# Distributed Optimization of Multi-Beam Directional Communication Networks

## TL;DR

This paper develops a distributed optimization framework for multi-beam airborne networks to maximize data rates, employing convex programming and augmented Lagrangian methods, demonstrating fast convergence and improved performance over traditional routing.

## Contribution

It introduces a convex multi-commodity flow formulation and a distributed augmented Lagrangian algorithm for optimizing multi-beam directional communication networks.

## Key findings

- Fast convergence compared to primal-dual methods
- Significant performance gains over minimum distance routing
- Effective handling of intra-network rate demands

## Abstract

We formulate an optimization problem for maximizing the data rate of a common message transmitted from nodes within an airborne network broadcast to a central station receiver while maintaining a set of intra-network rate demands. Assuming that the network has full-duplex links with multi-beam directional capability, we obtain a convex multi-commodity flow problem and use a distributed augmented Lagrangian algorithm to solve for the optimal flows associated with each beam in the network. For each augmented Lagrangian iteration, we propose a scaled gradient projection method to minimize the local Lagrangian function that incorporates the local topology of each node in the network. Simulation results show fast convergence of the algorithm in comparison to simple distributed primal dual methods and highlight performance gains over standard minimum distance-based routing.