than the number of antennas at the BS. For MIMO channel simulation the QuaDRiGa channel model reflecting the real propagation conditions is used. The obtained performance of MU-MIMO ZF precoding in spatially correlated channel are compared with DFT precoding based on the empirical cumulative density function of the sum rate of multiple users. Numerical results show that the ZF precoder outperforms the DFT precoder in channel with less spatial correlation. The DFT precoder in spatially more correlated channel has advantage over ZF precoder.

Innovative Approach of Spectral Efficiency Optimization over Various Pilot Reuse Factors

The Massive MIMO with TDD is breakthrough technology for spectral efficiency gains. The CSI is essential for spectral efficiency gains and CSI can be obtained by channel estimation methods. The channel estimation methods employ known pilot s sequences to estimate the channel before actual data transmission. However, the channel coherence is time and frequency limited, which reflects the trade-off between the resources available for pilots and those available for data in coherent block for transmission. The pilot sequences reuse in other cells can reduce pilot overhead, called pilot reuse. However potential interference is introduced, by pilot reuse, in the channel estimation phase, called pilot contamination. The aim is to determine optimum pilot reuse factor for better SE using low computational complexity estimation method. The sum averaged SE has been obtained for pilot reuse factor 1, 2, 4, 8 and 16 using MMSE, EW-MMSE and LS channel estimation methods. The obtained results reveals that the difference between values of SEs for f=1 to f=2 to f=4 is very low and performance difference between estimators decreases as pilot reuse factor increases, which allows to use LS estimator to get performance almost equal to MMSE with less computational complexity. Also, the pilot reuse factor f=4 found optimum among all for given constraint.

Deep learning based channel estimation method for mine OFDM system

In this paper, we present a channel estimation approach based on deep learning to solve the problem that the orthogonal frequency division multiplexing (OFDM) system channel estimation algorithm cannot accurately obtain the channel state information in the complex environment of the mine, resulting in system performance degradation. First, LS channel estimation matrix is considered as a low-resolution image and the actual channel state information is considered as a high-resolution image. Then the optimization of the LS channel estimation matrix is achieved by the FSRCNN image super-resolution algorithm. We validate the effectiveness of the proposed algorithm by conducting experiments in different channel environments, different number of pilots, and mismatched signal-to-noise ratio scenarios. The simulation results show that the proposed scheme is much better than the traditional LS channel estimation method and the DFT-LS channel estimation method, and the accuracy of the proposed scheme approaches that of the MMSE channel estimation method when the number of pilots is low.

Turbo-Coded MIMO-OFDM Channel Estimation Using the Chaotic Grey Wolf Optimizer and Genetic Algorithm

Turbo-Coded multiple-input multiple-output orthogonal frequency division multiplexing Channel Estimation, using Chaotic Grey Wolf Optimizer and the Genetic Algorithm (TC-MIMO-OFDM-Hybrid-CGWO-GA), is proposed in this manuscript. To enhance the performance of the bit error rate (BER), the proposed turbo code lessens the maximal correlation of the channel in the frequency domain. Initially, the channel is estimated using LS-MMSE individually using Chaotic Grey Wolf Optimizer (CGWO) and the LS-MMSE is combined with a genetic algorithm to scale the best channel for reducing the error. The encoding and decoding methods are done with the help of Turbo-codes for LS channel estimation. Then, the performance of the proposed method is analyzed with different performance metrics, such as the bit error rate, mean square error, channel estimation, computational cost, overhead, and symbol error rate. From the analysis, the proposed method attains lower computational costs of 99.67%, 98.38%, 92.34% and 97.45%, lower bit error rates of 98.33%, 89.34%, 83.12% and 88.96% and lower mean square errors of 93.15%, 91.25%, 79.90% and 92.88% analyzed to the existing methods, such as TSO-CE-TC-MIMO-OFDM, MFPA-CE-MIMO-OFDM, OSBS-CEA-MIMO-OFDM, and IAMO-CE-MIMO-OFDM.

Efficient Image Transmission Over OFDM Channel with Energy Conservation and LS Channel Estimation with Various Modulation Schemes

To send data like text, images, music, and videos, packetization procedures are commonly used in communication apps. Just a portion of carriers in OFDM systems may be suitable for transmitting images due to channel fading. To reduce high channel errors at the receiver, the transmitter can proactively map descriptions onto good subcarriers and delete remaining descriptions if it has access to channel status information. In this study, discrete wavelet processing as an energy-saving approach for transmitting compressed image frames via OFDM channels. By utilizing one-bit channel status information at the transmitter, the descriptions are sorted and allocated to high-quality channels in a descending order of importance. To conserve energy, faulty sub-channel descriptions are removed at the transmitter. In this project, image transmission across OFDM channels is packetized to enable efficient data transfer while minimizing energy consumption. We also use the LS Channel estimation method to get a more accurate channel estimate with different modulation schemes and SNR. We obtained energy savings of up to 38% and improved channel estimation with various modulation schemes by utilizing MATLAB simulations to test on a variety of modulation schemes and channel SNR ratios.

A Pilot-Based Integration Method of Ranging and LS Channel Estimation for OFDM Systems

In the design of unmanned aerial vehicle (UAV) communication systems, orthogonal frequency division multiplexing (OFDM) is a commonly used communication technology. An efficient channel estimation and equalization algorithm is required to recover the amplitude, phase, and frequency of the signal in OFDM systems. At present, the more precise channel estimation method is based on the pilot. However, its spectrum utilization is relatively low. Therefore, this paper presents the design of a new pilot based on the LS channel estimation, which extends the role of the traditional pilot and improves the utilization of the spectrum. In addition to the channel estimation and equalization, the new pilot can also be utilized for ranging. Simulation results show that the proposed scheme can achieve both channel estimation and communication ranging functions by using the new pilot, and it outperforms the conventional method in channel estimation performance. The proposed method can complete ranging when the bit error rate (BER) is above 0 dB. Moreover, compared with the traditional channel estimation, it reduces the requirement for SNR by about 1 dB under the same BER.

The research on channel estimation and signal‐noise ratio estimation based on minimum error entropy kalman filter for single carrier frequency domain equalization system

SummaryIn this paper, the channel estimation and signal‐to‐noise ratio (SNR) estimation technique of single‐carrier frequency domain equalization (SC‐FDE) system under low SNR in aeronautical multipath channel are studied, a SNR estimation algorithm which is easy to implement in engineering and an improved LS channel estimation algorithm based on Kalman filter using minimum error entropy (MEE‐KF) are proposed. This paper first introduces the SC‐FDE system and introduces the principle of MEE‐KF, and then, the channel estimation flow based on MEE‐KF is obtained by combining it with the traditional LS channel estimation algorithm, which makes the estimation results perform better. Simulation results show that after getting more accurate noise variance, the channel estimation results can better follow the changes of the channel after MEE‐KF processing, so as to resist the doppler frequency offset effect and make the channel estimation results more accurate, that is the channel response results of the data part can be closer to the real situation, so that the communication performance of SC‐FDE system has also been greatly improved.

Performance Analysis of Compressive Sensing based LS and MMSE Channel Estimation Algorithm

In this paper, the optimality of Compressive Sensing based Least Square (LS-CS) and Compressive Sensing based Minimum Mean Square (MMSE-CS) channel estimation algorithms in Multi Input Multi Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) system is investigated for a sparse communication channel. The performance of LS, MMSE, LS-CS and MMSE-CS channel estimation algorithms in terms of sparsity of the channel, compressive sensing and mathematical complexity is investigated and analyzed so that optimum ranges can be recommended.

Performance of MIMO-OFDM Systems

Orthogonal frequency division multiplexing (OFDM) is one of the new modulation techniques which is used to combat the frequency-selectivity of the transmission channel models achieving high data rate without inter-symbol interference. OFDM may be combined with antenna arrays at the transmitter and receiver to increase the system capacity on time-variant and frequency-selective channel models resulting in a multiple-input multiple-output (MIMO) configuration. In this paper, SISO, SIMO, MISO and MIMO-OFDM configurations of OFDM systems are proposed. LS channel estimator is used to calculate the channel coefficients. The four different OFDM systems are analysed and simulated. The simulation consists of four parameters namely bit error rate, mean square error, symbol error rate and capacity of the channel for MIMO-OFDM systems. The error rate values are minimised in 2×2 MIMO-OFDM systems compared to other 1×1, 1×2, 2×1 OFDM systems. Similarly channel capacity is maximised in 2×2 MIMO-OFDM systems, compared to the other OFDM systems. These performances are implemented using MATLAB software.

Channel Estimation and Data Transmission in Massive MIMO TDD Systems

This paper mainly elaborates the studies of channel estimation and downlink data transmission in Massive MIMO. As there are different types of interference in single-cell and multi-cell systems, this paper establishes different models for them separately. In terms of uplink training, for getting channel state information, we introduce LS and MMSE channel estimation algorithms and make a comparison between them. At the same time, the problem of pilot contamination is solved by cell classification and pilot identification. Next, this paper defines mathematical models for downlink data transmission. We use pre-coding methods (including Zero-forcing and Maximal Ratio Combining schemes) and optimize power distribution to improve channel capacity and transmission rate. Furthermore, this paper provides numerical results to show the simulation performance in both single-cell and multi-cell systems and extends to prospects in the future.

Improvement in Time Varying Multipath Channel Estimation

In this work, we propose a new algorithm that modifies the LS channel estimation technique by using a feedback LS channel estimator to improve upon its estimation capabilities and still retaining its simplicity. To examine its usefulness especially for wireless mobile applications, performance in terms of the symbol error rate is analyzed with varying Doppler frequency and delay parameters, which is found to be quite optimistic in comparison to LMMSE and LRMMSE algorithms.

LS Channel Estimation in Multicell Multiuser MIMO OFDM with Zadoff Chu Sequence as Pilots

High spectral efficiency and the ability to combat multipath fading are the key requirements of future wireless communication systems. Multiple-input multiple-output (MIMO) combined with orthogonal frequency- division multiplexing (OFDM) has been widely considered to be a promising candidate. Moreover Multi user MIMO OFDM can provide good spatial multiplexing with single antenna for each user. In the case of multicell multiuser MIMO OFDM system main challenge is to acquire the channel state information. In order to get the channel state information, channel estimation is to be done. Here pilot aided LS channel estimation is used to obtain the estimates. For multi cell multi user MIMO OFDM, channel estimation using pilot subcarriers causes pilot contamination. This is due to the reuse of pilot subcarriers in different cells. In order to reduce the impact of pilot contamination, pilot sequences must select properly. Here Zadoff Chu sequences are used for the design of pilots in different cells.

LS Channel Estimation and Signal Separation for UHF RFID Tag Collision Recovery on the Physical Layer.

In a passive ultra-high frequency (UHF) radio-frequency identification (RFID) system, tag collision is generally resolved on a medium access control (MAC) layer. However, some of collided tag signals could be recovered on a physical (PHY) layer and, thus, enhance the identification efficiency of the RFID system. For the recovery on the PHY layer, channel estimation is a critical issue. Good channel estimation will help to recover the collided signals. Existing channel estimates work well for two collided tags. When the number of collided tags is beyond two, however, the existing estimates have more estimation errors. In this paper, we propose a novel channel estimate for the UHF RFID system. It adopts an orthogonal matrix based on the information of preambles which is known for a reader and applies a minimum-mean-square-error (MMSE) criterion to estimate channels. From the estimated channel, we could accurately separate the collided signals and recover them. By means of numerical results, we show that the proposed estimate has lower estimation errors and higher separation efficiency than the existing estimates.

Performance analysis of OFDM receiver with modified PTS technique

Modified partial transmit sequence PTS technique with adjacent partitioning and four phase factors is implemented to enhance the PAPR performance of OFDM system and its PAPR reduction performance is evaluated in terms of the complementary cumulative distribution function CCDF. A multipath fading environment is considered and the performance of the OFDM receiver is evaluated using least square LS and minimum mean square error MMSE channel estimation methods. The performance of the receiver is evaluated in terms of mean square error MSE and bit error rate BER. The performance of the OFDM receiver is enhanced with PAPR reduction.

Channel Estimation and Equalization of DVB-T in Fast Fading Multipath Channels

Early Terrestrial Digital Video Broadcasting Systems (DVB-T) were planned to work in Line-of-Sight (LOS) environments where a significantly high Signal to Noise Ratio (SNR) is guaranteed for all times. However, the recent DVB standards are sought to work in non-LOS conditions where the SNR is relatively low. Hence, in such environments, there is a need for a successful channel estimation and detection technique to take care of the fast channel fluctuations. This article is dealing with LS channel estimation and detection method for DVB-T signals over various multipath fading channel. The proposed method has been proven successful for up to 16 Quadrature Amplitude Modulation (QAM) constellations even when there is no LOS. However, the performance is found to be insufficient for an acceptable user experience for higher number of constellations. 

Novel Sub-carriers Iterative Restructuring Method in OFDM: Toward Extra Equalization Omitted

This study considered a receiver equalization problem in wireless communication area. Receiver equalizations in OFDM proceeding through sub-carriers variedness in frequency guarantee transmit system bit error rate thus are essential. E.g., Zero-Forcing (ZF) equalization, Minimal Mean Square Equalization (MMSE), which can also be considered as channel Estimation (EE) and detection schemes. However, some methods like adaptive equalizations must count priority transceiver information thus may be not practical although they raise performances for receiver. This study considers equalization and detection problems through a sub-carriers restructuring method to omit any complicated extra equalization in receiver except ZF equalization following the elemental LS channel estimation. Optimal constraints model is utilized which leads to an optimal sub-carriers derivation, moreover, we iteratively set up constraints model and give derivation, then under our new sub-carriers no extra equalization is needed. Simulations verify the novel method can approach MMSE equalization although it may bring transceiver complexity.

Frequency domain based LS channel estimation in OFDM based Power line communications

This paper is focused on low voltage power line communication (PLC) realization with an emphasis on channel estimation techniques. The Orthogonal Frequency Division Multiplexing (OFDM) scheme is preferred technology in PLC systems because of its effective combat with frequency selective fading properties of PLC channel. As the channel estimation is one of the crucial problems in OFDM based PLC system because of a problematic area of PLC signal attenuation and interference, the improved LS estimation technique is proposed. We investigate and evaluate proposed frequency domain LS channel estimation method for OFDM based power line communication system. Also performance comparing with existing pilot based estimation algorithms towards proposed method in terms of their computational complexity, error correction, and suitability conditions is made.

Scattered pilot-based channel estimation with joint noise reduction for PDM-CO-OFDM system

To improve the accuracy of the channel estimation in PDM-CO-OFDM system when LS algorithm is used, a joint denoising method is proposed in this paper. We have theoretically proposed and simulated designed scattered pilot pattern for PDM-CO-OFDM system, then multi-wavelet is used to denoise the LS channel estimation results which have been interpolation in frequency domain, ISFA is further used to denoise signals after interpolation in time domain. Simulation results show that for 4-QAM, 80GB/s PDM-CO-OFDM system, the proposed joint denoising method has improved 0.96dB in OSNR sensitivity at the BER of 1e−3 (threshold of FEC) compared to the common LS method and reduced system overhead at the same time.

Performance Analysis of LS and LMMSE Channel Estimation Techniques for LTE Downlink Systems

The main purpose of this paper is to study the performance of two linear channel estimators for LTE Downlink systems, the Least Square Error (LSE) and the Linear Minimum Mean Square Error (LMMSE). As LTE is a MIMO-OFDM based system, a cyclic prefix is inserted at the beginning of each transmitted OFDM symbol in order to completely suppress both inter-carrier interference (ICI) and inter-symbol interference (ISI). Usually, the cyclic prefix is equal to or longer than the channel length but in some cases and because of some unforeseen channel behaviour, the cyclic prefix can be shorter. Therefore, we propose to study the performance of the two linear estimators under the effect of the channel length. Computer simulations show that, in the case where the cyclic prefix is equal to or longer than the channel length,LMMSE performs better than LSE but at the cost of computational complexity.In the other case, LMMSE continue to improve its performance only for low SNR values but it degrades for high SNR values in which LS shows better performance for LTE Downlink systems. MATLAB Monte-Carlo simulations are used to evaluate the performance of the studied estimators in terms of Mean Square Error (MSE) and Bit Error Rate (BER) for 2x2 LTE Downlink systems.