Robust precoder and receiver filter design for Gaussian interference channel

1. A method for providing at least one transmit precoder, the method comprising:

transforming at least one of a weighted sum-rate and max-min rate objective into two or more sub-problems by introducing at least one slack variable;

iterating the two or more sub-problems on a computer readable storage medium to provide at least one transmit precoder for each transmitter; and

applying the Schur complement to convert infinitely many inequalities into finitely many linear matrix inequalities,

wherein the at least one objective includes imperfect channel state information.

2. The method as recited in claim 1 , wherein the at least one objective includes perfect channel state information.

3. The method as recited in claim 2 , further comprising formulating the two or more sub-problems as a convex optimization problem.

4. The method as recited in claim 1 , wherein a robustness can be tuned using the values of an error-in-norm.

5. The method as recited in claim 1 , wherein the two or more sub-problems are formulated as at least one of a convex optimization problem and a problem explicitly solvable in closed form.

6. The method as recited in claim 1 , wherein transmit precoders allow for different weights per transmitter-receiver pair.

7. The method as recited in claim 1 , wherein iterating further provides at least one receiver filter for each transmitter-receiver pair.

8. A non-transitory computer readable storage medium comprising a computer readable program, wherein the computer readable program when executed on a computer causes the computer to execute the method as recited in claim 1 .

9. A method for providing at least one transmit precoder, the method comprising:

transforming at least one of a weighted sum-rate and max-min rate objective into two or more sub-problems by introducing at least one slack variable, wherein the at least one objective has imperfect channel state information, and further wherein the two or more sub-problems are formulated as at least one of a convex optimization problem and a problem explicitly solvable in closed form;

applying the Schur complement to convert infinitely many inequalities into a finite number of linear matrix inequalities; and

iterating the two or more sub-problems on a computer readable storage medium to provide at least one transmit precoder for each transmitter.

10. The method as recited in claim 9 , wherein a robustness can be tuned using the values of an error-in-norm.

11. A system for providing at least one transmit precoder, the system comprising:

a transformation module configured to transform at least one of a weighted sum-rate and a max-min rate objective into two or more sub-problems by introducing at least one slack variable;

a solving module configured to iterate the two or more sub-problems on a computer readable storage medium to provide at least one transmit precoder for each transmitter; and

an applying module configured to apply the Schur complement to convert infinitely many inequalities into finitely many linear matrix inequalities,

wherein the at least one objective includes imperfect channel state information.

12. The system as recited in claim 11 , wherein the at least one objective includes perfect channel state information and further comprising formulating the two or more sub- problems as a convex optimization problem.

13. The system as recited in claim 11 , wherein a robustness can be tuned using the values of an error-in-norm.

14. The system as recited in claim 11 , wherein the two or more sub-problems are formulated as at least one of a convex optimization problem and a problem explicitly solvable in closed form.

15. The system as recited in claim 11 , wherein transmit precoders allow for different weights per transmitter-receiver pair.

16. The system as recited in claim 11 , wherein the solving module further provides at least one receiver filter for each transmitter-receiver pair.