Waveform Design of UAV-Enabled Integrated Sensing and Communication in Marine Environment
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摘要: 未来6G将实现万物智能互联、虚拟和现实结合的全新时代，这离不开通信与感知技术的发展。但由于频率资源的稀缺，二者在频率资源上的共享是一个亟待解决的问题。通信感知一体化(ISAC)技术为解决这一问题提供了新的思路，它允许通信与感知共用一套设备、共享频率资源，可以同时完成目标探测和信息通信，被认为是6G的关键技术之一。同时我国是一个海洋大国，海洋资源丰富，海洋通信与海上目标感知需求急剧增加。该文对海洋环境下的ISAC技术进行了研究，提出一种海洋环境下的加权波形优化设计方法。通过仿真实验发现通信与感知的功率比值在[0.2, 0.5]区间内时，一体化波形不仅具有良好的通信性能，也具有不错的感知性能。最后对未来的工作内容进行了展望。Abstract: In the future, 6G will realize a new era of intelligent interconnection of everything and the combina-tion of virtual and reality, which can not be separated from the development of communication and perception technology. However, due to the scarcity of frequency resources, the sharing of frequency resources between them is an urgent problem to be solved. Integrated Sensing and Communication (ISAC) technology provides a new way to solve this problem. It allows communication and perception to share a set of equipment and frequency resources, and can simultaneously complete target detection and information communication, which is considered to be one of the key technologies of 6G. At the same time, China is powerful in the ocean with abundant marine resources and the demands for marine communication and marine targets perception have increased greatly. In this paper, ISAC technology in marine environment was studied, and a weighted waveform optimization design method is proposed. The simulation results show that when the power ratio of communication to perception is within the range of [0.2, 0.5], the integrated waveform not only has good communication performance, but also has good perception performance. Finally, the future work is prospected.
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1 基于连续凸逼近的无人机通信感知加权波形优化算法
输入：每个用户的权重${\alpha _k}$，初始迭代点$\{ {\boldsymbol{W}}_k^{(0)}\} $，加权系数$\rho $，SCA迭代精度$\delta $，SCA最大迭代次数${N_{{\mathrm{iter}}}}$，迭代步长$\gamma $，无人机和$K$个通
信用户的位置坐标，探测目标个数$M$及方位，发射天线个数$N$，中间参数${{\boldsymbol{B}}_k}$和$ {A_k} $输出：问题(P4)的局部最优解$\{ {{\boldsymbol{W}}_k}\} $ 1：根据无人机和用户的位置坐标计算无人机到每个用户之间的近海面无线信道$\{ {{\boldsymbol{h}}_k}\} $ 2：根据问题(P1)计算MIMO雷达波束图样${{\boldsymbol{R}}_1}$ 3：变量初始化，令$ p = 0 $；$\{ {\boldsymbol{W}}_k^{(p)}\} = \{ {\boldsymbol{W}}_k^{(0)}\} $ 4：for i=1, 2,···, ${N_{{\mathrm{iter}}}}$ 5： 利用CVX等凸优化求解器进行求解问题(SDR5.$ p $)，$ \{ \bar {\boldsymbol{W}}_k^*\} $是问题(SDR5.$ p $)的解 6： 使用SVD或高斯随机化得到秩一解$ \{ {\boldsymbol{W}}_k^*\} $ 7： if $ \displaystyle\sum\nolimits_{k = 1}^K {{{\left\| {{\boldsymbol{W}}_k^* - {\boldsymbol{W}}_k^{(p)}} \right\|}^2}} \le \delta $ 8： break 9： else 10： ${\boldsymbol{W}}_k^{(p + 1)} \leftarrow \gamma {\boldsymbol{W}}_k^* + (1 - \gamma ){\boldsymbol{W}}_k^{(p)}$ 11： end if 12：end for 13：输出$\{ {{\boldsymbol{W}}_k}\} = \{ {\boldsymbol{W}}_k^*\} $ 
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表 1 仿真参数设置
仿真参数 参数值 仿真参数 参数值 无人机高度(m) 100 蒸发导管有效高度(m) 300 通信用户个数 3 最大SCA迭代次数 10 通信用户1水平坐标(m) (200, –200) SCA迭代精度 10–4 通信用户2水平坐标(m) (100, –100) SCA迭代步长 0.9 通信用户3水平坐标(m) (–50, –100) 每个用户的权重${\alpha _k}$ 1 探测目标个数 1 地球半径(km) 6371 探测目标方向(°) 45 最小有效链路距离(km) 2.6 最大发射功率(W) 10 $ {A_0} $(dB) 100.7 发射天线个数 10 $ {n_{\mathrm{A}}} $ 1.9 接收天线高度(m) 1 $ {X_{\mathrm{A}}} $(dB) 18.5
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