An asynchronous distributed power allocation scheme for sum-rate maximization on cognitive GMACs

This paper considers an asynchronous distributed power allocation scheme for sum-rate-maximization under cognitive Gaussian multiple access channels (GMACs), where primary users and secondary users may communicate under mutual interference with the Gaussian noise. Formulating the problem as a standard nonconvex quadratically constrained quadratic problem (QCQP) provides a simple distributed method to find a solution using iterative Jacobian method instead of using centralized schemes. A totally asynchronous distributed power allocation for sum-rate maximization on cognitive GMACs is suggested. Simulation results show that this distributed algorithm for power allocation converges to a fixed point and the solution achieves almost the same performance as exhaustive search.