This paper evaluates the performance of uplink integrated sensing and communication systems in the presence of gain and phase imperfections. Specifically, we consider multiple unmanned aerial vehicles (UAVs) transmitting data to a multiple-input-multiple-output base-station (BS) that is responsible for estimating the transmitted information in addition to localising the transmitting UAVs. The signal processing at the BS is divided into two consecutive stages: localisation and communication. A maximum likelihood (ML) algorithm is introduced for the localisation stage to jointly estimate the azimuth-elevation angles and Doppler frequency of the UAVs under gain-phase defects, which are then compared to the estimation of signal parameters via rotational invariance techniques (ESPRIT) and multiple signal classification (MUSIC). Furthermore, the Cramer-Rao lower bound (CRLB) is derived to evaluate the asymptotic performance and quantify the influence of the gain-phase imperfections which are modelled using Rician and von Mises distributions, respectively. Thereafter, in the communication stage, the location parameters estimated in the first stage are employed to estimate the communication channels which are fed into a maximum ratio combiner to preprocess the received communication signal. An accurate closed-form approximation of the achievable average sum data rate (SDR) for all UAVs is derived. The obtained results show that gain-phase imperfections have a significant influence on both localisation and communication, however, the proposed ML is less sensitive when compared to other algorithms. The derived analysis is concurred with simulations.

翻译：本文评估了增益和相位不完善条件下上行通感一体化系统的性能。具体而言，我们考虑多架无人飞行器向多输入多输出基站传输数据，该基站需在定位无人飞行器的同时估计传输信息。基站的信号处理分为两个连续阶段：定位与通信。针对增益相位缺陷下无人机方位角-俯仰角及多普勒频率的联合估计，本文在定位阶段引入最大似然算法，并与基于旋转不变性的信号参数估计技术和多重信号分类算法进行性能对比。进一步推导了克拉美罗下界以评估渐近性能，并量化了分别采用莱斯分布和冯·米塞斯分布建模的增益相位不完善度的影响。随后在通信阶段，利用第一阶段估计的位置参数估算通信信道，将其输入最大比合并器对接收通信信号进行预处理。推导出所有无人机可达平均和数据速率的精确闭式近似。结果表明增益相位不完善对定位和通信均有显著影响，但所提最大似然算法相较于其他算法具有更低敏感性。仿真结果与理论分析一致。