Modeling Acoustic Channel Variability in Underwater Network Simulators from Real Field Experiment Data

Abstract

The underwater acoustic channel is remarkably dependent on the considered scenario and the environmental conditions. In fact, channel impairments differ significantly in shallow water with respect to deep water, and the presence of external factors such as snapping shrimps, bubbles, rain, or ships passing nearby, changes of temperature, and wind strength can change drastically the link quality in different seasons and even during the same day. Legacy mathematical models that consider these factors exist, but are either not very accurate, like the Urick model, or very computationally demanding, like the Bellhop ray tracer. Deterministic models based on lookup tables (LUTs) of sea trial measurements are widely used by the research community to simulate the acoustic channel in order to verify the functionalities of a network in certain water conditions before the actual deployment. These LUTs can characterize the link quality by observing, for instance, the average packet error rate or even a time varying packet error rate computed within a certain time window. While this procedure characterizes well the acoustic channel, the obtained simulation results are limited to a single channel realization, making it hard to fully evaluate the acoustic network in different conditions. In this paper, we discuss the development of a statistical channel model based on the analysis of real field experiment data, and compare its performance with the other channel models available in the DESERT Underwater network simulator.

On the Effect of Channel Knowledge in Underwater Acoustic Communications: Estimation, Prediction and Protocol

“…On the other hand, using long guard intervals (in the order of the frame time T f ) between two consecutive pilots allows the receiving nodes to acquire more significant channel statistics. In principle, there are several ways to estimate the channel coefficients starting from the received signal in Equation (9).…”

“…In fact, besides the intrinsic (technological) bandwidth limitation, due to transmitter capabilities, underwater acoustic channels are also characterized by severe multipath delays and highly selective frequency responses, as well as non-negligible propagation delays due to the relatively slow speed of sound in water [7]. The UWAC propagation scenario exhibits a peculiar time-varying behavior [8,9], due to sea temperature and salinity gradients, sea stream, and wind speed, as well as relative positional changes due to motion of the terminals [10]. Multipath time delay and frequency-selective deep fading affect the UWAC channel in different ways, depending on the propagation environment, typically classified as shallow, medium or deep water [11].…”

A Network Infrastructure for Monitoring Coastal Environments and Study Climate Changes in Marine Systems

“…Transmission frequency is 25 kHz and the bandwidth is 5 kHz. The protocol stack of these nodes is shown in Figure 4: they use a CBR application layer, static routing consisting in all the nodes transmitting directly to their 1-hop nearest receiver (i.e., a forwarder node), a Time Division Multiple Access (TDMA) MAC layer and, finally, a Hidden Markov Model (HMM) physical layer, as described in [29]. In particular, according to this physical model, each underwater node is linked to its destination by a channel whose behavior is statistically determined by means of its initial condition and the set of transition probabilities, which specifies the probability that, given the current state, the channel conditions in the next slot will be better, worse, or remain the same.…”