Use of linear programminig for dynamic subcarrier and bit allocation in OFDMA systems and its extension to MIMO-OFDMA systems

Abstract

In this dissertation, we propose adaptive subcarrier and bit allocation method for multiuser orthogonal frequency division multiple access (OFDMA) systems and its extension to multi-input and multi-output (MIMO) environments. For OFDMA systems, the subcarrier and bit allocation problems are previously formulated as nonlinear optimizations. However, this approach does not yield optimal solution and requires the heavy computational load. We converted these original problem that are formulated as nonlinear optimization into linear optimizations and solved by integer programming (IP). Based on the observation that the bits loaded to the subcarriers allocated to a given user tend to be identical, two-step suboptimal approach is proposed that performs subcarrier allocation and bit loading separately. It is shown that the subcarrier allocation in this approach can be optimized by the linear programming (LP) relaxation of IP, while the bit loading can be performed in a manner similar to single-user OFDM. In addition, a heuristic method for solving the LP problem is presented. The LP-based suboptimal and heuristic algorithms are considerably simpler to implement than optimal IP, plus their performances are close to those of the optimal approach. Moreover, the alternative formulations that are modification of original optimization is suggested. The proposed optimal and suboptimal approach is applicable to these alternative problems. The subcarrier, antenna, and bit allocation methods for MIMO-OFDMA systems are also proposed. Based on the observation that all antennas of an subcarrier must be assigned to an user, optimal IP can be formulated by inserting an additional constraint. The proposed suboptimal algorithm that is originally derived in OFDMA is applicable to MIMO-OFDMA systems. Like in OFDMA, the performance of the suboptimal algorithms is close to those of the optimal approach.