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                          <header>
                              <identifier>oai:research.chalmers.se:158177</identifier>
                              <datestamp>2018-03-29T15:09:37Z</datestamp>
                              <setSpec>openaire</setSpec>
                          </header>
                          <metadata>
                              <oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
                                  <dc:title>Uniformly Reweighted Belief Propagation for Estimation and Detection in Wireless Networks</dc:title>
                                  <dc:identifier>https://research.chalmers.se/en/publication/158177</dc:identifier>
                                  <dc:date>2012</dc:date>
                                              <dc:creator>Wymeersch, Henk</dc:creator>
                                              <dc:creator>Penna, Federico</dc:creator>
                                              <dc:creator>Savic, Vladimir</dc:creator>
                                                  <dc:subject>Computer and Information Science</dc:subject>
                                              <dc:subject>belief propagation</dc:subject>
                                              <dc:subject>distributed detection</dc:subject>
                                              <dc:subject>sum-product algorithm</dc:subject>
                                              <dc:subject>message passing</dc:subject>
                                              <dc:subject>approximate inference</dc:subject>
                                              <dc:subject>models</dc:subject>
                                              <dc:subject>multiple sensors</dc:subject>
                                              <dc:subject>factor graph approach</dc:subject>
                                              <dc:subject>localization</dc:subject>
                                              <dc:subject>sensor networks</dc:subject>
                                              <dc:subject>Distributed inference</dc:subject>
                                              <dc:subject>factor graphs</dc:subject>
                                          <dc:description>In this paper, we propose a new inference algorithm, suitable for distributed processing over wireless networks. The algorithm, called uniformly reweighted belief propagation (URW-BP), combines the local nature of belief propagation with the improved performance of tree-reweighted belief propagation (TRW-BP) in graphs with cycles. It reduces the degrees of freedom in the latter algorithm to a single scalar variable, the uniform edge appearance probability rho. We provide a variational interpretation of URW-BP, give insights into good choices of rho, develop an extension to higher-order potentials, and complement our work with numerical performance results on three inference problems in wireless communication systems: spectrum sensing in cognitive radio, cooperative positioning, and decoding of a low-density parity-check (LDPC) code.</dc:description>
                                                                  <dc:relation>info:eu-repo/grantAgreement/EC/FP7/258418//Cooperative Situational Awareness for Wireless Networks (COOPNET)/</dc:relation>
                                                  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1109/twc.2012.021412.111509</dc:relation>
                                          <dc:type>info:eu-repo/semantics/article</dc:type>
                                          <dc:source>IEEE Transactions on Wireless Communications (1536-1276) vol.11(2012)</dc:source>
                                                      <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:language>eng</dc:language>
                                  <dc:audience>Researchers</dc:audience>
                                  <dc:format>application/pdf</dc:format>
                              </oai_dc:dc>
                          </metadata>
                      </record>