On Capacity Scaling of Underwater Networks: An Information-Theoretic Perspective

TL;DR

This paper analyzes the capacity scaling laws of underwater acoustic networks, revealing that the network's throughput is highly power-limited and depends on attenuation, with a simple multi-hop scheme achieving near-optimal performance under certain conditions.

Contribution

It provides the first information-theoretic capacity scaling laws for underwater networks, highlighting the impact of attenuation and proposing an order-optimal multi-hop scheme.

Findings

Upper bound on throughput scales inversely with attenuation.

Multi-hop scheme is order-optimal when attenuation grows exponentially with √n.

Capacity is more influenced by attenuation than spreading factor.

Abstract

Capacity scaling laws are analyzed in an underwater acoustic network with $n$ regularly located nodes on a square. A narrow-band model is assumed where the carrier frequency is allowed to scale as a function of $n$. In the network, we characterize an attenuation parameter that depends on the frequency scaling as well as the transmission distance. A cut-set upper bound on the throughput scaling is then derived in extended networks. Our result indicates that the upper bound is inversely proportional to the attenuation parameter, thus resulting in a highly power-limited network. Interestingly, it is seen that unlike the case of wireless radio networks, our upper bound is intrinsically related to the attenuation parameter but not the spreading factor. Furthermore, we describe an achievable scheme based on the simple nearest neighbor multi-hop (MH) transmission. It is shown under extended networks that the MH scheme is order-optimal as the attenuation parameter scales exponentially with $\sqrt{n}$ (or faster). Finally, these scaling results are extended to a random network realization.