Blind Channel Estimation Algorithm Research Articles

New precoding blind channel estimation and channel order estimation algorithm in OFDM systems with cyclic prefix

Using different precoding matrices, a new blind channel estimation in OFDM systems with cyclic prefix is presented in this paper. The proposed method employs one column of the correlation matrix directly, unlike the traditional precoding techniques where the elements of precoding matrix is been removed. Results show the impact of this method specially when using the type of precoding matrices which include the circulant property in its design. Since channel order estimation is an important task in blind methods, a new and simple algorithm is also investigated with no additional complexity been added to the system. A proof of diagonalizability property is mentioned which can be implemented for other investigations concerning this type of matrices.

Unsourced Multiple Access Based on Sparse Tanner Graph—Efficient Decoding, Analysis, and Optimization

We propose novel sparse-graph-based transmission schemes and receiver algorithms for unsourced multiple access (UMA) in MIMO channels. The channel coherence interval is divided into a number of sub-slots and each active transmitter selects certain sub-slots to repeatedly transmit its codeword according to a sparse Tanner graph. We propose iterative receiver algorithms that at each iteration decode either a single codeword, or two or three codewords jointly, and then subtract the decoded codewords from received signals during all sub-slots. The keys to these decoders are novel blind channel estimation algorithms when the received signal contains one, two, or three codewords. We perform density evolution analysis on the proposed UMA systems to obtain the asymptotic upper bounds on the maximum achievable rates for different decoders under both regular and irregular Tanner graphs. Extensive simulation results are provided to illustrate the performance of the proposed UMA systems, and its advantages over existing compressed-sensing (CS)-based UMA schemes.

A Novel Blind mmWave Channel Estimation Algorithm Based on ML-ELM

The millimeter wave (mmWave) with wide frequency spectrum can fulfill the demand of escalating communication system capacity. However, the large bandwidth of the mmWave channel leads to a large increase in the dimension of the received signal, which results to the increased computational complexity for channel estimation. In this letter, we propose a novel blind channel estimation algorithm based on Manifold Learning-Extreme Learning Machine (ML-ELM) in mmWave communication system. In the proposed ML-ELM algorithm, Manifold Learning (ML) is employed to reduce the feature dimension of received signal, and Extreme Learning Machine (ELM) with one-shot training is applied for the estimated Channel State Information (CSI). The ELM channel estimator is trained using the channel fading features extracted by ML algorithm. The CSI estimated by ML-ELM algorithm is more accurate, and the computational complexity satisfied the real-time requirements. Simulation results show that the proposed ML-ELM algorithm without any pilot aided achieves better MSE performance of CSI compared with the non-blind channel estimation algorithms in higher SNR scenarios, and better compared with pilot-based LS algorithm in lower SNR scenarios.

Blind Channel Estimation for Downlink Massive MIMO Systems With Imperfect Channel Reciprocity

We consider the performance of time-division duplex (TDD) massive multiple-input multiple-output (MIMO) with imperfect calibration of the transmit and receive radio frequency chains. By deriving the achievable signal-to-interference-plus-noise ratio (SINR) and the per-user bit error rate (BER) for constant modulus constellations, we establish that, under linear precoding, reciprocity imperfections can result in substantial reduction of the array gain. To mitigate this loss, we propose an algorithm for blind estimation of the effective channel gain at each user. We show that, with sufficiently many downlink data symbols, our blind channel estimation algorithm restores the array gain. In addition, the proposed blind gain estimation algorithm can improve performance compared to standard hardening-based receivers even under perfect reciprocity. Following this, we derive the BERs for non-constant modulus constellations, viz. $M$ -PAM and $M$ -QAM. We corroborate all our derived results using numerical simulations.

Short-Packet Physical-Layer Network Coding

This paper explores the application of physical-layer network coding (PNC) for short-packet transmissions. PNC can potentially reduce the communication delay in relay-assisted wireless networks and can thus be instrumental in realizing short-packet communication systems with stringent delay requirements. In this work, first, we first derive an achievability bound for channel-coded short-packet PNC systems. Based on the random-coding error-exponent, the bound serves as a benchmark for short-packet PNC operating with traditional preamble-aided channel estimation and XOR channel decoding. Second, we design a blind channel estimation algorithm and a code-aided channel estimation algorithm for short-packet PNC systems. Both outperform the traditional preamble-aided channel estimation for PNC systems operating with mismatched channel-state-information. As a case study, we compare the three algorithms for packets of 128 symbols over a two-way relay channel. The results show that the blind algorithm outperforms the code-aided algorithm and preamble-aided algorithm by almost 0.2 and 1.5 dB respectively. Furthermore, the blind algorithm achieves the target packet error rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> within 0.5 dB of the random coding bound of an imaginary system in which perfect channel-state-information is available at the relay at no cost (i.e., channel estimation is not required in the imaginary system). The bound and the algorithms give us a fundamental framework for applying PNC to short-packet transmissions.

A new blind algorithm for channel estimation in OFDM-based amplify-and-forward two-way relay networks

In this paper, we propose a blind channel estimation algorithm for the amplify-and-forward (AF) two-way relay network (TWRN) which consists of two terminal nodes and one relay node. The orthogonal frequency division multiplexing (OFDM) modulation is adopted for frequency selective channel. Both cyclic prefix (CP) and zero padding (ZP) are considered. The two cascaded channels are estimated in two steps. First, the cascaded channel causing the self-interference is estimated using a proposed power reduction method. Then, the other cascaded channel from source to destination is estimated by subspace method. Closed-form formulas for channel estimates are derived. In addition, we also carry out the theoretical mean square error analysis and derive the approximated Cramer-Rao bounds.

Robust blind channel estimation algorithm for linear STBC systems using fourth order cumulant matrices

A novel blind channel estimation algorithm, based on fourth-order cumulant matrices, is proposed and applied to linear Space–Time Block Coded (STBC) for Multiple Input Multiple Output systems. Contrary to subspace and Second-Order Statistics (SOS) methods, the presented approach estimates the channel matrix without any modification of the transmitter. It takes advantage of the statistical independence of the signals in front of the space–time encoding. In this paper, the presented algorithm estimates the channel matrix by minimizing a cost function based on the higher cumulant matrices after Zero-Forcing equalization to mitigate the computational complexity and improve the performance. We employ the proposed method to the STBC systems including Spatial Multiplexing, Orthogonal, quasi-Orthogonal and Non-Orthogonal STBC systems. Symbol error rate and Normalized Mean Square Error simulations of the proposed algorithm are shown for a different number of users, signal to noise ratios and different number of symbols per user in comparison with subspace and Second-Order Statistics (SOS) methods. The results show that the presented method performs well and outperforms other methods in estimating the channel matrix from the received data. Moreover, the proposed method presents high convergence speed in estimating the channel matrix.

Distributed Cophasing With Autonomous Constellation Selection

In this paper, we consider a distributed cophasing (DCP) technique in which multiple sensor nodes (SNs) communicating their observations to a fusion center (FC) need to transmit different classes of data requiring different levels of error protection. To achieve this, we propose a variant of the DCP technique with autonomous constellation selection at the different SNs. In the first stage of the two-stage time division duplexed cophasing scheme, the SNs obtain estimates of the channels to the FC using pilot symbols transmitted by the latter. Following this, the SNs simultaneously transmit their data symbols prerotated according to the estimated channel phases to combine coherently at the FC. The symbols transmitted by different SNs are drawn from possibly different constellations selected based on the estimated instantaneous channel gains. We show that this scheme is equivalent to transmitting symbols from hierarchical constellations. Based on the properties of hierarchical constellations, we develop recursive expressions for the BER of the proposed system. Following this, we use the properties of the effective channel coefficients to show that it is possible to recover the transmitted data bits from the signal received at the FC blindly, without requiring explicit pilot symbols to be sent by the power-starved SNs. We develop three blind channel estimation and data detection schemes for the presented system model. Using Monte Carlo simulations, we show that the proposed blind channel estimation algorithm achieves a probability of error performance close to that with genie aided perfect channel state information (CSI) at the FC, while using only a moderate number of unknown data symbols for channel estimation.

Multistream Distributed Cophasing

In this paper, we develop a distributed cophasing (DCP) technique for physical layer fusion of multiple data streams in a wireless sensor network with multiple destination nodes (DNs). The DNs can either be connected to a fusion center (referred to as centralized data processing; CDP) or process data independently and communicate with each other via a rate-limited link (referred to as distributed data processing; DDP). In the first stage of this two-stage cophasing scheme, sensors estimate the channel to the DNs using pilot symbols transmitted by the latter; following which they simultaneously transmit multiple streams of data symbols by prerotating them according to the estimated channel phases to the different DNs. The achievable rates for both CDP and DDP are derived to quantify the gains obtainable by the multistream DCP. In order to aid data detection at the receiver, we propose a least-squares-based iterative algorithm for blind channel estimation in CDP–DCP. Following this, we develop a message passing based blind channel estimation algorithm for DDP–DCP. It is found using Monte Carlo simulations that for the CDP system, the proposed blind channel estimation algorithm achieves a probability of error performance very close to that with perfect CSI at the DNs, while using only a moderate number of unknown data symbols for channel estimation. We also derive approximate expressions for the error probability performance of the proposed system for both CDP and DDP and validate their accuracy using Monte Carlo simulations.

Blind channel estimation for FBMC‐based PMR transmission

AbstractThe paper presents a blind channel estimation algorithm for an filter bank multicarriers waveform in a fast varying channel context for professional mobile radio transmissions. A fourth‐order power elevation algorithm, followed by an auto‐deconvolution step, directly leading to the channel impulse response estimation, is developed. Simulation results are adapted to the Tetra enhanced data services standard context for hilly terrain propagation model. Copyright © 2016 John Wiley &amp; Sons, Ltd.

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.

Low-complexity fractional phase estimation for totally blind channel estimation

To remove the scalar ambiguity in conventional blind channel estimation algorithms, totally blind channel estimation (TBCE) is proposed by using multiple constellations. To estimate the unknown scalar, its phase is decomposed into a fractional phase and an integer phase. However, the maximum-likelihood (ML) algorithm for the fractional phase does not have closed-form solutions and suffers from high computational complexity. By exploring the structures of widely used constellations, this paper proposes a low-complexity fractional phase estimation algorithm which requires no exhaustive search. Analytical expressions of the asymptotic mean squared error (MSE) are also derived. The theoretical analysis and simulation results indicate that the proposed fractional phase estimation algorithm exhibits almost the same performance as the ML algorithm but with significantly reduced computational burden.

Blind Identification for Communication Signals: An Approach Exploiting Sampling Phase Diversity

This paper presents a second-order blind channel estimation algorithm for digital communication signals under natural asynchronous conditions where no synchronization exists between transmitting and receiving antennas. The approach exploits sampling phase diversity provided by the cyclostationary nature of the received signals as an advantageous means of constructing multiple sets of distinct autocorrelation matrices evaluated at equals time lags. Channel estimation is then performed via joint diagonalization of a set of differential autocorrelation target matrices, avoiding the need of noise power or statistical distribution estimation. A broad set of simulation experiments is presented in distinctive signal contexts as a means of supporting and evidencing the potential of the proposed estimation method.

Blind Channel Estimation for Amplify-and-Forward Two-Way Relay Networks Employing $M$-PSK Modulation

We consider the problem of channel estimation for amplify-and-forward (AF) two-way relay networks (TWRNs). Most works on this problem focus on pilot-based approaches which impose a significant training overhead that reduces the spectral efficiency of the system. To avoid such losses, this work proposes blind channel estimation algorithms for AF TWRNs that employ constant-modulus (CM) signaling. Our main algorithm is based on the deterministic maximum likelihood (DML) approach. Assuming M-PSK modulation, we show that the resulting estimator is consistent and approaches the true channel with high probability at high SNR for modulation orders higher than 2. For BPSK, however, the DML performs poorly and we propose an alternative algorithm that performs much better by taking into account the BPSK structure of the data symbols. For comparative purposes, we also investigate the Gaussian maximum-likelihood (GML) approach which treats the data symbols as Gaussian-distributed nuisance parameters. We derive the Cramer-Rao bound and use Monte-Carlo simulations to investigate the mean squared error (MSE) performance of the proposed algorithms. We also compare the symbol-error rate (SER) performance of the DML algorithm with that of the training-based least-squares (LS) algorithm and demonstrate that the DML offers a superior tradeoff between accuracy and spectral efficiency.

Blind channel estimation and discrete speed tracking in wireless systems using independent component analysis with particle filtering

For high data rate multiple-input multiple-output (MIMO) systems, a joint blind channel estimation and data recovery algorithm is presented for where the relative speed of the transmit/receive terminals may change. This changing speed is called manoeuvering. The blind technique is based on non-stationary independent component analysis with a generalised exponential density function to separate each source signal, and it uses particle filtering to track the time-varying channel. The presented technique also uses a hard decision switching block which adaptively selects between discrete speeds of a manoeuvering terminal. The speed can be therefore tracked within a mobile data communication link, that is, by using only the received data information signal. The performance is evaluated by simulation and is compared with optimal coherent detection as benchmark. A large degradation in system error performance is observed if the switching block is disabled within the algorithm, confirming its advantage. Moreover, to assess the impact of the presented blind channel estimation on system error performance more directly, a fair comparison with a known blind technique based on Kalman filtering and two known pilot-aided systems is presented with the assumption of non-manoeuvering terminals. Improved performance is observed using the presented technique.

Blind Reduced-Rank Adaptive Receivers for DS-UWB Systems Based on Joint Iterative Optimization and the Constrained Constant Modulus Criterion

A novel linear blind adaptive receiver based on joint iterative optimization (JIO) and the constrained-constant-modulus design criterion is proposed for interference suppression in direct-sequence ultrawideband systems. The proposed blind receiver consists of the following two parts: 1) a transformation matrix that performs dimensionality reduction and 2) a reduced-rank filter that produces the output. In the proposed receiver, the transformation matrix and the reduced-rank filter are jointly and iteratively updated to minimize the constant-modulus cost function subject to a constraint. Adaptive implementations for the JIO receiver are developed using the normalized stochastic gradient (NSG) and recursive least squares (RLS) algorithms. To obtain a low-complexity scheme, the columns of the transformation matrix with the RLS algorithm are individually updated. Blind channel estimation algorithms for both versions (i.e., NSG and RLS) are implemented. Assuming perfect timing, the JIO receiver only requires the spreading code of the desired user and the received data. Simulation results show that both versions of the proposed JIO receivers have excellent performance in terms of suppressing the intersymbol interference (ISI) and multiple-access interference (MAI) with low complexity.

Blind Channel Estimation with Lower Complexity Algorithm for OFDM System

Fast and reliable channel estimation is very important for wireless communication transmission. This paper addresses blind channel estimation for OFDM systems. A novel One-Cycle blind channel estimation algorithm based on cyclostationarity properties of OFDM signal is proposed in this paper, the new algorithm is formed from using the two related z-transform values of delay parameter . Furthermore, a lower complexity algorithm based on partial spectrum information can further reduce computational complexity. Computer simulation results verify that the performance of the proposed One-Cycle algorithm is superior to that of the Two-Cycle algorithm. Although the lower complexity One-Cycle algorithm has some performance loss in high SNR region, its computational complexity is dramatically reduced

Blind Channel Estimation for UWB Systems Based on the Cyclic Convolution Property of the Receive Signal’s Expectation

In this paper,a novel blind channel estimation algorithm based on tap detecting is proposed for the chip-rate discrete channel of the TH-PPM Ultra Wide-Band(UWB)communications.In this algorithm,the cyclic convolution property of the receive signal's expectation is used to model the question of channel estimation,and the sparse and cluster structure of UWB channel is used to avoid non-important zero taps' estimation which promoted the performance of the algorithm.Simulation results demonstrated that the mean square error of the UWB-TDLS is 5.5dB's lower than UWB-LS in the situation of low signal to noise ratio(0-15dB),while it is 3.SdB lower in the situation of high signal to noise ratio(15dB),moreover the UWB-TDLS also has better signal-error-ratio performance than UWB-LS.

Blind Channel Estimation for STBC Systems Using Higher-Order Statistics

This paper describes a new blind channel estimation algorithm for Space-Time Block Coded (STBC) systems. The proposed method exploits the statistical independence of sources before space-time encoding. The channel matrix is estimated by minimizing a kurtosis-based cost function after Zero-Forcing equalization. In contrast to subspace or Second-Order Statistics (SOS) approaches, the proposed method is more general since it can be employed for the general class of linear STBCs including Spatial Multiplexing, Orthogonal, quasi-Orthogonal and Non-Orthogonal STBCs. Furthermore, unlike other approaches, the method does not require any modification of the transmitter and, consequently, is well-suited for non-cooperative context. Numerical examples corroborate the performance of the proposed algorithm.

Subspace-Based Blind Channel Estimation for MIMO-OFDM Systems With Reduced Time Averaging

Among the various approaches recently proposed for blind estimation of wideband multiple-input-multiple-output (MIMO) wireless channels, subspace-based algorithms are particularly attractive due to their good performance and simple structure. These algorithms primarily exploit the orthogonality of the noise and signal subspaces of the correlation matrix of the received signals to estimate the unknown channel coefficients. In practice, the correlation matrix is unknown and must be estimated through time averaging over multiple received samples. To this end, the unknown channel must remain time invariant through the averaging process, which may pose a serious problem in practical applications. In this paper, to relax this requirement, we propose a novel subspace-based blind channel-estimation algorithm with reduced time averaging, as obtained by exploiting the frequency correlation among adjacent subcarriers in MIMO orthogonal frequency-division multiplexing (OFDM) systems. Simulation results show that the proposed approach outperforms other previously proposed methods within a reasonable averaging time over a Third-Generation Partnership Project (3GPP) spatial channel model.