Abstract
Under finite-alphabet inputs, the mutual information (MI) and minimum mean-square error (MMSE) matrix have no closed-form solutions, and numerical integrations, e.g., Monte Carlo simulation, endure prohibitive computational burden for massive multiple-input multiple-output (MIMO) systems. In this letter, we propose a new method based on the approximate message passing (AMP) algorithm for estimating the MI and MMSE matrix. By the new method, the computational complexity is reduced from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal{ O}}(N_{\mathrm{ sa}}N_{r}N_{t}M^{2N_{t}})$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal{ O}}({N_{r}N_{t}})$ </tex-math></inline-formula> for a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N_{r}\times N_{t}$ </tex-math></inline-formula> MIMO system, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> is the cardinality of the input and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N_{\mathrm{ sa}}$ </tex-math></inline-formula> is the number of samples. Simulation results also demonstrate the effectiveness and high-performance of the proposed new method.
Accepted Version
Published Version
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