Channel Estimation For MIMO-OFDM Systems Research Articles

A systematic literature review on channel estimation in MIMO-OFDM system: performance analysis and future direction

Abstract Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is a familiar modern wireless broadband technology due to its resistance against multipath fading, high data transmission rate, and spectral efficiency. This technology delivers dependable communication as well as a large range of coverage. The precise recovery of Channel State Information (CSI) and synchronization among the receiver and transmitter are two major challenges for MIMO-OFDM systems. Several estimate procedures, like blind, pilot-aided, and semi-blind channel estimating, are used to recover channel state information. Yet, those systems have several flaws that cause them to perform poorly. Hence, this paper describes the basic introduction of the Channel Estimation (CE) process in the MIMO-OFDM system. The main goal of this survey is to study analyzing and categorizing the channel estimation algorithms, and simulation tools in different contributions. Further, the performance analysis with different performance metrics from diverse contributions is pointed out. Thus, this review article presents a detailed overview of the various channel estimation schemes that have been exploited in the OFDM channel to enhance the estimation of the CSI in the MIMO-OFDM systems. This work presents and discusses the relevant comparative results and computational complexity for all of these CE systems. Furthermore, there is a list of open study directions for further exploration.

Deep Learning for Joint Pilot Design and Channel Estimation in MIMO-OFDM Systems.

In MIMO-OFDM systems, pilot design and estimation algorithm jointly determine the reliability and effectiveness of pilot-based channel estimation methods. In order to improve the channel estimation accuracy with less pilot overhead, a deep learning scheme for joint pilot design and channel estimation is proposed. This new hybrid network structure is named CAGAN, which is composed of a concrete autoencoder (concrete AE) and a conditional generative adversarial network (cGAN). We first use concrete AE to find and select the most informative position in the time-frequency grid to achieve pilot optimization design and then input the optimized pilots to cGAN to complete channel estimation. Simulation experiments show that the CAGAN scheme outperforms the traditional LS and MMSE estimation methods with fewer pilots, and has good robustness to environmental noise.

Variable pilot assisted channel estimation in MIMO-OFDM system with STBC and different modulation modes

High speed and low latency have become the development trend of mobile communication network. Orthogonal frequency division multiplexing (OFDM) technology has become the key technology of mobile communication network due to its efficient transmission method. Space time block coding (STBC) introduces time and space correlation in the signals sent by different antennas, so that diversity gains and coding gains are obtained for the multiple input multiple output-OFDM (MIMO-OFDM) receiver without sacrificing bandwidth. STBC is a signal coding technology that combines higher transmission rates and it is a combination of space and time transmission signals. STBC uses the spatial diversity of multiple transmitters and multiple receivers in space to improve the capacity and information rate of the OFDM system. It proceeds parallel multiplex transmission in the transmission channel, which effectively improves spectrum utilization. For obtaining diversity gains and spectrum efficiency, this paper introduces a variable pilot assisted channel estimation method with different modulation modes. In this paper, the new type pilot sequences with different modulation orders are proposed for improving spectrum efficiency. The proposed variable pilot assisted channel estimation method in MIMO-OFDM systems is adopted with different low and high order modulations. Simulation results show that the proposed variable pilot assisted channel estimation method has 1–2 dB signal-to-noise ratio (SNR) gains for bit error rate (BER) performance of MIMO-OFDM systems.

Analysis on Wavelet Family based Channel Estimation for MIMO-OFDM System

Channel estimation for (MIMO-OFDM) is an important part for present and future generation broadband wireless communications. OFDM, which uses for the spaced subcarriers to improving the performance. The channel estimation schemes based on pilot reduces the transmission rate and spectral efficiency. Many conventional schemes of channel estimation are not effective in reducing noise. It leads to poor quality signals at receiver at final stage. To rectify this problem, in this paper a wavelet family based channel estimation technique, is proposed and analyzed. The proposed research aims to estimate channels and reconstruct signal via wavelet transform, dyadic wavelet transform and fractional spline wavelet transform which enhances the spectrum efficiency and transmission rate. Simulation results shows that the fractional spline wavelet transform performs well for channel estimation and data signal reconstruction.

Channel Estimation For Mimo-Ofdm Systems of Joint Angle-of-Departure (AOD) or Angle-of-Arrival (AOA)

Beamforming with gathering mechanical assembly displays has been considered as an engaging advancement in future remote correspondence structures. To coordinate beamforming, one has to know the angle-of-departure (AoD) or angle-of-arrival (AoA). For data disclosure, the beneficiary in like manner has to know station response. In this paper, we propose another joint AoD, AoA, and channel estimation contrive for pilot-helped MIMO-OFDM systems. Starting, a compressive distinguishing system is used to survey the channel drive response, abusing the sparsity property of remote channels. By then, AoA and AoD are as one surveyed for each perceived path by the most maximum likelihood (ML) procedure. The successive Cramer-Rao lower bound (SCRLB) is moreover gathered and a transmit beamforming plan is proposed as necessities be. In the circumstance of available prior information, a most outrageous a posteriori estimation is proposed. Amusement results exhibit that the proposed procedures can approach the SCRLB achieve a comparable supernatural viability as that got with the ideal direct in millimetre-wave correspondences. the SCRLB is low multifaceted nature computation and to constrain misstep rate and improve sign to fuss extent.

Structured Compressive Sensing Based Block-Sparse Channel Estimation for MIMO-OFDM Systems

In this paper, a compressive sensing based method named Priori-Information Aided Modified-SAMP algorithm is proposed to solve the problem of channel estimation in MIMO-OFDM systems. Firstly, coarse channel state information (CSI) as a priori-information of channel is obtained by using the complete pseudo-random noise (PN) sequences. Due to noise and the interference among antennas caused by the non-orthogonality of PN sequences, then, the accuracy of channel estimation is not so high that the priori-information aided modified-SAMP algorithm based on the obtained CSI is proposed to estimate CSI more accurately in temporal domain. Though the proposed method is based on the sparsity adaptive matching pursuit (SAMP) algorithm, there are some significant differences with each other in signal structure, support set selection, and adaptive step size etc. Theoretical analysis shows that the proposed algorithm has good convergence, moderate computational complexity and less training sequence overhead. Finally, the performance of the proposed method is verified through experimental simulations which show that compared with other algorithms, especially the orthogonal matching pursuit algorithm, the proposed algorithm not only improves the estimation accuracy but also greatly reduces the training sequence overhead.

Joint AoD, AoA, and Channel Estimation for MIMO-OFDM Systems

Beamforming with antenna arrays has been considered as an enabling technology in future wireless communication systems. To conduct beamforming, one has to know the angle-of-departure (AoD) or angle-of-arrival (AoA). For data detection, the receiver also has to know channel response. In this paper, we propose a new joint AoD, AoA, and channel estimation scheme for pilot-assisted multiple-input-multiple-output–orthogonal-frequency-division-multiplexing (MIMO-OFDM) systems. First, a compressive-sensing technique is employed to estimate the channel impulse response, exploiting the sparsity property of wireless channels. Then, AoA and AoD are jointly estimated for each detected path by the maximum likelihood method. The Cramer–Rao lower bound (CRLB) is also derived and a transmit beamforming scheme is proposed accordingly. In the scenario of available prior information, a maximum a posteriori estimation is proposed. The Bayesian CRLB (BCRLB) for the problem is also derived and a transmit beamforming scheme is further proposed. It turns out that only two training OFDM symbols are required for the estimation. Simulation results show that the proposed methods can approach the CRLB/BCRLB in both scenarios and achieve the same spectral efficiency as that obtained with the ideal channel in millimeter-wave communications.

Investigations on wavelet and Fourier transform based channel estimation in MIMO-OFDM system

In this paper channel estimation methods for MIMO-OFDM system are investigated based on Fourier Transform and Wavelet Transform. The channel estimation algorithm based on Discrete Fourier Transform (DFT) cause energy leakage in multipath channel with non-sample-spaced time delays. Discrete Cosine Transform (DCT) based channel estimator can mitigate the drawback of Discrete Fourier Transform based channel estimator, when the non-sample spaced path delays are available in multipath fading channels. Wavelet based systems provide better spectral efficiency because of no cyclic prefix requirement, with narrow side lobes and also exhibit improved BER performance. Simulation results reveal that the DWT based transform outperforms the conventional DFT and DCT based channel estimator in terms of bit error rate and mean square error.

Optimised periodic precoder-based blind channel estimation for MIMO-OFDM systems

ABSTRACTIn modern wireless communication systems, multiple antennas at transmitter and receiver are employed along with orthogonal frequency-division multiplexing to achieve very high data rate with significant system reliability. To further enhance system performance, blind channel estimation techniques are employed whenever data symbols are transmitted in blocks. These estimation techniques are computationally expensive at almost all signal-to-noise ratio values. In this paper, periodic precoder-based blind channel estimation is used to tackle problems like complexity, channel order overestimation and bandwidth expansion. The system reliability can be improved, if precoder coefficients are chosen in such a way that they have no dependency or minimum correlation with noise coefficients. The overall bit error rate (BER) is derived and simulated with dependency on periodic sequence and channel estimation error. The simulation results shows that the presented precoding scheme performs better in terms of BER with minimum channel estimation error as compared to other state-of-the-art schemes.

Channel Estimation in MIMO-OFDM System using LS, LMMSE and STBC Technique

Channel Estimation in MIMO-OFDM System using LS, LMMSE and STBC Technique

A Time-Domain Channel Estimation Method for MIMO-OFDM Systems with Low-Precision Quantization

Considering the cost and power consumption problems of the high-speed wireless communication receiver for high-precision quantization, this paper discusses the time-domain channel estimation for MIMO-OFDM systems with low-precision quantization. The low-precision quantization affects the performance of the system, however, by selecting the appropriate training sequence information, introducing the feedback dither control signal and using the optimized channel estimation algorithm, even with such low-precision (e.g., 1---3 bits) quantization, also can be obtained the performance of comparable the full precision MMSE criterion. Thereby reducing the complexity of the subsequent signal processing, cutting down the cost and power consumption, and effectively reducing the effect of low-precision quantization on the system performance. Theoretical analysis and simulation results show that this method has the characteristics of the fast convergence rate, higher estimation accuracy. Implement the receiver of the low cost, low power consumption and higher transmission efficiency of the system.

An Improved Compressed Sensing based Sparse Channel Estimation for MIMO-OFDM Systems with an Efficient Pilot Insertion Scheme

In this paper, we proposed an improved recovery algorithm, with efficient pilot placement, for Compressed Sensing (CS)-based sparse channel estimation based on Sparsity Adaptive Matching Pursuit (SAMP) in Orthogonal Frequency Division Multiplexing (OFDM) communication systems. The proposed algorithm does not require a priori knowledge of the sparsity, and to approach the true sparsity, adjusts the step size adaptively. Furthermore, estimation accuracy can be affected by different measurement matrices in CS, due to different pilot arrangements. It is known that, the Cyclic Difference Set (CDS) is the optimal setoff pilot locations when the signal is sparse on the unitary Discrete Fourier Transform (DFT) matrix by minimizing the mutual coherence of the measurement matrix. Based on this, an efficient pilot insertion scheme is introduced in cases where Cyclic Different Set does not exist. Simulation results in the paper show that the channel estimation algorithm, with the new pilot placement scheme, which offers a better trade-off between the performances in terms of Bit Error Rate (BER), Mean Squared Error (MSE) and the complexity, when compared to previous estimation algorithms.

An enhanced semi-blind channel estimation for MIMO-OFDM systems

In wireless communication an orthogonal frequency division multiplexing (OFDM) system is efficiently used in recent years. The OFDM method in wireless channels can create deep fades, as a result of multipath dispersive spreading. Thus, a channel state information is essential when coherent detection is involved in OFDM system. Therefore, in many coherent communication systems, a good channel estimation method is necessary for OFDM receiver design. And semi-blind channel estimation is one of these good channel estimation methods. Conventionally, a semi-blind channel estimation uses least square (LS) technique. In our paper, we applied the scaled least square (SLS) to enhance the performance of the semi-blind channel estimator. The SLS requires less knowledge of the channel second-order statistics and has improved performance than the LS technique. Simulation results show that the proposed SLS semi-blind channel estimation method enhances the performance reasonably than conventional semi-blind channel estimation.

Optimization on semi-blind channel estimation for MIMO-OFDM systems

An orthogonal frequency division multiplexing (OFDM) is one of the effective techniques used in wireless communication. In OFDM systems, channel impairments due to multipath dispersive spreading can cause deep fades in wireless channels. Thus, the OFDM receiver requires channel state information when coherent detection is involved. Therefore, to overcome the impact of channel fades good channel estimation (CE) methods are needed in OFDM systems. And one of these CE methods is a semi-blind CE. However, the semi-blind method requires a large number of processing operations. In order to avoid the high computing complexity of the existing method, scaled least square (SLS) technique is applied to improve the performance of the semi-blind channel estimator which require less knowledge of the channel second-order statistics and have better performance than the least square (LS) which used in semi-blind CE. Simulation results shows, this proposed method of semi-blind CE has the capacity of elevating CE performance in multiple-input multiple-output (MIMO) OFDM systems.

A Hybrid of BFO and MCS Algorithm for Channel Estimation of MIMO-OFDM System

A Hybrid of BFO and MCS Algorithm for Channel Estimation of MIMO-OFDM System

On the optimality of training signals for MMSE channel estimation in MIMO-OFDM systems

In this paper, we investigate the optimality of training signals for linear minimum mean square error (LMMSE) channel estimation in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) with frequency-selective fading channels. This is a very challenging problem due to its mathematical intractability and has not been analytically solved in the literature. Using the Lagrange multiplier method, we derive the optimality conditions for training signal design. Important findings revealed on optimal training signals are twofold: (i) the energies of the training signals on each subcarrier are equal, and (ii) on each subcarrier, the training signals transmitted from the different antennas are orthogonal and of equal energy. We verify that our results are in line with the design principles that have been derived in single-carrier MIMO systems. Two types of optimal training signal examples that satisfy the optimality conditions are presented for practical implementations in MIMO-OFDM systems. Simulation results show that the training signals based on the optimality conditions outperform other non-optimal training signals in terms of channel estimation performance.

A New Precoder Design for Blind Channel Estimation in MIMO-OFDM Systems

A new precoder for precoding-based blind channel estimation for MIMO-OFDM systems is proposed. In the proposed scheme, only a small number of data symbols, commensurate with the channel length, are linearly precoded prior to transmission to induce the signal correlation needed for the scheme to blindly estimate the channels. Similar to how pilot symbols are transmitted, the subcarriers carrying these linearly precoded data symbols are equi-spaced across the frequency band. The other subcarriers carry data symbols in the standard way, enabling MLD per subcarrier and also allowing for MIMO linear precoding across antennas at each subcarrier. This is in contrast to previous precoding-based blind channel estimation schemes which precode all of the data symbols so that every subcarrier carries a linear combination of symbols, aking the resulting joint MLD problem infeasible. In addition, this also makes it infeasible to employ MIMO precoding per subcarrier across the transmit antennas. The proposed precoder is designed via a multi-stage optimization process that seeks to minimize both channel estimation error and symbol estimation error. For channel estimation purposes, the resulting optimal design offers low-cost features such as sign change and FFT while providing reasonable channel estimation performance for low mobility applications.

A Hybrid of Bacterial Foraging and Modified Cuckoo Search Optimization for Pilot Symbol Design in MIMO-OFDM Systems

Modern mobile telecommunication systems are using MIMO combined with OFDM, which is known as MIMO-OFDM systems, to provide robustness and higher spectrum efficiency. The major challenge in this scenario is to obtain an accurate channel estimation to detect information symbols, once the receiver must have the channel state information to equalize and process the received signal. Channel estimation is an essential task in MIMO-OFDM systems for coherent demodulation and data detection. Also designing pilot tones that affect the channel estimation performance is an important issue for these systems. For this reason, in this study we propose a Hybrid optimization algorithm (HBFOMCS) based on Bacterial Foraging Optimization (BFO) and Modified Cuckoo Search algorithm (MCS) to optimize placement of the pilot tones that are used for Least Square (LS) channel estimation in MIMO-OFDM systems. Simulation results show that designing pilot tones using the hybrid algorithm outperforms other considered placement strategies in terms of high system performance and low computational complexity.

Joint IQ Imbalance and Channel Estimation for MIMO-OFDM Systems with Sparse Multipath Channels

摘要: 该文针对稀疏多径信道环境中的MIMO-OFDM系统所存在的IQ不平衡问题，提出了一种IQ不平衡参数和信道的联合估计方法。推导了联合估计的时域模型，提出了基于l1l2优化模型和平行坐标下降算法的联合估计算法。仿真结果表明，所提方法与传统的频域最小二乘算法和匹配追踪算法相比，显著提高了系统性能。 关键词: 无线通信 / MIMO / OFDM / IQ不平衡 / 稀疏多径信道 / 估计

Channel Estimation for OFDM

Orthogonal frequency division multiplexing (OFDM) has been widely adopted in modern wireless communication systems due to its robustness against the frequency selectivity of wireless channels. For coherent detection, channel estimation is essential for receiver design. Channel estimation is also necessary for diversity combining or interference suppression where there are multiple receive antennas. In this paper, we will present a survey on channel estimation for OFDM. This survey will first review traditional channel estimation approaches based on channel frequency response (CFR). Parametric model (PM)-based channel estimation, which is particularly suitable for sparse channels, will be also investigated in this survey. Following the success of turbo codes and low-density parity check (LDPC) codes, iterative processing has been widely adopted in the design of receivers, and iterative channel estimation has received a lot of attention since that time. Iterative channel estimation will be emphasized in this survey as the emerging iterative receiver improves system performance significantly. The combination of multiple-input multiple-output (MIMO) and OFDM has been widely accepted in modern communication systems, and channel estimation in MIMO-OFDM systems will also be addressed in this survey. Open issues and future work are discussed at the end of this paper.