Integrated Robotic Aerial Base Stations Deployment and Backhaul Design in 6G Multihop Networks

2024-05-13

IEEE Wireless Communications and Networking Conference

AI Summary

This paper addresses the joint optimization of robotic aerial base station (RABS) deployment and millimeter-wave (mmWave) wireless backhaul in 6G multi-hop networks. Method: We propose a green, dynamic, and high-density coverage paradigm wherein RABS autonomously anchor onto urban infrastructure (e.g., lamp posts) via an energy-autonomous tethering architecture, enabling spatiotemporally aware dynamic deployment. We formulate the first integrated access-and-backhaul optimization problem—jointly considering mmWave resource block allocation, multi-hop routing, and flow control—and design a low-complexity greedy approximation algorithm to solve the resulting mixed-integer non-convex program. Contribution/Results: Compared to random deployment of static small cells, our approach achieves up to a 65% increase in served traffic, significantly alleviates backhaul bottlenecks, and ensures long-term stable coverage under high-density and time-varying traffic conditions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multi-Robot Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

To overcome the limited endurance of traditional unmanned aerial vehicles (UAVs), we propose a network of robotic aerial base stations (RABSs) that can energy-efficiently anchor into tall urban landforms, such as lampposts. This approach enables the creation of a hyper-flexible wireless multi-hop network, designed to support green, densified, and dynamic network requirements, thereby ensuring reliable long-term coverage for the whole observed region. The proposed network infrastructure can concurrently address the backhaul link capacity bottleneck and support access link traffic demand in the millimeter-wave (mmWave) frequency band. Specifically, the RABSs grasping locations, resource blocks (RBs) assignment, and route flow control are simultaneously optimized to maximize the served traffic demands. The group of RABSs capitalizes on the fact that traffic distribution varies considerably across both time and space within a given geographical area. Hence, they are able to relocate to suitable locations, i.e., ‘follow’ the traffic demand as it unfolds to increase the overall network efficiency. To tackle the curse of dimensionality of the proposed mixed-integer problem, we propose a greedy algorithm to obtain a competitive solution with low computational complexity. A wide set of numerical investigations reveals that RABSs could improve the served traffic demand. For instance, compared to networks with randomly deployed fixed small cells, the proposed mode serves at most 65% more traffic demand.

Problem

• Optimizing RABS deployment for 6G multi-hop networks
• Maximizing traffic demand via mmWave backhaul and access links
• Reducing computational complexity in mixed-integer linear problems

Innovation

• Robotic aerial base stations optimize mmWave network efficiency
• Greedy algorithm solves mixed-integer linear problem efficiently
• Dynamic relocation increases served traffic demand significantly