Self-Interference Cancellation Technology of Integrated Sensing and Communications System for Unmanned Aerial Vehicles
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摘要:
在无人机通信场景中,机载通信感知一体化系统受到本地信号发射带来的强烈自干扰影响,导致系统对目标的感知性能降低. 针对这一挑战,提出基于正交频分复用(OFDM)的通信感知一体化自干扰消除方法. 首先,建立基于OFDM的通信感知一体化系统回波模型,并引入自干扰信号;然后,利用最小二乘算法估计自干扰信号的信道增益,并利用该增益对自干扰信号进行重建和抑制;最后,通过无人机通信场景下的仿真实验验证所提方法的有效性. 结果表明,该方法能够将自干扰信号抑制到噪声功率水平,将目标回波信号的信干噪比提升近10.00 dB,从而有效提高系统的感知性能.
Abstract:In the communication scenario of unmanned aerial vehicles (UAVs), the airborne integrated sensing and communications (ISAC) system is affected by the strong self-interference caused by local signal transmission, which degrades the sensing performance of the system to the target. To solve this issue, a self-interference cancellation technology of the ISAC system based on orthogonal frequency division multiplexing (OFDM) was proposed. Firstly, the echo model of the ISAC system based on OFDM was established, and a self-interference signal was introduced. Then, the channel gain of the self-interference signal was estimated by the least squares algorithm, and the self-interference signal was reconstructed and suppressed by the gain. Finally, the effectiveness of the proposed method was verified by simulation experiments in the communication scenario of UAVs. The results show that the proposed method can suppress the self-interference signal to the noise power level and increase the signal-to-interference plus noise ratio (SINR) of the target echo signal by nearly 10.00 dB, so as to effectively improve the sensing performance of the system.
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表 1 OFDM-based ISAC系统参数
Table 1. Parameters of OFDM-based ISAC system
变量 数值 ${f_{\text{c}}}$/GHz 24 M/个 256 N/个 1024 $\Delta f$/kHz 90.909 $T$/μs $11$ ${T_{{\text{CP}}}}$/μs $1.375$ $T_{\mathrm{O} }$/μs $12.375$ $B$/MHz 93.1 ${R_{\max }}$/m 206 $\Delta R$/m 1.61 ${v_{\max }}$/(m·s−1) ±252.3 $\Delta v$/(m·s−1) 1.97 表 2 SIC处理前后SINR
Table 2. SINRs before and after SIC processing
dB RT/m 目标 1 目标 2 SIC 前 SIC 后 SIC 前 SIC 后 13.38 −32.93 −23.73 −32.93 −23.73 3.72 −7.03 2.18 −7.03 2.18 1.63 2.51 6.84 2.51 6.84 -
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