Blind multiuser detection algorithms based on set theory
This paper proposes three blind multiuser detection algorithms based on set theory. They all originate from the set-membership filter (SMF). Our algorithms have some advantages over traditional adaptive algorithms. First, They provide region estimates in addition to point estimates. Second, they exhibit better convergence due to the optimization of the weighting sequence. Third, our algorithms feature a unique selective update criterion that requires parameter updates to be computed for only a small fraction of the data. Simulation shows our algorithms are superior to other blind algorithms in serious near-far effect.