  In this work, we study complex-valued data detection performance in massive
multiple-input multiple-output (MIMO) systems. We focus on the problem of
recovering an $n$-dimensional signal whose entries are drawn from an arbitrary
constellation $\mathcal{K} \subset \mathbb{C}$ from $m$ noisy linear
measurements, with an independent and identically distributed (i.i.d.) complex
Gaussian channel. Since the optimal maximum likelihood (ML) detector is
computationally prohibitive for large dimensions, many convex relaxation
heuristic methods have been proposed to solve the detection problem. In this
paper, we consider a regularized version of this convex relaxation that we call
the regularized convex relaxation (RCR) detector and sharply derive asymptotic
expressions for its mean square error and symbol error probability. Monte-Carlo
simulations are provided to validate the derived analytical results.
