User-Level Scheduling and Resource Allocation for Multi-Beam Satellite Systems with Full Frequency Reuse

Multi-beam satellite communication systems can improve the resource utilization and system capacity effectively.However,the inter-beam interference,especially for the satellite system with full frequency reuse,will degrade the system performance greatly due to the characteristics of multi-beam satellite antennas.In this article,the user scheduling and resource allocation of a multi-beam satellite system with full frequency reuse are jointly studied,in which all beams can use the full bandwidth.With the strong inter-beam interference,we aim to minimize the system latency experienced by the users during the process of data downloading.To solve this problem,deep reinforcement learning is used to schedule users and allocate bandwidth and power resources to mitigate the inter-beam interference.The simulation results are compared with other reference algorithms to verify the effectiveness of the proposed algorithm.

Iterative multiuser detection for high spectral efficiency reverse-link of multibeam satellite via expectation propagation

Multibeam satellite communications employing full frequency reuse have the potential to increase spectral efficiency.However,they suffer from severe inter-beam interference.An expectation propagation based message passing algorithm is proposed for decoding multi-user transmissions in the reverse link of multi-beam satellite communications with full frequency reuse.Compared with an iterative MMSE(Minimum Mean Square Error)interference cancellation algorithm,the proposed algorithm reduces the cubic complexity to square complexity in the number of interfering beams.Numerical results show that the proposed algorithm outperforms the iterative MMSE algorithm slightly in terms of bit error rate when the energy per bit to noise power spectral density ratio is low.The performance of both algorithms is the same for other cases.