This paper investigates the problem of resource allocation for joint communication and radar sensing system on rate-splitting multiple access (RSMA) based unmanned aerial vehicle (UAV) system. UAV simultaneously communicates with multiple users and probes signals to targets of interest to exploit cooperative sensing ability and achieve substantial gains in size, cost and power consumption. By virtue of using linearly precoded rate splitting at the transmitter and successive interference cancellation at the receivers, RSMA is introduced as a promising paradigm to manage interference as well as enhance spectrum and energy efficiency. To maximize the energy efficiency of UAV networks, the deployment location and the beamforming matrix are jointly optimized under the constraints of power budget, transmission rate and approximation error. To solve the formulated non-convex problem efficiently, we decompose it into the UAV deployment subproblem and the beamforming optimization subproblem. Then, we invoke the successive convex approximation and difference-of-convex programming as well as Dinkelbach methods to transform the intractable subproblems into convex ones at each iteration. Next, an alternating algorithm is designed to solve the non-linear and non-convex problem in an efficient manner, while the corresponding complexity is analyzed as well. Finally, simulation results reveal that proposed algorithm with RSMA is superior to orthogonal multiple access and power-domain non-orthogonal multiple access in terms of power consumption and energy efficiency.


翻译:本文调查了用于联合通信和雷达系统的资源配置问题,涉及分速多存以无人驾驶飞行器(无人驾驶飞行器)为基础的多存取系统。无人驾驶飞行器同时与多个用户进行通信,并向感兴趣的目标发出信号,以利用合作遥感能力,在规模、成本和电力消耗方面实现重大收益。通过在发射机上使用线性预先编码的分解率,并在接收器上连续取消干扰,因此,将空间飞行器作为管理干扰以及提高频谱和能效的一个有希望的范例。为了最大限度地提高无人驾驶飞行器网络的能源效率,在电力预算、传输率和近似错误的限制下,将部署地点和波形组合矩阵联合优化。为了高效地解决已拟订的不凝固问题,我们将其分解为UAV部署的子问题和波状优化子问题。然后,我们引用了连续的螺旋线近似和锥形相偏差程序以及Dinkelbach方法,将精密的子相标变成每个网络。下一步,在电力预算、传输率和近似差误差差差差的矩阵中,在不精确的混合的电算法中,在最终分析后,对精度的获取结果的电压方法将最终和后,将可分析后算法式的电流的进入式的电压法则用于解的进入的序列式的电压法系。

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