概率LoS信道下能量受限空中CRNs的3D轨迹设计
3D Trajectory Design for Energy-constrained Aerial CRNs Under Probabilistic LoS Channel
AI总结：
研究概率LoS信道下能量受限的空中认知无线电网络，通过联合优化UAV的3D轨迹、发射功率与用户调度最大化平均可达速率，利用块坐标下降与逐次凸逼近求解非凸问题，验证了3D轨迹对系统性能的提升。
摘要：
无人机（UAVs）备受关注，因为在高层城市区域中，UAVs与地面节点之间有很大几率获得视距链路。在本研究中，我们通过联合设计三维（3D）轨迹、UAV的发射功率和用户调度来研究认知无线电网络。考虑到UAV的机载能耗，我们构建了一个优化问题，通过联合优化UAV的3D轨迹、发射功率和用户调度来最大化所考虑系统的平均可达速率。针对该非凸优化问题，我们利用平均可达速率的下界来降低求解复杂度。随后，利用块坐标下降法将原始优化问题解耦为四个子问题，并通过引入松弛变量和逐次凸逼近将每个子问题转化为可处理的凸优化问题。数值结果验证了所提算法的有效性，并表明UAV的3D轨迹能够提升空中CRNs的平均可达速率。
英文摘要：
Unmanned aerial vehicles (UAVs) have been attracting significant attention because there is a high probability of line-of-sight links being obtained between them and terrestrial nodes in high-rise urban areas. In this work, we investigate cognitive radio networks (CRNs) by jointly designing three-dimensional (3D) trajectory, the transmit power of the UAV, and user scheduling. Considering the UAV's onboard energy consumption, an optimization problem is formulated in which the average achievable rate of the considered system is maximized by jointly optimizing the UAV's 3D trajectory, transmission power, and user scheduling. Due to the non-convex optimization problem, a lower bound on the average achievable rate is utilized to reduce the complexity of the solution. Subsequently, the original optimization problem is decoupled into four subproblems by using block coordinate descent, and each subproblem is transformed into manageable convex optimization problems by introducing slack variables and successive convex approximation. Numerical results validate the effectiveness of our proposed algorithm and demonstrate that the 3D trajectories of UAVs can enhance the average achievable rate of aerial CRNs.
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