Power and rate optimization in wireless networks using convex relaxation

Abstract

In wireless communication, many resource allocation and management problems are related with optimization problems. In this dissertation, we consider nonconvex power and rate optimization problems in wireless networks such as DS-CDMA, OFDMA, cognitive network using convex relaxation. This dissertation deals with efficient numerical solutions for nonconvex optimization problems for a class of optimization problems in wireless communications systems. By using convex relaxation, we can approximate the solution of nonconvex optimization problems in wireless communication systems. In this dissertation, we considers a distributed utility maximization power control scheme in up-link DS-CDMA systems. Maximizing a utility function is solved by Lagrangian dual decomposition. Numerical result shows that the proposed algorithm achieves around 95% of the optimal utility maximization with a modest computational burden. Also, we considers a sum-rate maximizing power allocation problem under Gaussian cognitive multiple-access channel (MAC) environment, where primary and secondary users may communicate under mutual interference. Formulating the problem as a standard nonconvex quadratically constrained quadratic problem (QCQP) provides a simple method to find a solution using semidefinite relaxation (SDR). Numerical results show that the solution achieves the similar performance of the exhaustive search, within polynomial time. In addition, we considers a novel auction algorithm for subchannel allocation using the difference of throughput among subchannels to allow users to compete through bidding in OFDMA system. The algorithm we proposed can achieve a competitive fair subchannel allocation through auction mechanism. Numerical result shows that the proposed algorithm has the similar performance with sum rate maximization and guarantees fairness by minimizing user``s potential throughput loss.