基于无人机中继的星地认知网络波束成形算法

针对基于无人机中继的星地认知网络，提出了两种波束成形（beamforming, BF）算法，通过对各种干扰进行抑制，实现系统间的频谱共享。具体而言，在基于无人机中继的卫星网络作为次级网络、地面网络作为主网络的情况下，以无人机最大发射功率和主用户所受干扰为约束条件，建立次级用户信干噪比最大化准则的优化问题；接下来在已知次级用户统计信道状态信息的条件下，提出一种基于迭代的BF算法对优化问题进行求解；更进一步，为了降低迭代算法的实现复杂度，提出了一种基于迫零的BF算法。最后，计算机仿真验证了所提两种波束成形方案的正确性与有效性。

Beamforming algorithm for cognitive satellite and terrestrial network based on UAV relay

Two beamforming (BF) schemes to achieve spectrum sharing by suppressing inter-system interferences in cognitive satellite and terrestrial networks were proposed, where the satellite and UAV cooperative network was used as the secondary network, while the terrestrial network was used as the primary network.Specifically, considering that only the statistical channel state information was available, a constrained optimization problem was formulated to maximize the signal-to- interference-plus-noise ratio of the secondary user under the constraints of the maximum transmit power at the UAV and the interference power of the primary user.Then, an iteration-based BF scheme was proposed to solve the constrained optimization problem.To reduce the computational complexity of the iterative algorithm, a zero-forcing based BF scheme was further proposed.Finally, computer simulations verify the correctness and effectiveness of the proposed BF schemes.