Sensing and Classification Using Massive MIMO: A Tensor Decomposition-Based Approach

B. R. Manoj, Guoda Tian, Sara Gunnarsson, Fredrik Tufvesson, Erik G. Larsson

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review


Wireless-based activity sensing has gained significant attention due to its wide range of applications. We investigate radio-based multi-class classification of human activities using massive multiple-input multiple-output (MIMO) channel measurements in line-of-sight and non line-of-sight scenarios. We propose a tensor decomposition-based algorithm to extract features by exploiting the complex correlation characteristics across time, frequency, and space from channel tensors formed from the measurements, followed by a neural network that learns the relationship between the input features and output target labels. Through evaluations of real measurement data, it is demonstrated that the classification accuracy using a massive MIMO array achieves significantly better results compared to the state-of-the-art even for a smaller experimental data set.

Sidor (från-till)2649-2653
TidskriftIEEE Wireless Communications Letters
Tidigt onlinedatum2021
StatusPublished - 2021

Bibliografisk information

Publisher Copyright:

Ämnesklassifikation (UKÄ)

  • Kommunikationssystem


Utforska forskningsämnen för ”Sensing and Classification Using Massive MIMO: A Tensor Decomposition-Based Approach”. Tillsammans bildar de ett unikt fingeravtryck.

Citera det här