Topology Control Schema for Better QoS in Hybrid RF/FSO Mesh Networks

TL;DR

This paper presents a topology control scheme for hybrid RF/FSO mesh networks that optimizes QoS by adaptively adjusting transmission parameters using ILP, LR, and PSO techniques.

Contribution

It introduces a novel adaptive topology control scheme for hybrid RF/FSO networks utilizing ILP, LR, and PSO for improved QoS management.

Findings

Effective encoding of QoS constraints as ILP problems.

Lagrangian Relaxation and heuristics provide near-optimal solutions.

Particle Swarm Optimization enables scalability for large networks.

Abstract

The practical limitations and challenges of radio frequency (RF) based communication networks have become increasingly apparent over the past decade, leading researchers to seek new hybrid communication approaches. One promising strategy that has been the subject of considerable interest is the augmentation of RF technology by Free Space Optics (FSO), using the strength of each communication technology to overcome the limitations of the other. In this article, we introduce a new scheme for controlling the topology in hybrid Radio-Frequency/Free Space Optics (RF/FSO) wireless mesh networks. Our scheme is based on adaptive adjustments to both transmission power (of RF and FSO transmitters) and the optical beam-width (of FSO transmitters) at individual nodes, with the objective of meeting specified Quality of Service (QoS) requirements, specifically end-to-end delay and throughput. We show how one can effectively encode the instantaneous objectives and constraints of the system as an instance of Integer Linear Programming (ILP). We demonstrate that the technique of Lagrangian Relaxation (LR), augmented with iterative repair heuristics, can be used to determine good (albeit sub-optimal) solutions for the ILP problem, making the approach feasible for mid-sized networks. We make the proposed scheme viable for large-scale networks in terms of number of nodes, number of transceivers, and number of source-destination pairs by solving the ILP problem using a Particle Swarm Optimization (PSO) implementation.