# SPACNet: A Simulation Platform of an Acoustic Cognitive Network
###### Abstract
In [13], a receiver-initiated spectrum management system for underwater cognitive acoustic networks (UACNs) is proposed with the purpose of improving the performance of UACNs through a collaboration of a physical layer and a medium access control (MAC) layer. Ref. [14] proposed a cognitive acoustic transmission scheme by employing a probabilistic method to capture the stochastic characteristics of dolphins' communications, and mathematically describing the dolphin-aware constraint to maximize the end-to-end throughput. Both studies in [13; 14] adopted the simulation for the verification and performance evaluation of the proposed UACNs schemes. Thus far, the simulation platform for UANs is still in its initial stage and is mainly based on the mature terrestrial simulation platforms, e.g., NS2, NS3, and OPNET. NS2 [15] is an object-oriented network simulation tool, i.e., essentially a discrete event-driven simulator, in which the running state of the entire network can be simulated. NS2 implements a hierarchical design for network simulations, including a compilation layer and an interpretation layer, responsible for processing data packets and configuring and establishing the simulation environment, respectively. Although the name NS3 [16] is similar to NS2 and it is driven by a discrete time mechanism, NS3 is not an extension of NS2 and does not support the NS2 application programming interface (API). Due to the advantage of abstracting common network devices and encapsulating them into corresponding classes, NS2 has been gradually replaced by NS3 in recent years. OPNET [17] is a commercial simulation platform used for network design, planning, and network protocol analysis and development. OPNET has a user-friendly interface, adopts layered modeling and discrete event-driven simulation mechanisms, and has a relatively complete model library. To improve the quality of the overall architecture, the Aqua-Sim for NS2 and Aqua-Sim for NS3 were designed in [18; 19], respectively, and various models were constructed, including the energy module of underwater communication aircraft, signal-to-interference-plus-noise ratio detection, a propagation model of underwater communication channels, underwater noise, etc. [20; 21]. To facilitate the acquisition of real underwater acoustic channel simulation experiments, DESERT and SUNSET were designed in [22; 23; 24], respectively. DESERT and SUNSET not only improve the experimental accuracy but also have the convenience of software simulation protocol superposition and have successfully conducted multiple on-site tests [25; 26; 27]. In [28], a multithreaded NS3-based underwater acoustic network simulation architecture was designed for the parallel processing of multiple nodes and data packets. The simulation operates in a mode that combines real underwater environments, hardware nodes, and virtual protocol stacks. It can be directly ported to hardware platforms for experiments and verified using an underwater acoustic modem developed by the Chinese Academy of Sciences. In addition, some representative simulation and experimental platforms were introduced in [29], which are divided into three types: laboratory-level simulation platforms, short-term experimental verification platforms, and long-term on-site testing experimental platforms. However, due to the significant differences between the implementation of underwater cognitive mechanisms and wireless scenarios, the physical layer communication modules and channel models of traditional network simulation platforms, such as OPNET, NS2, and NS3, are not suitable for UACNs. For example, when conducting UAN simulation in OPNET, special modifications and configurations need to be made to the wireless communication channel to simulate the underwater acoustic channel [30]. However, this method cannot provide the external environment information and node internal information required for a cognitive network, making it difficult to support UACNs.