SYMBOL-LEVEL PRECODING IS RELATED TO PARAMETER ESTIMATION FROM QUANTIZED DATA

Symbol-level precoding (SLP) has received tremendous interest in MIMO communications in recent years. In particular, much attention has been paid to the formulation and optimization aspects. In this paper we contribute to these aspects by drawing a connection between SLP and a seemingly unrelated topic—namely, parameter estimation from quantized data. Specifically, we illustrate that the maximum detection probability formulation for SLP is basically the same as the maximum likelihood estimation formulation for quantized linear regression (QLR). This dual relationship is not just an interesting fundamental result. Using this relationship, we show how the expectation maximization (EM) method for QLR, a popular way to deal with QLR, can be repurposed to perform optimization for SLP. Our numerical results suggest that the accelerated EM method for SLP, as a new possibility, is highly efficient.