On decoding of digital data sent over a noisy MIMO channel

TL;DR

This paper introduces novel randomized decoding schemes for digital data transmitted over noisy MIMO channels, combining linear algebra and probability techniques, and demonstrates their effectiveness through numerical experiments.

Contribution

It presents new randomized algorithms for decoding in MIMO channels, integrating standard linear algebra and probabilistic methods, with performance validated by simulations.

Findings

Decoding schemes outperform traditional methods in noisy environments

Numerical experiments confirm the efficiency of the proposed algorithms

The methods are applicable to high-dimensional MIMO systems

Abstract

The transmission of digital data is one of the principal tasks in modern wireless communication. Classically, the communication channel consists of one transmitter and one receiver; however, due to the constantly increasing demand in higher transmission rates, the popularity of using several receivers and transmitters has been rapidly growing. In this paper, we combine a number of fairly standard techniques from numerical linear algebra and probability to develop several (apparently novel) randomized schemes for the decoding of digital messages sent over a noisy multivariate Gaussian channel. We use a popular mathematical model for such channels to illustrate the performance of our schemes via numerical experiments.