Maximization of the sum of mutual information terms

In summary, the conversation discusses finding the optimal power distribution for a communication scheme that maximizes the sum data rate. The problem involves a non-convex function and constraints on the power allocation. The speaker is seeking advice on how to proceed with finding the optimal solution, including techniques such as testing for quasi-convexity and using Lagrange multipliers. They are also open to finding solutions for a relaxed version of the problem.
  • #1
miggimig
3
0
I would like to compute the power distribution that maximizes the sum data rate of a certain communication scheme. The expression follows from the sum of the rate of four messages which interfere with each other. A certain amount of power (here 1 Watt) is assigned to these messages, and there is a further constraint on the sum rate of the first three messages.

[itex] \begin{equation}
\begin{split}
& \mathrm{maximize} \qquad \underbrace{\log_2\det \left( 1 + \frac{p_{1}}{ p_{2} + \lambda(p_{3}+p_{4}) + \sigma^2} \right)}_{R_{1}} \\
&\qquad\qquad\qquad + \underbrace{\log_2\det \left( 1 + \frac{p_{3}}{p_{4} + \lambda p_{2}+\sigma^2} \right)}_{R_{3}}\\
&\qquad\qquad\qquad + \underbrace{\log_2\det \left( 1 + \frac{p_{2}}{\lambda
p_{4}+\sigma^2} \right)}_{R_{2}}
+ \underbrace{\log_2\det \left( 1 + \frac{p_{4}}{\sigma^2} \right)}_{R_{4}}
\\
& \mathrm{subject\ to}\qquad p_{1}+p_{2} \leq 1;\ p_{3}+p_{4} \leq 1;\ R_{1}+R_{2}+R_{3} \leq 1
\end{split}
\end{equation} [/itex]

The problem is not convex. However, I still would like to find the optimal power assignment [itex] p_1, p_2, p_3, p_4 [/itex]. I have little knowledge of optimization techniques, therefore I would be happy about any advice how to proceed.

My first idea is, to test if the function is quasi-convex, but I do not know how to proof this property.

I also would be very happy about concrete advices which technique or solver is suitable to find the solution.

If the second constraint is a big problem, I would also be happy to find the optimal power assignment for the relaxed problem without this constraint.

Thank you very much for your attention!

Michael
 
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  • #2
You could start and compute the function value at the vertices, take the maximal vertex, and see if they increase or decrease if you leave this point. Another idea is to use Lagrange multipliers.
 

1. What is the concept of maximizing the sum of mutual information terms?

The concept of maximizing the sum of mutual information terms is a technique used in information theory to optimize the transmission of information between two or more systems. It involves finding the optimal encoding and decoding schemes that result in the maximum amount of mutual information being transmitted between the systems.

2. How is mutual information defined?

Mutual information is a measure of the amount of information that is shared between two random variables. It is calculated by taking the sum of the probabilities of all possible outcomes and multiplying them by the log of the ratio between the joint probability and the product of the individual probabilities.

3. What is the relationship between mutual information and entropy?

Mutual information and entropy are closely related concepts in information theory. Entropy measures the amount of uncertainty or randomness in a system, while mutual information measures the amount of information that is shared between two systems. Maximizing the sum of mutual information terms results in minimizing the overall entropy of the system, leading to more efficient communication.

4. How does maximizing the sum of mutual information terms improve communication?

By maximizing the sum of mutual information terms, we are able to find the optimal encoding and decoding schemes that result in the maximum amount of information being transmitted between two systems. This leads to more efficient communication, as the transmitted information is less affected by noise and errors.

5. Can maximizing the sum of mutual information terms be applied in real-world scenarios?

Yes, maximizing the sum of mutual information terms has practical applications in various fields such as telecommunications, data compression, and machine learning. It is used to improve the efficiency and reliability of communication systems by finding the best encoding and decoding schemes for transmitting information.

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