A survey of information geometry in iterative receiver design, and its application to cooperative positioning
Dapeng Liu · Chalmers Publication Library (Chalmers University of Technology) · 2012
Belief propagation is a powerful procedure to perform inference, and exhibits near-optimal performance in iterative decoding/demapping.However, a complete analysis of the iterative process is still lacking, and convergence of the procedure has not proved in general cases.Information geometry offers a new view from differential geometry to understand this problem.In this thesis, we try to understand belief propagation as an iterative projection procedure by studying various information-geometric interpretations from the literature.We extend these insights to distributed inference, in particular to cooperative positioning.Cooperative Bayesian algorithms have outstanding performance in many network scenarios.However, they suffers from a large computational complexity when messages are represented by a grid.We propose three techniques to reduce the complexity and improve the accuracy of cooperative Bayesian positioning: geometrical pre-location, dynamic grid, and clipping.The performance of the new algorithm compares favorably with the original algorithm, with considerably complexity and network traffic reduction.