Joint box-constraint and deregularization in multiuser detection

Multiuser detection can be described as a quadratic optimization problem with binary constraint. Many techniques are available to find approximate solution to this problem. These techniques can be characterized in terms of complexity and detection performance. The “efficient frontier” of known techniques include the decision-feedback (DF), branch-and-bound (BB) and probabilistic data association (PDA) detectors. We propose a novel iterative multiuser detection technique based on joint deregularized and box-constrained solution to quadratic optimization with iterations similar to that used in the nonstationary Tikhonov iterated algorithm. The deregularization maximizes the energy of the solution; this is opposite to the Tikhonov regularization where the energy is minimized. However, combined with box-constraints, the deregularization forces the solution to be close to the binary set. Our development improves the “efficient frontier” in multiuser detection, which is illustrated by simulation results.