Equivalent Signal Model Research Articles

Phase Noise in MIMO Systems: Bayesian Cramér–Rao Bounds and Soft-Input Estimation

This paper addresses the problem of estimating time varying phase noise caused by imperfect oscillators in multiple-input multiple-output (MIMO) systems. The estimation problem is parameterized in detail and based on an equivalent signal model its dimensionality is reduced to minimize the overhead associated with phase noise estimation. New exact and closed-form expressions for the Bayesian Cramer-Rao lower bounds (BCRLBs) and soft-input maximum a posteriori (MAP) estimators for online, i.e., filtering, and offline, i.e., smoothing, estimation of phase noise over the length of a frame are derived. Simulations demonstrate that the proposed MAP estimators' mean-square error (MSE) performances are very close to the derived BCRLBs at moderate-to-high signal-to-noise ratios. To reduce the overhead and complexity associated with tracking the phase noise processes over the length of a frame, a novel soft-input extended Kalman filter (EKF) and extended Kalman smoother (EKS) that use soft statistics of the transmitted symbols given the current observations are proposed. Numerical results indicate that by employing the proposed phase tracking approach, the bit-error rate performance of a MIMO system affected by phase noise can be significantly improved. In addition, simulation results indicate that the proposed phase noise estimation scheme allows for application of higher order modulations and larger numbers of antennas in MIMO systems that employ imperfect oscillators.

Space-alternating generalised expectation–maximisation-based H-infinity channel estimator for multiple-input multiple-output-orthogonal frequency division multiplexing systems

This study presents a low-complexity and robust H-infinity channel estimator for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The H-infinity estimator, which has never been devoted to MIMO-OFDM systems, could be implemented by applying a multiple-order auto-regression (AR) model. However, it may increase the design complexity of receivers and lead to poor real-time property when this model is used for MIMO-OFDM systems, which makes the authors abandon AR model. In order to reduce the number of matrices manipulations because of the each received OFDM symbols from different transmit antennas, the iterative space-alternating generalised expectation–maximisation (SAGE) algorithm is adopted. Furthermore, to deal with the effect of non-Gaussian noise (NGN) channels, because of various natural or man-made impulsive sources, an equivalent signal model (ESM) is introduced to alleviate the effect of this issue and enhance the robust of SAGE-based H-infinity estimator. Simulation results show that H-infinity estimator has almost the same bit error rate performance as optimal maximum a posteriori estimator. The performance gain afforded by using ESM can be substantial when compared with using the traditional signal model, which dramatically enhance the robustness of SAGE-based H-infinity estimator against NGN channels.

Optimum receiver design for wireless broad-band systems using OFDM. I

Orthogonal frequency-division multiplexing (OFDM) is the technique of choice in digital broad-band applications that must cope with highly dispersive transmission media at low receiver implementation cost. In this paper, we focus on the inner OFDM receiver and its functions necessary to demodulate the received signal and deliver soft information to the outer receiver for decoding. The effects of relevant nonideal transmission conditions are thoroughly analyzed: imperfect channel estimation, symbol frame offset, carrier and sampling clock frequency offset, time-selective fading, and critical analog components. Through an appropriate optimization criterion (signal-to-noise ratio loss), minimum requirements on each receiver synchronization function are systematically derived. An equivalent signal model encompassing the effects of all relevant imperfections is then formulated in a generalized framework. The paper concludes with an outline of synchronization strategies.