# Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine

## TL;DR

This paper introduces a Tucker decomposed extreme learning machine (TDELM) for multi-antenna channel interpolation, leveraging tensor processing to improve accuracy and efficiency in estimating wireless channel state information.

## Contribution

The paper proposes a novel TDELM model that processes tensorial data for channel interpolation, achieving better performance and shorter runtime than traditional ELMs.

## Key findings

- Achieves comparable MSE with 15% shorter runtime
- Outperforms other methods with 20% lower MSE
- Validates theoretical interpolation capability

## Abstract

Channel interpolation is an essential technique for providing high-accuracy estimation of the channel state information (CSI) for wireless systems design where the frequency-space structural correlations of multi-antenna channel are typically hidden in matrix or tensor forms. In this letter, a modified extreme learning machine (ELM) that can process tensorial data, or ELM model with tensorial inputs (TELM), is proposed to handle the channel interpolation task. The TELM inherits many good properties from ELMs. Based on the TELM, the Tucker decomposed extreme learning machine (TDELM) is proposed for further improving the performance. Furthermore, we establish a theoretical argument to measure the interpolation capability of the proposed learning machines. Experimental results verify that our proposed learning machines can achieve comparable mean squared error (MSE) performance against the traditional ELMs but with 15% shorter running time, and outperform the other methods for a 20% margin measured in MSE for channel interpolation.