On Delay Constrained Multicast Capacity of Large-Scale Mobile Ad-Hoc Networks

TL;DR

This paper analyzes the maximum multicast capacity in large-scale mobile ad hoc networks under delay constraints, deriving capacity bounds and proposing a joint coding/scheduling scheme that achieves near-optimal throughput.

Contribution

It provides the first capacity bounds for delay-constrained multicast in large-scale MANETs and introduces an effective joint coding and scheduling algorithm.

Findings

Capacity per session is bounded by O(min{1, (log p)(log(n_sp)) sqrt(D/n_s)}).

Proposed algorithm achieves throughput of Θ(min{1, sqrt(D/n_s)}).

Simulations confirm the algorithm's effectiveness under different mobility models.

Abstract

This paper studies the delay constrained multicast capacity of large scale mobile ad hoc networks (MANETs). We consider a MANET consists of $n_s$ multicast sessions. Each multicast session has one source and $p$ destinations. The wireless mobiles move according to a two-dimensional i.i.d. mobility model. Each source sends identical information to the $p$ destinations in its multicast session, and the information is required to be delivered to all the $p$ destinations within $D$ time-slots. Given the delay constraint $D,$ we first prove that the capacity per multicast session is $O(\min\{1, (\log p)(\log (n_sp)) \sqrt{\frac{D}{n_s}}\}).$ Given non-negative functions $f(n)$ and $g(n)$: $f(n)=O(g(n))$ means there exist positive constants $c$ and $m$ such that $f(n) \leq cg(n)$ for all $ n\geq m;$ $f(n)=\Omega(g(n))$ means there exist positive constants $c$ and $m$ such that $f(n)\geq cg(n)$ for all $n\geq m;$ $f(n)=\Theta(g(n))$ means that both $f(n)=\Omega(g(n))$ and $f(n)=O(g(n))$ hold; $f(n)=o(g(n))$ means that $\lim_{n\to \infty} f(n)/g(n)=0;$ and $f(n)=\omega(g(n))$ means that $\lim_{n\to \infty} g(n)/f(n)=0.$ We then propose a joint coding/scheduling algorithm achieving a throughput of $\Theta(\min\{1,\sqrt{\frac{D}{n_s}}\}).$ Our simulations show that the joint coding/scheduling algorithm achieves a throughput of the same order ($\Theta(\min\{1, \sqrt{\frac{D}{n_s}}\})$) under random walk model and random waypoint model.