Semi-blind Channel Research Articles

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.

Optimal number of user antennas in a constrained pilot‐length massive MIMO system

In this study, the authors analyse the performance of a massive multiple-input multiple-output network with N-antenna users. Equipping the user terminal (UT) with a multiple of antennas is usually challenging since it increases the overhead of the channel estimation process (linearly with N). In this study, the authors use semi-blind (data-aided) channel estimation that significantly reduces the pilot overhead and analytically obtains the optimal number of antennas that can be mounted at the UT. The proposed channel estimation algorithm is especially useful in cases where a UT can be assigned a single, or generally a few numbers of, pilot sequence(s) although it might have a number of antennas greater than the number of sequences (as the case of 5G proposed solutions); therefore the network does not have to assign more pilots to the user in order to estimate the additional antennas. The authors also characterise the user outage performance when a part of its data is used for the blind channel estimation.

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.

Fast Convergence on Blind and Semi-Blind Channel Estimation for MIMO–OFDM Systems

In this paper, blind and semi-blind subspace channel estimations with fast convergence rate are proposed for multiple-input multiple-output orthogonal frequency division multiplexing (OFDM) systems in the presence of virtual carriers. The subspace method has a main drawback of slow convergence rate when deriving the noise subspaces from the second-order statistics of received signals. This phenomenon is especially evident when the size of received signals is large. Inspired by this fact, we present a block matrix scheme (BMS) to generate a group of sub-vectors from each OFDM symbol when virtual carriers are used. The number of equivalent signals is increased and therefore, the convergence rate of channel estimation is enhanced. The semi-blind method is also investigated by incorporating subspace technology with least square scheme. The identifiability of the BMS-based channel estimation is analyzed to derive the applicable range of the BMS size. The computational complexity of the proposed channel estimation is calculated at the end. Computer simulations show that the proposed blind and semi-blind methods converge very well in channel estimation and equalization.

Adaptive Joint Semi-blind Estimation of CFO and Channel for OFDM Systems

The large number of subcarriers, used in OFDM systems, increases the sensivity to frequency offset which results from a Doppler shift, due to mobile movement, as well as from a mismatch between the carrier frequencies at the transmitter and receiver. The problem of channel estimation in a practical OFDM receiver that suffers from CFO has been addressed in this paper. We first derived the modified MUSIC based blind CFO estimator. Next, we propose new joint training-based and joint semi-blind CFO and channel estimation for OFDM systems. Whereas the training-based estimator only assumes knowledge of pilot tones, the semi-blind estimator assumes knowledge of the virtual subcarriers (VSCs). For both estimators we derive a maximum likelihood (ML) algorithm for block processing and LMS algorithm for adaptive processing. It is demonstrated that the CFO estimators are able to perform well with a very small number of OFDM blocks. The performance of our estimators was investigated by simulations.

A Matched Filtering Approach for Phase Noise Suppression in CO-OFDM Systems

This letter presents a computationally efficient scheme to mitigate the effect of phase noise, including intercarrier interference, in coherent optical orthogonal frequency-division-multiplexing systems. Utilizing the fact that phase noise in the time domain can be modeled as a circular convolution with a finite impulse response filter in the frequency domain, and its matched filter is the optimal solution to cancel the phase noise, semiblind adaptive channel equalization algorithms are applied to directly remove the effect of phase noise without the estimation process. Simulation results demonstrate substantial improvement in phase noise suppression using the proposed algorithm.

Maximum-LikelihoodSemiblind Equalization of Doubly Selective Channels Using the EM Algorithm

Maximum-likelihood semi-blind joint channel estimation and equalization for doubly selective channels and single-carrier systems is proposed. We model the doubly selective channel as an FIR filter where each filter tap is modeled as a linear combination of basis functions. This channel description is then integrated in an iterative scheme based on the expectation-maximization (EM) principle that converges to the channel description vector estimation. We discuss the selection of the basis functions and compare various functions sets. To alleviate the problem of convergence to a local maximum, we propose an initialization scheme to the EM iterations based on a small number of pilot symbols. We further derive a pilot positioning scheme targeted to reduce the probability of convergence to a local maximum. Our pilot positioning analysis reveals that for high Doppler rates it is better to spread the pilots evenly throughout the data block (and not to group them) even for frequency-selective channels. The resulting equalization algorithm is shown to be superior over previously proposed equalization schemes and to perform in many cases close to the maximum-likelihood equalizer with perfect channel knowledge. Our proposed method is also suitable for coded systems and as a building block for Turbo equalization algorithms.

Iterative Pilot-Layer Aided Channel Estimation with Emphasis on Interleave-Division Multiple Access Systems

Channel estimation schemes suitable for interleave-division multiple access (IDMA) systems are presented. Training and data are superimposed. Training-based and semiblind linear channel estimators are derived and their performance is discussed and compared. Monte Carlo simulation results are presented showing that the derived channel estimators in conjunction with a superimposed pilot sequence and chip-by-chip processing are able to track fast-fading frequency-selective channels. As opposed to conventional channel estimation techniques, the BER performance even improves with increasing Doppler spread for typical system parameters. An error performance close to the case of perfect channel knowledge can be achieved with high power efficiency.

A semi-blind joint data and channel estimation based receiver for meteor burst communication

According to the analysis of meteor burst communication (MBC) mechanism, a model of signal processing based on the structure of data frame is suggested for adaptive modulation and coding (AMC) of MBC system in this paper. There are two distinct modes of operation for signal processing: acquisition and tracking. The acquisition mode is a training period to initialize the channel estimation by frame header. The tracking mode is jointly to equalize payload data and to trace channel, where the principle of per-survivor processing (PSP) for maximum likelihood sequence detection (MLSD) is performed. A suboptimal method called D-PSP is adopted to save the computational time and memory size, which agrees with the slow-fading characteristic of meteor channel and makes the MLSD possible for adaptive modulation and coding of MBC system. Computer simulation results are included to support our development.

Joint blind and semi-blind detection and channel estimation for space–time trellis coded modulation over fast faded channels

A method for decoding of space–time trellis coded modulation (STTCM) without a need to transmit separate training sequences is developed. The novel technique uses only a single initial estimate to acquire and track multiple-input multiple-output (MIMO) channels while performing STTCM detection. The technique is akin to blind trellis search techniques (per-survival processing, PSP) and adaptive Viterbi. The solution consists of the deployment of a bank of Kalman filters. The bank of Kalman filters is coupled with Viterbi-type decoders, which produce tentative decisions based on Kalman channel predictions. In return the bank of Kalman filters use the tentative decisions to update and track the MIMO channels corresponding to a number of tracked hypotheses. The proposed technique is applicable to space–time single carrier systems in rapidly fading environments, and also to space–frequency coded MIMO-OFDM.