Joint channel estimation and MAP detection in time-selective CDMA channels

In this paper, a joint channel estimation and turbo equalization scheme is proposed with low complexity and high accuracy for doubly selective fading channels. Based on the traditional data frame, by adding several pre-training sequences, the spectral efficiency is improved in the proposed two stage scheme. In the first stage, with the adaptive subblock-wise inverse QR recursive least square (IQR-RLS) filter, more trustworthy estimated values of the channel coefficients are generated with a non-priori model compared with the first order autoregressive (AR) modeling extended Kalman filter (EKF), based on complex exponential basis expansion models (CE-BEMs). Particularly, the calculation of operations is significantly reduced. In the second stage, a turbo equalizer is provided by utilizing a non-linear Kalman filter, which has a remarkable improvement in BER performance over EKF. Furthermore, complexity analysis is presented and illustrates the advantage of the proposed scheme. Finally, simulation results demonstrate the validity of the proposed scheme.

Most existing space-time (ST) coding schemes have been developed for flat fading channels. To offer antenna diversity gains, they rely on channel state information (CSI) acquired at the receiver through training, or, via blind channel estimation. Differential ST modulation obviates the need for CSI but is less effective in rapidly fading environments. This paper develops novel ST coders and decoders that enjoy transmit-diversity gains in the presence of time-selective fading channels. Modeling the time-selective channels as random processes, we employ Kalman filtering to estimate them and mitigate their effects so that symbol detection benefits from transmit-diversity. Computer simulations confirm that the proposed scheme achieves robust performance in time-selective channels with a few training symbols.

In this paper, we present two finite-dimensional iterative algorithms for maximum a posteriori (MAP) state sequence estimation of bilinear systems. Bilinear models are appealing in their ability to represent or approximate a broad class of nonlinear systems. Our iterative algorithms for state estimation are based on the expectation-maximization (EM) algorithm and outperform the widely used extended Kalman smoother (EKS). Unlike the EKS, these EM algorithms are optimal (in the MAP sense) finite-dimensional solutions to the state sequence estimation problem for bilinear models. We also present recursive (on-line) versions of the two algorithms and show that they outperform the extended Kalman filter (EKF). Our main conclusion is that the EM-based algorithms presented in this paper are novel nonlinear filtering methods that perform better than traditional methods such as the EKF.

In this contribution, we present new algorithms to source separation for the case of noisy instantaneous linear mixture, within the Bayesian statistical framework. The source distribution prior is modeled by a mixture of Gaussians [Moulines97] and the mixing matrix elements distributions by a Gaussian [Djafari99a]. We model the mixture of Gaussians hierarchically by mean of hidden variables representing the labels of the mixture. Then, we consider the joint a posteriori distribution of sources, mixing matrix elements, labels of the mixture and other parameters of the mixture with appropriate prior probability laws to eliminate degeneracy of the likelihood function of variance parameters and we propose two iterative algorithms to estimate jointly sources, mixing matrix and hyperparameters: Joint MAP (Maximum a posteriori) algorithm and penalized EM algorithm. The illustrative example is taken in [Macchi99] to compare with other algorithms proposed in literature. Keywords: Source separation, Gaussian mixture, classification, JMAP algorithm, Penalized EM algorithm.

This paper proposes a new blind algorithm, based on Mixture Kalman Filtering (MKF), for joint carrier recovery and channel estimation in time-selective Rayleigh fading channels. MKF is a powerful tool for estimating unknown parameters in non-linear, non-Gaussian, real-time applications. We use a combination of Kalman filtering and Sequential Monte Carlo Sampling to estimate the channel fading coefficients and joint posterior probability density of the unknown carrier offset and transmitted data respectively. We study the effect of Signal to Noise Ratio (SNR) and doppler shift on Mean Square Error (MSE) and Bit Error Rate (BER) performance of the proposed algorithm through computer simulations. The results show that BER of the proposed algorithm achieves the theoretical performance slope for the full acquisition range of normalized carrier frequency offset.

The paper presents a method for joint channel and data estimation for transmission over frequency-selective and time-variant channels. Hereby, fast time-variant channels can be tracked, which is important for mobile communications at very high velocities. We approach the problem by estimating the scattering function, which is assumed to be limited in delay and Doppler frequency. At the transmitter, a short training sequence is inserted at the beginning of the data block. This sequence is used for initializing a Kalman filter, which carries out the joint channel and data estimation adaptively. After the training sequence, hypothetical data symbols are considered to extend the persecuted paths in the data tree.

Joint data and channel estimation for mobile communication receivers can be realized by employing a Viterbi detector along with channel estimators which estimate the channel impulse response. The behavior of the channel estimator has a strong impact on the overall error rate performance of the receiver. Kalman filtering is an optimum channel estimation technique which can lead to significant improvement in the receiver bit error rate (BER) performance. However, a Kalman filter is a complex algorithm and is sensitive to roundoff errors. Square-root implementation methods are required for robustness against numerical errors. Real-time computation of the Kalman estimator in a mobile communication receiver calls for parallel and pipelined structures to take advantage of the inherent parallelism in the algorithm. In this paper different implementation methods are considered for measurement update and time update equations of the Kalman filter. The unit-lower-triangular-diagonal (LD) correction algorithm is used for the time update equations, and systolic array structures are proposed for its implementation. For the overall implementation of joint data and channel estimation, parallel structures are proposed to perform both the Viterbi algorithm and channel estimation. Simulation results show the numerical stability of different implementation techniques and the number of bits required in the digital computations with different estimators.

In this paper, an integrated Orthogonal Frequency Division Multiplexing (OFDM) system is developed to improve Bit Error Rate (BER) performance of communication systems in order to meet the requirements of advanced communication systems like long term evaluation advanced (LTE –A). The proposed OFDM system consists of channel estimation using comb type pilot arrangement and convolution encoder with the best known Coding Bounds and Maximum A Posteriori (MAP) decoder. Using pilot symbols, the channel quality is estimated. The BER performance of OFDM system is improved using pilot carriers, channel estimation schemes and MAP decoder at the best known coding bounds. The system performance is analyzed with recursive systematic and non-systematic convolution codes, ML and MAP decoders. From the results, it is evident that the proposed OFDM system with channel estimation and MAP decoder at the best known coding bounds improves the system performance significantly.

In this paper, we propose a joint channel estimation and symbol detection (JCESD) algorithm relying on message-passing algorithms (MPA) for orthogonal frequency division multiple access (OFDMA) systems. The channel estimation and symbol detection leverage the framework of expectation propagation (EP) and belief propagation (BP) with the aid of Gaussian approximation, respectively. Furthermore, to reduce the computation complexity involved in channel estimation, the matrix inversion is transformed into a series of diagonal matrix inversions through the Sherman-Morrison formula. Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%, compared with the traditional linear minimum mean square error (LMMSE) algorithm, and can approach to the bit error rate (BER) performance bound of perfectly known channel state information within 0.1 dB.

In this paper a proposal of joint channel and carrier estimation using extended Kalman filter (EKF) is presented. For the proposed algorithm simulations are performed and the results are compared with the separate channel estimation based on the Kalman filter (KF) and carrier estimation based on the extended Kalman filter (EKF).

This paper studies three-dimensional multi-robot Cooperative Localization (CL) problem. Most of existing CL strategies adopt Extended Kalman Filter (EKF) or Maximum a Posteriori (MAP). In this paper, a novel approach based on Moving Horizon Estimation (MHE) is proposed. The main contribution of this paper is twofold: 1) MHE is integrated with EKF for three-dimensional CL using single mobile beacon, which can bound localization error, impose various constraints on states and noises, and make use of previous range mea- surements for current estimation. 2) A sufficient condition on observability of multi-robot CL is derived by using Fisher Information Matrix. Simulation is conducted to verify that the proposed MHE based CL algorithm outperforms EKF based method in terms of localization accuracy, and two scenarios where our algorithm is superior to EKF are discussed.

This paper proposes a novel joint channel and data estimation (JCDE) algorithm via parametric bilinear Gaussian belief propagation (PBiGaBP) for orthogonal time frequency space (OTFS) demodulation. In doubly-selective fading channels, since the Doppler shift breaks the orthogonality between subcarriers, the signal demodulation process requires estimating the path gains and data symbols from the high-dimensional signals that are multiplexed in the frequency-time (FT) domain. To obtain highly accurate channel state information (CSI) using a typical channel estimation scheme based only on reference signals, an increase in pilot overhead to suppress inter-symbol interference (ISI) is unavoidable. In addition, large-scale matrix operations based on the size of OTFS-equivalent channels also pose problems in terms of computational cost. To address these issues, we focus on the fact that OTFS demodulation can be formulated as a large-scale parametric bilinear inference (PBI) problem, and solve it using the Gaussian belief propagation (GaBP) approach, which enables an approximate implementation of the sum-product algorithm (SPA), based on the central limit theorem (CLT), to design a low-complexity and high-accuracy JCDE algorithm using short pilots. Simulation results show that the proposed PBiGaBP-based JCDE algorithm outperforms the state-of-the-art (SotA) schemes and approaches the performance of idealized (genie-aided) scheme in terms of mean square error (MSE) and bit error rate (BER) performances.

This article addresses the target tracking problem based on the received signal strength (RSS) and angle of arrival (AOA) in wireless sensor networks (WSNs). The tracking problem is formulated in the framework of the maximum a posteriori (MAP), in which the prior knowledge of moving target nodes (TNs) is exploited. Due to the fact that the cost function of the tracking problem is highly nonlinear and nonconvex, most of the existing algorithms tend to approximate and relax the cost function. As a result, the tracking accuracy is usually compromised. In this article, we propose a tracking algorithm based on evolutionary techniques that do not require an approximation of the cost function, resulting in a considerable improvement in tracking accuracy. The proposed tracking algorithm is compared with state-of-the-art algorithms such as the MAP, particle filter (PF), and Kalman filter (KF). Simulation and real experiment results demonstrate that the proposed tracking algorithm provides an improvement roughly by 16%, 11%, and 18% over the MAP, PF, and KF, respectively, in the tracking accuracy.

The performance of signal detection in a multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) optical wireless communication (OWC) system is significantly affected by channel fading and atmospheric turbulence. Accurate channel state information (CSI) is crucial for reliable channel estimation, but obtaining it typically requires a high pilot overhead. To address this issue, we propose a joint channel estimation and signal detection (JCESD) algorithm that reduces the pilot overhead while improving the detection performance with a moderate increase in computational complexity. By leveraging convolutional neural network (CNN) and long short-term memory (LSTM) networks, the JCESD algorithm learns the mapping relationship between atmospheric turbulence channels and signals, thereby mitigating the challenges associated with high-complexity signal processing at the receiver. Additionally, layer normalization is applied during data pre-processing to enhance overall performance. Simulation results demonstrate that the proposed JCESD algorithm outperforms the LS-ZF algorithm and achieves an approximate 1dB gain over the LS-MMSE algorithm under weak turbulence conditions at a bit error rate (BER) of 10-5 in the 2×2 system when using eight pilots, with a 0.9dB gain when using four pilots-highlighting its capability to maintain superior performance while reducing pilot overhead. Additionally, the JCESD algorithm exhibits superior robustness in atmospheric turbulence channels while effectively reducing pilot overhead and maintaining manageable computational complexity.

This paper presents a variational framework for dense diffeomorphic atlas-mapping onto high-throughput histology stacks at the 20 μm meso-scale. The observed sections are modelled as Gaussian random fields conditioned on a sequence of unknown section by section rigid motions and unknown diffeomorphic transformation of a three-dimensional atlas. To regularize over the high-dimensionality of our parameter space (which is a product space of the rigid motion dimensions and the diffeomorphism dimensions), the histology stacks are modelled as arising from a first order Sobolev space smoothness prior. We show that the joint maximum a-posteriori, penalized-likelihood estimator of our high dimensional parameter space emerges as a joint optimization interleaving rigid motion estimation for histology restacking and large deformation diffeomorphic metric mapping to atlas coordinates. We show that joint optimization in this parameter space solves the classical curvature non-identifiability of the histology stacking problem. The algorithms are demonstrated on a collection of whole-brain histological image stacks from the Mouse Brain Architecture Project.