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This paper covers location determination in wireless cellular networks based on time difference of arrival (TDoA) measurements in a factor graphs framework. The resulting nonlinear estimation problem of the localization process for the mobile station cannot be solved analytically. The well-known iterative Gauss-Newton method as standard solution fails to converge for certain geometric constellations and bad initial values, and thus, it is not suitable for a general solution in cellular networks. Therefore, we propose a TDoA positioning algorithm based on factor graphs. Simulation results in terms of root-mean-square errors and cumulative density functions show that this approach achieves very accurate positioning estimates by moderate computational complexity.

This paper proposes a new class of low-density generator-matrix codes (LDGM) based on chaotic dynamical systems. The codes are designed by controlling symbolic dynamics and using linear convolutional codes. Analyzing the complex structure of chaotic systems, iterative decoding is developed. The communication performance is studied, and convergence analysis of the iterative-decoding system is presented. Finally, comparison and advantages over LDGM linear block codes in terms of encoding complexity and bit-error-rate performance are described, and possible applications of our codes are discussed.

We address the problem of online Bayesian state and parameter tracking in autoregressive (AR) models with time-varying process noise variance. The involved marginalization and expectation integrals cannot be analytically solved. Moreover, the online tracking constraint makes sampling and batch learning methods unsuitable for this problem. We propose a hybrid variational message passing algorithm that robustly tracks the time-varying dynamics of the latent states, AR coefficients and process noise variance. Since message passing in a factor graph is a highly modular inference approach, the proposed methods easily extend to other non-stationary dynamic modeling problems.