# Federated Dropout Learning for Hybrid Beamforming With Spatial Path Index Modulation In Multi-User mmWave-MIMO Systems

arXiv: https://arxiv.org/abs/2102.07450 · HTML: https://mlsanity.com/paper/2102.07450/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [Expanding the Reach of Federated Learning by Reducing Client Resource Requirements](https://mlsanity.com/paper/1812.07210/) (2018)
- [Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO](https://mlsanity.com/paper/2008.10846/) (2020)
- [Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems](https://mlsanity.com/paper/2001.11085/) (2020)
- [Deep-Learning-based Millimeter-Wave Massive MIMO for Hybrid Precoding](https://mlsanity.com/paper/1901.06537/) (2019)
- [Joint Antenna Selection and Hybrid Beamformer Design using Unquantized and Quantized Deep Learning Networks](https://mlsanity.com/paper/1905.03107/) (2019)
- [Toward Millimeter Wave Joint Radar-Communications: A Signal Processing Perspective](https://mlsanity.com/paper/1905.00690/) (2019)
- [A Deep Learning Framework for Hybrid Beamforming Without Instantaneous CSI Feedback](https://mlsanity.com/paper/2006.10971/) (2020)
- [Cognitive Learning-Aided Multi-Antenna Communications](https://mlsanity.com/paper/2010.03131/) (2020)
- [Intelligent Time-Varying Metasurface Transceiver for Index Modulation in 6G Wireless Networks](https://mlsanity.com/paper/2006.04889/) (2020)

## Built on by (most cited first, 2 of 2)

- [Federated Learning for Physical Layer Design](https://mlsanity.com/paper/2102.11777/) (2021)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)

## Abstract (arXiv)

Millimeter wave multiple-input multiple-output (mmWave-MIMO) systems with small number of radio-frequency (RF) chains have limited multiplexing gain. Spatial path index modulation (SPIM) is helpful in improving this gain by utilizing additional signal bits modulated by the indices of spatial paths. In this paper, we introduce model-based and model-free frameworks for beamformer design in multi-user SPIM-MIMO systems. We first design the beamformers via model-based manifold optimization algorithm. Then, we leverage federated learning (FL) with dropout learning (DL) to train a learning model on the local dataset of users, who estimate the beamformers by feeding the model with their channel data. The DL randomly selects different set of model parameters during training, thereby further reducing the transmission overhead compared to conventional FL. Numerical experiments show that the proposed framework exhibits higher spectral efficiency than the state-of-the-art SPIM-MIMO methods and mmWave-MIMO, which relies on the strongest propagation path. Furthermore, the proposed FL approach provides at least 10 times lower transmission overhead than the centralized learning techniques.