| 多小区协作无人机通信感知一体化系统的资源分配 |
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| 引用本文： | 李培铭,吕忠昊,方园,许杰.多小区协作无人机通信感知一体化系统的资源分配[J].信号处理,2022,38(8):1592-1600. |
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| 作者姓名： | 李培铭 吕忠昊 方园 许杰 |
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| 作者单位： | 1.香港中文大学(深圳)理工学院与未来智联网络研究院, 广东 深圳 518172 |
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| 基金项目： | 国家自然科学基金项目61871137广东省省级科技计划项目2021A0505030002深圳市基础研究面上项目20210318123512002 |
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| 摘 要： | 在通信感知一体化系统中，由于通信与感知两者的目的并不完全一致，这使得通信和感知之间的性能折中问题尤为关键。本文研究多小区协作联网无人机网络的通信感知一体化问题，其中基站作为通信感知收发器与无人机用户通信，同时估计感知目标的位置。基于此，联合优化多个基站协作发射功率控制以及无人机用户的轨迹来平衡感知和通信性能，在满足无人机用户的信干噪比需求和感知目标定位的克拉美罗下界需求基础上，最小化基站的能量消耗。该问题是一个非凸优化问题，通常难以直接进行求解。为解决该问题，本文提出基于交替优化的联合基站功率控制与无人机轨迹优化方案，分别利用半正定松弛技术和连续凸近似技术对基站功率控制和无人机轨迹进行优化设计。最后，实验仿真结果验证了所提联合优化方案的性能。
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| 关 键 词： | ??通信感知一体化 多小区协作 无线通信 无人机轨迹设计 资源分配 |
| 收稿时间： | 2022-03-31 |
Resource Allocation for Multi-Cell Cooperative Integrated Sensing and Communication with UAVs |
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| Affiliation: | 1.School of Science and Engineering and Future Network of Intelligence Institute，The Chinese University of Hongkong （Shenzhen），Shenzhen，Guangdong 518172，China2.School of Information Engineering，Guangdong University of Technology，Guangzhou，Guangdong 510006，China |
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| Abstract: | ? ?The integrated sensing and communication is to realize the dual functions of wireless communication and radar sensing at the same time through the reuse of radio frequency signals to achieve mutual benefit. On the one hand， different types of sensing missions can be processed based on existing communication systems； on the other hand， the sensing results can be used to assist communication， so as to improve the QoS （quality-of-service） and communication efficiency. However， since the purposes of sensing and communication are not exactly the same， this makes the performance trade-off between sensing and communication very critical. This paper studied the multi-cell cooperative integrated sensing and communication （ISAC） with unmanned aerial vehicles （UAVs）， in which the base stations （BSs） act as ISAC transceivers to send individual messages to their respective cellular-connected UAV users， and at the same time estimate the location of the sensing target. As such， we jointly designed the cooperative power control among BSs and UAV trajectory to minimize the energy consumption of the BSs， while satisfying the signal-to-interference-noise ratio （SINR） requirements of UAV users and the Cramer-Rao lower bound （CRLB） requirement for target location estimation. Since the variables are coupled together and the SINR constraints are non-convex， the formulated problem is non-convex and difficult to be tackled in general. To deal with this challenge， we proposed an efficient algorithm based on the alternating optimization to jointly optimize the BSs’ power control as well as the UAV trajectory. In particular， the power control problem and the UAV trajectory problem are solved by using the techniques of semi-definite relaxation （SDR） and successive convex approximation （SCA）， respectively. For each iteration， the updated objective value of the original problem is ensured to be monotonically non-decreasing， and as a result， the convergence of the proposed algorithm is ensured. Finally， the performance of the proposed joint design is verified by numerical results. |
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