# A Fast Acquisition Algorithm for Hybrid Signals of 5G and BeiDou B1
## 1 Introduction
proposed a solution in the context of 5G and machine learning to improve the positioning accuracy in urban areas by obtaining the location of user equipment (UE) from beamforming radio signal strength (RSS) measurements and fusing them with GNSS-based positioning data to improve the overall positioning performance [7]. The following research focuses on the key issues affecting the performance of GNSS and 5G signals regarding positioning, timing, and accuracy on hybrid architectures and platforms. [PERSON] et al. discuss the architecture and key technologies of a 5G system based on a satellite platform, evaluate the impact of satellite channel characteristics on the physical and media access control layers, and propose different solutions based on the services and architectures considered [8]. [PERSON] et al. proposed a simplified 5G/GNSS fused positioning system architecture using only a 5G base station and observations from a GNSS satellite. The improvement of fused positioning over pure 5G positioning is more than 32%, independent of the time synchronization error between the 5G base station and the GNSS satellite [9]. [PERSON] et al. keep the time alignment error in 5G base station transmit ports required for coordinated multipoint technology below 65 ns by evaluating mass-market GNSS receivers' relative time (phase) synchronization [10]. [PERSON] et al. proposed a 5G coding and carrier phase receiver incorporating a round-trip time (RTT) ranging method and an integrated 5G/GNSS positioning architecture. Experimental results showed that the integrated 5G/GNSS positioning improved the standalone GNSS solution's accuracy and availability. The proposed 5G RTT ranging method showed a maximum root mean square error (RMSE) of 1.109 m [11]. [PERSON] et al. developed a navigation method using 5G signals, which utilizes the downlink channel and proposes a structure that can generate 5G reference signals using software radio (SDR). The experimental results show that the standard deviation of the ranging error between SDR and accurate 5G signals is 1.19 m [12]. [PERSON] et al. proposed a combined BDS and 5G PPP observation model for the problem of the long convergence time of receivers in urban areas with severe satellite occlusion. The experimental results show that this technique can effectively accelerate convergence while improving positioning accuracy [13]. [PERSON] et al. proposed a multi-rate adaptive Kalman filter (MRAKF) for GNSS-5G hybrid positioning with a hybrid sequence fusion scheme, which can effectively solve the problems of considerable measurement noise uncertainty and degradation of positioning accuracy and system convergence in GNSS-5G hybrid systems due to the near-far effect of 5G base stations [14]. [PERSON] et al. proposed an algorithm to eliminate neighborhood differences and improve the time synchronization accuracy between 5G base stations. This effectively solved the problem of significant time synchronization errors caused by different visual satellite groups. The accuracy of time synchronization between 5G base stations is improved [15]. [PERSON] et al. conducted a study by deriving the Fisher information matrix (FIM) and subsequent lower bounds for position, rotation, and clock-offset errors for the 5G-GNSS combined positioning system, demonstrating that the 5G-GNSS combined positioning technique has significant advantages in terms of positioning accuracy, coverage, rotation estimation, and clock error estimation [16]. [PERSON] et al. proposed a simulation environment based on a ray-tracing (RT) channel model simulating global navigation satellite system (GNSS) signals, which was validated and extended to simulate 5G PRS and detection reference signals (SRS). The simulated 5G signals improved GNSS positioning accuracy with hybrid positioning methods, especially in complex channel conditions, such as typical industrial environments [17]. [PERSON] et al.