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Equivalent MIMO Channel Matrix Sparsification for Enhancement of Sensor Capabilities.

Mikhail Bakulin¹, Vitaly Kreyndelin¹, Sergei Melnik¹

¹Faculty of Radio and Television, Moscow Technical University of Communications and Informatics (MTUCI), 111024 Moscow, Russia.

Sensors (Basel, Switzerland)

|March 10, 2022

Summary

New mobile communication methods use large antenna systems. This study transforms multiple-input multiple-output (MIMO) channels into sparse matrices, enabling efficient signal detection using message-passing algorithms (MPAs).

Keywords:

MIMO Turbo coding sparse matrix

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Area of Science:

Electrical Engineering
Computer Science
Information Theory

Background:

Next-generation mobile communications increasingly rely on multiple-input multiple-output (MIMO) channels with numerous antennas.
Existing signal detection methods face significant energy efficiency and complexity challenges, especially when comparing optimal maximum likelihood algorithms with simpler linear approaches.

Purpose of the Study:

To develop a novel method for transforming MIMO channels into a sparse matrix model.
To enable the use of efficient iterative signal detection algorithms for large-scale MIMO systems.

Main Methods:

Representing the MIMO channel as a sparse matrix with a limited number of non-zero elements per row.
Modeling the MIMO channel as a Markov process.
Applying iterative demodulation algorithms like message-passing algorithms (MPAs) and Turbo codes.

Main Results:

Successfully demonstrated that a MIMO channel can be effectively represented as a sparse matrix.
Established that the MIMO channel can be modeled as a Markov process.
Showed the feasibility of using iterative algorithms for signal detection in these transformed channels.

Conclusions:

The proposed sparse matrix transformation offers a viable approach for simplifying signal detection in large-scale MIMO systems.
Modeling MIMO channels as Markov processes opens avenues for employing efficient iterative demodulation techniques.
This research contributes to overcoming the complexity and energy efficiency gap in advanced mobile communication systems.