Semi-blind Estimation Research Articles

Channel Estimation for Full-Duplex Multi-Antenna Ambient Backscatter Communication Systems

Ambient backscatter communication (AmBC) is a highly promising technology that enables the ubiquitous deployment of low-cost, low-power devices to support the next generation of Internet-of-Things (IoT) applications. This paper addresses channel estimation for full-duplex multi-antenna AmBC systems. This is highly challenging due to the large number of channel parameters resulting from the use of multiple antennas, the presence of self-interference, and the dependence of the backscattering channel on the state of the backscattering device. Considering both pilot-based and semi-blind estimation strategies, we propose three solutions for this problem. The first is the pilot-based maximum-likelihood (ML) estimator. The second is a semi-blind estimator based on the expectation maximization (EM) framework, which provides higher accuracy than the ML, at the cost of higher computational complexity. The third is a semi-blind estimator based on the decision-directed (DD) strategy, which provides a tradeoff between the ML and the EM. Additionally, we derive the exact Cramer-Rao bound (CRB) for pilot-based estimation and the modified CRB for semi-blind estimation. Simulations show that the ML and the EM perform very close to their respective CRBs, and that the semi-blind estimators offer significantly higher estimation accuracy, as well as superior symbol-error-rate performance, compared to the ML estimator.

Semi-blind estimation of CFO and channels for STBC-OFDM system

Semi-blind estimation of CFO and channels for STBC-OFDM system

Semi-blind estimation of CFO and channels for STBC-OFDM system

Semi-blind estimation of CFO and channels for STBC-OFDM system

Semi-Blind Channel Estimation for MIMO Diffusive Molecular Communication

Multiple-input multiple-output (MIMO) molecular communication (MC) systems can provide significantly better data rates than single-input single-output (SISO) systems. A major challenge in MIMO MC systems is the presence of inter-link interference, in addition to inter-symbol interference. Accurate estimation of all the channel information is thus essential for interference mitigation in MIMO MC systems. The pilot-based estimators proposed in existing works impose a heavy training overhead. In contrast, we address this problem using semi-blind estimation strategies. In particular, we propose one semi-blind estimator based on the expectation-maximization (EM) criterion. Moreover, as alternatives to the iterative EM estimator, we develop two other estimators that are based on the decision-directed criterion. As a benchmark on estimation accuracy, we derive the semi-blind Cramer-Rao bound. Through simulation studies, we demonstrate that our proposed estimators outperform the previously proposed pilot-based estimators. Moreover, they can be used to considerably reduce the number of transmitted pilot symbols, while maintaining high estimation accuracy.

Joint Channel, Carrier Frequency Offset and I/Q Imbalance Estimation in Ambient Backscatter Communication Systems

Ambient backscatter communication systems (ABCSs) have been gaining popularity as a green alternative to conventional communication systems. To deploy such systems, it is important to acquire the channel state information (CSI) and to account for hardware imperfections such as carrier-frequency offsets (CFOs) and inphase/quadrature (I/Q) imbalance. In this work, we present two algorithms for the joint estimation of the channel, CFO and I/Q imbalance at the receivers of ABSCs. The first algorithm is the pilot-based maximum-likelihood estimator, while the second is a semi-blind estimator based on the expectation maximization (EM) framework. While both estimators converge to their respective Cramer-Rao bounds, the EM algorithm provides significantly higher estimation accuracy, leading to better bandwidth utilization.

Efficient Channel Estimation in Massive MIMO Partially Centralized Cloud-Radio Access Network Systems

This article investigates channel estimation problem in massive MIMO partially centralized cloud-RAN (MPC-RAN). The channel estimation was realized through compressed data method to minimize the huge pilot overhead, then combined with parallel Givens data projection method (PGDPM) to form a semi-blind estimator. Comparison and analysis of improved minimum mean square error (MMSE), fast data projection method (FDPM), compressed data, and PGDPM techniques was evaluated for achievable normalized mean square error (NMSE) in MPC-RAN. The PGDPM-based estimator had the lowest normalized mean square error. The FDPM and PGDPM based methods are comparable in performance with PGDPM based estimator having a slight edge over FDPM-based estimator. This vindicates PGDPM-based estimator as a method to be utilized in channel estimation since it compresses the massive MIMO channel information, hence mitigating the fronthaul finite capacity problem, and at the same time, it is geared towards efficient parallelization for optimal BBU resource utilization.

Semi-Blind Channel Estimation for Diffusive Molecular Communication

In this letter, we consider the problem of channel estimation for diffusive molecular communication (MC) systems. The presence of memory in diffusive MC channels, along with channel noise caused by various sources, necessitate the development of accurate channel estimators to acquire the channel impulse response (CIR). Previous works proposed pilot-based estimators based on the maximum likelihood (ML) and least squares (LS) criteria. In contrast, we propose three novel semi-blind estimators, one based on the expectation maximization (EM) framework and two based on the decision-directed (DD) estimation strategy. We also obtain the corresponding semi-blind Cramer-Rao bound (CRB). Our simulation results show that all the proposed semi-blind estimators offer substantially lower mean-squared error than the existing pilot-based estimators. The EM estimator provides the highest accuracy and converges to the semi-blind CRB, while the DD estimators offer convenient low-complexity alternatives. Importantly, the proposed estimators allow for a significant reduction in the number of transmitted pilots, without compromising the estimation accuracy.

Reduced-Rank Filtering-Based Semiblind MIMO-OFDM Sparse Channel Estimation

This article derives and analyzes novel, semiblind data/channel estimation algorithms for block-based multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) transmission in sparse channels. A novel reduced equalizer (with few active taps) is introduced and the semiblind sparse channel/data estimation problem is solved via reduced-rank filtering methods. A novel arrangement of the “full” equalizer matrix allows data/channel estimation, with fewer received OFDM blocks (in channels with short coherence time). The “reduced” equalizer then further reduces the number of OFDM blocks for estimation. The novel, reduced-rank filters are analyzed and shown to be equivalent to some nonblind Bayesian and “natural gradient” methods, widely used for sparse estimation. Thus, the novel method can be considered a blind Bayesian method, which is not available in the literature, and whose perturbation analysis is performed. Simulation results illustrate that the novel semiblind estimators perform much better than existing blind/training-based sparse methods (even including the popular compressed sensing and Bayesian methods), when few training subcarriers are available (which may occur in futuristic, pilot-starved massive MIMO-OFDM systems), at much reduced complexity.

Semi-Blind Image Resampling Factor Estimation for PRNU Computation

Camera sensor fingerprints for digital camera forensics are formed by Photo-Response Non-Uniformity (PRNU), or more precisely, by estimating PRNU from a set of images taken with a camera. These images must be aligned with each other to establish sensor location pixel-to-pixel correspondence. If some of these images have been resized and cropped, the transformations need to be reversed. In this work we deal with estimation of resizing factor in the presence of one reference image from the same camera. For this problem we coin the term semi-blind estimation of resizing factor. We post two requirements that any solution of this problem should meet. It needs to be reasonably fast and exhibit very low estimation error. Our work shows that this problem can be solved using established image matching in Fourier-Mellin transform applied to vertical and horizontal projections of noise residuals (also called linear patterns).

Semi-Blind Joint Timing-Offset and Channel Estimation for Amplify-and-Forward Two-Way Relaying

In this paper, we consider the problem of joint timing-offset and channel estimation for amplify-and-forward (AF) two-way relay networks (TWRNs). This problem is solved for generic pulse-shaping filters, taking into account the filter truncation in practical communication and considering both pilot-based and semi-blind estimation strategies. Beginning with pilot-based estimation, we propose a novel Maximum-likelihood joint timing-offset and channel estimator, as well as an alternative estimator based on the special properties of Zadoff-Chu sequences. The first algorithm offers high accuracy, almost overlapping with the Cramer-Rao bound (CRB), while the second offers very low computational complexity. We then develop a semi-blind estimator based on the expectation maximization (EM) framework, exploiting the underlying Hidden Markov Model to apply Baum-Welch forward-backward recursion. The semi-blind CRB is also obtained as an indicator of the best achievable performance. Using simulations, we show that the semi-blind algorithm yields superior accuracy to pilot-based estimation, as well as improved symbol-error-rates and performs very close to the semi-blind CRB. Additionally, a low-complexity approximate EM algorithm is proposed for the case of rectangular pulses. Finally, we consider the possibility of errors in integer-offset estimation and propose pilot-based and semi-blind generalized likelihood ratio test (GLRT) schemes for correcting such errors.

Performance Analysis of a SAGE-Based Semi-Blind Channel Estimator for Pilot Contaminated MU Massive MIMO Systems

Massive multiple-input multiple-output (M-MIMO) is one of the key ingredients in the upcoming 5G technology. The benefits associated with M-MIMO rely largely on the accuracy of channel state information (CSI) available at the base station (BS). The existing literature mostly employs pilot-aided schemes which require additional pilots for improving their CSI accuracy; additionally, pilot contamination degrades their performance to a large extent. In this paper, we design a Space-Alternating Generalized Expectation-Maximization (SAGE) based semi-blind estimator for pilot contaminated multi-user (MU) M-MIMO systems. It utilizes both pilot and a few data symbols for CSI estimation through two stages, namely initialization and iterative SAGE (ISAGE). We obtain an initial channel estimate with the help of a pilot-aided linear minimum mean squared error (LMMSE) estimator in the initialization stage. The acquired initial estimate from the former stage is then iteratively updated by SAGE algorithm with the joint usage of pilot and a few data symbols in ISAGE stage. The inclusion of data information in ISAGE stages' estimation process aids in simultaneous improvement of CSI accuracy and spectral efficiency (SE) of a M-MIMO system; which is unlikely for a pilot based estimation scheme. Through simulations, we show that our estimator obtains a considerable improvement over the existing pilot-aided schemes in terms of mean squared error (MSE), bit error rate (BER), SE, and energy efficiency (EE) at a nominal increase in complexity. Besides, on average, it achieves convergence in almost two iterations. We also derive modified Cramer-Rao lower bound (MCRLB) to validate the estimation efficacy of our estimator. We evaluate a closed-form expression for lower bound on UL achievable rate of MU M-MIMO systems under both perfect and imperfect CSI scenario. We also discuss the trade-off between SE and EE.

PARATUCK semi-blind receivers for relaying multi-hop MIMO systems

In this paper, two receivers are proposed for a multiple-input multiple-output (MIMO) relaying multi-hop communication system using a Khatri-Rao space-time (KRST) coding at the source and amplify-and-forward (AF) relays. It is shown that the third-order tensor of signals received at the destination satisfies a PARATUCK-(K+1) tensor model, where K is the number of relays. After formulating the system model, the expressions of the Cramér-Rao bound (CRB) for the communication channels are derived for the particular case of a two-hop system, i.e., K=1. The presented tensorial modeling enables a joint semi-blind estimation of the transmitted symbols and the channels. The first proposed estimation algorithm is a non-iterative technique based on a rearrangement of the Kronecker product, while the second proposed receiver is based on the alternating least squares (ALS) algorithm. The uniqueness of the tensor decomposition and the identifiability conditions of the proposed algorithms are discussed. The performance of these receivers is evaluated by means of Monte Carlo simulations.

A Comprehensive Review on Various Estimation Techniques for Multi Input Multi Output Channel

The problem of wireless channel estimation has been evolving due to some undesirable effects of channel physical properties on transmitted signals. At the receiver end, distortions, delays, attenuations, interferences, and phase shifts are the most issues encounter together with the received signals. In order to overcome channel effects and provide almost a perfect quality of data transmission, channel parameter estimation is needed. In Multiple Input-Multiple Output systems (MIMO), channel estimation is a more complicated step as compared with the Single Input-Single Output systems, SISO, because of the fact that the number of sub-channels that needs estimate is much greater than SISO systems. The fundamental objective of this research paper is to go over the famous and efficient algorithms that have been innovated to solve the problem of MIMO channel estimation in wireless communication systems. In this paper, these techniques have been classified into three groups: non-blind, semi-blind and blind estimation. For each group, a brief illustration is presented for familiar estimation algorithms. Finally, we compare between these techniques based on computational complexity, latency and estimation accuracy.

Spectrum Efficient Joint Frequency Offset and Channel Estimation for Time-Asynchronous Amplify-and Forward Two-Way Relay Networks

This paper considers the problem of the joint channel and carrier frequency offset (CFO) estimation for asynchronous amplify-and-forward (AF) two-way relay networks (TWRNs). This problem has not been solved to the best of our knowledge, and the presence of timing offsets between the two terminals, in addition to the frequency offsets, makes it necessary to develop signal sampling and joint estimation schemes that explicitly take into account both offsets. As potential solutions, we investigate two estimation strategies. The first strategy is pilot-based estimation, and in this context, we develop the maximum-likelihood joint CFO and channel estimator. Then, we consider the semi-blind estimation strategy, proposing a novel estimator based on expectation maximization (EM). As an alternative to the EM, we also investigate decision-directed (DD) semi-blind estimation. The semi-blind Cramer-Rao bound (CRB) is also obtained as a benchmark. Extensive experiments are used to compare the different estimation methods, showing that the proposed EM algorithm yields significantly lower mean-squared error than both the pilot-based estimator and the DD estimator and converges to the obtained CRB. It also yields much better symbol error rates. This is achieved at a reasonable computational complexity, and only a limited number of EM iterations are sufficient for convergence. The main contribution of our work is the development of the EM estimator, the corresponding CRB, and the use of simulations to demonstrate the superior performance of the proposed EM algorithm compared to other potential solutions.

Iterative SAGE-Based Joint MCFOs and Channel Estimation for Full-Duplex Two-Way Multi-Relay Systems in Highly Mobile Environment

Two-way relay network based on full-duplex technique has the potential to enhance the spectral efficiency significantly, and increase capacity in future 5G mobile communication systems. However, the self-interference of full-duplex communication severely limits the performance of a two-way relay network. The cancellation of self-interference for multi-relay full-duplex two-way relay systems in highly mobile environment is very challenging due to time-frequency doubly selective channel on the one hand and multiple carrier frequency offsets on the other hand. In this paper, we propose a novel semi-blind estimator to jointly estimate multiple carrier frequency offsets and doubly selective self-interference channels in highly mobile two-way relay systems with orthogonal frequency-division multiplexing modulation in the presence of residual self-interference. We use discrete prolate spheroidal basis expansion model to capture rapid time variations of the channel. The proposed iterative space-alternating generalized expectation maximization-based semi-blind algorithm uses received data symbols along with received pilot symbols to obtain improved frequency offsets and channel estimate with significantly less number of pilot overhead. The proposed estimator converges in almost two iterations and achieves significant improvement over the pilot-based method. The Cramer–Rao lower bounds of the semi-blind joint estimation are also derived.

Semi-Blind Sparse Channel Estimation Using Reduced Rank Filtering

This paper develops a novel method for semi-blind estimation of sparse channels in single-input, multiple-output and multiple-input, multiple-output single-carrier transmission systems, using single/multiple transmit and multiple receive antennas. Very little work exists on semi-blind sparse channel estimation. By a novel formulation of the received data matrix (called reduced data matrix) for sparse channels, semi-blind channel/data estimation problem has been cast into a reduced-rank filtering problem. The estimation operates on selected, non-uniformly spaced equalizer taps. Then two classes of semi-blind channel/data estimation are developed, the first being a pseudo-inverse based linear prediction method and the second based on reduced rank multi-stage Wiener filtering. The second class has the advantage of not requiring the rank of the reduced data matrix, but estimating it adaptively via stage-by-stage updates. The computational complexity of the novel reduced channel estimators are compared with the full channel estimators, which use all available equalizer taps. Simulation results show that the reduced semi-blind estimators perform much better than the full estimators and existing sparse channel estimators, with limited number of training symbols, at much reduced computational complexity.

Semi-blind Channel Estimation for Multiuser Massive MIMO Systems

Motivated by recent developments in time-division duplex massive multiple-input multiple-output (MIMO) systems, this paper investigates semi-blind channel estimation for multiuser MIMO systems. An expectation-maximization (EM) algorithm is derived for semi-blind channel estimation and a tractable EM algorithm is obtained by assuming a Gaussian distribution for the unknown data symbols, which improves channel estimates even when the data symbols are drawn from a finite constellation, such as quadrature phase-shift keying. An alternate EM algorithm is also derived by employing suitable priors on the channel coefficients and it is shown to outperform the EM algorithm with no priors in the low signal-to-noise ratio regime. To analytically understand the performance of the semi-blind scheme, Cramer–Rao bounds (CRBs) for semi-blind channel estimation are derived for deterministic and stochastic (Gaussian) data symbol models. To get insight into the behavior of a massive MIMO system, the asymptotic behavior of the CRBs as the number of antennas at the base station grows is analyzed. The numerical results show the benefits of semi-blind estimation algorithm as measured by the mean squared error. In particular, the performance of the EM algorithm becomes closer to the genie-aided maximum likelihood estimator based on known data symbols as the number of antennas increases. This result is consistent with the asymptotic analysis of the two CRBs indicating that semi-blind channel estimation for massive MIMO systems is very promising.

Joint Impulsive Noise Estimation and Data Detection Conceived for LDPC-Coded DMT-Based DSL Systems

Impulsive noise is one of the most challenging issues in digital subscriber lines (DSL). In order to mitigate the deleterious effects of impulsive noise, the conventional automatic repeat request invokes cyclic redundancy checking (CRC) in order to estimate the existence of impulsive noise and then triggers retransmission, which degrades the spectral efficiency attained. More straightforward techniques of mitigating impulsive noise, such as blanking and clipping, require specific design, which increases the implementation complexity. Against the background, we propose a novel two-stage joint impulsive noise estimation and data detection scheme conceived for low-density parity-check (LDPC) coded discrete multitone (DMT)-based DSL systems. More explicitly, first of all, we propose a semi-blind estimation method, which is capable of estimating the arrival of impulsive noise without using CRC and additionally evaluating the power of impulsive noise with an adequate accuracy. Second, in order to improve the accuracy of impulsive noise estimation in more advanced LDPC-coded DMT-based DSL systems, we propose a decision-directed method for the second stage of channel decoding and data detection with the aid of extrinsic information transfer (EXIT) charts. Our proposed two-stage scheme is capable of approaching the performance of the idealistic scenario of perfectly knowing both the arrival time and the instantaneous power of impulsive noise. Moreover, we analyze the mean square error of the proposed schemes in order to quantify the estimation accuracy and to reduce the estimation complexity. Our simulation results demonstrate that our proposed scheme is capable of achieving a near-capacity performance to using our LDPC coded DMT-based DSL system in the presence of impulsive noise.

Semi-blind pilot-aided channel estimation in uplink cloud radio access networks

In this paper, a quasi-Newton method for semi-blind estimation is derived for channel estimation in uplink cloud radio access networks (C-RANs). Different from traditional pilot-aided estimation, semi-blind estimation utilizes the unknown data symbols in addition to the known pilot symbols to estimate the channel. An initial channel state information (CSI) obtained by least-squared (LS) estimation is needed in semi-blind estimation. BFGS (Brayben, Fletcher, Goldfarb and Shanno) algorithm, which employs data as well as pilot symbols, estimates the CSI though solving the problem provided by maximum-likelihood (ML) principle. In addition, mean-square-error (MSE) used to evaluate the estimation performance can be further minimized with an optimal pilot design. Simulation results show that the semi-blind estimation achieves a significant improvement in terms of MSE performance over the conventional LS estimation by utilizing data symbols instead of increasing the number of pilot symbols, which demonstrates the estimation accuracy and spectral efficiency are both improved by semi-blind estimation for C-RANs.

A review of channel estimation and security techniques for CRNS

Cognitive Radio Network (CRN) is an intelligent wireless communication system that adapts itself to variations in the incoming radio frequency stimuli by modifying the operating parameters. Using the spectrum sensing techniques, the idle channels are detected, and allocated to the Secondary Users (SUs). The existing cooperative spectrum sensing techniques such as centralized sensing technique, Distributed sensing technique, and External sensing technique exploit efficient prediction models for allocating the frequency spectrum to SUs. For an optimal assignment of the channel using channel parameters, the channel estimation techniques such as pilot-assisted channel estimation, blind and semi blind estimation technique, and decision directed channel estimation technique are analyzed. The flexible nature of the CRN introduces various security attacks such as Primary User Emulation Attack, Objective Function Attack, Jamming Attack, Spectrum Sensing Data Falsification (SSDF), Control Channel Saturation DoS Attack (CCSD), Selfish Channel Negotiation (SCN), Sinkhole Attacks, HELLO Flood Attacks and Lion Attack. From the surveyed results, it is observed that the existing spectrum sensing, and prediction-based techniques consume more energy, and minimal data transmission rate for detecting the idle channel. Further, the end-to-end delay, energy consumption, end-to-end delay, and bandwidth are not minimized by the existing techniques.