Research on Resource Allocation and Trajectory Optimization of a Multitask Unmanned Aerial Vehicles Assisted Communication Network
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摘要: 装载各种有效荷载的无人机(UAV)能够实现传感、通信和计算等多任务，因而常被部署到数据采集(DA)和辅助计算等领域。但是到目前为止，绝大多数研究仅专注于单一功能的无人机辅助的通信网络资源分配与轨迹优化，对于面向多任务的资源分配和轨迹优化问题还未解决。为此，该文提出一种综合考虑无人机数据采集、数据广播以及计算任务卸载的无人机辅助的通信网络资源优化的分配策略，旨在通过联合优化传输占空比、用户发射功率与无人机轨迹，在满足目标位置采集数据实时广播的前提下，最大化用户卸载量。为了解决多变量耦合优化问题，提出了基于块坐标下降(BCD)和连续凸逼近(SCA)的高效迭代优化算法，将耦合优化问题分解为3个子问题进行迭代优化。最后，大量仿真结果表明，该算法在公平性和总卸载计算量方面都优于其他测试方案。Abstract: Unmanned Aerial Vehicles (UAV) loaded with various payloads can achieve multiple tasks such as sensing, communication, and computing, and are often deployed in fields such as Data Acquisition (DA) and auxiliary computing. However, so far, the vast majority of research has only focused on single function drone resource allocation and trajectory optimization, and the problem of multi task oriented drone resource allocation and trajectory optimization has not been solved yet. Therefore, an allocation strategy for optimizing drone communication network resources is proposed that comprehensively considers drone data acquisition, data broadcasting, and computing task offloading. The aim is to maximize user offloading by jointly optimizing transmission duty cycle, user transmission power, and drone trajectory, while meeting the real-time broadcast of target location data collection. In order to solve the problem of multivariable coupled optimization, an efficient iterative optimization algorithm based on Block Coordinate Descent (BCD) and Successive Convex Approximate (SCA) is proposed. The coupled optimization problem is decomposed into three sub problems for iterative optimization. Finally, a large number of simulation results show that the algorithm outperforms other testing schemes in terms of fairness and total offloading computation.
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1 面向多任务的无人机轨迹和资源迭代优化算法
(1) 初始化最大误差 $ \varepsilon $ 、总量吐量迭代值obj、最大迭代次数 $ \alpha $ 与
$ {{{D}}^i}\left( n \right) = \left\{ {{\boldsymbol{A}}_n^i,{\boldsymbol{Q}}_n^i,{\boldsymbol{P}}_{m,n}^i} \right\} $(2) $ {\bf{while}} $ $ i \lt \alpha $ $ {\bf{do}} $ (3) $ i = i + 1, $ (4) 给定 $ \left\{ {{\boldsymbol{Q}}_n^i,{\boldsymbol{P}}_{m,n}^i} \right\} $ ，求解占空比子问题P1，得到最优情况
$ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{Q}}_n^i,{\boldsymbol{P}}_{m,n}^i} \right\} $ ;(5) 给定 $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{Q}}_n^i} \right\} $ ，求解功率子问题P2，得到最优情况
$ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{Q}}_n^i,{\boldsymbol{P}}_{m,n}^{i + 1}} \right\} $ ;(6) 给定 $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{P}}_{m,n}^{i + 1}} \right\} $ ，求解轨迹子问题P3.1，得到最优情
况 $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{Q}}_n^{i + 1},{\boldsymbol{P}}_{m,n}^{i + 1}} \right\} $ ;(7) 将 $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{Q}}_n^{i + 1},{\boldsymbol{P}}_{m,n}^{i + 1}} \right\} $ 带入目标函数中计算当前总吞吐
量迭代值objnew;(8) $ {\bf{if }}{\text{ abs}}\left( {{\text{objnew}} - {\text{obj}}} \right) \le \varepsilon {\text{ }}{\bf{then}} $ break (9) else $ {\text{obj}} = {\text{objnew}} $ ; (10) end while (11) 输出最优参数值 $ \left\{ {{\boldsymbol{A}}_n^*,{\boldsymbol{Q}}_n^*,{\boldsymbol{P}}_{m,n}^*} \right\} = \left\{ {{\boldsymbol{A}}_n^i,{\boldsymbol{Q}}_n^i,{\boldsymbol{P}}_{m,n}^i} \right\} $ ，
计算获得当前最大的总吞吐量为 $ {\text{ob}}{{\text{j}}^*} = {\text{objnew}} $ ;
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表 1 关键的仿真参数
参数 取值 参数 取值 飞行高度$ H $ 100 m 最大传输能量$ {E_{\max }} $ 0.4 J 飞行时隙$ N $ 60 最小传输速率$ {R_{\min }} $ 4×104 bit/s 飞行周期$ T $ 60 s 最大平均功率$ {P_{\max }} $ 500 mW 信道带宽$ B $ 1 MHz 目标区域半径$ {d_{{\text{set}}}} $ 10 m 最大速度$ {V_{\max }} $ 20 m/s 功率谱密度$ {N_0} $ –90 dBm/Hz 信道功率增益$ {\beta _0} $ –60 dB 目标位置数据量$ {D_k} $ 7×106 bit
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