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:
Objective In the future, 6G will usher in a new era of intelligent interconnection and the integration of virtual and physical environments. This vision relies heavily on the deployment of numerous communication and sensing devices. However, the scarcity of frequency resources presents a significant challenge for sharing these resources efficiently. Integrated Sensing and Communication (ISAC) technology offers a promising solution, enabling both communication and sensing to share a common set of equipment and frequency resources. This allows for simultaneous target detection and information transmission, positioning ISAC as a key technology for 6G. ISAC research can be divided into two main approaches: Coexisting Radar and Communication (CRC) and Dual-Function Radar Communication (DFRC). The CRC approach designs separate systems for radar and communication, aiming to reduce interference between the two; however, this leads to increased system complexity. The DFRC approach integrates radar and communication into a single system, simplifying the design while still achieving both radar detection and communication functions. As a result, DFRC is the primary focus of ISAC research. Waveform design is a crucial component of ISAC systems, with two primary strategies: non-overlapped resource waveform design and fully unified waveform design. The fully unified design can be further classified into three types: sensing-centric, communication-centric, and joint design. Previous research has predominantly focused on sensing-centric or communication-centric designs, which limit the flexibility of the integrated waveform in balancing communication and sensing performance. Additionally, limited research has addressed ISAC in marine environments. This paper investigates waveform design for ISAC in marine environments, proposing a joint design approach that uses a weighting coefficient to adjust the communication and sensing performance of the integrated waveform. Methods Considering the characteristics of the marine environment, this paper proposes using Unmanned Aerial Vehicles (UAVs) as nodes in the ISAC system, owing to their flexibility, portability, and cost-effectiveness. The integrated waveform transmitted by UAVs can both communicate with downlink users and detect targets. The communication performance is evaluated using the achievable sum rate, while the sensing performance is assessed by the error between the covariance matrix of the integrated waveform and the standard radar covariance matrix. The optimization objective is to maximize the weighted sum of these two performance indices, subject to the constraint that UAV power does not exceed the maximum allowable value. The weighting coefficient represents the ratio of communication power to sensing power. Due to the non-convex rank-1 constraint and objective function, the optimization problem is non-convex. This paper decomposes the non-convex optimization problem into a series of convex subproblems using the Successive Convex Approximation (SCA) algorithm. The local optimal solution of the original problem is obtained by solving these convex subproblems. The communication and sensing performance of the integrated waveform can be adjusted by varying the weighting coefficient. The performance of the weighted integrated waveform design in a marine environment is simulated, and the results are presented. Results and Discussions Simulation results indicate that the integrated beam pattern exhibits two large lobes: one directed towards the target for detection, and the other towards the communication user (Fig.4). As the weighting coefficient increases, the lobes directed towards the communication users become more pronounced, reflecting the increased emphasis on communication performance. Furthermore, as the weighting coefficient increases, the sensing performance error (smaller error indicates better sensing performance) initially increases slowly before rising more rapidly. Meanwhile, the achievable sum rate of communication increases sharply. Eventually, both the sensing performance error and the communication sum rate curves flatten out ( Fig. 6 ). Since the UAV's maximum power is limited to 10 W, further increases in the weighting coefficient beyond a certain point lead to diminishing returns in communication performance, as power constraints limit further improvement. At this point, the sensing performance error remains stable.Conclusions This paper investigates the waveform design for UAV-enabled ISAC systems in marine environments. A wireless propagation model for UAVs in such environments is developed, and an integrated waveform optimization method based on a weighted design is proposed. The SCA algorithm is used to solve the convex approximation. Simulation results demonstrate that when the weighting coefficient is between 0.2 and 0.5, the integrated waveform ensures strong communication performance while maintaining good sensing performance. -
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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