Channel Estimation For Communications Research Articles

Estimation of Time-varying Communication Channels Using Reduced Length Chirp Pilots

Estimation of Time-varying Communication Channels Using Reduced Length Chirp Pilots

Sparse Bayesian Learning for Channel Estimation in Time-Varying Underwater Acoustic OFDM Communication

In this paper, we study the sparse Bayesian learning (SBL) framework for channel estimation in underwater acoustic (UWA) orthogonal frequency-division multiplexing (OFDM) communication systems, which provides a desirable property of preventing structural error with fewer convergence errors for sparse signal reconstruction compared with the compress sensing (CS)-based methods. First, we design a SBL-based channel estimator for block-by-block processing using the channel sparse structure independently in each block. Then, we propose a joint channel model after Doppler compensation for multi-block joint processing, where the delays of the channels for several consecutive blocks are similar and the path gains exhibit temporal correlation, and we denote a temporal correlation coefficient for path gains to evaluate the strength of the correlation. Furthermore, we propose the temporal multiple SBL (TMSBL)-based channel estimator to jointly estimate the channels by taking advantage of the channel coherence between consecutive OFDM blocks. Results of numerical simulation and sea trial demonstrate the effectiveness of the SBL and TMSBL channel estimator algorithms in time-varying UWA channel, which achieve better channel estimation performance and lower bit error rate compared with the existing CS-based methods, such as orthogonal matching pursuit (OMP) and simultaneous OMP, especially the TMSBL estimator achieves the best performance in strong temporal correlated channels and maintains robustness in weak temporal correlated channels.

On the use of EMD based adaptive filtering for OFDM channel estimation

Hilbert Huang transform (HHT) based data driven empirical mode decomposition (EMD) in conjunction with adaptive filter (AF) is proposed for estimation of communication channel in OFDM system. EMD can be viewed as alike of wavelet decomposition which decomposes the signal of interest to intrinsic mode functions (IMF), whose basis function is derived from signal itself. In this method, the length of channel impulse response (CIR), is approximated using Akaike information criterion (AIC). Then the estimation of CIR is performed using adaptive filter with EMD decomposed IMF of the received OFDM symbol. Conventional AF uses random initial weight vector. The novelty of the proposed method lies in the fact that it uses decimated version of one of the decomposed IMFs of received OFDM symbol as initial weight vector. The selection of useful IMF component is done based on correlation and kurtosis measures. This makes the proposed EMD based AF method converge to minimum mean square error (MMSE) in less number of iterations resulting in almost 50% saving of computations. Bit error rate (BER), mean square error (MSE) and normalized root mean square error (NRMSE) are computed. The simulation studies established the efficacy of proposed method; and comparative studies under different modulation schemes and fading conditions revealed improved performance. Simulations have shown an average improvement of 3 dB in BER performance for proposed EMD based AF as compared to conventional AF.

An Enhanced Set-Membership PNLMS Algorithm with a Correntropy Induced Metric Constraint for Acoustic Channel Estimation

In this paper, a sparse set-membership proportionate normalized least mean square (SM-PNLMS) algorithm integrated with a correntropy induced metric (CIM) penalty is proposed for acoustic channel estimation and echo cancellation. The CIM is used for constructing a new cost function within the kernel framework. The proposed CIM penalized SM-PNLMS (CIMSM-PNLMS) algorithm is derived and analyzed in detail. A desired zero attraction term is put forward in the updating equation of the proposed CIMSM-PNLMS algorithm to force the inactive coefficients to zero. The performance of the proposed CIMSM-PNLMS algorithm is investigated for estimating an underwater communication channel estimation and an echo channel. The obtained results demonstrate that the proposed CIMSM-PNLMS algorithm converges faster and provides a smaller estimation error in comparison with the NLMS, PNLMS, IPNLMS, SM-PNLMS and zero-attracting SM-PNLMS (ZASM-PNLMS) algorithms.

Modeling Contrast-Imaging-Assisted Optimal Targeted Drug Delivery: A Touchable Communication Channel Estimation and Waveform Design Perspective.

To maximize the effect of treatment and minimize the adverse effect on patients, we propose to optimize nanorobots-assisted targeted drug delivery (TDD) for locoregional treatment of tumor from the perspective of touchable communication channel estimation and waveform design. The drug particles are the information molecules; the loading/injection and unloading of the drug correspond to the transmitting and receiving processes; the concentration-time profile of the drug particles administered corresponds to the signaling pulse. Given this analogy, we first propose to use contrast-enhanced microwave imaging (CMI) as a pretherapeutic evaluation technique to determine the pharmacokinetic model of nanorobots-assisted TDD. The CMI system applies an information-theoretic-criteria-based algorithm to estimate drug accumulation in tumor, which is analogous to the estimation of channel impulse response in the communication context. Subsequently, we present three strategies for optimal targeted therapies from the communication waveform design perspective, which are based on minimization of residual drug molecules at the end of each therapeutic session (i.e., inter-symbol interference), maximization of duration when the drug intensity is above a prespecified threshold during each therapeutic session (i.e., non-fade duration), and minimization of average rate that a therapeutic operation is not received correctly at tumor (i.e., bit error rate). Finally, numerical examples are applied to demonstrate the effectiveness of the proposed analytical framework.

Multi-dimensional sinusoidal order estimation using angles between subspaces

Multi-dimensional harmonic retrieval (HR) in white noise is required in numerous applications such as channel estimation in wireless communications and imaging in multiple-input multiple-output radar. In this paper, we propose two R-dimensional (R-D) extensions of the subspace-based MUSIC model order selection scheme, for R≥2, to detect the number of multi-dimensional cisoids. The key idea in the algorithm development is to utilize the principle angles between multilinear signal subspaces via the truncated higher-order singular value decomposition. The first method is designed for multiple-snapshot scenarios. It considerably outperforms existing algorithms in terms of both detection accuracy and identifiability particularly when a large number of snapshots are available. However, its computational cost is relatively quite high. The second method is computationally much simpler and performs almost as well as the first one when the number of snapshots is small. Simulation results are conducted to demonstrate the performance of the proposed estimators.

Compressed sensing-based channel estimation for ACO-OFDM visible light communications in 5G systems

In this paper, we propose a compressive sensing (CS)-based channel estimation technique for asymmetrically clipped optical-orthogonal frequency division multiplexing (ACO-OFDM) visible light communications (VLC) in 5G systems. We proposed a modified version of sparsity adaptive matching pursuit (SAMP) algorithm which is named as self-aware step size sparsity adaptive matching pursuit (SS-SAMP) algorithm. It utilizes the built-in features of SAMP and with additional ability to select step size according to the present situation, hence term self-aware, can provide better accuracy and low computational cost. It also does not require any prior knowledge of the sparsity of the signal which makes it self-sufficient. CS-based algorithms such as orthogonal matching pursuit (OMP), SAMP, and our proposed SS-SAMP were implemented on ACO-OFDM VLC. The paper is supported by simulation results which demonstrate performance of proposed scheme in terms of bit error rate (BER), mean square error (MSE), computational complexity, and key VLC parameter (LED nonlinearity, shot noise, thermal noise, channel response, and peak-to-average power ratio (PAPR). It is shown that the SS-SAMP is a good candidate for ACO-OFDM-based VLC that are mobile and have limited processing power, based on its performance and computational complexity.

Three Natural Computation methods for joint channel estimation and symbol detection in multiuser communications

This paper studies three of the most important optimization algorithms belonging to Natural Computation (NC): genetic algorithm (GA), tabu search (TS) and simulated quenching (SQ). A concise overview of these methods, including their fundamentals, drawbacks and comparison, is described in the first half of the paper. Our work is particularized and focused on a specific application: joint channel estimation and symbol detection in a Direct-Sequence/Code-Division Multiple-Access (DS/CDMA) multiuser communications scenario; therefore, its channel model is described and the three methods are explained and particularized for solving this. Important issues such as suboptimal convergence, cycling search or control of the population diversity have deserved special attention. Several numerical simulations analyze the performance of these three methods, showing, as well, comparative results with well-known classical algorithms such as the Minimum Mean Square Error estimator (MMSE), the Matched Filter (MF) or Radial Basis Function (RBF)-based detection schemes. As a consequence, the three proposed methods would allow transmission at higher data rates over channels under more severe fading and interference conditions. Simulations show that our proposals require less computational load in most cases. For instance, the proposed GA saves about 73% of time with respect to the standard GA. Besides, when the number of active users doubles from 10 to 20, the complexity of the proposed GA increases by a factor of 8.33, in contrast to 32 for the optimum maximum likelihood detector. The load of TS and SQ is around 15–25% higher than that of the proposed GA.

Time and Frequency Domain Channel Estimation in MIMO-OFDM Co-operative Communication

Time and Frequency Domain Channel Estimation in MIMO-OFDM Co-operative Communication

PSO tuned ANFIS equalizer based on fuzzy C-means clustering algorithm

Wireless communication always suffers from intersymbol interference (ISI), multipath fading, co-channel interference (CCI). Equalization techniques try to combat these phenomena to recover distorted data at receivers. Most techniques need a separate process to estimate channel profile before equalization. In this paper, Particle Swarm Optimization tuned Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) based channel equalizer, which is capable of system identification, estimation and equalization of wireless communication channels, is proposed. PSO-ANFIS equalizer uses the training data and employs fuzzy C-means (FCM) clustering to model a wireless communication channel without knowledge of channel dynamics. The PSO-ANFIS equalizer was simulated on a mobile communication model with ISI, CCI and the Additive White Gaussian Noise. The used training method and FCM provided the best regression of system modeling to fit to wireless channel. The performances of PSO-ANFIS equalizer were evaluated and compared to the equalizers of Maximum-Likelihood Sequence Estimation, Recursive Least Squares, ANFIS, Functional Link Artificial Neural Network, PSO-Variable Constriction Factor and Artificial Neural Network-PSO in terms of BER–SNR. The simulation results showed that the performance of the PSO-ANFIS equalizer with FCM clustering gives the best performance among the mentioned nonlinear equalization techniques.

A Complete Analysis of Channel Estimation and Peak to Average Power Ratio in Wireless Communication Using Discrete Fourier Transform

In wireless communication peak to average power and channel estimation are important parameters which should be considered for efficient communication. At the receiver end, the signal is normally distorted due to channel interference and noise. In this paper we focus on complete analysis of PAPR and channel estimation in wireless communication. For this purpose we use discrete Fourier transform which has many application. In this paper we analyze the effect of increase in number of subcarrier on the performance of PAPR using discrete Fourier transform technique. The paper also describes how discrete Fourier transform can be used to remove the distortion from received signal. Our main motive is to improve the peak to average power ratio with the help of discrete Fourier transform approach. This will improve the power efficiency. All the mathematical equations and simulation for our analysis are done with the help of MATLAB software.

Tensor CP Decomposition With Structured Factor Matrices: Algorithms and Performance

The canonical polyadic decomposition (CPD) of high-order tensors, also known as Candecomp/Parafac, is very useful for representing and analyzing multidimensional data. This paper considers a CPD model having structured matrix factors, as e.g. Toeplitz, Hankel or circulant matrices, and studies its associated estimation problem. This model arises in signal processing applications such as Wiener-Hammerstein system identification and cumulant-based wireless communication channel estimation. After introducing a general formulation of the considered structured CPD (SCPD), we derive closed-form expressions for the Cramer-Rao bound (CRB) of its parameters under the presence of additive white Gaussian noise. Formulas for special cases of interest, as when the CPD contains identical factors, are also provided. Aiming at a more relevant statistical evaluation from a practical standpoint, we discuss the application of our formulas in a Bayesian context, where prior distributions are assigned to the model parameters. Three existing algorithms for computing SCPDs are then described: a constrained alternating least squares (CALS) algorithm, a subspace-based solution and an algebraic solution for SCPDs with circulant factors. Subsequently, we present three numerical simulation scenarios, in which several specialized estimators based on these algorithms are proposed for concrete examples of SCPD involving circulant factors. In particular, the third scenario concerns the identification of a Wiener-Hammerstein system via the SCPD of an associated Volterra kernel. The statistical performance of the proposed estimators is assessed via Monte Carlo simulations, by comparing their Bayesian mean-square error with the expected CRB.

Sparse channel estimation and pilot optimization for underwater acoustic orthogonal frequency division multiple access uplink communications

Considering that the conventional channel interpolation method with sparse and irregular spaced pilots will lead to an error floor in underwater acoustic (UWA) orthogonal frequency division multiple access (OFDMA) uplink communications, a method for sparse channel estimation and pilot optimization is proposed in this paper. A compressed sensing (CS) algorithm is utilized for sparse channel impulse response estimation, which performs well in sparse and irregular spaced pilots and significantly decreases the channel estimation error. Besides, the pilots’ pattern and power joint optimization algorithm based on the random search technique is proposed for the minimum mutual coherence criterion in CS theory, which further improves the performance of CS estimation algorithm. During each iteration step, we randomly pick a pilots’ pattern from the subcarrier index set and a pilots’ power subset from the available power set. Then we perform this step iteratively within a certain searching time. Finally, the local optimal solution of the objective function for minimizing mutual coherence is considered as the feasible pilots’ pattern and power. Simulation results show that the convergence performance of the pilots’ pattern and power joint optimization algorithm is much better than that of the pilots’ pattern optimization algorithm. Furthermore, the channel estimation error of the proposed method is much lower than that of conventional least-squares channel estimator based on linear interpolation, CS channel estimator without pilot optimization, and CS channel estimator merely with pilots’ pattern optimization in channels of different multipath delay spreads. Finally, performance of the proposed method is demonstrated in the UWA uplink OFDMA systems with interleaved and generalized carrier assignment schemes respectively in the two-user case in a pool experiment. Experimental results show that the proposed method decreases dramatically the bit error rate in both carrier assignment schemes, and simultaneous reception for two users is achieved when signal noise ratio is larger than 10 dB.

Estimation of Symmetric Channels for Discrete Cosine Transform Type-I Multicarrier Systems: A Compressed Sensing Approach.

The problem of channel estimation for multicarrier communications is addressed. We focus on systems employing the Discrete Cosine Transform Type-I (DCT1) even at both the transmitter and the receiver, presenting an algorithm which achieves an accurate estimation of symmetric channel filters using only a small number of training symbols. The solution is obtained by using either matrix inversion or compressed sensing algorithms. We provide the theoretical results which guarantee the validity of the proposed technique for the DCT1. Numerical simulations illustrate the good behaviour of the proposed algorithm.

Design and implementation of an active multibeam antenna system with 64 RF channels and 256 antenna elements for massive MIMO application in 5G wireless communications

This paper focuses on the design and implementation of an active multibeam antenna system for massive MIMO applications in 5G wireless communications. The highly integrated active multibeam antenna system is designed and implemented at 5.8 GHz with 64 RF Channels and 256 antenna elements. The 64-channel highly integrated active multibeam antenna system provides a verification platform for digital beamforming algorithm and massive MIMO channel estimation for next generation wireless communications.

Compressed Sensing Aided Channel Estimation for Power Line Communications

In this paper, we address a channel estimation scheme for power line communication systems based on compressed sensing techniques. With the properly designed pilot symbols, the received signals at the receiver can be reconstructed from a set of random projections, benefiting from a reduced sampling rate. Moreover, we propose a novel channel estimation structure for PLC systems, which can be applied for appropriate system design. Eventually, simulation results demonstrate that the proposed algorithm outperforms other algorithms and reduces the sample rate significantly.

Particle filtering based channel estimation in OFDM power line communication

Particle filtering (PF) algorithm has the powerful potential for coping with difficult non-linear and non-Gaussian problems. Aiming at non-linear, non-Gaussian and time-varying characteristics of power line channel, a time-varying channel estimation scheme combined PF algorithm with decision feedback method is proposed. In the proposed scheme, firstly the indoor power line channel is measured using the pseudo-noise (PN) correlation method, and a first-order dynamic autoregressive (AR) model is set up to describe the measured channel, then, the channel states are estimated dynamically from the received signals by exploiting the proposed scheme. Meanwhile, due to the complex noise distribution of power line channel, the performance of channel estimation based on the proposed scheme under the Middleton class A impulsive noise environment is analyzed. Comparisons are made with the channel estimation scheme respectively based on least square (LS), Kalman filtering (KF) and the proposed algorithm. Simulation indicates that PF algorithm dealing with this power line channel estimation difficult non-linear and non-Gaussian problems performance is superior to those of LS and KF respectively, so the proposed scheme achieves higher estimation accuracy. Therefore, it is confirmed that PF algorithm has its own unique advantage for power line channel estimation.

Improved smoothed L0 reconstruction algorithm for ISI sparse channel estimation

In this paper, the problem of inter symbol interference (ISI) sparse channel estimation in wireless communication with the application of compressed sensing is investigated. However, smoothed L0 norm algorithm (SL0) has ‘notched effect’ due to the negative iterative gradient direction. Moreover, the property of continuous function in SL0 is not steep enough, which results in inaccurate estimations and low convergence. Afterwards, we propose the Lagrange multipliers as well as Newton method to optimize SL0 algorithm in order to obtain a more rapid and efficient signal reconstruction algorithm, improved smoothed L0 (ISL0). ISI channel estimation will have a direct effect on the performance of ISI equalizer at the receiver. So, we design a pre-filter model which with no considerable loss of optimality and do analyses of the equalization methods of the sparse multi-path channel. Real-time simulation results clearly show that the ISL0 algorithm can estimate the ISI sparse channel much better in both signal noise ratio (SNR) and compression levels. In the same channel conditions, ISL0 algorithm has been greatly improved when compared with the SL0 algorithm and other compressed-sensing algorithms.

A Fast and Accurate Sparse Continuous Signal Reconstruction by Homotopy DCD with Non-Convex Regularization

In recent years, various applications regarding sparse continuous signal recovery such as source localization, radar imaging, communication channel estimation, etc., have been addressed from the perspective of compressive sensing (CS) theory. However, there are two major defects that need to be tackled when considering any practical utilization. The first issue is off-grid problem caused by the basis mismatch between arbitrary located unknowns and the pre-specified dictionary, which would make conventional CS reconstruction methods degrade considerably. The second important issue is the urgent demand for low-complexity algorithms, especially when faced with the requirement of real-time implementation. In this paper, to deal with these two problems, we have presented three fast and accurate sparse reconstruction algorithms, termed as HR-DCD, Hlog-DCD and Hlp-DCD, which are based on homotopy, dichotomous coordinate descent (DCD) iterations and non-convex regularizations, by combining with the grid refinement technique. Experimental results are provided to demonstrate the effectiveness of the proposed algorithms and related analysis.

Blind Deconvolution Using Convex Programming

We consider the problem of recovering two unknown vectors, w and x, of length L from their circular convolution. We make the structural assumption that the two vectors are members of known subspaces, one with dimension N and the other with dimension K. Although the observed convolution is nonlinear in both w and x, it is linear in the rank-1 matrix formed by their outer product wx*. This observation allows us to recast the deconvolution problem as low-rank matrix recovery problem from linear measurements, whose natural convex relaxation is a nuclear norm minimization program. We prove the effectiveness of this relaxation by showing that, for “generic” signals, the program can deconvolve w and x exactly when the maximum of N and K is almost on the order of L. That is, we show that if x is drawn from a random subspace of dimension N, and w is a vector in a subspace of dimension K whose basis vectors are spread out in the frequency domain, then nuclear norm minimization recovers wx* without error. We discuss this result in the context of blind channel estimation in communications. If we have a message of length N, which we code using a random L x N coding matrix, and the encoded message travels through an unknown linear time-invariant channel of maximum length K, then the receiver can recover both the channel response and the message when L ≳ N + K, to within constant and log factors.