Manifold Learning Inspired Dynamic Hybrid Precoding With Antenna Partitioning Algorithm for Dual-Hop Hybrid FSO-RF Systems

Abstract

The decode-and-forward (DF) based free-space optical-radio frequency (FSO-RF) hybrid system combines the advantages of both FSO links and RF links, which makes the system easy to be deployed and enables the extended transmission coverage. In the case of multiple users, the unreasonable dynamic antenna selection algorithm will lead to multi-user interference (MUI) and reduce the spectral efficiency of the system. Therefore, we propose hybrid precoding based on manifold learning with the antenna partitioning algorithm for dual-hop hybrid FSO-RF systems. In the hybrid precoding of dynamic subarray structure, the high-dimensional channels are embedded into the low-dimensional manifolds by the optimized multidimensional scaling (MDS). The potential spatial correlations of the high-dimensional channel are preserved by the scaling by majorizing a complicated function (SMACOF) algorithm. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi-conjugate gradient methods. Meanwhile, an antenna subarray partitioning algorithm is proposed, so that each antenna unit can be assigned to an RF chain based on the increment of the user’s maximum signal to interference noise ratio (SINR). By calculating the simulated equivalent channel SINR for the selected users, the antenna division can greatly reduce the computational complexity and the size of the search space, and ensure fairness among users. Simulation results show that this method can obtain almost the best summation rate and higher spectral efficiency compared with the conventional method.