Jointly Optimized Deployment and Power for Unmanned Aerial Vehicle - Satellite Assisted Cell-Free Massive MIMO Systems
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摘要: 为了解决传统去蜂窝大规模多输入多输出(CF-mMIMO)通信系统认知局限、资源短缺、覆盖盲区的问题，针对传统覆盖受限的去蜂窝网络下行传输系统，该文提出无人机-近地轨道卫星辅助的空天地一体化CF-mMIMO的功率分配和无人机位置部署方法。根据已知的用户位置以及地面接入点的部署缺陷，考虑各通信接入点的覆盖约束、最大功率约束、跨层干扰约束，以最大化用户最小速率为目标，建立联合用户关联、功率分配以及无人机放置的混合资源分配模型。基于块坐标下降方法和连续凸优化方法，将原本的非凸优化问题转化为3个子问题，并交替求得子问题近似解，最终得到原问题的最优近似解。仿真结果表明，所提方法能够合理安排系统的资源放置，显著提高系统通信覆盖，提升用户的平均吞吐量。Abstract:
Objective This study addresses persistent limitations in resource availability, cognitive adaptability, and spatial coverage in traditional Cell-Free massive Multiple-Input Multiple-Output (CF-mMIMO) systems. A novel framework is proposed that integrates power control and Unmanned Aerial Vehicle (UAV) placement within a Low Earth Orbit (LEO) satellite-assisted downlink architecture. The objective is to enhance communication efficiency and system robustness in coverage-constrained wireless environments, particularly under dynamic user distributions and challenging propagation conditions. Methods The proposed framework adopts a hybrid optimization model that jointly considers user association, power allocation, and UAV deployment, based on the known spatial distribution of ground users and access points. With LEO satellite support, the architecture extends coverage and strengthens transmission links. The optimization problem aims to maximize the minimum achievable user data rate, subject to constraints on coverage, power, and cross-layer interference. Owing to the nonconvex and coupled nature of the variables, an iterative algorithm is developed using block coordinate descent and successive convex approximation. The original problem is decomposed into three interdependent subproblems—user association, power allocation, and UAV positioning—which are solved alternately to obtain a near-optimal solution. Results and Discussions Simulation results confirm that the proposed framework significantly improves system-wide throughput, communication robustness, and spectral efficiency. Compared with conventional CF-mMIMO systems, the integration of UAVs and LEO satellites enhances adaptability to non-uniform user distributions and challenging wireless environments. The strategy enables real-time adjustment of UAV positions and transmission power, improving load balancing, reducing interference, and expanding service coverage. Performance metrics, including the minimum user rate and total system capacity, demonstrate the proposed method’s effectiveness in complex, heterogeneous network settings. Conclusions This study proposes a scalable and adaptive approach for next-generation communication networks by integrating aerial and satellite components into terrestrial CF-mMIMO systems. The combination of intelligent UAV deployment and adaptive power control enables efficient resource management while maintaining high reliability and wide-area coverage. The proposed strategy represents a promising direction for future air-space-ground integrated networks, supporting high-throughput, energy-efficient, and resilient wireless services in both urban and remote scenarios. -
1 空天地一体化CF-mMIMO系统无人机部署和功率分配算法
输入：$ {{\boldsymbol{\chi}} }^{0},{{\boldsymbol{q}}}^{0},{{}{{\boldsymbol{p}}}^{0},{{\boldsymbol{\rho}} }^{0}\text{}} $, $ r = 0 $, 误差$ \varpi $； 输出：$ {{\boldsymbol{\chi}} }^{r},{{\boldsymbol{q}}}^{r},{{}{{\boldsymbol{p}}}^{r},{{\boldsymbol{\rho }}}^{r}\text{}} $ 循环 (1) 对于给定的$ \{ {{\boldsymbol{\chi }}^r},{{\boldsymbol{q}}^r},{{\boldsymbol{P}}^r}\} $，求解问题式(17)得到问题优解$ {{\boldsymbol{\chi }}^{r + 1}} $; (2) 对于给定的$ \{ {{\boldsymbol{\chi }}^{r + 1}},{{\boldsymbol{q}}^r},{{\boldsymbol{P}}^r}\} $，求解问题式(21)得到问题优解
$ {{\boldsymbol{q}}^{r + 1}} $;(3) 对于给定的$ \{ {{\boldsymbol{\chi }}^{r + 1}},{{\boldsymbol{q}}^{r + 1}},{{\boldsymbol{P}}^r}\} $，求解问题式(26)得到问题优解
$ {{\boldsymbol{P}}^{r + 1}} $;(4) 更新$ r = r + 1 $; (5) 直到$ \left| {\lambda _{}^{r + 1} - \lambda _{}^r} \right| \lt \varpi $。 
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