A BP-MF-EP Based Iterative Receiver for Joint Phase Noise Estimation, Equalization and Decoding

In this work, with combined belief propagation (BP), mean field (MF) and expectation propagation (EP), an iterative receiver is designed for joint phase noise (PN) estimation, equalization and decoding in a coded communication system. The presence of the PN results in a nonlinear observation model. Conventionally, the nonlinear model is directly linearized by using the first-order Taylor approximation, e.g., in the state-of-the-art soft-input extended Kalman smoothing approach (soft-in EKS). In this work, MF is used to handle the factor due to the nonlinear model, and a second-order Taylor approximation is used to achieve Gaussian approximation to the MF messages, which is crucial to the low-complexity implementation of the receiver with BP and EP. It turns out that our approximation is more effective than the direct linearization in the soft-in EKS with similar complexity, leading to significant performance improvement as demonstrated by simulation results.Comment: 5 pages, 3 figures, Resubmitted to IEEE Signal Processing Letter

Turbo-Equalization Using Partial Gaussian Approximation

This paper deals with turbo-equalization for coded data transmission over intersymbol interference (ISI) channels. We propose a message-passing algorithm that uses the expectation-propagation rule to convert messages passed from the demodulator-decoder to the equalizer and computes messages returned by the equalizer by using a partial Gaussian approximation (PGA). Results from Monte Carlo simulations show that this approach leads to a significant performance improvement compared to state-of-the-art turbo-equalizers and allows for trading performance with complexity. We exploit the specific structure of the ISI channel model to significantly reduce the complexity of the PGA compared to that considered in the initial paper proposing the method.Comment: 5 pages, 2 figures, submitted to IEEE Signal Processing Letters on 8 March, 201

Hybrid Message Passing Algorithm for Downlink FDD Massive MIMO-OFDM Channel Estimation

The design of message passing algorithms on factor graphs has been proven to be an effective manner to implement channel estimation in wireless communication systems. In Bayesian approaches, a prior probability model that accurately matches the channel characteristics can effectively improve estimation performance. In this work, we study the channel estimation problem in a frequency division duplexing (FDD) downlink massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. As the prior probability, we propose the Markov chain two-state Gaussian mixture with large variance difference (TSGM-LVD) model to exploit the structured sparsity in the angle-frequency domain of the massive MIMO-OFDM channel. In addition, we present a new method to derive the hybrid message passing (HMP) rule, which can calculate the message with mixed linear and non-linear model. To the best of the authors' knowledge, we are the first to apply the HMP rule to practical communication systems, designing the HMP-TSGM-LVD algorithm under the structured turbo-compressed sensing (STCS) framework. Simulation results demonstrate that the proposed HMP-TSGM-LVD algorithm converges faster and outperforms its counterparts under a wide range of simulation settings

Combined Message Passing Based SBL with Dirichlet Process Prior for Sparse Signal Recovery with Multiple Measurement Vectors

This paper concerns the problem of sparse signal recovery with multiple measurement vectors, where the sparse signal vectors share multiple supports (i.e., the signal vectors can be clustered and the vectors in a cluster share a common support) and the prior knowledge on the supports of the vectors is unknown. This problem can be solved using sparse Bayesian learning (SBL) with Dirichlet process (DP) as hyper-prior, which is named DP-SBL in this paper. This work aims to design efficient inference algorithms. The variational inference for DP mixtures, in particular mean field (MF) inference, has been studied, and applying it to the problem in this paper leads to an MF-DP-SBL algorithm. In this paper, we propose a combined message passing (CMP) approach, where a factor graph representation is designed to enable a more efficient implementation with both the MF and approximate message passing (AMP), leading to a CMP-DP-SBL algorithm. It is shown that, compared to MF-DP-SBL, CMP-DP-SBL delivers the same or even better performance with significantly lower complexity. As an example, we apply it to massive MIMO channel estimation where, due to the large number of antennas deployed at base station, the channel impulse responses measured at receive antennas can share multiple supports. It is shown that CMP-DP-SBL delivers considerably better performance than existing algorithms