Interference Prediction in Mobile Ad Hoc Networks with a General Mobility Model

TL;DR

This paper presents a novel interference prediction method for MANETs using a general mobility model, enabling more accurate and adaptive communication strategies compared to traditional long-term average approaches.

Contribution

It introduces a general-order linear mobility model and a compound Gaussian point process functional for interference prediction, providing analytical tools and conditions for validity of BPP approximations.

Findings

The proposed model accurately predicts time-varying interference in MANETs.

Analytical results match simulation data, confirming effectiveness.

Conditions identified when BPP approximations are valid.

Abstract

In a mobile ad hoc network (MANET), effective prediction of time-varying interferences can enable adaptive transmission designs and therefore improve the communication performance. This paper investigates interference prediction in MANETs with a finite number of nodes by proposing and using a general-order linear model for node mobility. The proposed mobility model can well approximate node dynamics of practical MANETs. In contrast to previous studies on interference statistics, we are able through this model to give a best estimate of the time-varying interference at any time rather than long-term average effects. Specifically, we propose a compound Gaussian point process functional as a general framework to obtain analytical results on the mean value and moment-generating function of the interference prediction. With a series form of this functional, we give the necessary and sufficient condition for when the prediction is essentially equivalent to that from a Binomial Point Process (BPP) network in the limit as time goes to infinity. These conditions permit one to rigorously determine when the commonly used BPP approximations are valid. Finally, our simulation results corroborate the effectiveness and accuracy of the analytical results on interference prediction and also show the advantages of our method in dealing with complex mobilities.