Efficient Channel Estimation Algorithm Research Articles

Second-Order Statistics-Aided Channel Estimation for Multipath Massive MIMO-OFDM Systems

This paper develops an efficient channel estimation algorithm based on second-order statistics of time division duplex (TDD) multiuser massive multiple-input multiple-output (MIMO) systems. The algorithm uses the received signal correlation to determine the most significant lags (MSLs) of the received signal. We first employ these MSLs to propose a novel set containing the channel’s four most significant taps (MSTs). Then, by using them, we propose an efficient semi-blind iterative algorithm called enhanced modified-subspace pursuit (EM-SP). It uses the set mentioned above and two theoretical results ( <xref ref-type="lemma" rid="lemma1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lemma 1</xref> and <xref ref-type="theorem" rid="theorem1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Theorem 1</xref> ) to estimate an arbitrary number of MSTs efficiently. Simulation results show that the normalized mean square error (NMSE) of the proposed EM-SP algorithm is much smaller than that of the subspace pursuit (SP) and orthogonal matching pursuit (OMP) algorithms at the cost of 0.3 % and 2 % more computational complexity for the channels with three and six nonzero paths, respectively. Moreover, the NMSE of it is very close to that of the optimal genie-aided least square algorithm.

A Pilot-Based Integration Method of Ranging and LS Channel Estimation for OFDM Systems

In the design of unmanned aerial vehicle (UAV) communication systems, orthogonal frequency division multiplexing (OFDM) is a commonly used communication technology. An efficient channel estimation and equalization algorithm is required to recover the amplitude, phase, and frequency of the signal in OFDM systems. At present, the more precise channel estimation method is based on the pilot. However, its spectrum utilization is relatively low. Therefore, this paper presents the design of a new pilot based on the LS channel estimation, which extends the role of the traditional pilot and improves the utilization of the spectrum. In addition to the channel estimation and equalization, the new pilot can also be utilized for ranging. Simulation results show that the proposed scheme can achieve both channel estimation and communication ranging functions by using the new pilot, and it outperforms the conventional method in channel estimation performance. The proposed method can complete ranging when the bit error rate (BER) is above 0 dB. Moreover, compared with the traditional channel estimation, it reduces the requirement for SNR by about 1 dB under the same BER.

Efficient Beamforming Training and Channel Estimation for Millimeter Wave OFDM Systems

We study the problem of downlink beamforming training and channel estimation for millimeter wave (mmWave) OFDM systems, where a hybrid analog and digital beamforming structure is employed at the transmitter (i.e., base station) and an omni-directional antenna or an antenna array is used at the receiver (i.e., user). To efficiently probe the channel, we form multiple directional beams simultaneously at the transmitter and steer them towards different directions. The objective is to devise the beam training sequence and develop an efficient algorithm to estimate the channel. By exploiting the sparse scattering nature of mmWave channels, the above problem is formulated as one of sparse encoding and signal recovery, which involves finding a sparse sensing matrix to compress the sparse channel and an efficient channel estimation algorithm to recover the sparse channel from compressive measurements. In this article, we propose a sparse bipartite graph code-based algorithm, where a set of bipartite graphs are employed to encode the sparse channel and a simple decoding procedure that relies on the presence of a No-Multiton-graph (NM-graph) is used to reconstruct the sparse channel. Theoretical analysis shows that our proposed method can help achieve a substantial training overhead reduction. Simulations are provided to show the effectiveness of the proposed algorithm and its performance advantage over compressed sensing-based methods.

Millimeter Wave Massive MIMO Channel Estimation Using Subspace Pursuit Greedy Algorithm

Achieving high data rate communications and increasing system reliability are the essentials for 5G networks. Massive MIMO and millimeter wave (mmWave) communications along with the ongoing developments in the underlying infrastructure promise to sharply increase the aggregate capacity. The communication system harnessing the odds of the unused spectrum in mmWave bands is vulnerable to the extreme path loss that severely weakens the strength of the information-bearing signal. Nevertheless, exploiting hybrid beamforming and efficient high-speed channel estimation algorithm can help address this problem and create the communication systems required for 5G. Our main contribution in this paper is to propose a very high speed accurate greedy algorithm called Subspace Pursuit (SP) and we also compare the performance of this algorithm with two other greedy algorithms. Simulation results show that SP algorithm achieves a noticeable improvement in comparison with Orthogonal Matching Pursuit (OMP) and Regularized Orthogonal Matching Pursuit (ROMP) algorithms in terms of both the spectral efficiency and runtime.

An efficient RNQ channel estimation algorithm for AMC communication techniques

ABSTRACT The adaptive communication systems require a good estimation technique to offer beneficial performances. In this work, a new robust estimation algorithm based on a non-quadratic error function (RNQ) was proposed for Bit Error Rate (BER) reduction, and improving of the spectral efficiency (SE) and transmission throughput in Adaptive Modulation and Coding-Coded Orthogonal Frequency Division Multiplexing (AMC-COFDM) systems. Also, this technique is compared to the Normalised Least Mean Square (NLMS) estimation method. The obtained results show clearly that the estimation method based on the RNQ algorithm is very efficient in term of the convergence speed and minimum average error and works very satisfactorily.

Efficient Channel Estimation for mmWave MIMO With Transceiver Hardware Impairments

Transceiver hardware impairments (e.g., phase noise, high power amplifier nonlinearities, in-phase/quadrature-phase imbalance, and quantization errors) degrade the performance of channel estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. Although compensation methods can be exploited to mitigate the impact of hardware impairments, there always remains residual impairments that will distort the training pilots and received signals. In this paper, we reformulate the channel estimation with transceiver impairments into a sparse recovery problem from a Bayesian perspective and propose an efficient channel estimation algorithm. The proposed algorithm can effectively deal with the perturbation in channel estimation problem caused by the transmitter hardware impairments with small amount computation times. Simulation results demonstrate the superior performance of the proposed algorithm compared to the conventional orthogonal matching pursuit algorithm (OMP) based channel estimation method and Bayesian inference method.

Irregular Array Manifold Aided Channel Estimation in Massive MIMO Communications

Angle information aided channel estimation has recently attracted extensive attention since its exploitation of the underlying angular channel structure has brought significant performance improvement. Nevertheless, current research works are mostly tailored to one dimensional (1D) or two dimensional (2D) antenna arrays. Being heavily dependent on the shift-invariant structure of the arrays, these existing results cannot be easily extended to irregular three dimensional (3D) arrays, which however are indispensable for base stations with large numbers of antennas but limited space. To extend the angle aided channel estimation to the regime of 3D irregular arrays, the channel estimation problem for an irregularly shaped array is linked to an array manifold constrained complex-valued tensor decomposition problem under missing data, and an efficient two-stage channel estimation algorithm that can automatically estimate the channel path number is further proposed. Simulation results are presented to demonstrate the excellent performance of the proposed algorithm in channel estimation, including path number enumeration.

Antithetic Dithered 1-Bit Massive MIMO Architecture: Efficient Channel Estimation via Parameter Expansion and PML

Drawing from a recent work on negative noise correlation in quantization and statistics, we propose a novel antithetic dithered 1-bit massive MIMO receiver architecture and develop efficient channel estimation algorithms that exploit the natural and induced negative correlated noise in the system. We illustrate that both linear and nonlinear estimators can benefit from negative correlation. We provide a rigorous analysis of a low-complexity nonlinear estimator for channel estimation. In the process, we developed a generalized statistical framework to analyze correlated quantized output arising from this generalized linear model. We formalized the approximation technique used in this work as a special case of the more general pseudo maximum likelihood method. A parameter expanded expectation maximization (PX-EM) algorithm applied to such a system is shown to exhibit fast convergence, possessing an upper bounded convergence guarantee and a graceful monotonic estimation performance over a large SNR range. Stochastic Gibbs sampling algorithms are constructed to evaluate truncated multivariate normal distributions and to implement an asymptotically exact data augmentation algorithm for comparison.

Sparse Channel Estimation for OFDM-Based Underwater Acoustic Systems in Rician Fading With a New OMP-MAP Algorithm

In this paper, a new channel estimation algorithm is proposed that exploits channel sparsity in the time domain for an orthogonal frequency division multiplexing (OFDM)-based underwater acoustical (UWA) communications systems in the presence of Rician fading. A path-based channel model is used, in which the channel is described by a limited number of paths, each characterized by a delay, Doppler scale, and attenuation factor. The resulting algorithm initially estimates the overall sparse channel tap delays and Doppler shifts using a compressed sensing approach, in the form of the orthogonal matching pursuit (OMP) algorithm. Then, a computationally efficient and novel channel estimation algorithm is developed by combining the OMP and maximum a posteriori probability (MAP) techniques for estimating the sparse complex channel path gains whose prior densities have complex Gaussian distributions with unknown mean and variance vectors, where a computationally efficient maximum likelihood algorithm is proposed for their estimation. Monte Carlo simulation results show that the mean square error and symbol error rate performances of the OMP–MAP algorithm uniformly outperforms the conventional OMP-based channel estimation algorithm, in case of uncoded OFDM-based UWA communications systems.

Error Probability Analysis and Applications of Amplitude-Coherent Detection in Flat Rayleigh Fading Channels

This paper presents the detector design and symbol error rate (SER) analysis of $M$ -ary amplitude shift keying over multipath fading channels using amplitude-coherent detection (ACD). The optimum detector is derived using the maximum likelihood criterion, and then it is used to derive two efficient low-complexity suboptimal detectors. The probability distribution function of the decision variables and the SER of the two suboptimal detectors are expressed using simple closed-form analytical formulas when single receiving antenna is used. The SER with receiver diversity is obtained using Monte Carlo simulation. The obtained analytical and simulation results reveal that ACD can provide reliable SER as compared with noncoherent detection. Moreover, we present an efficient blind channel estimation algorithm using ACD and hybrid modulation frame structures.

TDD LTE 네트워크에서 궤환 채널의 Reciprocity를 이용한 효율적인 TDD LTE ICS 구조 설계

It is essential to develop an estimation and cancellation algorithm for feedback signals in designing the interference cancellation systems (ICS) as a solution to extending the coverage of the TDD LTE networks. Unlike the radio access of FDD LTE networks, TDD networks utilize the same frequency band in downlink and uplink for radio access. For this reason, downlink and uplink are repeated in time and thus the estimation of the feedback signals in uplink and downlink, respectively, is periodically paused to cause performance degradation. In this study, the reciprocity of downlink and uplink is examined through experiments and exploiting the same frequency band in uplink and downlink, an efficient channel estimation and elimination algorithm is proposed since the algorithm utilizes the same structure for both downlink and uplink.

DFT-based optical offset-QAM OFDM: analytical, numerical, and experimental studies

We investigate discrete Fourier transform-based offset quadrature amplitude modulation (offset-QAM) orthogonal frequency division multiplexing (OFDM) technology. We derive a closed-form expression for the de-multiplexed signal and analyze the influence of crosstalk on implementation algorithms and system performance. It is found that channel estimation in offset-QAM OFDM is different from that in conventional OFDM (C-OFDM) due to the residual crosstalk terms and requires particular study. We propose simple and efficient channel estimation algorithms and show, in a 38-Gbit/s offset-16QAM OFDM experiment with 840-km single-mode fiber, that these algorithms can enable the system performance close to the theoretical limit. By using these algorithms, we compare this technology with C-OFDM and Nyquist FDM (N-FDM) and numerically and experimentally show that DFT-based offset-QAM OFDM can greatly enhance the net data rate for fiber transmissions compared to C-OFDM and exhibit lower complexity than N-FDM. These advantages together with the successfully developed implementation algorithms make this technology very promising for optical communication systems.

Training-Aided Frequency-Domain Channel Estimation and Equalization for Single-Carrier Coherent Optical Transmission Systems

Frequency-domain equalization supported by training-aided 2 × 2 multi-input multi-output channel estimation (CE) is proposed for coherent optical transmission systems. In particular, novel training sequence (TS) schemes based on a constant amplitude zero auto-correlation code, efficient CE algorithms and equalizer-tap updating solutions are derived from the theoretical linear fiber channel model and investigated through simulations. The robustness of the proposed methods is demonstrated with respect to the main degrading optical propagation effects, such as amplified spontaneous emission noise, chromatic dispersion, polarization-mode dispersion, polarization-dependent loss, time misalignment between received and reference TS, frequency offset between transmitter and receiver lasers, as well as dynamic state of polarization rotation.

Nondata-Aided Joint Channel Estimation and Equalization for OFDM Systems in Very Rapidly Varying Mobile Channels

This paper is concerned with the challenging and timely problem of joint channel estimation and equalization for orthogonal frequency division multiplexing (OFDM) systems in the presence of frequency selective and very rapidly time varying channels. The resulting algorithm is based on the space alternating generalized expectation maximization—maximum a posteriori probability (SAGE-MAP) technique which is particularly well suited to multicarrier signal formats. The algorithm is implemented in the time-domain which enables one to use the Gaussian approximation of the transmitted OFDM samples. Consequently, the averaging process of the nonpilot data symbols becomes analytically possible resulting in a feasible and computationally efficient channel estimation algorithm leading to a receiver structure that yields also an equalized output from which the data symbols are detected with excellent symbol error rate (SER) performance. Based on this Gaussian approximation the exact Bayesian Cramér Rao lower bound (CRLB) as well as the convergence rate of the algorithm are derived analytically. To reduce the computational complexity of the algorithm, discrete Legendre orthogonal basis functions are employed to represent the rapidly time-varying fading channel. It is shown that, depending on the normalized Doppler frequency, only a small number of expansion coefficients is sufficient to approximate the channel very well and there is no need to know the correlation function of the input signal. The computational complexity of the algorithm is shown to be <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\sim}O(NL)$</tex></formula> per detected data symbol and per SAGE-MAP algorithm cycle where <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$N$</tex></formula> is the number of OFDM subcarriers and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$L$</tex></formula> is the number of multipath components.

Improved Soft Iterative Channel Estimation for Turbo Equalization of Time Varying Frequency Selective Channels

In this paper, we present computationally efficient iterative channel estimation algorithms for Turbo equalizer-based communication receiver. Least Mean Square (LMS) and Recursive least Square (RLS) algorithms have been widely used for updating of various filters used in communication systems. However, LMS algorithm, though very simple, suffers from a relatively slow and data dependent convergence behaviour; while RLS algorithm, with its fast convergence rate, finds little application in practical systems due to its computational complexity. Variants of LMS algorithm, Variable Step Size Normalized LMS (VSSNLMS) and Multiple Variable Step Size Normalized LMS algorithms, are employed through simulation for updating of channel estimates for turbo equalization in this paper. Results based on the combination of turbo equalizer with convolutional code as well as with turbo codes alongside with iterative channel estimation algorithms are presented. The simulation results for different normalized fade rates show how the proposed channel estimation based-algorithms outperformed the LMS algorithm and performed closely to the well known Recursive least square (RLS)-based channel estimation algorithm.

An Efficient Adaptive Channel Estimation Algorithm for MIMO OFDM Systems—Study of Doppler Spread Tolerance

In this paper we develop an adaptive MIMO channel estimation algorithm for space---time block coded OFDM systems. The presented algorithm is based on Expectation Maximization (EM) technique by decomposing the superimposed received signals into their signal components, and estimating the channel parameters of each signal component separately. We also study and compare our proposed EM-based algorithm with a previously introduced recursive-least-squares based algorithm for MIMO OFDM systems. At each iteration the EM algorithm decomposes the problem of multi-channel estimation into channel estimation for each transmit---receive link. In this paper we also study the Doppler spread tolerance of our proposed algorithm in a fast fading environment, and investigate how it affects the system BER performance.