Semi-blind Channel Estimation Research Articles

Channel estimation for pilot contamination in massive MIMO-NOMA system

Channel estimation is crucial for massive multiple-input multiple-output (MIMO) systems to scale up multi-user (MU) MIMO, providing great improvement in spectral and energy efficiency. The nature of non-orthogonal cause pilot contamination is experienced only while estimating multi-cell MIMO scheme with the training and it is misplaced while narrowing concentration to multi-cell or one-cell setting, where information of the channel is assumed to be obtainable at no cost. Non-orthogonal multiple access (NOMA) serves numerous users concurrently utilizing channel gain differences. The advancement in massive MIMO-NOMA technology has offered diverse techniques recently for reducing pilot contamination in massive MIMO-NOMA based on pilot allocation. Here, a new approach called War Strategy Chimp Optimization+Deep Neuro-Fuzzy Network (WSChO+DNFN) is designed for the estimation of channels to reduce pilot contamination in a massive MIMO-NOMA system. It takes place in two phases, the transmitter and the receiver phase. The channel estimation is conducted by DNFN that is tuned by devised WSChO. Furthermore, WSChO is an amalgamation of War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). Additionally, the WSChO+DNFN attained minimal values of BER and normalized MSE of 0.000103 and 0.000074, respectively. The proposed method has achieved a performance gain of 44.39%, 19.26%, 9.17%, 5.22%, 9.92%, and 6.03% compared to the Orthogonal Frequency Division Multiplexing (OFDM), Group Successive Interference Cancellation assisted Semi-Blind Channel Estimation Scheme (GSIC_SBCE), Sector-Based Pilot Assignment Scheme (PAS), Convolutional Neural Network (CNN), User Segregation based Channel Estimation (USCE), Optimal Channel Estimation using Hybrid Machine Learning (OCE_HML), respectively.

Blind and Semi-blind Channel Estimation and Collision Resolution for the Uplink of MU-MIMO and Massive MIMO Systems for B5G Networks

Blind and Semi-blind Channel Estimation and Collision Resolution for the Uplink of MU-MIMO and Massive MIMO Systems for B5G Networks

Data Detection With CFO Uncertainty and Nonlinearity for mmWave MIMO-OFDM Systems

The nonlinear distortions attributed by radio frequency power amplifiers (PAs) are inevitable in millimeter-wave (mmWave) systems due to the high frequency and large bandwidth. This nonlinearity induces multiplicative distortion and intercarrier interference in mmWave multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing systems, which in collusion with a frequency-selective channel and a carrier frequency offset (CFO) brings great challenges to signal detection. Furthermore, the nonlinearity destroys orthogonality of the training pilots and degrades the estimation accuracy of the channel gains and the CFO. Also, the target posterior distribution for data detection becomes analytically intractable due to the nonlinearity. In this article, we present an importance-sampling-based framework for estimating the CFO in the presence of PA impairment, which utilizes few pilots and initializes the channel estimator. We propose a particle-filter-based iterative algorithm for data detection, where the intractable posterior distribution is approximated by weighted random measures. Furthermore, a sequential-maximum-likelihood-based semiblind channel estimator in the presence of nonlinearity is proposed. Performances of the estimator and the detector are evaluated by means of normalized mean square error, mean square error, and bit error rate. To validate the efficacy of the channel and the CFO estimator, the Cramer–Rao bound (CRB) and the weighted Bayesian CRB are derived, respectively.

Semi-Blind Channel Estimation for RIS-Assisted MISO Systems Using Expectation Maximization

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Reconfigurable intelligent surface (RIS) is a passive antenna array composed of a large number of reflecting elements. In a RIS-assisted communication system, it is a challenging task to acquire accurate channel state information (CSI). In this paper, we propose a semi-blind channel estimation method for a RIS-assisted massive multiple-input single-output (MISO) system. Assuming a Gaussian</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <b>priori</b> </i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on the data symbols, an iterative expectation maximization (EM)-based algorithm is developed to obtain the maximum likelihood (ML) estimate of the cascaded RIS channel. Different from existing pilot-based methods, the proposed semi-blind channel estimation method exploits the data symbols for channel estimation enhancement and only a fraction of full-pilot signaling overhead is required. Simulation results verify that the proposed method achieves significant improvement in the accuracy of the channel estimation while it noticeably reduces the training pilot overhead.</b>

Blind and Semi-Blind Channel Estimation/Equalization for Poisson Channels in Optical Wireless Scattering Communication Systems

The communication systems for Poisson channels require long pilot for channel estimation and may result in large percentage of overhead due to the signal-dependent noise of Poisson-distributed signal, i.e., both signal part and noise part experience random processes. In this paper, both blind and semi-blind channel estimation methods are studied to shorten the overhead and increase the transmission efficiency for Poisson channels. First, fractionally spaced equalizers are proposed based on modified constant modulus algorithm (CMA) and subspace (SS). Second, a data-aided iterative channel estimation (ICE) is designed and analyzed in terms of its asymptotic unbiasedness and convergence. The proposed methods are evaluated based on both a constant channel scenario and a varying channel scenario. Numerical simulation results show that the modified CMA has the worst bit-error rate performance but requires the lowest computational complexity. Besides, both SS based channel estimation and ICE have negligible overhead and comparable bit-error rate performances with respect to the conventional periodic pilot based channel estimation having 50% overhead.

Secrecy Coding Analysis of Short-Packet Full-Duplex Transmissions with Joint Iterative Channel Estimation and Decoding Processes.

This paper studies the secrecy coding analysis achieved by the self-jamming technique in the presence of an eavesdropper by considering a short-packet Full-Duplex (FD) transmission developed based on iterative blind or semi-blind channel estimation and advanced decoding algorithms. Indeed, the legitimate receiver and eavesdropper can simultaneously receive the intended signal from the transmitter and broadcast a self-jamming or jamming signal to the others. Unlike other conventional techniques without feedback, the blind or semi-blind algorithm applied at the legitimate receiver can simultaneously estimate, firstly, the Self-Interference (SI) channel to cancel the SI component and, secondly, estimate the propagation channel, then decode the intended messages by using 5G Quasi-Cyclic Low-Density Parity Check (QC-LDPC) codes. Taking into account the passive eavesdropper case, the blind channel estimation with a feedback scheme is applied, where the temporary estimation of the intended channel and the decoded message are fed back to improve both the channel estimation and the decoding processes. Only the blind algorithm needs to be implemented in the case of a passive eavesdropper because it achieves sufficient performances and does not require adding pilot symbols as the semi-blind algorithm. In the case of an active eavesdropper, based on its robustness in the low region of the Signal-to-Noise Ratio (SNR), the semi-blind algorithm is considered by trading four pilot symbols and only requiring the feedback for channel estimation processes in order to overcome the increase in noise in the legitimate receiver. The results show that the blind or semi-blind algorithms outperform the conventional algorithm in terms of Mean Square Error (MSE), Bit Error Rate (BER) and security gap (). In addition, it has been shown that the blind or semi-blind algorithms are less sensitive to high SI and self-jamming interference power levels imposed by secured FD transmission than the conventional algorithms without feedback.

Semi-Blind Channel Estimation for Intelligent Reflecting Surfaces in Massive MIMO Systems

Intelligent reflecting surface (IRS) is considered as a promising technology for enhancing the transmission rate in cellular networks. Such improvement is attributed to considering a large IRS with high number of passive reflecting elements, optimized to properly focus the incident beams towards the receiver. However, to achieve this beamforming gain, the channel state information (CSI) should be be efficiently acquired at the base station (BS). Unfortunately, the traditional pilot estimation method is challenging, because the passive IRS does not have radio frequency (RF) chains and the number of channel coefficients is proportional to the number of IRS elements. In this paper, we propose a novel semi-blind channel estimation method where the reflected channels are estimated using not only pilot but also data symbols, reducing the channel estimation overhead. The performance of the system is analytically investigated in terms of the uplink achievable sum-rate. The proposed scheme achieves higher energy and spectrum efficiency, while being robust to channel estimation errors. For instance, the proposed scheme achieves a 80% increase in spectrum efficiency compared to pilot-only based schemes, for IRSs with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> = 32 elements.

Semi-blind channel estimation based on modified CMA and unitary scrambling for massive MIMO systems

Pilot contamination is one of the main impairments in multi-cell massive Multiple-Input Multiple-Output systems. In order to improve the channel estimation in this context, we propose to use a semi-blind channel estimator based on the constant modulus algorithm (CMA). We consider an enhanced version of the CMA namely the Modified CMA which modifies the cost function of the CMA algorithm to the sum of cost functions for real and imaginary parts. Due to pilot contamination, the channel estimator may estimate the channel of a contaminating user instead of that of the user of interest (the user for which the Base Station wants to estimate the channel and then the data). To avoid this, we propose to scramble the users sequences before transmission. We consider different methods to perform unitary scrambling based on rotating the transmitted symbols (one Dimensional (1-D) scrambling) and using unitary matrices (two-Dimensional (2-D) scrambling). At the base station, the received sequence of the user of interest is descrambled leading to a better convergence of the channel estimator. We also consider the case where the Automatic Repeat reQuest protocol is used. In this case, using scrambling leads to a significant gain in terms of BLock Error Rate due to the change of the contaminating users data from one transmission to another induced by scrambling.

Group Successive Interference Cancellation Assisted Semi-Blind Channel Estimation in Multi-Cell Massive MIMO-NOMA Systems

Both non-orthogonal multiple access (NOMA) and massive multiple-input multiple-output (MIMO) are the promising techniques to satisfy the foreseeable massive connectivity requirements. In this letter, a novel semi-blind channel estimation method is proposed for multi-cell massive MIMO-NOMA systems, in which a group successive interference cancellation (GSIC) assisted method is developed to mitigate pilot contamination. Specifically, the users in each cell are divided into multiple groups and the same pilot sets are reused by the different groups in order to reduce pilot overhead. Besides, by following the NOMA principle, a GSIC assisted method is proposed to eliminate inter-group pilot contamination. In simulations, it is shown that the proposed channel estimation method is capable of reducing the overlapping probability of different groups as the number of receiving antennas increases. In addition, the proposed scheme outperforms the conventional channel estimation methods in terms of normalized mean square error (NMSE) performance.

Particle Filter Based Nonlinear Data Detection in Presence of CFO for Frequency Selective mmWave MIMO-OFDM Systems

The nonlinear distortions attributed by the radio frequency (RF) power amplifier (PA) and other RF circuits from the enormous bandwidth of millimeter wave (mmWave) and high frequency design limitations of the integrated circuits involved, degrade the performance of the hybrid mmWave multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. This nonlinear distortions in the hybrid mmWave systems cause nonlinear coupling of the hybrid precoder with the data signals. Further, it induces intercarrier interference (ICI) in the OFDM systems, which also involves the interference from the precoders of other subcarriers. These difficulties in collusion with frequency selective channel and carrier frequency offset (CFO) may pose great challenges to the estimation accuracy of these parameters (channel gains and CFO) and signal detection. The nonlinearity causes the target posterior distribution for data detection as non-Gaussian and analytically intractable. In this work, we present a novel pilot assisted maximum likelihood (ML) based framework for estimating the CFO with PA impairment in the time domain by formulating a low dimensional equivalent channel matrix. The low dimensional equivalent channel matrix is composed of hybrid precoders-combiners, high dimensional MIMO channel, and the nonlinearly distorted components from PA. We further propose a group-sparse Bayesian learning (G-SBL) based semi-blind channel estimator in the time domain, which incorporates the CFO and nonlinearity of PA in its estimation process. With the estimated channel, a procedure to obtain the hybrid precoder and combiner matrices for data transmission is developed next. An iterative algorithm based on particle filter (PF) that effectively handles the nonlinear coupling of precoder matrix with data, and ICI is also proposed for data detection. The performance of the algorithms are evaluated on two different channel models: (a) synthetic geometric based mmWave channel (b) realistic mmWave channel for an urban microcell (UMi) environment. To validate the efficacy of the CFO and channel estimator, the Cramer-Rao bound (CRB) and Bayesian CRB (BCRB) are derived respectively.

Deep Learning Based Semi-Blind Tracking for Aging Wireless Communication Channels

The time-varying nature of the wireless propagation channel under high user mobilities, termed as channel aging, is a major performance impediment in many communication systems. In this paper, we discuss deep learning models for semi-blind channel estimation in a single input single output wireless communication system under channel aging. In our proposed scheme, we first use pilot based training to obtain initial channel estimates. Following this, we treat the detected symbols as pilots and perform further channel estimation using an Encoder-Decoder LSTM network for constant and sliding window schemes. To show the effectiveness of our method, we show the training capabilities of our models and the BER vs SNR graphs for multiple simulations. We discuss integrating these Encoder-Decoder LSTM models with deep learning enabled symbol detection techniques like the DetNet to further improve spectral efficiency. The Encoder-Decoder LSTM network gives us a low BER, with the moving window scheme outperforming the constant window scheme.

MIMO Semi-blind Channel Estimation by using Tikhonov Regularized MMSE and MAP Algorithms with Householder Transformation based QR Decomposition

MIMO Semi-blind Channel Estimation by using Tikhonov Regularized MMSE and MAP Algorithms with Householder Transformation based QR Decomposition

Designing Tensor-Train Deep Neural Networks For Time-Varying MIMO Channel Estimation

This paper proposes a novel tensor-train deep neural network (TT-DNN) based channel estimator to tackle challenges of time-varying channel estimation in multiple-input multiple-output (MIMO) systems. A centralized DNN channel estimator can be realized by a distributed TT-DNN with parallel paths. The TT-DNN provides a compact representation by decomposing each DNN layer into a TT format with fewer model parameters, and is well-designed to adapt to the block structure, pilot density, and the number of MIMO antennas. Moreover, the channel estimation is performed in block-by-block and antenna-by-antenna manners such that both input dimensions of TT-DNN and the number of model parameters can be further reduced. In addition, a preliminary stage of model pre-training is set for the DNN/TT-DNN channel estimator which boosts the channel estimation accuracy. Moreover, the proposed TT-DNN is generalized to semiblind channel estimation scenarios where there exists only preamble training symbols. Our experiments show that the proposed TT-DNN based channel estimator outperforms the DNN counterparts in terms of convergence rate, estimation accuracy, and robustness, and presents better performance than recurrent neural network for semiblind channel estimation.

Precoding Aided Data Correlation Scheme for Channel Estimation Technique in MIMO-OFDM System

Channel estimation is considered as an important phase in Multiple Input Multiple Output – Orthogonal Frequency Division Multiplexing (MIMO-OFDM) networks which can enhances the performance significantly. Channel estimation widely classified as pilot based, blind and semi-blind channel estimation. The pilot-based channel estimation decreases the data transmission rate and spectral efficiency. To overcome these issues of existing schemes, we present a novel blind channel estimation technique. According to proposed scheme, we transmit the data in a block-wise manner. The proposed scheme uses precoding technique to establish the correlation between these blocks. Further, we use channel correlation to solve the diagonal uncertainty of correlation matrix which helps to improve the system performance. We present a comparative analysis study which shows that proposed approach can achieve better performance in terms of Normalized Mean Square Error (NMSE) and Mean Square Error (MSE) when compared with existing techniques.

Semi-Blind Channel Estimation and Data Detection for Multi-Cell Massive MIMO Systems on Time-Varying Channels

We study the problem of semi-blind channel estimation and symbol detection in the uplink of multi-cell massive MIMO systems with spatially correlated time-varying channels. An algorithm based on expectation propagation (EP) is developed to iteratively approximate the joint a posteriori distribution of the unknown channel matrix and the transmitted data symbols with a distribution from an exponential family. This distribution is then used for direct estimation of the channel matrix and detection of data symbols. A modified version of the popular Kalman filtering algorithm referred to as KF-M emerges from our EP derivation and it is used to initialize the EP-based algorithm. Performance of the Kalman smoothing algorithm followed by KF-M is also examined. Simulation results demonstrate that channel estimation error and the symbol error rate (SER) of the semi-blind KF-M, KS-M, and EP-based algorithms improve with the increase in the number of base station antennas and the length of the transmitted frame. It is shown that the EP-based algorithm significantly outperforms KF-M and KS-M algorithms in channel estimation and symbol detection. Finally, our results show that when applied to time-varying channels, these algorithms outperform the algorithms that are developed for block-fading channel models.

Second-Order Statistics-Based Semi-Blind Techniques for Channel Estimation in Millimeter-Wave MIMO Analog and Hybrid Beamforming

Semi-blind (SB) channel estimation is conceived for millimeter wave (mmWave) analog-beamforming (AB) and hybrid-beamforming (HB)-based multiple-input multiple-output (MIMO) systems, which also exploits the data symbols for improving the estimation accuracy. A novel aspect of the proposed framework is that it directly estimates the analog beamformer/ combiner weights without necessitating the estimation of the entire mmWave MIMO channel matrix. By involving powerful matrix perturbation theoretic techniques, a closed-form expression is derived for the mean-squared-error (MSE) of the mmWave-AB-SB algorithm. As a further novelty, our mmWave-HB-SB technique relies on the decomposition of the channel matrix as the product of a decorrelating and a unitary matrix. Subsequently, the former is estimated purely relying on the unknown data symbols, whereas the latter is estimated exclusively from the training vectors. A lower bound on the MSE of the proposed mmWave-HB-SB technique is derived using the constrained Cramer-Rao lower bound (CRLB) framework. Furthermore, the performance gain of our mmWave-HB-SB technique over the conventional purely training-based scheme is also quantified analytically. Our simulation results demonstrate the superiority of the techniques advocated over the existing solutions and also verify the accuracy of our analytical findings.

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.

Improved Semi-Blind Channel Estimation With Time Domain Cancellation for LDM-LSI

The adoption of OFDM as the physical layer waveform in ATSC 3.0 leaves the door open for the implementation of Single Frequency Networks (SFN). In addition, Layered Division Multiplexing (LDM), which can be understood as a low complexity approach for Non-Orthogonal Multiple Access (NOMA) techniques, has also been included as a baseline technology. These two new features can be combined to offer LDM based Local Contents (LC) in SFNs with unprecedented spectrum efficiency. Up to now, the design of a reliable channel estimation algorithm that does not require any modification of the standard has been the main challenge for the successful development of this approach. This paper aims at providing tools for satisfactory and efficient application of LDM-LSI (Local Service Insertion). In particular, in this paper, three major contributions are presented: a new semi-blind channel estimation algorithm that takes advantage of the LDM structure of the network, a time domain cancellation technique for NOMA, and the implementation of the proposed solution in a fully compliant ATSC 3.0 transceiver.

Pilot Decontamination in Multi-Cell Massive MIMO Systems via Combining Semi-Blind Channel Estimation With Pilot Assignment

In the multi-cell massive multiple-input multiple-output (MIMO) systems, the mitigation of pilot contamination is an important issue in the semi-blind channel estimations. For the pilot decontamination schemes, the performance of the semi-blind based estimation approaches is constrained by the ratio of the interference signal power to the target signal power. In order to eliminate pilot contamination effectively, the eigenvalues of the target signals should be separated from those of the interference signals. In this paper, we propose a novel uplink channel estimation for mitigating pilot contamination in time division duplex massive MIMO systems, which combines pilot assignments with semi-blind channel estimation methods. In order to reduce the search complexity for the optimal pilot assignment under the condition of a large number of users per cell, we proposed a sector-based pilot assignment method, including inter-sector pilot assignment and intra-sector pilot optimization. Simulation results verify that the joint pilot allocation and semi-blind channel estimation method is capable of improving the system achievable rates and the normalized mean square error performance.

Semi-Blind Data Detection and Non-Linear Equalization in Full-Duplex TWR-OFDM Systems With High Mobility

Full-duplex two-way relay (FD-TWR) system has potential to increase the spectral efficiency in the future 5G wireless system. Full-duplex transceiver suffers from inevitable self-interference (SI) which can be alleviated by active self-interference cancellation (SIC) method. However, the mitigation capability of SIC mechanism is limited specifically due to inherent non-linearities of transmitter and receiver front end. As a consequence, residual self-interference (RSI) will degrade the system’s signal-to-noise ratio (SNR) and throughput. Non-linearity in RF power amplifier in collusion with time-variant channel results is a great challenge in efficient signal detection and successful SI suppression. In contrast to classical schemes, which consider non-linear distortion at the transmitter, we present a semi-blind data detection and non-linear channel estimation in the presence of RSI at the receiver. Attributed to non-linearity, the target posterior probability density function is mathematically intractable. In this paper, a sequential importance sampling based particle filtering is used for joint data detection and estimation. Intractable distribution is approximated by using weighted random measures. A Taylor’s series expansion is used to locally linearize the non-analytic form of distribution. Numerical results validate the joint detection and channel estimation scheme. The robustness of the scheme is verified in presence of RSI under high mobility.