## 3 Proposed Scheduling Scheme
### Recurrent Learning
```
Function to perform Recurrent Learning and Power Allocation FunctionRecurrentLearningPowerAllocation \((r_{0},S,i_{d},q_{0},0_{0},\
abla_{1},\
abla_{2},v_{n},r_{n},g_{t},p_{i},c_{k},w_{c}, \gamma,h_{a},X,e_{t})\) Input: \(r_{0},S,i_{d},q_{0},0_{0},\
abla_{1},\
abla_{2},v_{n},r_{n},g_{t},p_{i},c_{k},w_{c },\gamma,h_{a},X,e_{t}\) Output: Power allocation, Service dissemination, Deficiency, Service distribution Step 1: Recurrent Learning Phase \(k_{0}\gets u(p_{i}+r_{a}(S+q_{0})*o_{0})\)
```  
**Algorithm 3**Recurrent learning  
**Algorithm 3**_Cont._  
Step 2: First Hidden Layer Calculation  
Initialize gradient array  
for each service rate \(r_{i}\) in \(r\) do  
calculate gradient \(o_{i}\)  
\(o_{i}\leftarrow(S+r_{1})*\left(\frac{M+i_{i}}{q_{0}+\
abla_{1}}\right)+b^{ \prime}-e_{t}\)  
add \(o_{i}\) to the gradient array  
return gradient array  
Step 3: Second Hidden Layer Calculation  
Initialize gradient array  
for each index \(i\) from \(0\)_to_\(n-1\) do  
Initialize sum = 0  
for each coefficient \(c_{k}\) do  
\(\sum\gets sum+v[i]*c_{k}*r[i]\)  
calculate gradient \(o_{i}\)  
\(o_{i}=q[i]*\left(\frac{a_{i}}{r[i]}\right)+sum\)  
add \(o_{i}\) to the gradient array  
return gradient array  
Step 4: Power Management  
Initialize \(M=0\)  
Initialize \(g^{\prime}=0\)  
Initialize \(\gamma=0\)  
for each index \(i\) from \(0\)_to_\(n-1\) do  
calculate \(g^{\prime}=(\
abla_{1}+\
abla_{2})*p_{i}\)  
calculate \(\gamma\)  
\(\gamma=p_{i}+w_{c}*(h_{t}+\tau_{n})\),  
calculate \(M\)  
\(M\leftarrow(\
abla_{1}+\
abla_{2})*\left[\left(\frac{a/p}{S}\right)+(v_{0}*i_{ d})\right]*b^{\prime}+g^{\prime}+\gamma*q_{0}(v_{n})+k_{0}\)  
return \(M\)  
Step 5: Prediction and Analysis  
Initialize \(\delta=0\)  
// Calculate the product of the terms inside the sigma  
\(sigma\_product=1\)  
for each coefficient \(c_{k}\) in \(c\) do  
\(sigma\_product=s(sigma\_product*((a_{0}+gt_{i})\)  
\(*(((i_{d}\) / \(M) / \(partial\_derivative+s_{u}))+(b^{\prime}*p_{i})))\)  
// Calculate the \(\delta\) value  
\(\delta=\left(\frac{1}{v_{n}+\tau_{n}}\right)*sigma\_product-e_{t}\)  
return delta  
Step 6: Service Dissemination Calculation  
Initialize \(partial\_derivative=0\)  
for each coefficient \(c_{k}\) in \(c\) do  
// Calculate the inner sum  
\(inner\_sum=0\)  
for each coefficient \(c_{k}\) in \(c\) do  
\(inner\_sum=inner\_sum+(w_{c}+p_{i})\)  
// Calculate the term inside the parenthesis  
\(term\_inside\_parenthesis=\frac{h_{t}+c_{k}}{\frac{a_{i}}{m}}*((k_{0}+V)*(inner\_ sum/g_{t}))\)  
// Add the contribution of each term to the partial derivative  
\(partial\_derivative=partial\_derivative+term\_inside\_parenthesis\)  
// Add the contribution of \(o_{0}*(\
abla_{1}+\
abla_{2})\) to the partial derivative  
\(partial\_derivative=partial\_derivative+o_{0}*(\
abla_{1}+\
abla_{2})\)  
return \(partial\_derivative\)  
Step 7: Output Results  
Return \(\vartheta\), \(\delta\)  
End FunctionIn discussing the performance of the scheduling system, Table 1 provides crucial insights into the system's behavior across various scheduling intervals. The table presents data on queued requests, service dissemination success rates, and the number of failed requests, which are vital metrics for evaluating the effectiveness of the scheduling algorithm. The sharing interval is determined for the varying scheduling intervals for the queued request and increases if the shared interval increases. Suppose the queued request decreases and the service dissemination increases for varying intervals. In another case, if the queued request decreases, the failure request also decreases (Table 1). The iterates utilized at different sharing intervals are tabulated in Table 2.  
The goal of the suggested method is to minimize latency while ensuring adequate power distribution to every user. Allocated power is computed based on an optimization model that distributes the total available power and total among users to reduce the overall latency. Required power signifies the theoretical power necessary to achieve a target latency for every user or sharing interval.