Variational-Bayes-Assisted Joint Signal Detection, Noise Covariance Estimation, and Channel Tracking in MIMO-OFDM Systems

Abstract

This paper introduces an improved variational bayes (Improved-VB) receiver algorithm for joint signal detection, noise covariance matrix estimation and channel impulse response (CIR) tracking in MIMO-OFDM systems over time varying channels. The variational bayes (VB) framework and turbo principle are combined to accomplish the parameter estimation and data detection. In the proposed Improved-VB receiver, a modified linear minimum mean-square-error interference cancellation (LMMSE-IC) soft detector is developed based on the VB theory, which adaptively sets the log-likelihood ratio (LLR) clipping value according to the reliability of detection on each subcarrier to mitigate the error propagation. Following the signal detection, an adaptive noise covariance matrix estimator is derived for the effective noise covariance estimation. Furthermore, in order to track time varying channels, a VB softinput Kalman filter (VB-Soft-KF) is first derived. However, unreliable soft symbols introduce outliers, which degrade the performance of VB-Soft-KF. To tackle this problem, we propose a robust VB softinput Kalman filter (VB-Robust-KF) based on the Huber M estimation theory. Finally, the performance The material has been presented in parts at IEEE PIMRC 2010, Istanbul, Turkey, Sep. 26-30.

EM‐based poor slot update method for MIMO IDD receiver in fast time‐varying channels

“…where y(n) ∈ ℂ N R × 1 is the received signal vector in time instant n, H(n) ∈ ℂ N R × N T is the spatially uncorrelated flat Rayleigh fading MIMO channel matrix, s(n) ∈ ℂ N T × 1 is the transmitted symbol vector, and w(n) ∈ ℂ N R × 1 is the noise vector with i.i.d. AWGN elements with variance σ w 2 . An element h i, j (n) of channel matrix H(n) represents the Rayleigh fading channel coefficient between the jth transmitting antenna and the ith receiving antenna of the MIMO channel.…”

“…The DD channel tracking scheme can be integrated into a turbo iterative receiver in order to maximise the performance of the receiver, and it can also be performed jointly with the detection process [1,2]; however, in this study, we chose to focus on a separate channel tracking algorithm [3][4][5][6] in order to reduce the complexity and provide us with the flexibility to evaluate a wide range of detection and channel tracking algorithms. The iterative DD channel tracking techniques that have been developed for turbo iterative receivers employ separate adaptive filters, such as least mean squares (LMS) [3], recursive least squares (RLS) [4], and Kalman filtering (KF) [5,6].…”

“…i. Most previous work on DD channel tracking [1,2,[5][6][7] relies on a KF-based approach to achieve optimal performance, which is prohibitively complex for MIMO IDD receivers with a large number of antennas. In contrast, we propose a new RLS-based channel tracking method with enhanced EM-based update process for MIMO IDD receivers.…”

Message-Passing Receiver for Joint Channel Estimation and Decoding in 3D Massive MIMO-OFDM Systems

“…To overcome this problem, various message-passing algorithms based on approximate inference have been proposed [8], [16]- [23]. In existing approaches, the message passing strategies include loopy belief propagation (LBP) [8], [16], [19]- [21], variational methods [17], [23], [24], and a hybrid of both [18], [22]. LBP has a high complexity when applied to graphical models that involve both discrete and continuous random variables.…”

Power of Error Correcting Codes for SFBC-OFDM Classification Over Unknown Channels

“…1) Error-correcting codes have been intensively studied for MIMO-OFDM communication systems. Examples include low-density parity-check (LDPC) codes [21], turbo codes [22], and convolutional codes [23]. They also have been adopted in many practical MIMO-OFDM systems such as 5G, LTE, WiMAX, and IEEE 802.11x WiFi systems [24].…”