# SPACNet: A Simulation Platform of an Acoustic Cognitive Network
## 2 Design of SPACNet
### Integrated Environment-Driven UWA Channel Model  
From the perspectives of nodes and links, the communication network system adopts an integrated environment-driven UWA channel model. The environment model defines the parameters of the water area where the network is located and inputs them into the UAC module to simulate the network data transmission process. In addition, based on actual simulation requirements, the network size, topology, and work scenario are defined through the environment-driven UWA channel model, including node location information, external environmental parameters, and link states between nodes.  
Considering the time-varying characteristic of underwater acoustic channels, the intensity of each multipath is modeled as a process obeying Gaussian random variation, where a random number with a mean of \(\bar{a}_{p}\) is superimposed on the time-varying diameter. The channel impulse response \(h(\tau,t)\) is represented by the following formula:  
\[h(\tau,t)=\sum_{p=1}^{N_{p}}a_{p}(t)\delta\big{(}\tau-\tau_{p}\big{)}\, \tag{1}\]  
where \(t\) is time, \(\tau_{p}\) is the time delay of the \(p\)th path, \(N_{p}\) is the number of multipaths, and \(\delta(\tau)\) is the Dirac function. Hence, the received signal can be written as follows:  
\[y(t)=\sum_{p=1}^{N_{p}}a_{p}(t)s\big{(}t-\tau_{p}\big{)}+n(t)\, \tag{2}\]  
where \(s(t)\) is the transmitted signal and \(n(t)\) is the noise. Then, a Doppler factor is introduced, and Equation (2) can be rewritten as follows:  
\[y(t)=\sum_{p=1}^{N_{p}}a_{p}(t)s\big{(}(1+\Delta_{p})t-\tau_{p}\big{)}+n(t)\, \tag{3}\]  
where \(\Delta_{p}=\frac{\tau_{p}}{c}\) is the Doppler factor in the \(p\)th path, \(v\) is the relative velocity between transmitter and receiver, and \(c\) is the underwater acoustic velocity. To produce a more  
Figure 2: Discrete event-driven simulation mechanism.  
realistic underwater acoustic channel, Equation (1) between nodes is generated by the Bellhop model based on environmental parameters. The noise and Doppler factor are added in Equation (3). The environment-driven parameters of Equation (3) depend on the environmental parameters of Equation (4), which reflect the true environment.  
In practical applications, to unify the form and facilitate the simulation, the strategy of mapping from 3D to 2D was adopted. Only by accurately expressing the interconnection status of links between nodes can a network topology model be established. In the specific implementation process, a \(N\times N\times K\) three-dimensional matrix is constructed in the topology module to store the link distance, boundary information, Doppler factor, and other information between the transmitting node and all receiving nodes. The three-dimensional information matrix can be expressed in Equation (4):  
\[\mathbf{G}=\left[\begin{array}{cccc}\mathbf{g}_{11}&\mathbf{g}_{12}&\cdots& \mathbf{g}_{1N}\\ \mathbf{g}_{21}&\mathbf{g}_{22}&\cdots&\mathbf{g}_{2N}\\ \vdots&\vdots&\ddots&\vdots\\ \mathbf{g}_{N1}&\mathbf{g}_{N2}&\cdots&\mathbf{g}_{NN}\\ \end{array}\right]\, \tag{4}\]  
\[\mathbf{g}_{ij}=\left(\begin{array}{cccc}g_{ij1}&g_{ij2}&\cdots&g_{ijk}\\ \end{array}\right)\, \tag{5}\]  
where \(g_{ijk}\) is the \(k\)-th environmental parameter from node \(i\) to node \(j\), \(\mathbf{g}_{ij}\) is the total environmental parameters from node \(i\) to node \(j\), and \(\mathbf{G}\) is the total environmental parameters in the network. Notation \(N\) is the number of nodes in the network, and \(K\) is the number of channel modeling parameters, which is determined by the channel simulator and varies according to the number of input parameters.  
In addition, in our designed SPACNet, the Bellhop model and real environmental noise are used to formulate the characteristics of an underwater acoustic channel with high fidelity.