Linear Capacity Scaling in Wireless Networks: Beyond Physical Limits?

TL;DR

This paper explores how wireless network capacity scales with size and environment, showing hierarchical cooperation can achieve linear capacity growth beyond physical limits in certain regimes.

Contribution

It identifies three regimes based on network area and wavelength, demonstrating hierarchical cooperation's optimality in achieving linear capacity scaling beyond previous multi-hopping methods.

Findings

In small-area networks, capacity scales as sqrt{n} with multi-hopping.

In larger-area networks, degrees of freedom increase, enabling hierarchical cooperation to achieve linear scaling.

Hierarchical cooperation outperforms multi-hopping in regimes where sqrt{A}/lambda > n.

Abstract

We investigate the role of cooperation in wireless networks subject to a spatial degrees of freedom limitation. To address the worst case scenario, we consider a free-space line-of-sight type environment with no scattering and no fading. We identify three qualitatively different operating regimes that are determined by how the area of the network A, normalized with respect to the wavelength lambda, compares to the number of users n. In networks with sqrt{A}/lambda < sqrt{n}, the limitation in spatial degrees of freedom does not allow to achieve a capacity scaling better than sqrt{n} and this performance can be readily achieved by multi-hopping. This result has been recently shown by Franceschetti et al. However, for networks with sqrt{A}/lambda > sqrt{n}, the number of available degrees of freedom is min(n, sqrt{A}/lambda), larger that what can be achieved by multi-hopping. We show that the optimal capacity scaling in this regime is achieved by hierarchical cooperation. In particular, in networks with sqrt{A}/lambda> n, hierarchical cooperation can achieve linear scaling.