Near-Optimal and Efficient Heuristic Algorithms for Resource Allocation in MISO-OFDM Systems

MISO-OFDM systems enable the allocation of resources in the frequency and spatial domain to increase the data throughput of wireless systems. In this paper, we initially present a near-optimal solution to the MISO-OFDM Resource Allocation (RA) problem. This solution performs an exhaustive search over all possible user sets and then uses a dual-based approach to optimally assign power to the selected user sets. However, this algorithm requires computing the pseudo-inverse of all subchannel matrices. In order to reduce its computational complexity we derive an efficient heuristic RA algorithm. We first propose an approximation method that only computes the determinant of the squared subchannel matrices and selects the sets with maximum determinant per subcarrier. We then further reduce the computational cost by using a greedy search method which decreases the number of candidate users for each subcarrier. We present simulation results which show that the performance gap of the approximate method is under 0.6% with respect to the near-optimal solution, and under 1.4% when we use the greedy search method pre-selecting 4 sets. These results show that the proposed heuristic algorithms can achieve a performance very close to the optimal at a low computational cost even with a large number of users in the cell.