# SPACNet: A Simulation Platform of an Acoustic Cognitive Network
###### Abstract  
Originating from the concept of cognitive networks (CNs), which are becoming popular in wireless terrestrial communication scenarios, underwater acoustic cognitive networks (UACNs) are drawing more and more attention in the field of the Internet of Underwater Things (IoUT). However, as the implementation of cognitive mechanisms in underwater acoustic networks is different from that of wireless scenarios, it is impossible or difficult for traditional simulation platforms to carry out simulations of UACNs. There is a lack of specialized simulation tools in terms of UACNs. To enable the quantitative evaluation of the effectiveness and performance enhancement of a UACNs in an adverse underwater environment, a simulation platform of acoustic cognitive networks (SPACNet) was designed and investigated in this article. First, based on a state machine-based protocol programming framework, the SPACNet is capable of supporting the implementation of different state-transform types associated with cognitive networking protocols. Moreover, to facilitate the realization of cognitive function at comprehensive levels of signal, information, and link, an underwater acoustic channel model with an environmental parameter input is integrated in SPACNet to generate underwater environment-driven multiple-aspect behaviors. Moreover, a simplified collision model consisting of an environment factor, channel response, and node location is used to reduce the complexity of the simulation of UACNs signal reception. A simulation was carried out to verify the effectiveness of SPACNet in evaluating the cognitive capabilities of UACNs. Finally, a field UACNs experiment was performed to validate the general consistency between the conclusion obtained with the SPACNet-based simulation and that from the field test. underwater acoustic network (UAN); underwater acoustic cognitive network (UACNs); simulation platform; cognitive network (CN) +
Footnote †: journal: Marine Science and Engineering
network aided by an unmanned aerial vehicle (UAV), which contains a number of uplink cellular users as primary users, active device-to-device (D2D) users as secondary users, and idle D2D users that act as a virtual multiple-input multiple-output (MIMO) node. In [10], a UAVs-assisted two-hop cognitive radio network (CRN) was proposed, where multiple UAVs are employed to act as air access points and relay nodes. Ref. [11] reviews the research of covert communications in cooperative cognitive radio networks via three intelligent covert transmission approaches, namely, intelligent parasitic covert transmission, intelligent jammer-aided covert transmission, and intelligent reflecting surface-assisted covert transmission. Based on the point-to-point linkage provided by underwater acoustic modems, it is possible to construct a large-scale UAN consisting of a substantial number of nodes under the framework of the Internet of Underwater Things (IoUT) [12]. However, the performance of IoUT suffers from adverse underwater environments and random temporal-spatial-spectral uncertainties. By incorporating CNs with UANs, underwater acoustic cognitive networks (UACNs) offer a promising solution for the research and practical implementation of IoUT. However, while cognitive networks have achieved significant progress in academic research, the R&D of the standard network model, and practical commercial applications based on established wireless channel sensing and cognitive optimization strategies [1; 2; 3; 4; 5; 6; 7; 8], the specific implementation of CNs in the underwater acoustic channel still encounter many difficulties due to the highly diverse channel patterns, underwater acoustic (UWA) communication modes, and network protocols. Moreover, considering the vast cost and resource associated with the underwater hardware, the supporting facilities, and the vessels required by underwater acoustic network field tests, simulation platforms are recognized as a low-cost and effective way for the comprehensive evaluation of UACNs schemes.