Recall that to solve (P2) in the tth time frame, we observe ξt 􏰗 {hti, Qi(t), Yi(t)}Ni=1, consisting of the channel gains {hti}Ni=1 and the system queue states {Qi(t),Yi(t)}Ni=1, and accordingly decide the control action {xt, yt}, including the binary offloading decision xt and the continuous resource allocation yt 􏰗 􏰄τit, fit, eti,O, rit,O􏰅Ni=1. A close observation shows that although (P2) is a non-convex optimization problem, the resource allocation problem to optimize yt is in fact an “easy” convex problem if xt is fixed. In Section IV.B, we will propose a customized algorithm to efficiently obtain the optimal yt given xt in (P2). Here, we denote G􏰀xt,ξt􏰁 as the optimal value of (P2) by optimizing yt given the offloading decision xt and parameter ξt. Therefore, solving (P2) is equivalent to finding the optimal offloading decision (xt)∗, where，yt的内容是靠什么确定的

yt的内容是通过解决(P2)中的资源分配问题来确定的。具体来说，给定二进制卸载决策xt和参数ξt，我们需要优化yt来最大化(P2)的性能指标，其中yt包括τit，fit，eti，O，rit，O。虽然(P2)是一个非凸优化问题，但是当xt固定时，优化yt的资源分配问题实际上是一个凸优化问题。因此，在第IV.B节中，我们将提出一种定制的算法，有效地获得在(P2)中给定xt的最优yt。因此，通过解决(P2)中的资源分配问题来确定yt的内容，并最终找到最优的卸载决策(xt)*，以最大化系统的性能指标。

相关问题

design a DHNN to store a pattern then use the designed DHNN to recall the corrupted pattern

Sure, here's a brief overview of how you can design a DHNN to store and recall patterns:

1. First, choose the number of neurons in the hidden layer of your DHNN. This will depend on the complexity of the patterns you want to store.

2. Initialize the weights between the input layer and the hidden layer randomly.

3. Present the pattern you want to store to the input layer of the DHNN.

4. Compute the output of the hidden layer using the weights and biases.

5. Use a Hebbian learning rule to update the weights between the input layer and the hidden layer. This will strengthen the connections between the neurons that fire together.

6. Repeat steps 3-5 for each pattern you want to store.

7. To recall a pattern, present a corrupted version of the pattern to the input layer of the DHNN.

8. Compute the output of the hidden layer using the updated weights and biases.

9. Use an inverse Hebbian learning rule to update the weights between the hidden layer and the output layer. This will help the DHNN to reconstruct the original pattern.

10. Compute the output of the output layer using the updated weights and biases. This should be a close approximation of the original pattern.

11. Repeat steps 7-10 for each corrupted pattern you want to recall.

I hope this helps! Let me know if you have any other questions.

Classification metrics can't handle a mix of binary and continuous targets 如何处理

If you have a mix of binary and continuous targets in a classification problem, you need to convert the continuous targets into binary targets before using binary classification metrics such as accuracy, precision, recall, or F1-score.

One approach is to use a threshold value to convert the continuous targets into binary targets. For example, if the threshold value is set to be 0.5, any value above 0.5 is considered as 1, and any value below 0.5 is considered as 0. This is a common technique used in logistic regression models to convert predicted probabilities into binary predictions.

Alternatively, you can use regression metrics such as mean squared error (MSE) or mean absolute error (MAE) to evaluate the performance of the model on the continuous targets. However, keep in mind that this approach does not take into account the binary nature of the target variable.

In summary, the approach you choose depends on the specific requirements of your problem and the metric you want to use to evaluate the performance of your model.