scholarly journals Frequency Domain Preamble-Based Channel Estimation and Equalization in LoRa

2021 ◽  
Author(s):  
Vincent Savaux ◽  
Patrick Savelli
Keyword(s):  
Channel Estimation ◽  
Frequency Domain ◽  
Minimum Mean Square Error ◽  
Least Square ◽  
Estimation Methods ◽  
Multicarrier Systems ◽  
Cyclic Property ◽  
Selective Channel ◽  
Channel Estimation And Equalization ◽  
Phase Equalizer

This paper deals with multipath channel estimation and equalization in LoRa. It is suggested to take advantage of the cyclic property of the symbols in the LoRa frame preamble to obtain an interference-free version of the symbols in the frequency domain. Then, estimation methods used in multicarrier systems can be applied, such as the least square (LS), and the minimum mean square error (MMSE) estimators. It is shown that the cyclic property in LoRa is inherently independent of the length of the channel, making these estimation techniques robust to any frequency-selective channel. In addition the frequency domain zero-forcing (ZF) equalizer is used, and an original phase equalizer is introduced, taking advantage of the constant modulus property of LoRa symbols in the frequency domain. The performance of the investigated estimators and equalizers is shown through simulations, and applications to the presented results are further discussed.

scholarly journals Frequency Domain Preamble-Based Channel Estimation and Equalization in LoRa

2021 ◽  
Author(s):  
Vincent Savaux ◽  
Patrick Savelli
Keyword(s):  
Channel Estimation ◽  
Frequency Domain ◽  
Minimum Mean Square Error ◽  
Least Square ◽  
Estimation Methods ◽  
Multicarrier Systems ◽  
Cyclic Property ◽  
Selective Channel ◽  
Channel Estimation And Equalization ◽  
Phase Equalizer

This paper deals with multipath channel estimation and equalization in LoRa. It is suggested to take advantage of the cyclic property of the symbols in the LoRa frame preamble to obtain an interference-free version of the symbols in the frequency domain. Then, estimation methods used in multicarrier systems can be applied, such as the least square (LS), and the minimum mean square error (MMSE) estimators. It is shown that the cyclic property in LoRa is inherently independent of the length of the channel, making these estimation techniques robust to any frequency-selective channel. In addition the frequency domain zero-forcing (ZF) equalizer is used, and an original phase equalizer is introduced, taking advantage of the constant modulus property of LoRa symbols in the frequency domain. The performance of the investigated estimators and equalizers is shown through simulations, and applications to the presented results are further discussed.

scholarly journals A Comprehensive Review on Channel Estimation in OFDM System

2019 ◽  
Vol 5 (3) ◽  
pp. 6 ◽  
Author(s):  
Neha Dubey ◽  
Ankit Pandit
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Orthogonal Frequency Division Multiplexing ◽  
Transmission Rate ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Least Square ◽  
Estimation Methods ◽  
Mean Square ◽  
Mimo Ofdm

In wireless communication, orthogonal frequency division multiplexing (OFDM) plays a major role because of its high transmission rate. Channel estimation and tracking have many different techniques available in OFDM systems. Among them, the most important techniques are least square (LS) and minimum mean square error (MMSE). In least square channel estimation method, the process is simple but the major drawback is it has very high mean square error. Whereas, the performance of MMSE is superior to LS in low SNR, its main problem is it has high computational complexity. If the error is reduced to a very low value, then an exact signal will be received. In this paper an extensive review on different channel estimation methods used in MIMO-OFDM like pilot based, least square (LS) and minimum mean square error method (MMSE) and least minimum mean square error (LMMSE) methods and also other channel estimation methods used in MIMO-OFDM are discussed.

scholarly journals Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems

2018 ◽  
Vol 8 (9) ◽  
pp. 1607 ◽  
Author(s):  
Xiao Zhou ◽  
Chengyou Wang ◽  
Ruiguang Tang ◽  
Mingtong Zhang
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Confidence Level ◽  
Orthogonal Frequency Division Multiplexing ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Least Square ◽  
Estimation Methods ◽  
Mean Square ◽  
Inverse Operation

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.

A Two-Way Relay Channel Estimation Method Based on Sparsity Adaptive Matching Pursuit Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 1037-1042
Author(s):  
Ju Rong Wang ◽  
Jin He Zhou
Keyword(s):  
Channel Estimation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Prior Information ◽  
Matching Pursuit ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Reconstruction Algorithm ◽  
Least Square ◽  
Estimation Methods ◽  
Relay Channel

To solve the problem that many existing two-way relay channel (TWRC) estimation methods require the sparse degree of the channel as prior information, we introduced a novel iterative greedy reconstruction algorithm based on compressed sensing (CS), called the sparisty adaptive matching pursuit (SAMP) to reconstruct the channel impulse response under orthogonal frequency division multiplexing (OFDM) system. The most innovative feature of SAMP is its capability of channel reconstruction without prior information of the sparse degree. Under the same condition we compared the algorithm with the other channel estimation methods including conventional least square (LS) algorithm, minimum mean square error (MMSE) algorithm and a orthogonal matching pursuit (OMP) algorithm based on CS. Simulation results show that the proposed algorithm has a better estimation performance and the algorithm improves the utilization of communication resources such as spectrum and energy. Thus it is suitable for real application.

scholarly journals Optimization of Training Signal Transmission for Estimating MIMO Channel under Antenna Mutual Coupling Conditions

2010 ◽  
Vol 2010 ◽  
pp. 1-10 ◽  
Author(s):  
Xia Liu ◽  
Marek E. Bialkowski
Keyword(s):  
Channel Estimation ◽  
Mutual Coupling ◽  
Minimum Mean Square Error ◽  
Signal Transmission ◽  
Multiple Input Multiple Output ◽  
Least Square ◽  
Mimo Channel ◽  
Estimation Methods ◽  
Estimation Errors ◽  
Training Signal

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

scholarly journals Channel Estimation in MIMO-OFDM System

2014 ◽  
Vol 14 (2) ◽  
pp. 97-102
Author(s):  
SR Aryal ◽  
H Dhungana
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Multipath Fading ◽  
Spatial Multiplexing ◽  
Diversity Gain ◽  
Estimation Methods ◽  
Mean Square ◽  
Ofdm System ◽  
Mimo Ofdm

There are no limit of human desire, so day by day we need much higher data speed to facilitate our need but every physical resource like frequency band, transmit signal strength are finite. Within the given limited resource, higher data speed is accomplished by new proficiency called Multiple Input Multiple Output (MIMO), Orthogonal Frequency Division Multiplexing (OFDM) system. MIMO-OFDM fulfills the high data rate requirement through spatial multiplexing gain and improved link reliability due to antenna diversity gain. With this technique, both interference reduction and maximum diversity gain are achieved by increasing number of antennae on either side. Received signal in MIMO-OFDM system is usually distorted by multipath fading. In order to recover the transmitted signal correctly, channel effect must be estimated and repaired at receiver. In this paper the performance evaluating parameter mean square error and symbol error rate of least square error, minimum mean square error and DFT based channel estimation methods are estimated and appropriate solution is recommended. Furthermore, comparison among their characteristics is simulated in MATLAB and useful conclusion is delineated. DOI: http://dx.doi.org/10.3126/njst.v14i2.10421   Nepal Journal of Science and Technology Vol. 14, No. 2 (2013) 97-102

Performance of Pilot-Aided 3D- OFDM Channel Estimation using Different Antenna Configurations

2017 ◽  
Vol 8 (1) ◽  
pp. 77
Author(s):  
Dinesh N. Bhange ◽  
Chandrashekhar G. Dethe
Keyword(s):  
Channel Estimation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Rayleigh Fading ◽  
Minimum Mean Square Error ◽  
Vertical Direction ◽  
Performance Comparison ◽  
Least Square ◽  
Digital Video Broadcasting ◽  
Video Broadcasting ◽  
Mimo Ofdm

<p>This paper aims, a 3D-Pilot Aided Multi-Input Multi-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) Channel Estimation (CE) for Digital Video Broadcasting -T2 (DVB-T2)for the 5 different proposed block and comb pilot patterns model and performed on different antenna configuration. The effects of multi-transceiver antenna on channel estimation are addressed with different pilot position in frequency, time and the vertical direction of spatial domain framing. This paper first focus on designing of 5- different proposed spatial correlated pilot pattern model with optimization of pilot overhead. Then it demonstrates the performance comparison of Least Square (LS) &amp;Linear Minimum Mean Square Error (LMMSE), two linear channel estimators for 3D-Pilot Aided patterns on different antenna configurations in terms of Bit Error Rate. The simulation results are shown for Rayleigh fading noise channel environments. Also, 3x4 MIMO configuration is recommended as the most suitable configuration in this noise channel environments.</p>

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