Energy efficient power allocation for MIMO-NOMA-based integrated terrestrial-satellite networks

Millimeter-wave (mmWave) massive MIMO and non-orthogonal multiple access (NOMA) are promising ways for improving energy efficiency of integrated terrestrial-satellite networks. In this paper, we investigate the performance of integrated terrestrial-satellite networks, in which MIMO-NOMA-based terrestrial networks and satellite network cooperatively provide ubiquitous services for ground users while reusing the entire bandwidth. Both base stations (BSs) and satellite are equipped with multiple antennas, and beamforming technology is utilized to serve multiple users simultaneously. First, a user selection scheme based on channel gain ratio is proposed to select satellite users. Then, we design a user clustering algorithm for reducing the intra-beam and inter-beam interference of BSs users. Analog and digital beamforming techniques are also adopted to further increase beam gain and improve system energy efficiency. The terrestrial networks and satellite networks energy efficiency are separately optimized through beamforming design and power allocation scheme, and then, a joint iterative algorithm is proposed to maximize the system energy efficiency. In addition, we introduce the interference temperature threshold to limit the satellite interference to BSs users. Finally, the performance of the proposed algorithm is simulated in comparison with the existing algorithm. The results indicate that the presented algorithm has high superiority in system energy efficiency and it can be applied to integrated terrestrial-satellite networks. We investigate the performance of integrated terrestrial-satellite networks, in which MIMO-NOMA-based terrestrial networks and satellite network cooperatively provide ubiquitous services for ground users while reusing the entire bandwidth. Considering the user grouping and clustering strategy, a joint power optimization algorithm is proposed to maximize network energy efficiency. Numerical results demonstrate the superiority of our proposed power allocation algorithm, and we analyzed the trade-off between algorithm complexity and energy efficiency improvement.image