# Soft-Centralized Spectrum Resource Management in UAV-Assisted MANETs from Aggregate Multi-Hop Information Efficiency

## TL;DR

This paper introduces a new framework for managing spectrum resources in UAV-assisted ad hoc networks to improve communication efficiency.

## Contribution

The novel contribution is a soft-centralized resource management framework using AMIE to optimize spectrum allocation in UAMANETs.

## Key findings

- The proposed framework significantly improves Aggregate Multi-hop Information Efficiency (AMIE) compared to standard IEEE 802.11p settings.
- A modified MSF-PSO algorithm effectively optimizes resource allocation under MAC-layer constraints.
- AMIE captures the interplay between MAC contention, routing, and transmission reliability in dynamic UAV networks.

## Abstract

UAV-Assisted Mobile Ad Hoc Networks (UAMANETs) provide flexible communication support in dynamic and infrastructure-limited environments. This paper studies a representative UAMANET architecture in which a subset of UAVs forms stable task clusters with ground nodes while simultaneously acting as relays in an airborne backbone network. To characterize the network capacity under contention-based medium access and multi-hop routing, we introduce Aggregate Multi-hop Information Efficiency (AMIE), a capacity-oriented metric that jointly accounts for MAC-layer contention, multi-hop routing, and end-to-end transmission reliability. Based on an IEEE 802.11p access model, we extend Bianchi’s CSMA/CA analytical framework to UAMANETs, enabling a quantitative characterization of how spectrum resource allocation affects AMIE through link activation probability, transmission interruption, and end-to-end hop count. Building on the derived analytical insights, we further develop a soft centralized resource management framework, in which an existing MSF-PSO algorithm is employed as a numerical solver to optimize resource allocation under implicit MAC-layer coupling constraints. Numerical results demonstrate that, compared with conventional IEEE 802.11p spectrum resource settings, the proposed framework can achieve substantial AMIE improvements under representative network configurations.