## 2 System Model
The maximum number of packets that can be packed into the MPDU data area is set to be \(N\), and the time required to generate a frame is denoted as \(t_{sw}\). As shown in Equation (2), the probability density function for framing time \(t_{sw}\) within the interval \((0,T]\) is  
\[f_{sw}(t)=\lambda e^{-\lambda t}\frac{(\lambda t)^{N-1}}{(N-1)!} \tag{2}\]  
The probability that the framing time \(t_{sw}\) is below the threshold value \(T_{th}=T\) is  
\[P(t_{sw}=T)=\int_{T}^{+\infty}\lambda e^{-\lambda t}\frac{(\lambda t)^{N-1}}{ (N-1)!}dt \tag{3}\]  
The average framing time for the RTFG module is  
\[\Phi(S_{w})=\int_{0}^{T}tf_{sw}(t)dt+TP(t_{sw}=T)=\int_{0}^{T}t\lambda e^{- \lambda t}\frac{(\lambda t)^{N-1}}{(N-1)}dt+T\int_{T}^{+\infty}\lambda e^{- \lambda t}\frac{(\lambda t)^{N-1}}{(N-1)}dt \tag{4}\]  
Let \(l_{s}\) be the packet length and \(l_{ms}\) be the packet length of the MPDUs. The average multiplexing efficiency for the RTFG module is  
\[E(eff_{m})=P(t_{SW}<T)+\sum_{n=0}^{N-1}\frac{nl_{s}}{l_{ms}}P(A(T)=n)=\int_{0} ^{T}t\lambda e^{-\lambda t}\frac{(\lambda t)^{N-1}}{(N-1)}dt+\sum_{n=0}^{N-1} \frac{nl_{s}}{l_{ms}}\frac{e^{-\lambda t}(\lambda T)^{n}}{n!} \tag{5}\]  
Let \(T_{d}\) be the packet delay, \(P(A(t)=n)\) be the probability of reaching \(n\) packets in \(\left[0,T\right]\) time, and \(P(A(t)=N+x)\) be the probability of reaching \(N+x\) packets in \(\left[0,T\right]\) time, with the average packet delay of the RTFG module when \(i\) packets arrive being  
\[\vartheta(T_{d})=\frac{1}{N\cdot E(eff_{m})}\cdot\left(\sum_{n=1}^{N-1}P(A(t )=n)\cdot\frac{T}{2}\cdot n+\sum_{x=0}^{\infty}P(A(t)=N+x)\cdot\sum_{i=1}^{N} \frac{(N-i)\cdot T}{N+x+1}\right) \tag{6}\]  
On the other hand, the framing task of packets in the non-delay-sensitive service queue is handled by the high-efficiency framing (HEFG) module. The non-real-time packets are framed according to the maximum number of packets in the MPDU. Non-delay-sensitive traffic, such as observation or scientific payload data, often exhibits bursty behavior, long-range dependence, and variable flow sizes. To better model such phenomena, we introduce a self-similar ON/OFF traffic model, where each of the \(M\) sources alternates between ON (sending packets) and OFF (idle) periods. Both ON durations \(\tau_{l}\) and OFF durations \(\theta_{l}\) follow Pareto distributions, with tail behavior:  
\[P(\tau>t)\sim t^{-\alpha},\quad 1<\alpha<2 \tag{7}\]  
The aggregate active source count \(\xi\) at time \(t\) follows a binomial distribution with success probability \(p=\frac{E|\tau|}{E|\tau|+E|\theta|}\), and it can be approximated by a Poisson process with intensity \(\lambda=M\cdot p\) as \(M\rightarrow\infty\). In the queue model, we set \(S_{w}\) as the time required to generate a frame. Then, the average frame time of the HEFG module is  
\[\Phi(S_{w})=\int_{0}^{\infty}t\cdot f_{\text{ON}}(t)\,dt \tag{8}\]  
where \(f_{\text{ON}}(t)\) is the empirically derived arrival distribution during ON periods. The average queuing delay \(\vartheta(T_{d})\) for non-delay-sensitive services under self-similar ON/OFF traffic is obtained in a manner analogous to that for Poisson arrivals, but it is adapted to capture long-range dependence and burstiness. Assuming an aggregated arrival rate \(\lambda_{\text{eff}}\), the average frame generation time in this case is \[\Phi(S_{w})=\frac{N}{\lambda_{\text{eff}}}\] (9) Then, the average queuing delay can be estimated as Figure 1:0 (10) or, in simplified form, Figure 1:1 (11) where \(\rho\) is the queue utilization factor. This formulation captures the impact of long-term correlations in bursty traffic on the frame queuing behavior. Finally, all the encapsulated transmission frames subsequently enter the virtual channel scheduling process and carry out the next transmission.