Hierarchical Cooperation Achieves Optimal Capacity Scaling in Ad Hoc Networks

TL;DR

This paper demonstrates that hierarchical cooperation enables ad hoc networks to achieve optimal capacity scaling laws, significantly improving over previous multihop methods through distributed MIMO and digital architecture.

Contribution

It introduces a hierarchical cooperation scheme that achieves optimal capacity scaling in dense and extended ad hoc networks, surpassing prior multihop results.

Findings

Capacity scales linearly with n in dense networks.

In extended networks, capacity scales as n^{2 - alpha/2} for 2<alpha<3.

Hierarchical cooperation with distributed MIMO improves network capacity.

Abstract

n source and destination pairs randomly located in an area want to communicate with each other. Signals transmitted from one user to another at distance r apart are subject to a power loss of r^{-alpha}, as well as a random phase. We identify the scaling laws of the information theoretic capacity of the network. In the case of dense networks, where the area is fixed and the density of nodes increasing, we show that the total capacity of the network scales linearly with n. This improves on the best known achievability result of n^{2/3} of Aeron and Saligrama, 2006. In the case of extended networks, where the density of nodes is fixed and the area increasing linearly with n, we show that this capacity scales as n^{2-alpha/2} for 2<alpha<3 and sqrt{n} for alpha>3. The best known earlier result (Xie and Kumar 2006) identified the scaling law for alpha > 4. Thus, much better scaling than multihop can be achieved in dense networks, as well as in extended networks with low attenuation. The performance gain is achieved by intelligent node cooperation and distributed MIMO communication. The key ingredient is a hierarchical and digital architecture for nodal exchange of information for realizing the cooperation.