Merging Belief Propagation and the Mean Field Approximation: A Free Energy Approach

We present a joint message passing approach that combines belief propagation and the mean field approximation. Our analysis is based on the region-based free energy approximation method proposed by Yedidia et al. We show that the message passing fixed-point equations obtained with this combination correspond to stationary points of a constrained region-based free energy approximation. Moreover, we present a convergent implementation of these message passing fixedpoint equations provided that the underlying factor graph fulfills certain technical conditions. In addition, we show how to include hard constraints in the part of the factor graph corresponding to belief propagation. Finally, we demonstrate an application of our method to iterative channel estimation and decoding in an orthogonal frequency division multiplexing (OFDM) system

Low Complexity Scalable Iterative Algorithms for IEEE 802.11p Receivers

In this paper, we investigate receivers for Vehicular to Vehicular (V2V) and Vehicular to Infrastructure (V2I) communications. Vehicular channels are characterized by multiple paths and time variations, which introduces challenges in the design of receivers. We propose an algorithm for IEEE 802.11p compliant receivers, based on Orthogonal Frequency Division Multiplexing (OFDM). We employ iterative structures in the receiver as a way to estimate the channel despite variations within a frame. The channel estimator is based on factor graphs, which allow the design of soft iterative receivers while keeping an acceptable computational complexity. Throughout this work, we focus on designing a receiver offering a good complexity performance trade-off. Moreover, we propose a scalable algorithm in order to be able to tune the trade-off depending on the channel conditions. Our algorithm allows reliable communications while offering a considerable decrease in computational complexity. In particular, numerical results show the trade-off between complexity and performance measured in computational time and BER as well as FER achieved by various interpolation lengths used by the estimator which both outperform by decades the standard least square solution. Furthermore our adaptive algorithm shows a considerable improvement in terms of computational time and complexity against state of the art and classical receptors whilst showing acceptable BER and FER performance