Underwater Acoustic Networks: Channel Models and Network Coding based Lower Bound to Transmission Power for Multicast

TL;DR

This paper develops a tractable underwater acoustic channel model and proposes a network coding based lower bound on transmission power for multicast, enabling performance comparison of various network schemes.

Contribution

It introduces a closed-form approximate model for underwater acoustic channels and a network coding based lower bound for multicast transmission power.

Findings

The model accurately predicts power and frequency band as functions of distance and capacity.

Network coding approaches approach the lower bound in power efficiency.

Significant power savings are achievable with optimized network coding schemes.

Abstract

The goal of this paper is two-fold. First, to establish a tractable model for the underwater acoustic channel useful for network optimization in terms of convexity. Second, to propose a network coding based lower bound for transmission power in underwater acoustic networks, and compare this bound to the performance of several network layer schemes. The underwater acoustic channel is characterized by a path loss that depends strongly on transmission distance and signal frequency. The exact relationship among power, transmission band, distance and capacity for the Gaussian noise scenario is a complicated one. We provide a closed-form approximate model for 1) transmission power and 2) optimal frequency band to use, as functions of distance and capacity. The model is obtained through numerical evaluation of analytical results that take into account physical models of acoustic propagation loss and ambient noise. Network coding is applied to determine a lower bound to transmission power for a multicast scenario, for a variety of multicast data rates and transmission distances of interest for practical systems, exploiting physical properties of the underwater acoustic channel. The results quantify the performance gap in transmission power between a variety of routing and network coding schemes and the network coding based lower bound. We illustrate results numerically for different network scenarios.