scholarly journals Variable Is Better Than Invariable: Sparse VSS-NLMS Algorithms with Application to Adaptive MIMO Channel Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Guan Gui ◽  
Zhang-xin Chen ◽  
Li Xu ◽  
Qun Wan ◽  
Jiyan Huang ◽  
...  
Keyword(s):  
Channel Estimation ◽  
Communication Systems ◽  
Orthogonal Frequency Division Multiplexing ◽  
Computational Cost ◽  
Mimo Channel ◽  
Variable Step Size ◽  
Mean Square ◽  
Ofdm Systems ◽  
Step Size ◽  
Estimation Performance

Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparseinvariable step-size normalized least mean square(ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stablesparse variable step-sizeNLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics.

scholarly journals Sparse DFT Based Channel Estimation In OFDM Systems

2020 ◽  
Vol 3 (2) ◽  
pp. 1-10
Author(s):  
Maryam K. Abboud ◽  
Bayan M. Sabbar
Keyword(s):  
Channel Estimation ◽  
Communication Systems ◽  
Orthogonal Frequency Division Multiplexing ◽  
Channel Model ◽  
Doppler Frequency ◽  
Mobile Systems ◽  
Vehicle Speed ◽  
Ofdm Systems ◽  
Estimation Algorithms ◽  
Doppler Frequency Shifts

Channel estimation is an essential part of Orthogonal Frequency Division Multiplexing (OFDM) communication systems. In this paper, two Discrete Fourier Transform (DFT) improvement algorithms are proposed and compared where the 1st one exploits channel sparsity concept while the other considers significant channel coefficients only. In the proposed algorithms; Enhanced and Sparse DFT (E-DFT and S-DFT), different number of significant channel components is selected either by a threshold determining procedure such as in    E-DFT, or through determining channel sparsity level such as in S-DFT. In the presence of Doppler frequency shifts, the Inter Symbol Interference (ISI) effect on channel coefficients is successfully reduced using the proposed estimation algorithms. Vehicular A-ITU channel model is considered with a relatively high vehicle speed up to 68 Km/h in order to test the suitability of the proposed algorithms for mobile systems. E-DFT and S-DFT improves conventional as well as previous DFT improvement methods (I-DFT) suggested by [7], [8], [9], [15]. For 64 subcarriers, S-DFT outperforms E-DFT and I-DFT by about 3dB at a BER of 0.01 with a mobility reaches 45 Km/h, and by about 0.4dB and 2.5dB at a BER of 0.02 with a mobility reaches 68Km/h.

scholarly journals Channel Estimation and Data Detection Using Machine Learning for MIMO 5G Communication Systems in Fading Channel

Technologies ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 72 ◽  
Author(s):  
Sumitra Motade ◽  
Anju Kulkarni
Keyword(s):  
Channel Estimation ◽  
Bit Error Rate ◽  
Error Rate ◽  
Communication Systems ◽  
Orthogonal Frequency Division Multiplexing ◽  
Symbol Error Rate ◽  
Base Station ◽  
Data Detection ◽  
Ofdm Systems ◽  
K Nearest Neighbor

In multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, multi-user detection (MUD) algorithms play an important role in reducing the effect of multi-access interference (MAI). A combination of the estimation of channel and multi-user detection is proposed for eliminating various interferences and reduce the bit error rate (BER). First, a novel sparse based k-nearest neighbor classifier is proposed to estimate the unknown activity factor at a high data rate. The active users are continuously detected and their data are decoded at the base station (BS) receiver. The activity detection considers both the pilot and data symbols. Second, an optimal pilot allocation method is suggested to select the minimum mutual coherence in the measurement matrix for optimal pilot placement. The suggested algorithm for designing pilot patterns significantly improves the results in terms of mean square error (MSE), symbol error rate (SER) and bit error rate for channel detection. An optimal pilot placement reduces the computational complexity and maximizes the accuracy of the system. The performance of the channel estimation (CE) and MUD for the proposed scheme was good as it provided significant results, which were validated through simulations.

scholarly journals Channel Estimation Techniques for OFDM and UFMC Systems for 5G Communications

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1776-1778
Keyword(s):  
Channel Estimation ◽  
Communication Systems ◽  
Orthogonal Frequency Division Multiplexing ◽  
Wireless Communication Systems ◽  
Modulation Scheme ◽  
Frequency Division ◽  
Ofdm Systems ◽  
Machine Type ◽  
Machine Type Communications ◽  
Simulation Results

In this paper, pilot-assisted techniques for channel estimation (CE) are simulated for Universal Filtered Multi-Carrier (UFMC) modulation scheme. UFMC aims at replacing orthogonal frequency division multiplexing (OFDM) and improves performance and robustness in the case of timefrequency misalignment. These techniques efficiently support Internet of Things (IoT) and massive machine type communications (mMTC), which are identified as challenges for 5G wireless communication systems (WCS). Pilot-aided techniques are adopted and applied to OFDM and UFMC. Simulation results are supplemented to compare the performance of UFMC systems with conventional CP-OFDM systems.

scholarly journals Channel Estimation for MIMO Systems

2022 ◽  
Vol 10 (1) ◽  
pp. 201-204
Author(s):  
Sonti Swapna
Keyword(s):  
Channel Estimation ◽  
Communication Systems ◽  
Orthogonal Frequency Division Multiplexing ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Least Square ◽  
Spectrum Efficiency ◽  
Mean Square ◽  
Multiple Input ◽  
Input Multiple Output

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

scholarly journals A Novel LS/LMMSE Based PSO Approach for 3D-Channel Estimation in Rayleigh Fading

2018 ◽  
Vol 7 (4) ◽  
pp. 117-123
Author(s):  
D. N. Bhange ◽  
C. Dethe
Keyword(s):  
Channel Estimation ◽  
Bit Error Rate ◽  
Error Rate ◽  
Orthogonal Frequency Division Multiplexing ◽  
Transmission Rate ◽  
Least Square ◽  
High Signal ◽  
Mean Square ◽  
Ofdm Systems ◽  
Channel Parameters

A high transmission rate can be obtained using Multi Input Multi Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) model. The most commonly used 3D-pilot aided channel estimation (PACE) techniques are Least Square (LS) and Least Minimum Mean Square (LMMSE) error. Both of the methods suffer from high mean square error and computational complexity. The LS is quite simple and LMMSE being superior in performance to LS providing low Bit Error Rate (BER) at high Signal to Noise ratio (SNR). Artificial Intelligence when combined with these two methods produces remarkable results by reducing the error between transmission and reception of data signal. The essence of LS and LMMSE is used priory to estimate the channel parameters. The bit error so obtained is compared and the least bit error value is fine-tuned using particle swarm optimization (PSO) to obtained better channel parameters and improved BER. The channel parameter corresponding to the low value of bit error rate obtained from LS/LMMSE is also used for particle initialization. Thus, the particles advance from the obtained channel parameters and are processed to find a better solution against the lowest bit error value obtained by LS/LMMSE. If the particles fail to do so, then the bit error value obtained by LS/LMMSE is finally considered. It has emerged from the simulated results that the performance of the proposed system is better than the LS/LMMSE estimations. The performance of OFDM systems using proposed technique can be observed from the imitation and relative results.

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