TY - GEN
T1 - Pseudo Prior Belief Propagation for densely connected discrete graphs
AU - Goldberger, J.
AU - Leshem, Amir
N1 - Place of conference:Cairo
PY - 2010
Y1 - 2010
N2 - This paper proposes a new algorithm for the linear least squares problem where the unknown variables are constrained to be in a finite set. The factor graph that corresponds to this problem is very loopy; in fact, it is a complete bipartite graph. Hence, applying the Belief Propagation (BP) algorithm yields very poor results. The Pseudo Prior Belief Propagation (PPBP) algorithm is a variant of the BP algorithm that can achieve near maximum likelihood (ML) performance with low computational complexity. First, we use the minimum mean square error (MMSE) detection to yield a pseudo prior information on each variable. Next we integrate this information into a loopy Belief Propagation (BP) algorithm as a pseudo prior. We show that, unlike current paradigms, the Belief Propagation (BP) algorithm can be advantageous even for dense graphs with many short loops. The performance of the proposed algorithm is demonstrated on the MIMO detection problem based on simulation results.
AB - This paper proposes a new algorithm for the linear least squares problem where the unknown variables are constrained to be in a finite set. The factor graph that corresponds to this problem is very loopy; in fact, it is a complete bipartite graph. Hence, applying the Belief Propagation (BP) algorithm yields very poor results. The Pseudo Prior Belief Propagation (PPBP) algorithm is a variant of the BP algorithm that can achieve near maximum likelihood (ML) performance with low computational complexity. First, we use the minimum mean square error (MMSE) detection to yield a pseudo prior information on each variable. Next we integrate this information into a loopy Belief Propagation (BP) algorithm as a pseudo prior. We show that, unlike current paradigms, the Belief Propagation (BP) algorithm can be advantageous even for dense graphs with many short loops. The performance of the proposed algorithm is demonstrated on the MIMO detection problem based on simulation results.
UR - https://scholar.google.co.il/scholar?q=Pseudo+Prior+Belief+Propagation+for+densely+connected+discrete+graphs&btnG=&hl=en&as_sdt=0%2C5
UR - https://scholar.google.co.il/scholar?q=Pseudo+prior+belief+propagation+for+densely+connected+discrete+graphs&btnG=&hl=en&as_sdt=0%2C5
M3 - Conference contribution
BT - Information Theory (ITW 2010, Cairo), 2010 IEEE Information Theory Workshop on
PB - IEEE
ER -