Third party funded individual grant
Start date : 01.07.2018
End date : 30.06.2021
Extension date: 31.12.2021
The theoretical limits of distributed compressive sensing are studied by tools from both information theory and statistical physics. The investigations cover both noise-free and noisy distributed compressive sensing. The theoretical insights are utilized to design approximate message passing algorithms for joint recovery of large distributed compressive sensing networks with feasible computational complexity. These algo- rithms enable us to verify the non-rigorous results obtained by the replica method from statistical mechanics, and also, to propose theoretically optimal approaches for sampling and low complexity. The proposed research will lead to improved performance of reconstruction algorithms for distributed compressive sensing, e.g. higher compression rates and/or higher fidelity of reconstruction.
The theoretical limits of distributed compressive sensing are studied by tools from both information theory and statistical physics. The investigations cover both noise-free and noisy distributed compressive sensing. The theoretical insights are utilized to design approximate message passing algorithms for joint recovery of large distributed compressive sensing networks with feasible computational complexity. These algo- rithms enable us to verify the non-rigorous results obtained by the replica method from statistical mechanics, and also, to propose theoretically optimal approaches for sampling and low complexity. The proposed research will lead to improved performance of reconstruction algorithms for distributed compressive sensing, e.g. higher compression rates and/or higher fidelity of reconstruction.