Work

Probabilistic MIMO Symbol Detection With Expectation Consistency Approximate Inference

Year: 2017

Type: article

Abstract: In this paper, we explore low-complexity probabilistic algorithms for soft symbol detection in high-dimensional multiple-input multiple-output (MIMO) systems. We present a novel algorithm based on the expectation consistency (EC) framework, which describes the approximate inference problem as an optimization over a nonconvex function. EC generalizes algorithms such as belief propagation and expectation propagation. For the MIMO symbol detection problem, we discuss feasible methods to find stationary points of the EC function and explore their tradeoffs between accuracy and speed of convergence. The accuracy is studied, first in terms of input-output mutual information and show that the proposed EC MIMO detector greatly improves state-of-the-art methods, with a complexity order cubic in the number of transmitting antennas. Second, these gains are corroborated by combining the probabilistic output of the EC detector with a low-density parity-check channel code.

Source: IEEE Transactions on Vehicular Technology

DOI: 10.1109/tvt.2017.2786638

Authors: Javier Cespedes, Pablo M. Olmos, Matilde Sanchez-Fernandez, Fernando Perez-Cruz

Institutions: Universidad Carlos III de Madrid, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Swiss Data Science Center

Language: English

Topic: Advanced Wireless Communication Techniques

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Council for International Exchange of Scholars, Comunidad de Madrid

Awards: G3216000229, G3230842478, G5218671702