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

• Type: Article in International Scientific Journal
• Date: 2008

Authors

Daniel E. Lucani (Author) FEUP; Muriel Médard (Author) FEUP; Milica Stojanovic (Author) Other

Journal

Title: IEEE Journal on Selected Areas in Communications

Vol. 26 No. 9

Pages: 1708-1719

ISSN: 0733-8716

Publisher: IEEE

Indexing

ISI Web of Science

Scopus

INSPEC

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Technological sciences > Technology

Other information

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific