scholarly journals Channel Estimation Performance Analysis of FBMC/OQAM Systems with Bayesian Approach for 5G-Enabled IoT Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Han Wang ◽  
Wencai Du ◽  
Xianpeng Wang ◽  
Guicai Yu ◽  
Lingwei Xu
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Communication Systems ◽  
Channel Model ◽  
Matching Pursuit ◽  
Multiple Input Multiple Output ◽  
Filter Bank Multicarrier ◽  
Channel Parameter ◽  
Input Multiple Output ◽  
Estimation Scheme

A filter bank multicarrier (FBMC) with offset quadrature amplitude modulation (OQAM) (FBMC/OQAM) is considered to be one of the physical layer technologies in future communication systems, and it is also a wireless transmission technology that supports the applications of Internet of Things (IoT). However, efficient channel parameter estimation is one of the difficulties in realization of highly available FBMC systems. In this paper, the Bayesian compressive sensing (BCS) channel estimation approach for FBMC/OQAM systems is investigated and the performance in a multiple-input multiple-output (MIMO) scenario is also analyzed. An iterative fast Bayesian matching pursuit algorithm is proposed for high channel estimation. Bayesian channel estimation is first presented by exploring the prior statistical information of a sparse channel model. It is indicated that the BCS channel estimation scheme can effectively estimate the channel impulse response. Then, a modified FBMP algorithm is proposed by optimizing the iterative termination conditions. The simulation results indicate that the proposed method provides better mean square error (MSE) and bit error rate (BER) performance than conventional compressive sensing methods.

A New Method for MIMO-OFDM Channel Estimation Based on Discrete Wavelet Transform

2012 ◽  
Vol 182-183 ◽  
pp. 2066-2070
Author(s):  
Hui Shi ◽  
Ren Wang Song ◽  
Gang Fei Wang
Keyword(s):  
Wavelet Transform ◽  
Channel Estimation ◽  
Discrete Wavelet Transform ◽  
Orthogonal Frequency Division Multiplexing ◽  
Multiple Input Multiple Output ◽  
Mimo Channel ◽  
Discrete Wavelet ◽  
Input Multiple Output ◽  
Estimation Scheme ◽  
Mimo Ofdm

This paper puts forward a suitable channel estimation scheme for multiple input multiple output and orthogonal frequency division multiplexing system (MIMO-OFDM) based on discrete wavelet transform. According to the least-squares standard (LS), this plan uses pilot to estimate the unit impulse response of MIMO channel firstly, then does wavelet denoising in changing domain, in order to reduce the frequency spectrum leakage and improve the estimation precision. At the same time, this method does not need to know channel information in advance, and can follow up the changes of channel on time with good error rate performance.

scholarly journals A Deep Neural Network-Based Interference Mitigation for MIMO-FBMC/OQAM Systems

2021 ◽  
Vol 2 ◽  
Author(s):  
Abla Bedoui ◽  
Mohamed Et-tolba
Keyword(s):  
Neural Network ◽  
Communication Systems ◽  
Deep Neural Network ◽  
Interference Mitigation ◽  
Multiple Input Multiple Output ◽  
Filter Bank Multicarrier ◽  
Multiple Input ◽  
High Spectral Efficiency ◽  
Input Multiple Output ◽  
Imaginary Field

Offset quadrature amplitude modulation-based filter bank multicarrier (FBMC/OQAM) is among the promising waveforms for future wireless communication systems. This is due to its flexible spectrum usage and high spectral efficiency compared with the conventional multicarrier schemes. However, with OQAM modulation, the FBMC/OQAM signals are not orthogonal in the imaginary field. This causes a significant intrinsic interference, which is an obstacle to apply multiple input multiple output (MIMO) technology with FBMC/OQAM. In this paper, we propose a deep neural network (DNN)-based approach to deal with the imaginary interference, and enable the application of MIMO technique with FBMC/OQAM. We show, by simulations, that the proposed approach provides good performance in terms of bit error rate (BER).

scholarly journals Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Athar Waseem ◽  
Aqdas Naveed ◽  
Sardar Ali ◽  
Muhammad Arshad ◽  
Haris Anis ◽  
...  
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Massive Mimo ◽  
Communication Systems ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Sampling Theorem ◽  
Base Station ◽  
Mimo Channels ◽  
Pilot Design

Massive multiple-input multiple-output (MIMO) is believed to be a key technology to get 1000x data rates in wireless communication systems. Massive MIMO occupies a large number of antennas at the base station (BS) to serve multiple users at the same time. It has appeared as a promising technique to realize high-throughput green wireless communications. Massive MIMO exploits the higher degree of spatial freedom, to extensively improve the capacity and energy efficiency of the system. Thus, massive MIMO systems have been broadly accepted as an important enabling technology for 5th Generation (5G) systems. In massive MIMO systems, a precise acquisition of the channel state information (CSI) is needed for beamforming, signal detection, resource allocation, etc. Yet, having large antennas at the BS, users have to estimate channels linked with hundreds of transmit antennas. Consequently, pilot overhead gets prohibitively high. Hence, realizing the correct channel estimation with the reasonable pilot overhead has become a challenging issue, particularly for frequency division duplex (FDD) in massive MIMO systems. In this paper, by taking advantage of spatial and temporal common sparsity of massive MIMO channels in delay domain, nonorthogonal pilot design and channel estimation schemes are proposed under the frame work of structured compressive sensing (SCS) theory that considerably reduces the pilot overheads for massive MIMO FDD systems. The proposed pilot design is fundamentally different from conventional orthogonal pilot designs based on Nyquist sampling theorem. Finally, simulations have been performed to verify the performance of the proposed schemes. Compared to its conventional counterparts with fewer pilots overhead, the proposed schemes improve the performance of the system.

scholarly journals Hybrid Beamforming Analysis Based on MIMO Channel Measurements at 28 GHz

2020 ◽  
Author(s):  
Joerg Eisenbeis ◽  
Magnus Tingulstad ◽  
Nicolai Kern ◽  
Zsolt Kollár ◽  
Jerzy Kowalewski ◽  
...  
Keyword(s):  
Communication Systems ◽  
Channel Model ◽  
Multiple Input Multiple Output ◽  
Wireless Channel ◽  
Mimo Channel ◽  
Channel Measurements ◽  
Electromagnetic Propagation ◽  
Channel Models ◽  
Input Multiple Output ◽  
Hybrid Beamforming

<div>Hybrid beamforming systems represent an efficient</div><div>architectural solution to realize massive multiple-input multiple-output (MIMO) communication systems in the centimeter wave (cmW) and millimeter wave (mmW) region. These hybrid beamforming systems separate the beamforming process into a digital and analog beamforming network. The analog beamforming networks can be realized by different architectural solutions, which demand dedicated algorithms to determine the complex weighting factors in the digital and analog domain. To date, novel hybrid beamforming architectures and algorithms are solely compared in numerical simulations based on statistical channel models. These abstract channel models simplify the complicated electromagnetic propagation process, thereby not exactly reconstructing the wireless channel. Within this work, we present a measurement-based evaluation of hybrid beamforming algorithms and compare them with numerical results gained from a statistical path-based MIMO channel model. The results show that by adjustment of the channel model parameter the simulation achieves a good match with the measured maximum achievable spectral efficiencies.</div>

scholarly journals Channel Estimation Approach with Low Pilot Overhead in FBMC/OQAM Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jun Sun ◽  
Xiaomin Mu ◽  
Dejin Kong ◽  
Qian Wang ◽  
Xinmin Li ◽  
...  
Keyword(s):  
Channel Estimation ◽  
Filter Bank ◽  
Multiple Input Multiple Output ◽  
Performance Loss ◽  
Filter Bank Multicarrier ◽  
Error Ratio ◽  
Multiple Input ◽  
Bit Error Ratio ◽  
Input Multiple Output ◽  
The Cost

In filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems, a large pilot overhead is required due to the existence of the imaginary interference. In this paper, we present an approach to reduce the pilot overhead of channel estimation. A part of pilot overhead is used for transmitting data, and compensating symbols are required and designed to remove the imaginary interference. It is worthwhile to point out that the power of compensating symbols can be helpful for data recovery; hence, the proposed approach decreases the overhead of pilots significantly without the cost of additional pilot energy. In addition, the proposed scheme is extended into multiple input multiple output systems without the performance loss. Compared with the conventional preamble consisting of 3 columns symbols, the pilot overhead is equivalent to 2 column symbols in the proposed preamble. To verify the proposed preamble, numerical simulations are carried out with respects to bit error ratio.

scholarly journals Channel Estimation for Large Intelligent Surface-Based Transceiver Using a Parametric Channel Model

2021 ◽  
Author(s):  
mojtaba ghermezcheshmeh ◽  
Vahid Jamali ◽  
Haris Gacanin, ◽  
Nikola zlatanov
Keyword(s):  
Wireless Communication ◽  
Channel Estimation ◽  
Communication Systems ◽  
Channel Model ◽  
Estimation Error ◽  
Computational Cost ◽  
Channel Estimation Error ◽  
Wireless Communication Systems ◽  
Physical Channel ◽  
Estimation Scheme

<div>Large intelligent surface-based transceivers (LISBTs), in which a spatially continuous surface is being used for signal transmission and reception, have emerged as a promising solution for improving the coverage and data rate of wireless communication systems. To realize these objectives, the acquisition of accurate channel state information (CSI) in LISBT-assisted wireless communication systems is crucial. In this paper, we propose a channel estimation scheme based on a parametric physical channel model for line-of-sight dominated communication in millimeter and terahertz wave bands. The proposed estimation scheme requires only five pilot signals to perfectly estimate the channel parameters assuming there is no noise at the receiver. In the presence of noise, we propose an iterative estimation algorithm that decreases the channel estimation error due to noise. The training overhead and computational cost of the proposed scheme do not scale with the number of antennas. The simulation results demonstrate that the proposed estimation scheme significantly outperforms other benchmark schemes.</div>

SUPERIMPOSED TRAINING-BASED CHANNEL ESTIMATION FOR MIMO-MB-OFDM UWB SYSTEMS

2008 ◽  
Vol 17 (05) ◽  
pp. 865-870 ◽  
Author(s):  
QINGHAI YANG ◽  
HUAMIN ZHU ◽  
KYUNG SUP KWAK
Keyword(s):  
Channel Estimation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Multiple Input Multiple Output ◽  
Least Square ◽  
Ultra Wideband ◽  
Superimposed Training ◽  
Square Channel ◽  
Channel Estimate ◽  
Input Multiple Output ◽  
Estimation Scheme

A novel channel estimation scheme based on superimposed training is proposed for multiple-input multiple-output multi-band orthogonal frequency division multiplexing ultra-wideband systems. The optimal training symbols are derived with respect to the least-square channel estimate mean square error. Simulation shows that the proposed scheme benefits much higher effective data throughput over the conventional method.

Compressive Sensing-Based Angle Estimation for MIMO Radar with Multiple Snapshots

2013 ◽  
Vol 347-350 ◽  
pp. 1028-1032
Author(s):  
Jian Feng Li ◽  
Xiao Fei Zhang ◽  
Tong Hu
Keyword(s):  
Compressive Sensing ◽  
Complexity Analysis ◽  
Matching Pursuit ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Eigenvalue Decomposition ◽  
Angle Estimation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Esprit Algorithm

The issue of angle estimation for multiple-input multiple-output (MIMO) radar is studied and an algorithm for the estimation based on compressive sensing with multiple snapshots is proposed. The dimension of received signal is reduced to make the computation burden lower, and then the noise sensitivity is reduced by the eigenvalue decomposition (EVD) of the covariance matrix of the reduced-dimensional signal. Finally the signal subspace obtained from the eigenvectors is realigned to apply the orthogonal matching pursuit (OMP) for angle estimation. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, and reduced-dimension Capon. Furthermore, the proposed algorithm works well for coherent targets, and requires no knowledge of the noise. The complexity analysis and simulation results verify the effectiveness of the algorithm.

scholarly journals Hybrid Beamforming Analysis Based on MIMO Channel Measurements at 28 GHz

2020 ◽  
Author(s):  
Joerg Eisenbeis ◽  
Magnus Tingulstad ◽  
Nicolai Kern ◽  
Zsolt Kollár ◽  
Jerzy Kowalewski ◽  
...  
Keyword(s):  
Communication Systems ◽  
Channel Model ◽  
Multiple Input Multiple Output ◽  
Wireless Channel ◽  
Mimo Channel ◽  
Channel Measurements ◽  
Electromagnetic Propagation ◽  
Channel Models ◽  
Input Multiple Output ◽  
Hybrid Beamforming

<div>Hybrid beamforming systems represent an efficient</div><div>architectural solution to realize massive multiple-input multiple-output (MIMO) communication systems in the centimeter wave (cmW) and millimeter wave (mmW) region. These hybrid beamforming systems separate the beamforming process into a digital and analog beamforming network. The analog beamforming networks can be realized by different architectural solutions, which demand dedicated algorithms to determine the complex weighting factors in the digital and analog domain. To date, novel hybrid beamforming architectures and algorithms are solely compared in numerical simulations based on statistical channel models. These abstract channel models simplify the complicated electromagnetic propagation process, thereby not exactly reconstructing the wireless channel. Within this work, we present a measurement-based evaluation of hybrid beamforming algorithms and compare them with numerical results gained from a statistical path-based MIMO channel model. The results show that by adjustment of the channel model parameter the simulation achieves a good match with the measured maximum achievable spectral efficiencies.</div>

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