Throughput Scaling of Wireless Networks With Random Connections

TL;DR

This paper analyzes how the data throughput of large ad hoc wireless networks scales with the number of nodes under various connection models, revealing bounds and achievable rates for different schemes and fading conditions.

Contribution

It establishes throughput scaling bounds for large networks with random connections and demonstrates achievable rates under specific models and relaying strategies.

Findings

Single-hop throughput is bounded by O(n^{1/3})

Two-hop schemes can achieve Θ(n^{1/2}) throughput

Linear throughput scaling Θ(n) is possible with Pareto fading models

Abstract

This work studies the throughput scaling laws of ad hoc wireless networks in the limit of a large number of nodes. A random connections model is assumed in which the channel connections between the nodes are drawn independently from a common distribution. Transmitting nodes are subject to an on-off strategy, and receiving nodes employ conventional single-user decoding. The following results are proven: 1) For a class of connection models with finite mean and variance, the throughput scaling is upper-bounded by $O(n^{1/3})$ for single-hop schemes, and $O(n^{1/2})$ for two-hop (and multihop) schemes. 2) The $\Theta (n^{1/2})$ throughput scaling is achievable for a specific connection model by a two-hop opportunistic relaying scheme, which employs full, but only local channel state information (CSI) at the receivers, and partial CSI at the transmitters. 3) By relaxing the constraints of finite mean and variance of the connection model, linear throughput scaling $\Theta (n)$ is achievable with Pareto-type fading models.