# Federated Learning in MIMO Satellite Broadcast System

**Authors:** Raphael Pinard, Mitra Hassani, Wayne Lemieux  
**arXiv:** [2303.16603](https://arxiv.org/abs/2303.16603)  
**Date:** 2023-03-29  
**Summary page:** https://www.summarizepaper.com/en/paper/2303.16603v1/

## Key points

- Federated learning (FL) preserves clients' data privacy from adversaries and central servers.
- FL has limitations such as vulnerability to inference or decreased test accuracy.
- The paper proposes incorporating federated learning in the inner workings of multiple-input multiple-output (MIMO) systems to address these limitations.
- The paper focuses on MIMO satellite communication (SatCom), where the receiver is a linear array antenna.
- Researchers aim to find the channel capacity for low Earth orbiting satellites whose positions are unknown to the terrestrial receiver.
- To analyze channel capacity in MIMO systems, researchers typically find eigenvalue distribution for the channel matrix multiplied by its conjugate.
- This paper approximates average channel capacity and outage probability assuming perfect channel state information (CSI).
- Simulation results show that only three terms of an infinite summation would give us average capacity with high accuracy.
- Incorporating federated learning into MIMO systems can provide a unique solution for increasing demand for high-performance next-generation wireless communications while preserving clients' data privacy.

## Summary

Federated learning (FL) is a type of distributed machine learning that enables the preservation of clients' data privacy from adversaries and even the central server. To address existing FL limitations such as vulnerability to inference or decreased test accuracy, this paper proposes incorporating federated learning in the inner workings of multiple-input multiple-output (MIMO) systems. The paper focuses on MIMO satellite communication (SatCom), where the receiver is a linear array antenna. Researchers aim to find the channel capacity for low Earth orbiting satellites whose positions are unknown to the terrestrial receiver. In such scenarios, line-of-sight becomes more dominant and path loss reduces. To analyze channel capacity in MIMO systems, researchers typically find eigenvalue distribution for the channel matrix multiplied by its conjugate. When modeled as pure line-of-sight, the channel matrix H becomes a Vandermonde matrix and eigenvalue distribution is unknown; only asymptotic behavior studies have been conducted. This paper approximates average channel capacity and outage probability assuming perfect channel state information (CSI). Simulation results show that only three terms of an infinite summation would give us average capacity with high accuracy. Incorporating federated learning into MIMO systems can provide a unique solution for increasing demand for high-performance next-generation wireless communications while preserving clients' data privacy from adversaries and even central servers.