Efficient System Tracking With Decomposable Graph-Structured Inputs and Application to Adaptive Equalization With Cyclostationary Inputs - 2018 PROJECT TITLE :Efficient System Tracking With Decomposable Graph-Structured Inputs and Application to Adaptive Equalization With Cyclostationary Inputs - 2018ABSTRACT:This Project introduces the graph-structured recursive least squares (GS-RLS) algorithm, which could be a terribly economical suggests that to trace a linear time-varying system when the inputs to the system have structure that can be modeled employing a decomposable Gaussian graphical model. For graphs with small clique sizes, it's shown that GS-RLS will achieve tracking performance terribly close to that of the standard RLS algorithm for a fraction of the computational value. In particular, once proving that the outputs of wide-sense stationary time-varying Communication channels have graphical model structure if the inputs are cyclostationary, important computational gains are realized for adaptive equalization of the time-varying underwater acoustic Communication channel using the GS-RLS algorithm. This is often verified using field data from the SPACE08 underwater acoustic Communication experiment. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Efficient Partial-Sum Network Architectures for List Successive-Cancellation Decoding of Polar Codes - 2018 Efficient Wideband DOA Estimation Through Function Evaluation Techniques - 2018