## 3 DMCAP System
### SNIR Distribution and Analysis Using Multi-Channel Nonlinear Regression Model  
The generic nonlinear channel regression model is determined as shown in Equation (15). Generally, SNIR determined at the channel is nonlinear in nature. Similarly, the production of multi-channel SNIR falls completely under nonlinear functions. In this nonlinear production of timeline data of SNIR, the prediction and determination can be modelled with the help of multi-channel nonlinear regression model. As provided in Equation (15), the nonlinear function of channel quality management tuples shall be determined. \[\mathrm{y}(\mathrm{n})=\theta^{0}+(\mathrm{T}(\mathrm{S})\alpha^{\mathrm{i}}+ \mathrm{T}(\mathrm{P})\alpha^{\mathrm{i}})\cdot\theta^{1}+\theta^{1}\cdot \theta^{0}+(\mathrm{T}(\mathrm{S})\alpha^{\mathrm{i}}+\mathrm{T}(\mathrm{P}) \alpha^{\mathrm{i}})^{2}+\ldots \tag{15}\]  
\[\mathrm{y}(\mathrm{n})=\frac{\theta^{0}}{1+\theta^{1(\mathrm{T}(\mathrm{S}) \alpha^{\mathrm{i}}+\mathrm{T}(\mathrm{P})\alpha^{\mathrm{i}})-\theta^{2}}} \tag{16}\]  
* \(\theta^{\mathrm{i}}\)-Nonlinear quantity factor. From the nonlinear model determination, the nonlinear ability of each channel impacts the channel can be calculated. Let the multi-channel nonlinear regression model, \(\mathrm{m}(\mathrm{y}(\mathrm{n}))\) be expressed as  
\[\mathrm{y}(\mathrm{n})\alpha\:\mathrm{C}(\mathrm{I}) \tag{17}\]  
\[\mathrm{m}(\mathrm{y}(\mathrm{n}))=\mathrm{N}\cdot\mathrm{y}(\mathrm{n})\cdot \frac{\mathrm{d}\mathrm{C}\mathrm{i}}{\mathrm{d}\tau} \tag{18}\]  
Equation (18) denotes \(\mathrm{Ci}\) as channel identifier and \(\mathrm{N}\) as number of channels. The channel capacity and attenuation/energy nonlinearity determinations are closely coupled with SNIR production rate. As the rates of previous channel qualities and quantities vary, SNIR leads to distortions. At this moment, the learning and classification practices of nonlinear SNIR sequence make significant effects in WUGSN communication. This is modelled with the help of Ensemble Nonlinear SVM (ENLSVM). Generally, SNIR determines the upper bounds in information transferring for a wireless channel. The optimal determination of SNIR for each wireless channel shows the quality of wireless links against various signal distortions [38]. In WUGSN, SNIR is defined as provided Equation (19). \[\mathrm{SNIR}\left(\mathrm{I}^{\mathrm{i}}\right)=\frac{\mathrm{P}^{\mathrm{ sl}}}{\mathrm{I}^{\mathrm{i}}+\mathrm{Noise}^{\mathrm{i}}}\pm\varphi \tag{19}\]  
Equation (19) illustrates the SNIR determination at the active link, \(\mathrm{I}^{\mathrm{i}}\). In this equation, \(\mathrm{P}^{\mathrm{sl}}\) represents required signal quality, \(\mathrm{I}^{\mathrm{i}}\) and \(\mathrm{Noise}^{\mathrm{l}}\) denote interference and noise impacts in the channel, respectively. Equation (20) shows the multi-channel SNIR determination function for all active channels. The distribution of multi-channel SNIR \(\left(\mathrm{I}^{\mathrm{i}}\right)\) is determined with the Gaussian distribution model with \(\varphi\) as provided in Equation (21). \[\mathrm{S}\left(\mathrm{SNIR}\left(\mathrm{I}^{\mathrm{i}}\right)\right)= \mathrm{SNIR}\left(\mathrm{I}^{\mathrm{i}}\right)\cdot\mathrm{d}\mathrm{s} \cdot\mathrm{d}\tau\:\forall\:\mathrm{S}(\mathrm{L}) \tag{20}\]  
* \(\mathrm{I}^{\mathrm{i}}\)-a link at time \(\tau\);
* \(\mathrm{I}^{\mathrm{i}}\) can be either \(\mathrm{U}-\mathrm{U}\mathrm{i}\mathrm{or}\mathrm{U}\mathrm{i}-\mathrm{Si} \mathrm{or}\mathrm{Si}-\mathrm{Bi}\);
* \(\varphi\)-nonlinear event variance, where \(0\leq\varphi\leq 1\). In this case, \(\mathrm{m}\) is the mean and \(\varphi^{2}\) is the variance in distribution model. The interference likelihood function is formulated for given distribution model in Equation (21).