Throughput Scaling of Wireless Networks With Random Connections

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 Θ(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 Θ(n) is achievable with Pareto-type fading models.