# Machine Learning for Model Order Selection in MIMO OFDM Systems

arXiv: https://arxiv.org/abs/2106.11633 · HTML: https://mlsanity.com/paper/2106.11633/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Model-aided Deep Neural Network for Source Number Detection](https://mlsanity.com/paper/1909.13273/) (2019)
- [Model Order Selection in DoA Scenarios via Cross-Entropy based Machine Learning Techniques](https://mlsanity.com/paper/1910.09284/) (2019)

## Built on by (most cited first, 1 of 1)

- [Combining AI/ML and PHY Layer Rule Based Inference -- Some First Results](https://mlsanity.com/paper/2203.08074/) (2022)

## Abstract (arXiv)

A variety of wireless channel estimation methods, e.g., MUSIC and ESPRIT, rely on prior knowledge of the model order. Therefore, it is important to correctly estimate the number of multipath components (MPCs) which compose such channels. However, environments with many scatterers may generate MPCs which are closely spaced. This clustering of MPCs in addition to noise makes the model order selection task difficult in practice to currently known algorithms. In this paper, we exploit the multidimensional characteristics of MIMO orthogonal frequency division multiplexing (OFDM) systems and propose a machine learning (ML) method capable of determining the number of MPCs with a higher accuracy than state of the art methods in almost coherent scenarios. Moreover, our results show that our proposed ML method has an enhanced reliability.