6 research outputs found
On the Success Probability of the Box-Constrained Rounding and Babai Detectors
In communications, one frequently needs to detect a parameter vector \hbx
in a box from a linear model. The box-constrained rounding detector \x^\sBR
and Babai detector \x^\sBB are often used to detect \hbx due to their high
probability of correct detection, which is referred to as success probability,
and their high efficiency of implimentation. It is generally believed that the
success probability P^\sBR of \x^\sBR is not larger than the success
probability P^\sBB of \x^\sBB. In this paper, we first present formulas for
P^\sBR and P^\sBB for two different situations: \hbx is deterministic and
\hbx is uniformly distributed over the constraint box. Then, we give a simple
example to show that P^\sBR may be strictly larger than P^\sBB if \hbx is
deterministic, while we rigorously show that P^\sBR\leq P^\sBB always holds
if \hbx is uniformly distributed over the constraint box.Comment: to appear in ISIT 201