MIMO-OFDM Channel Estimation Research Articles

Learning-Based Block-Wise Planar Channel Estimation for Time-Varying MIMO OFDM

Learning-Based Block-Wise Planar Channel Estimation for Time-Varying MIMO OFDM

Enhancing MIMO-OFDM channel estimation in 5G and beyond with conditional self-attention generative adversarial networks

Enhancing MIMO-OFDM channel estimation in 5G and beyond with conditional self-attention generative adversarial networks

MIMO-OFDM channel estimation based on minimum error entropy criterion under non-Gaussian environment

ABSTRACT Using the orthogonal frequency division multiplexing (OFDM) technique, a multiple-input multiple-output (MIMO) system can provide high spectral efficiency and high data transmission over a fading channel. To achieve the best performance in a MIMO-OFDM system, high accuracy in channel estimation is a very important factor which leads to appropriate receiver design. Therefore, in the most channel estimation algorithms, the mean square error (MSE) is the main criterion for noise minimisation which is robust in the case of Gaussian noise. However, in telecommunication systems which do not have noise with Gaussian distribution, the MSE criterion is not appropriate. So as to tackle this problem, a robust adaptive filtering algorithm was proposed using minimum error entropy (MEE) criterion and improved least square (ILS) approach which by far is better than MMSE criterion to robust channel estimation. In this work, impulsive noise is used for non-Gaussian environment simulation. Furthermore, MEE is employed to attenuate the impulsive noise, and ILS is adopted to reduce LS estimation variance. Compared to the MSE-based algorithms, simulation results indicate that the proposed method outperforms the channel estimation in a non-Gaussian environment.

A Survey on MIMO-OFDM Systems: Review of Recent Trends

MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified.

Downlink transmission and channel estimation for cell-free massive MIMO-OFDM with DSDs

In a cell-free massive MIMO system, multiple users arrive at multiple access points at separate times, while in an OFDM system, different delays can be equivalent to delay spread differences (DSDs). Since DSDs are not all the same, in the downlink transmission process, it is necessary to consider its impact on transmission techniques, such as channel estimation and downlink precoding. In this paper, aiming at the performance loss caused by DSDs in cell-free massive MIMO-OFDM system, we propose a multi-RB precoding optimization algorithm that maximizes the downlink sum rate. We derive the sum rate maximization problem into an iterative second-order cone programming form to achieve convex approximation. Then, considering the impact of DSDs on the accuracy of cell-free massive MIMO-OFDM channel estimation, we propose a downlink channel estimation method, which jointly uses channel state information reference signal and demodulation reference signal. The simulation results show that the proposed multi-RB optimal precoding can effectively improve the downlink sum rate, and the proposed downlink channel estimation can obtain accurate multi-RB frequency-domain channel parameters.

Bilinear Channel Estimation for MIMO OFDM: Lower Bounds and Training Sequence Optimization

Time-varying narrowband Multiple-Input Multiple- Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) channel estimation is considered. The effect of finite bandwidth and practical pulse shapes renders the sparse MIMO-OFDM channel, non-sparse - this effect is denoted leakage. It is shown that the leaked MIMO OFDM channel is effectively separable in the delay and Doppler domains. A convex optimization approach, based on the atomic norm, is posed to estimate the channel parameters. With respect to the mean-squared error, the proposed scheme offers a 4 dB improvement over methods that ignore leakage and a 1.3 dB gain over a method that does consider leakage. The Cramér Rao bound (CRB) for the leaked channel parameters is derived and also exhibits decoupling in delay and Doppler. Training sequences that optimize the CRBs for delay and Doppler parameters are determined via solving key fixed-point equations. The optimized random sequences offer a performance gain on the order of 5-2.5 dB over purely random sequences. The proposed channel estimation algorithm nearly achieves the CRB, suggesting near optimality. Finally, the proposed estimation strategy, when employed on experimental data (SPACE'08), provides an average performance gain of 3 dB with respect to the probability of error in comparison to a traditional sparse channel estimation scheme.

RETRACTED: Research on multiple classified signal detection algorithms based on mimo-ofdm system

OFDM is a high-speed signal transmission technology in wireless environment. In wireless channels, the frequency response curve is generally non-flat. MIMO technology can effectively increase system capacity, improve system performance, and improve network coverage and transmission reliability. OFDM technology can effectively resist intersymbol crosstalk caused by multipath propagation by adding a certain length of redundant guard interval in front of transmission symbols. According to the channel estimation and signal detection algorithms in MIMOOFDM systems, this paper will choose a better algorithm to implement MIMOOFDM systems and channel estimation and signal detection algorithms. Theoretical analysis and simulation results show that the optimal detection algorithm has the best performance, but its complexity is exponentially related to the number of antennas and modulation order. The architecture for dense texture analysis is developed based on Field Programmable Gate Array FPGA device which is a general engine for high-speed image processing. In order to maximize the performance, our architecture is designed for minimizing consumed resources. As explained in subsection our architecture is composed of two main operations, namely the AH based sum and difference of images and the calculus of texture features

Structured Turbo Compressed Sensing for Downlink Massive MIMO-OFDM Channel Estimation

Compressed sensing has been employed to reduce the pilot overhead for channel estimation in wireless communication systems. Particularly, structured turbo compressed sensing (STCS) provides a generic framework for structured sparse signal recovery with reduced computational complexity and storage requirement. In this paper, we consider the problem of massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channel estimation in a frequency division duplexing (FDD) downlink system. By exploiting the structured sparsity in the angle-frequency domain (AFD) and angle-delay domain (ADD) of the massive MIMO-OFDM channel, we represent the channel by using AFD and ADD probability models and design message-passing based channel estimators under the STCS framework. Several STCS-based algorithms are proposed for massive MIMO-OFDM channel estimation by exploiting the structured sparsity. We show that, compared with other existing algorithms, the proposed algorithms have a much faster convergence speed and achieve competitive error performance under a wide range of simulation settings.

Sparse massive MIMO-OFDM channel estimation based on compressed sensing over frequency offset environment

In massive MIMO-OFDM systems, channel estimation is a significant module which can be utilized to eliminate multipath interference. However, in realistic communication systems, carrier frequency offset (CFO), which often exists in receive end, will deteriorate the performance of channel estimation. One of the effective solutions is to compensate CFO via the help of pseudo-noise (PN) sequence. At the beginning of this paper, to reduce system complexity and correctly compensate CFO, we propose an improved OFDM frame structure. Subsequently, we theoretically analyze the catastrophic influence of CFO on conventional PN-sequence-based compressed sensing (CS) channel estimation scheme. As our solution, based on the improved OFDM frame structure, a novel massive MIMO-OFDM channel estimation method under CFO environment is proposed. It first estimates CFO by utilizing differential correlation algorithm. Thereby, the interference caused by CFO can be eliminated. Then, relying on the PN sequence, the partial common support (PCS) information of each channel is obtained. Finally, using the PCS information as a priori information, we improve the CS reconstruction scheme to estimate the accurate channel. The simulation result shows that the proposed scheme demonstrates better MSE and BER performance than other mentioned schemes. The major advantage of our scheme is its anti-CFO ability and independence to channel sparsity level. Therefore, the proposed scheme is meaningful for practical use.

Weighted Neuro-Fuzzy Hybrid Algorithm for Channel Equalization in Time Varying Channel

In MIMO-OFDM communication systems, accurate and specific channel estimation and equalisations are plays a major role. In this paper, we use weighted neuro-fuzzy hybrid (WNFH) channel estimation algorithm for channel equalisation. The pilot is designed based on combination of neural network and fuzzy logic system. Scaled conjugate gradient (SCG) is mutual with group search optimiser (GSO) algorithm along with; the training procedure of neural network is prepared using the hybrid training algorithm. In the transmitter section, the projected system contains quadrature amplitude modulation (QAM) and transmitter. By considering the channel prediction error to recover the performance of symbol detection the minimum mean-square error (MMSE) estimation design is accomplished. To reduce the MMSE of channel estimation and the calculated pilot sequences present great superiority in MIMO-OFDM system. Experimentation outcome shows that the channel assessment is supportive.

Novel Spectral Efficient Technique for MIMO-OFDM Channel Estimation with Reference to PAPR and BER Analysis

With emerge of increasing research in domain of future wireless communications, massive multiple input multiple output (MIMO) attracted most of researchers interests. Massive MIMO is nothing but high speed wireless communication standards. The performance of MIMO systems is based on techniques used for channel estimation. Efficient channel estimation leads to spectral efficient wireless communications. There are number of channel estimation techniques presented recently in literature with pros and cons. The recent method shows the spectral and bit error rate (BER) efficiency, however apart from this, there is need of improving the peak to average power ratio (PAPR). Recently we proposed, novel channel estimation method as the existing channel estimation techniques failed to effectively solve the inter symbol interference (ISI) problem. The presence of ISI in MIMO-OFDM may leads to worst performance. Our proposed blind channel estimation is combined with independent component analysis (ICA) hence this method is called as hybrid ICA (HICA) to minimize the ISI effect. The extensive simulation analysis of proposed HICA required to claiming the scalability as well as reliability. In this paper, proposed study on additional performance metrics such as PAPR and computational costs (energy) along with BER and spectral efficiency performances. The result claims that HICA is not improving the PAPR and energy performances significantly.

Low-Complexity and High-Accuracy Semi-blind Joint Channel and Symbol Estimation for Massive MIMO-OFDM

In order to fully exploit the scarce spectrum, antenna arrays are incorporated into wireless communication devices in 4G and 5G communication networks to deploy MIMO-OFDM systems. Recently, the least squares Khatri–Rao factorization has been applied to MIMO-OFDM systems for semi-blind joint channel and symbol estimation. Its cubic computational complexity is prohibitive when the number of transmit and receive antennas is very large. Therefore, the average vector and Hadamard ratio rank one approximation has been proposed for MIMO-OFDM systems, showing a linear complexity, but being limited to channels and transmitted symbols with offsets. In this paper, we present four novel MIMO-OFDM algorithms for massive antenna array systems that outperform the state-of-the-art approaches in terms of complexity and/or accuracy. The four proposed schemes are the alternating least squares with vector selection initialization, the vector projection rank one approximation including vector selection rank one initialization, the factorization based on eigenvalue decomposition with eigenvector projection and the factorization based on sectional truncated singular value decomposition and vector projection. Our analytical complexity analysis and numerical results corroborate the trade-offs offered by the different receiver algorithms in terms of complexity, parallelism and performance.

SBL-Based Joint Sparse Channel Estimation and Maximum Likelihood Symbol Detection in OSTBC MIMO-OFDM Systems

This paper presents sparse Bayesian learning (SBL)-based schemes for approximately sparse channel estimation in an orthogonal space-time block coded (OSTBC) multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) wireless system. The parameterized prior-based SBL framework is employed to present a pilot scheme for an ill-posed OSTBC MIMO-OFDM channel estimation scenario. Maximum likelihood symbol detection (MLSD) has been incorporated in the expectation-maximization framework for SBL-based channel estimation. This has led to the development of a novel scheme for joint approximately sparse channel estimation and symbol detection. The proposed scheme performs SBL-based channel estimation in the E-step followed by a modified ML decision metric-based symbol detection in the M-step. Bayesian Cramer–Rao bounds are obtained for the genie minimum mean-squared error estimators corresponding to the SBL schemes. Closed-form bit error probability expressions are derived for the MLSD in the presence of SBL-based channel estimation errors. Simulation results are presented towards the end to validate the theoretical bounds and illustrate the performance of the proposed techniques.

Model-based compressed sensing algorithms for MIMO- OFDM channel estimation

High data rates on the wireless channel can be achieved by combining orthogonal frequency division multiplexing (OFDM) with multiple input multiple output (MIMO) communication modulation scheme. MIMO-OFDM system impulse response of the channel is approximately sparse. Sparse channelestimation can be done using Compressive Sensing (CS) techniques. In this paper, a low complexity model based CoSaMp Compressive Sensing (CS) algorithm with conventional tools namely Least Square (LS) and Least Mean Square (LMS) are used for MIMO-OFDM channel estimation. Simulation results show amodel based CoSaMP for MIMO-OFDM channel estimation with LMS tool the Normalized Mean Square Error(NMSE)reduced by 34%with very reduced complexity.

MIMO-OFDM Channel Estimation in Rayleigh and Rician Fading based on MMSE Estimator

MIMO-OFDM Channel Estimation in Rayleigh and Rician Fading based on MMSE Estimator

Channel Estimation and Combining Orthogonal Pilot Design in MIMO-OFDM System

The current technology of high speed wireless communication exist the following weaknesses, limited bandwidth, bad transmitted channel, complex mobile environment and the quality of service under limited service time. The dispersing pilot pattern and the method of channel estimation using in MIMO-OFDM system couldn’t eliminate the same frequency interference caused by the OFDM frequency among different transmit-receive antenna couples. Aiming at these problems, we proposed a method of MIMO-OFDM channel estimation combining orthogonal pilot design. The data format of pilot is designed. It can retain the orthogonality of different sub-carriers of OFDM which are produced by different transmit-receive antenna couples or transmit-receive antenna couple. In wireless mobile environment, it can better estimate the channel parameters without any prior channel information. This method proposed in the paper has finished the design of the completely orthogonal pilot data symbol among the different subcarriers position of different transmitting-receiving antenna pair. And the pilot data symbols are distributed in the entire time-frequency grid of the channel reasonably, therefore this paper uses a FFT unit whose points are equal to the number of the OFDM subcarriers to realize the least-square channel estimation algorithm. In the process of channel estimation, the channel parameters estimation precision has increased because of the noise getting better inhibition, which reduces the least squares estimate noise sensitivity to the requirements

Scheduling of the Activations in Iterative Detection, Decoding, and Channel Estimation for MIMO-OFDM

An iterative receiver for a multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system is considered to jointly decode the transmitted bits and estimate the channel state. The receiver consists of the a posteriori probability (APP) algorithm, the repeat-accumulate (RA) decoder, and the least-squares (LS) channel estimator. An obvious problem, with more than two blocks in an iterative receiver, is to find the optimal activation schedule of the different blocks. This paper proposes to use extrinsic information transfer (EXIT) charts to characterize the behavior of the receiver blocks and find out the optimal activation schedule for them. A semi-analytical expression of the EXIT function is derived for the decision directed LS channel estimator. An algorithm is proposed to generate the EXIT function of the APP algorithm as a function of channel estimate mutual information (MI). Surface fitting is used to get closed form expressions for the EXIT functions of the APP algorithm and the RA decoder. Trellis search based algorithms are shown to find the convergence with the lowest possible complexity using the EXIT charts. With the proposed concept, the activation scheduling can be adapted to prevailing channel circumstances and unnecessary iterations will be avoided.

Kalman Filtering based Channel Estimation for MIMO-OFDM

this paper, Kalman filtering is used for MIMO-OFDM channel estimation. First we uses the MIMO-OFDM Training sequences, the current channel response at the receiving end, and then use the Kalman filter method to estimate the channel response. The measure and tracking, using the Jakes channel transmission model. Through simulation can be seen, and compared to other channel estimation method proposed in this paper. Method can fast-track the time-varying channel where the channel response changes very quickly. At the same time, without excessive insertion of training sequences and pilot symbols, the performance of more greatly improved.

New MIMO-OFDM Channel Estimation Algorithm with Low Complexity

该文基于m序列设计了一种适合多天线收发-正交频分复用(MIMO-OFDM)系统的时域信道估计新方法。在时域OFDM符号之间插入附加循环前缀(CP)的m序列作为训练序列，在接收端将去掉循环前缀的接收序列与指定的循环移位m序列作相关运算，然后利用m序列的二值自相关特性获得信道的冲激响应估计，从而得到信道的频率响应估计。理论分析和仿真结果都表明：在频率选择性信道中，新方法在系统开销小、计算复杂度低的情况下可以取得比传统算法更好的性能。

Sparse Recovery Algorithms for Pilot Assisted MIMO OFDM Channel Estimation

In this letter, the sparse recovery algorithm orthogonal matching pursuit (OMP) and subspace pursuit (SP) are applied for MIMO OFDM channel estimation. A new algorithm named SOMP is proposed, which combines the advantage of OMP and SP. Simulation results based on 3GPP spatial channel model (SCM) demonstrate that SOMP performs better than OMP and SP in terms of normalized mean square error (NMSE). key words: MIMO, OFDM, channel estimation