Joint Blind Channel Estimation Research Articles

Signal Scrambling Based Joint Blind Channel Estimation, Activity Detection, and Decoding for Massive Random Access

Signal Scrambling Based Joint Blind Channel Estimation, Activity Detection, and Decoding for Massive Random Access

Joint Blind Channel Estimation, Channel Equalization, and Data Detection for Underwater Visible Light Communication Systems

In this letter, we jointly consider the problems of blind channel estimation (BCE), channel equalization and data detection in underwater visible light communication (UVLC) systems. Specifically, we propose to solve them with a model-driven deep learning (DL) approach. Inspired by the module-based signal processing methodology and communication theory, we devise a new blind receiver architecture called blind detection network (BDNet), which consists of three modules corresponding to BCE, channel equalization, and symbol demapping, respectively. Different from existing BCE schemes, the BDNet does not directly estimate the channel itself, but instead estimates the inverse channel regardless of the scalar ambiguity issue by learning the latent channel features from the received signal only. The UVLC channel effects can then be partially compensated by the zero-forcing equalization module of BDNet. Finally, the symbol demapping module recovers the transmitted symbols with the aid of the learned decision thresholds. Simulation results show the competitive bit error rate performance of BDNet in comparison to conventional pilot-aided schemes.

Partial automatic repeat request transceiver for bandwidth and power efficient multiple‐input–multiple‐output orthogonal frequency division multiplexing systems

In this study, the authors present a novel method to improve the performance of automatic repeat request (ARQ) protocol for multiple-input–multiple-output systems that use orthogonal frequency division multiplexing signalling by incorporating partial ARQ (PARQ) mechanism. As a typical ARQ frame has few number of errors, it is useful to retransmit partial sequence of symbols and recover from errors by consuming relatively low bandwidth. The observations from the first transmission and subsequent retransmission are used for joint detection. As a direct consequence of partial retransmission, the bandwidth and power efficiency of the overall system is improved considerably without substantial decrease in the performance. They achieve further bandwidth saving by omitting pilot sequence from the partial retransmission and acquire channel estimate in semi-blind fashion. Partial retransmission of the original information symbol sequence allows the authors to formulate second-order statistics based joint blind channel estimation problem by combining observations from two transmissions. Channel estimation ambiguity inherent to blind methods is resolved by combining cost functions of blind detection and least-square estimator. To evaluate efficacy of the proposed PARQ method, they compare throughput of chase-type hybrid-ARQ and the proposed PARQ methods.

Generalized Maximum Likelihood for Cross-Polarization Modulation Effects Compensation

We investigate the mitigation of nonlinearities with advanced digital signal processing focusing in particular on cross-polarization effects. Based on a relaxation of an analytical model derived for cross-polarization effects, this paper proposes a novel compensation method called generalized maximum likelihood. It performs a joint blind channel estimation and symbol detection, and it additionally accounts for the statistical prior distributions of the cross-polarization crosstalk coefficients. This avoids an overestimation of these crosstalk coefficients. A practical method for both fast computations and optimal performance is then presented, which allows nonlinear compensation for high-order modulations. Next, we present Monte-Carlo simulations showing that the proposed algorithm performs close to the theoretical limits. Large performance improvement can be obtained and this is particularly emphasized with higher order modulation such as a 16-ary quadrature amplitude modulation. Finally, using Nyquist pulse shaping and polarization-division multiplexed with a quadrature phase-shift keying modulation, the experiments are shown to be in accordance with the simulations and show up to 0.7 dB improvement in Q-factor for the worst-case samples.

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

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

PARAFAC-based channel estimation and data recovery in nonlinear MIMO spread spectrum communication systems

In this paper, a new tensorial modeling is first proposed for nonlinear multiple-input multiple-output (MIMO) direct sequence spread spectrum communication systems. The channel is modeled as an instantaneous MIMO Volterra system. Then, a direct data approach for joint blind channel estimation and data recovery is developed using the parallel factor (PARAFAC) decomposition of a third-order tensor composed of received signals, exploiting space, time and code diversities. A blind channel estimation method based on the PARAFAC decomposition of a fifth-order tensor composed of covariances of the received signals is also proposed, considering phase shift keying (PSK) modulated transmitted signals. The proposed estimation algorithms are evaluated by simulating a nonlinear uplink MIMO radio over fiber (ROF) communication system.

Space–time spreading–multiplexing for MIMO wireless communication systems using the PARATUCK-2 tensor model

In this paper, we present a new space–time spreading–multiplexing model for multiple-input multiple-output (MIMO) wireless communication systems relying on a tensor modeling of the transmitted and received signals. At the transmitter, we exploit the core of a PARATUCK-2 tensor model composed of a precoding matrix and two allocation matrices that allow to control the spreading and multiplexing of the data streams across the space dimension (transmit antennas) and time-dimension (time-slots). Different MIMO schemes combining space–time multiplexing and diversity can be derived from the proposed model. The identifiability and uniqueness of the PARATUCK-2 tensor model for the received signal are discussed and subsequently exploited for a joint blind channel estimation and symbol detection. The bit-error-rate performance of different transmit schemes derived from the proposed tensor model is evaluated by means of computer simulations.

A Low Complexity RLS-PASTd Algorithm for Blind Multiuser Detection in Dispersive CDMA Channels

For joint blind channel estimation and multiuser detection in direct-sequence code division multiple access (DS-CDMA) systems, a combined scheme is proposed, which couples the recursive least-squares (RLS) adaptations and the PASTd subspace tracking algorithm. A major advantage of the RLS-PASTd algorithm is that it admits an adaptive implementation with low computational complexity instead of singular value decomposition. Simulation results demonstrate the fast tracking capability and high steady-state estimation accuracy of the proposed algorithm in both the time-invariant and time-variant environments

Joint blind channel estimation and interference suppression with efficient implementation for OFDM systems

A joint blind channel estimation and interference suppression scheme with efficient implementation for orthogonal frequency-division multiplexing (OFDM) systems is presented. In particular, an equalising filterbank is designed and the channel estimate from a lower bound of the filterbank output power is derived. The proposed scheme and exhibits low sensitivity to unknown narrowband interference. It compares favourably with a subspace blind channel estimator.

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.

Adaptive Channel Estimation for Maximum Likelihood Sequence Estimation

Abstract The paper reviews the problem of adaptive channel estimation for maximum likelihood sequence estimation. It explains necessity of channel estimation for operation of the ML receiver, reviews a few of the most important receiver structures, discusses several adaptation algorithms and joint blind adaptive sequence and channel estimation.