Semi-blind Approach Research Articles

A Proposed Channel Estimation Based on Enhanced Sub-carrier Index Modulation and Packet-Discrete Wavelet Transform to Minimize Bit Error Rate

For Orthogonal Frequency Division Multiplexing (OFDM) and other communication systems, many estimating approaches have been developed to estimate the channel state information and lower the Bit Error Rate (BER). These estimating methods, however, are still subject to the influence of large peak powers compared to average powers. Reduced computational complexity is one of the most significant factors to consider while developing a new estimate algorithm. This study aims to provide a novel design of the Packet-Discrete Wavelet Transform (P-DWT) algorithm for channel estimation in wireless OFDM instead of the fast Fourier transform (FFT). It is presented to retrieve the code of a spread spectrum signal and transmitted data bits, and it is compared to particle swarm optimization PSO and least mean square (LMS) optimization. The suggested approach reduces the computing cost of DWT by recognizing the Packet Wavelet Transform (PWT) coefficients and local points, findings utilizing P-DWT channels generated from both models and measurements show that the proposed technique outperforms pilot-based channel estimation in terms of bit error rate under sparseness conditions BER. Moreover, as compared to typical semi-blind approaches, the estimation accuracy is enhanced while computing cost is reduced.

Role of the Ipsilateral Primary Motor Cortex in the Visuo-Motor Network During Fine Contractions and Accurate Performance.

It is widely recognized that continuous sensory feedback plays a crucial role in accurate motor control in everyday life. Feedback information is used to adapt force output and to correct errors. While primary motor cortex contralateral to the movement (cM1) plays a dominant role in this control, converging evidence supports the idea that ipsilateral primary motor cortex (iM1) also directly contributes to hand and finger movements. Similarly, when visual feedback is available, primary visual cortex (V1) and its interactions with the motor network also become important for accurate motor performance. To elucidate this issue, we performed and integrated behavioral and electroencephalography (EEG) measurements during isometric compression of a compliant rubber bulb, at 10% and 30% of maximum voluntary contraction, both with and without visual feedback. We used a semi-blind approach (functional source separation (FSS)) to identify separate functional sources of mu-frequency (8-13[Formula: see text]Hz) EEG responses in cM1, iM1 and V1. Here for the first time, we have used orthogonal FSS to extract multiple sources, by using the same functional constraint, providing the ability to extract different sources that oscillate in the same frequency range but that have different topographic distributions. We analyzed the single-trial timecourses of mu power event-related desynchronization (ERD) in these sources and linked them with force measurements to understand which aspects are most important for good task performance. Whilst the amplitude of mu power was not related to contraction force in any of the sources, it was able to provide information on performance quality. We observed stronger ERDs in both contralateral and ipsilateral motor sources during trials where contraction force was most consistently maintained. This effect was most prominent in the ipsilateral source, suggesting the importance of iM1 to accurate performance. This ERD effect was sustained throughout the duration of visual feedback trials, but only present at the start of no feedback trials, consistent with more variable performance in the absence of feedback. Overall, we found that the behavior of the ERD in iM1 was the most informative aspect concerning the accuracy of the contraction performance, and the ability to maintain a steady level of contraction. This new approach of using FSS to extract multiple orthogonal sources provides the ability to investigate both contralateral and ipsilateral nodes of the motor network without the need for additional information (e.g. electromyography). The enhanced signal-to-noise ratio provided by FSS opens the possibility of extracting complex EEG features on an individual trial basis, which is crucial for a more nuanced understanding of fine motor performance, as well as for applications in brain-computer interfacing.

Improving the Targets’ Trajectories Estimated by an Automotive RADAR Sensor Using Polynomial Fitting

A way to reduce the uncertainty at the output of a Kalman filter embedded into a tracker connected to an automotive RADAR sensor consists of the adaptive selection of parameters during the tracking process. Different informed strategies for automatically tuning the tracker’s parameters and to jointly learn the parameters and state/output sequence using: expectation maximization; optimization approaches, including the simplex algorithm; coordinate descent; genetic algorithms; nonlinear programming using finite differencing to estimate the gradient; Bayesian optimization and reinforcement learning; automatically tuning hyper-parameters in the least squares, were already proposed. We develop here a different semi-blind post-processing approach, which is faster and more robust. Starting from the conjecture that the trajectory is polynomial in Cartesian coordinates, our method supposes to fit the data obtained at the output of the tracker to a polynomial. We highlight, by simulations, the improvement of the estimated trajectory’s accuracy using the polynomial fitting for single and multiple targets. We propose a new polynomial fitting method based on wavelets in two steps: denoising and polynomial part extraction, which compares favorably with the classical polynomial fitting method. The effect of the proposed post-processing methods is visible, the accuracy of targets’ trajectories estimations being hardly increased.

Performance Analysis of a Semiblind Channel Estimator in One-Bit Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) suffers from RF hardware complexity, cost, and circuit power consumption. The high-resolution analog-to-digital converter (ADC) at the RF front end is one of the major contributors in power depletion. Thus, one-bit massive MIMO becomes an effective solution. However, the availability of accurate channel state information (CSI) at the base station remains a challenging task. In this article, we propose a semiblind channel estimator for one-bit massive MIMO systems and evaluate its uplink (UL) performance. The proposed algorithm consists of two stages: initialization and iteration. The initial channel estimate is obtained from a pilot-aided initialization stage, which is refined in the iteration stage with the help of both pilot and a few data symbols. The semiblind approach simultaneously improves the estimation accuracy and spectral efficiency with minimum pilot length. We also derive a theoretical lower bound on the UL achievable rate under perfect and imperfect CSI. Through simulations, we show that the semiblind scheme achieves a considerable improvement in mean square error, bit-error-rate, spectral efficiency, and bit energy against the existing one-bit pilot based estimators at the cost of a nominal increase in computational complexity.

New Iterative Method for Estimation of Carrier Frequency in Multicarrier Systems

The key problem for OFDM (Orthogonal Frequency Division Multiple) systems is who to Estimate carrier frequency offset (CFO) with reduced complexity and acceptable performance. The CFO must be compensated before DFT (Discrete Fourier Transform) in order to restore data correctly and enhance the system performance. This paper, present a low complexity estimator of CFO with Semi-Blind (SB) criterion based on pilot tones and on virtual subcarriers, and with the aid of subspace based method. However, MUSIC and ESPRIT based semi-blind algorithms require a highly computational complexity. To overcome this drawback, we use Taylor’s series for the first order as developed in [1]. The present methods developed in this paper are very suitable for Multi- Carrier (MC) systems when the CFO are present. Simulation results demonstrated that the semi-blind (SB) approach outperforms the blind-based approach.

Extended common spatial and temporal pattern (ECSTP): A semi-blind approach to extract features in ERP detection

Common spatial pattern (CSP) analysis and its extensions have been widely used as feature extraction approaches in the brain-computer interfaces (BCIs). However, most of the CSP-based approaches do not use any prior knowledge that might be available about the two conditions (classes) to be classified. Therefore, their applications are limited to datasets that contain enough variance information about the two conditions. For example, in some event-related potential (ERP) detection applications, such as P300 speller, the information is in the time domain but not in the variance of spatial components. To address this problem, first, we present a novel feature extraction method termed extended common spatial pattern (ECSP) analysis, which uses prior knowledge available from data to produce a broader range of features than that of conventional CSP analysis. Then, similarly, we introduce the extended common temporal pattern (ECTP) analysis. Finally, to exploit both spatial and temporal information, we propose extended common spatial and temporal pattern (ECSTP) analysis. We have used BCI competition III, dataset II as our main dataset to evaluate our proposed methods. In addition, we used two other datasets, namely BCI competition II, dataset IIb and BCI competition IV, dataset IIb, to further evaluate the performance of the proposed methods. In All the datasets, the proposed methods significantly outperform the conventional CSP, CTP, and CSTP methods. More specifically, ECSTP has the best performance among the proposed methods. Moreover, classification results show that the proposed methods are competitive with other state of the art methods applied to these datasets.

Tensor-Based Joint Channel Estimation and Symbol Detection for AF MIMO Relay Networks

A study on the joint channel and symbol estimation issue is provided for two hop relay networks which employ the amplify-and-forward (AF) relaying approach. The encoding scheme at the source node introduces the time-domain spreading with a time-varying linear constellation precoding. Then, a set of amplifying factors matrices is utilized by the relays to amplify and forward the received data to the destination. The received signal at the destination can be constructed as a fourth-order tensor model, which is referred to as the nested parallel factor (PARAFAC) model. And then, we present a novel Levenberg-Marquardt (LM) algorithm based on this tensor model. The proposed method does not require complex signal processing at the relay, which effectively reduces the burden of relay. As a semi-blind method, which does not require the pilot signal, the proposed receiver can jointly recover the channels and information symbols. Moreover, the proposed semi-blind receiver is robust as it can work in different wireless channel scenarios. Simulations are conducted to demonstrate the efficiency of the proposed semi-blind approach.

On Detection and Mitigation of Reused Pilots in Massive MIMO Systems

In a time-division duplex multiple antenna system the channel state information (CSI) can be estimated using reverse training. In multi-cell multi-user massive MIMO systems, pilot contamination degrades CSI estimation performance and adversely affects massive MIMO system performance. In this paper we consider a subspace-based semi-blind approach where we have training data as well as information bearing data from various users (both in-cell and neighboring cells) at the base station (BS). Existing semi-blind approaches assume that the interfering users from neighboring cells are always at distinctly lower power levels at the BS compared with the in-cell users. This requires (perfect) power control. In this paper we do not make any such assumption. Unlike existing approaches, the BS estimates the channels of all users: in-cell and significant neighboring cell users, i.e., ones with comparable power levels at the BS. We exploit both subspace method using correlation as well as blind source separation using higher-order statistics. Finally, the estimated channels are used to detect information symbols, which, in turn, are used as pseudo-pilots to re-estimate the in-cell users’ channels. The proposed approach is illustrated via simulation examples and compared with some existing semi-blind methods.

New semi-blind approach to optimize turbo decoding for a cauchy α-stable impulsive noise channel

Knowledge of the noise parameters that contaminate the signal sent is very important for many communication systems. In this work, we propose a new method to estimate the dispersion parameter of a symmetric α-stable impulse noise (SαS) of Cauchy, from the received signal only at the iterative decoder (turbo decoder). This method is based on the FLOS (Fractional Lower Order Statistics) and the probability that binds the received signal and the signal sent in BPSK modulation (Binary Phase Shift Keying). The results obtained, in terms of RMSE (Root Mean Square Error) and BER (Bit Error Rate) show the precision and the efficiency of our approach. Keywords: Cauchy impulse noise SαS, dispersion parameter, FLOS, RMSE, BER.

Performance Bounds Analysis for Semi-Blind Channel Estimation in MIMO-OFDM Communications Systems

Most communications systems require channel estimation for equalization and symbol detection. Currently, this is achieved by using dedicated pilot symbols, which consume a non-negligible part of the throughput and power resources, especially for large dimensional systems. The main objective of this paper is to quantify the rate of reduction of this overhead due to the use of a semi-blind channel estimation. Different data models and different pilot design schemes have been considered in this paper. By using the Cramer Rao Bound (CRB) tool, the estimation error variance bounds of the pilot-based and semi-blind based channel estimators for a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system are compared. In particular, for large MIMO-OFDM systems, a direct computation of the CRB is prohibitive, and hence, a dedicated numerical technique for its fast computation has been developed. Many key observations have been made from this comparative study. The most important one is that, thanks to the semi-blind approach, one can skip about 95% of the pilot samples without affecting the channel estimation quality.

Compressed Training Adaptive Equalization: Algorithms and Analysis

We propose “ compressed training adaptive equalization ” as a novel framework to reduce the quantity of training symbols in a communication packet. It is a semi-blind approach for communication systems employing time-domain/frequency-domain equalizers, and founded upon the idea of exploiting the magnitude boundedness of digital communication symbols. The corresponding algorithms are derived by combining the least-squares-cost-function measuring the training symbol reconstruction performance and the infinity-norm of the equalizer outputs as the cost for enforcing the special constellation boundedness property along the whole packet. In addition to providing a framework for developing effective adaptive equalization algorithms based on convex optimization, the proposed method establishes a direct link with compressed sensing by utilizing the duality of the $\ell _{1}$ and $\ell _\infty $ norms. This link enables the adaptation of recently emerged $\ell _{1}$ -norm-minimization-based algorithms and their analysis to the channel equalization problem. In particular, we show for noiseless/low noise scenarios, the required training length is on the order of the logarithm of the channel spread . Furthermore, we provide approximate performance analysis by invoking the recent MSE results from the sparsity-based data processing literature. Provided examples illustrate the significant training reductions by the proposed approach and demonstrate its potential for high bandwidth systems with fast mobility.

Constrained Null Space Component Analysis for Semiblind Source Separation Problem.

The blind source separation (BSS) problem extracts unknown sources from observations of their unknown mixtures. A current trend in BSS is the semiblind approach, which incorporates prior information on sources or how the sources are mixed. The constrained independent component analysis (ICA) approach has been studied to impose constraints on the famous ICA framework. We introduced an alternative approach based on the null space component (NCA) framework and referred to the approach as the c-NCA approach. We also presented the c-NCA algorithm that uses signal-dependent semidefinite operators, which is a bilinear mapping, as signatures for operator design in the c-NCA approach. Theoretically, we showed that the source estimation of the c-NCA algorithm converges with a convergence rate dependent on the decay of the sequence, obtained by applying the estimated operators on corresponding sources. The c-NCA can be formulated as a deterministic constrained optimization method, and thus, it can take advantage of solvers developed in optimization society for solving the BSS problem. As examples, we demonstrated electroencephalogram interference rejection problems can be solved by the c-NCA with proximal splitting algorithms by incorporating a sparsity-enforcing separation model and considering the case when reference signals are available.

BAYESIAN SEMI-BLIND COMPONENT SEPARATION FOR FOREGROUND REMOVAL IN INTERFEROMETRIC 21 cm OBSERVATIONS

ABSTRACT In this paper, we present a new Bayesian semi-blind approach for foreground removal in observations of the 21 cm signal measured by interferometers. The technique, which we call H i Expectation–Maximization Independent Component Analysis (HIEMICA), is an extension of the Independent Component Analysis technique developed for two-dimensional (2D) cosmic microwave background maps to three-dimensional (3D) 21 cm cosmological signals measured by interferometers. This technique provides a fully Bayesian inference of power spectra and maps and separates the foregrounds from the signal based on the diversity of their power spectra. Relying only on the statistical independence of the components, this approach can jointly estimate the 3D power spectrum of the 21 cm signal, as well as the 2D angular power spectrum and the frequency dependence of each foreground component, without any prior assumptions about the foregrounds. This approach has been tested extensively by applying it to mock data from interferometric 21 cm intensity mapping observations under idealized assumptions of instrumental effects. We also discuss the impact when the noise properties are not known completely. As a first step toward solving the 21 cm power spectrum analysis problem, we compare the semi-blind HIEMICA technique to the commonly used Principal Component Analysis. Under the same idealized circumstances, the proposed technique provides significantly improved recovery of the power spectrum. This technique can be applied in a straightforward manner to all 21 cm interferometric observations, including epoch of reionization measurements, and can be extended to single-dish observations as well.

Exact Cramer–Rao Bounds for Semiblind Channel Estimation in Amplify-and-Forward Two-Way Relay Networks Employing Square QAM

In this paper, we derive the Cramer-Rao bound (CRB) for semiblind channel estimation in amplify-and-forward two-way relay networks employing square QAM, assuming flat-fading channel conditions. The derived bound is exact as it is based on the true likelihood function that takes into account the statistics of the transmitted data symbols. Using the new bound, we show that exploiting even a limited number of transmitted data symbols in addition to the pilot symbols leads to substantial estimation accuracy improvements over conventional pilot-based estimation. We also propose a semiblind expectation-maximization-based estimation algorithm that performs very close to the exact CRB at an affordable computational cost. The superior accuracy of the semiblind approach makes it possible to significantly reduce the training overhead for channel estimation, thus offering a higher throughput and a better tradeoff between accuracy and spectral efficiency. We also derive the modified CRB, which approximates the exact CRB at high SNR for low modulation orders.

A Semiblind Two-Way Training Method for Discriminatory Channel Estimation in MIMO Systems

Discriminatory channel estimation (DCE) is a recently developed strategy to enlarge the performance difference between a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. Specifically, it makes use of properly designed training signals to degrade channel estimation at the UR which in turn limits the UR's eavesdropping capability during data transmission. In this paper, we propose a new two-way training scheme for DCE through exploiting a whitening-rotation (WR) based semiblind method. To characterize the performance of DCE, a closed-form expression of the normalized mean squared error (NMSE) of the channel estimation is derived for both the LR and the UR. Furthermore, the developed analytical results on NMSE are utilized to perform optimal power allocation between the training signal and artificial noise (AN). The advantages of our proposed DCE scheme are two folds: 1) compared to the existing DCE scheme based on the linear minimum mean square error (LMMSE) channel estimator, the proposed scheme adopts a semiblind approach and achieves better DCE performance; 2) the proposed scheme is robust against active eavesdropping with the pilot contamination attack, whereas the existing scheme fails under such an attack.

Semi-Blind Channel Estimation with Superimposed Training for OFDM-Based AF Two-Way Relaying

We consider the problem of channel estimation for OFDM-based amplify-and-forward (AF) two-way relay networks (TWRNs). While previous works have adopted a pilot-based approach, we propose a semi-blind approach that exploits both the transmitted pilots as well as the received data samples to improve the estimation performance. Our proposed semi-blind estimator is based on the Gaussian maximum likelihood (GML) criterion which treats that data symbols as Gaussian-distributed nuisance parameters. The GML estimates are obtained using an iterative quasi-Newton method. To assist in the estimation of the individual channels, we adopt a superimposed training strategy at the relay. We design the pilot vectors of the terminals and the relay to optimize the estimation performance. Furthermore, we derive the semi-blind and pilot-based Cramer-Rao bounds (CRBs) to use as performance benchmarks. Finally, we use simulation studies to show that the proposed method provides substantial improvements in estimation accuracy over the conventional pilot-based estimation and that it approaches the semi-blind CRB as SNR increases. These improvements are possible using only a limited number of OFDM data blocks, which demonstrates the practicality of the semi-blind approach.

Fast unmixing of multispectral optoacoustic data with vertex component analysis

Multispectral optoacoustic tomography enhances the performance of single-wavelength imaging in terms of sensitivity and selectivity in the measurement of the biodistribution of specific chromophores, thus enabling functional and molecular imaging applications. Spectral unmixing algorithms are used to decompose multi-spectral optoacoustic data into a set of images representing distribution of each individual chromophoric component while the particular algorithm employed determines the sensitivity and speed of data visualization. Here we suggest using vertex component analysis (VCA), a method with demonstrated good performance in hyperspectral imaging, as a fast blind unmixing algorithm for multispectral optoacoustic tomography. The performance of the method is subsequently compared with a previously reported blind unmixing procedure in optoacoustic tomography based on a combination of principal component analysis (PCA) and independent component analysis (ICA). As in most practical cases the absorption spectrum of the imaged chromophores and contrast agents are known or can be determined using e.g. a spectrophotometer, we further investigate the so-called semi-blind approach, in which the a priori known spectral profiles are included in a modified version of the algorithm termed constrained VCA. The performance of this approach is also analysed in numerical simulations and experimental measurements. It has been determined that, while the standard version of the VCA algorithm can attain similar sensitivity to the PCA–ICA approach and have a robust and faster performance, using the a priori measured spectral information within the constrained VCA does not generally render improvements in detection sensitivity in experimental optoacoustic measurements.

Semiblind frequency-domain timing synchronization and channel estimation for OFDM systems

In this article, we propose unit vectors in the high dimensional Cartesian coordinate system as the preamble, and then propose a semiblind timing synchronization and channel estimation scheme for orthogonal frequency division multiplexing (OFDM) systems. Due to the lack of useful information in the time-domain, a frequency-domain timing synchronization algorithm is proposed. The proposed semiblind approach consists of three stages. In the first stage, a coarse timing offset related to the delayed timing of the path with the maximum gain in multipath fading channels is obtained. Then, a fine time adjustment algorithm is performed to find the actual delayed timing in channels. Finally, the channel response in the frequency-domain is obtained based on the final timing estimate. Although the required number of additions in the proposed algorithm is higher than those in conventional methods, the simulation results show that the proposed approach has excellent performance of timing synchronization in several channel models at signal-to-noise ratio (SNR) smaller than 6 dB. In addition, for a low-density parity-check coded single-input single-output OFDM system, our proposed approach has better bit-error-rate performance than conventional approaches for SNR varying from 3 to 8 dB.

The efficacy of the semi-blind approach of transversus abdominis plane block on postoperative analgesia in patients undergoing inguinal hernia repair: a prospective randomized double-blind study

PurposeIn this prospective, randomized, double-blind study, our aim was to compare the analgesic efficacy of the semi-blind approach of transversus abdominis plane (TAP) block with a placebo block in patients undergoing unilateral inguinal hernia repair.MethodsAfter receiving hospital ethical committee approval and informed patient consents, American Society of Anesthesiologists (ASA) I–III patients aged 18–80 were enrolled in the study. Standard anesthesia monitoring was applied to all patients. After premedication, spinal anesthesia was administered to all patients with 3.5 mL heavy bupivacaine at the L3-L4 subarachnoid space. Patients were randomly allocated into 2 groups. Group I (n = 32) received a placebo block with 20 mL saline, Group II (n = 32) received semi-blind TAP block with 0.25% bupivacaine in 20 mL with a blunt regional anesthesia needle into the neurofascial plane via the lumbar triangle of Petit near the midaxillary line before fascial closure. At the end of the operation, intravenous (IV) dexketoprofen was given to all patients. The verbal analog scale (VAS) was recorded at 2, 4, 6, 12, and 24 hours postoperatively. Paracetamol IV was given to patients if their VAS score > 3. A rescue analgesic of 0.05 mg/kg morphine IV was applied if VA S > 3. Total analgesic consumption and morphine requirement in 24 hours were recorded.ResultsTAP block reduced VAS scores at all postoperative time points (P < 0.001). Postoperative analgesic and morphine requirement in 24 hours was significantly lower in group II (P < 0.01).ConclusionSemi-blind TAP block provided effective analgesia, reducing total 24-hour postoperative analgesic consumption and morphine requirement in patients undergoing elective unilateral inguinal hernia repair.

MIMO-OFDM Channel Tracking Using a Dynamic ANN Topology

All the available algorithms for blind estimation namely constant modulus algorithm (CMA), Decision-Directed Algorithm (DDA/DFE) suffer from the problem of convergence to local minima. Also, if the channel drifts considerably, any DDA looses track of the channel. So, their usage is limited in varying channel conditions. The primary limitation in such cases is the requirement of certain overhead bits in the transmit framework which leads to wasteful use of the bandwidth. Also such arrangements fail to use channel state information (CSI) which is an important aid in improving the quality of reception. In this work, the main objective is to reduce the overhead imposed by the pilot symbols, which in effect reduces the system throughput. Also we formulate an arrangement based on certain dynamic Artificial Neural Network (ANN) topologies which not only contributes towards the lowering of the overhead but also facilitates the use of the CSI. A 2×2 Multiple Input Multiple Output (MIMO) system is simulated and the performance variation with different channel estimation schemes are evaluated. A new semi blind approach based on dynamic ANN is proposed for channel tracking in varying channel conditions and the performance is compared with perfectly known CSI and least square (LS) based estimation. Keywords—MIMO, Artificial Neural Network (ANN), CMA, LS, CSI.