Jiaqi Li

Fifth-generation (5G) networks can be integrated with global navigation satellite systems (GNSS) to improve positioning accuracy in urban canyon environments, where GNSS signals are frequently degraded by limited satellite visibility and severe multipath effects. Although 5G positioning reference signals (PRS) provide higher update rates than GNSS, existing hybrid GNSS–5G approaches often fail to fully exploit this advantage. Moreover, most adaptive fusion methods involve high computational complexity, limiting their suitability for real-time applications in dynamic urban scenarios. To address these challenges, this work introduces a fuzzy logic–based hybrid GNSS–5G positioning scheme (FL-HPS). In the proposed method, a fuzzy logic controller dynamically selects between a standard extended Kalman filter (EKF) and an adaptive EKF, thereby ensuring both robustness and efficiency in position estimation. Simulation results across pedestrian, vehicular, and UAV scenarios demonstrate that FL-HPS consistently outperforms GNSS-only and conventional tightly coupled methods. In the UAV case, for instance, FL-HPS achieves a mean positioning error of 1.04 m and submeter-level accuracy in 48.13% of epochs, compared with only 0.94% for a tightly coupled EKF. Beyond accuracy improvements, FL-HPS delivers substantial computational gains. By operating at the position level and avoiding iterative raw measurement processing, it reduces average computation time to less than 0.00025 s per epoch—over 25 times faster than the tightly coupled approach.

Accurate positioning in urban canyon environments poses significant challenges owing to signal obstructions, multipath effects, and limited satellite visibility. This paper evaluates the performance of standalone Global Navigation Satellite System (GNSS) positioning in urban canyon environments over three scenarios such as pedestrian, vehicle, and unmanned aerial vehicle (UAV) for various altitudes and speeds. To improve the performance of the standalone GNSS in the urban canyon conditions, two hybrid positioning solutions such as GNSS with the Quasi-Zenith Satellite System (QZSS) and GNSS with 5G are proposed and their performances are investigated. Through extensive simulations, it is shown that higher speeds and lower receiver altitudes result in higher positioning errors for the standalone GNSS positioning. Additionally, in the UAV scenario, the hybrid solution with GNSS and QZSS demonstrates robust performance with 0% positioning failures and low positioning errors. Meanwhile, in the pedestrian and vehicle scenarios where satellite visibility is severely restricted, the hybrid solution with GNSS and 5G outperforms the hybrid solution with GNSS and QZSS, resulting in reduced positioning errors owing to the superior 5G terrestrial infrastructure.

Reformation of the 3.7–4.0 GHz band to expand 5G communication deployment poses a risk of 5G signals disrupting radar altimeter operation, leading to data loss or inaccuracies. Thus, this paper proposes a guard band protection method to facilitate the coexistence of 5G base stations and radar altimeters operating in the 4.2–4.4 GHz band. To enhance the adjacent channel leakage ratio (ACLR), we implemented spectral regrowth on an oversampled waveform using a high-power amplifier model, filtering out-of-band spectral emissions. The results demonstrated that a 150 MHz guard band enables coexistence, except in the case of the 16-by-16 antenna array in rural environments. Notably, for the 4-by-4 antenna array in urban environments, coexistence can be achieved using a 50 MHz guard band. The proposed mitigation techniques may also be extended to promote coexistence between non-terrestrial networks and 5G communication systems, including satellites, unmanned aerial vehicles, and hot air balloons.

With the growing demand for 5G, many countries have allocated additional spectrum above 3.7 GHz, potentially causing serious interference to radar altimeters operating at the 4.2–4.4 GHz band. Therefore, in this paper, a general mathematical model is proposed to estimate interference from 5G base stations to a radar altimeter which can be applied for aircraft, helicopters, unmanned aerial vehicles, satellites, and hot-air balloons. Using this model, the interference at the radar altimeter is evaluated in several environments such as rural, suburban, and urban. Two types of antennas are considered which are omnidirectional and multiple-input and multiple-output antennas (4-by-4, 8-by-8, and 16-by-16). Additionally, a power control-aided distance protection method is introduced to enable the coexistence of the 5G base stations and the radar altimeters. To validate the effectiveness of the proposed protection method, the Monte Carlo method is employed. Numerical results present the heights of the radar altimeter where the interference-to-noise ratio becomes higher than the interference protection threshold of -6 dB. In addition, it shows that the radar altimeter can coexist with the 5G base stations with the proposed protection scheme.

In this paper, we extend a Mixed Integer Linear Programming (MILP) model to study the impact of renewable energy available represented by wind farms and solar power on the data centre over a multi-layer distributed IoT architecture supported by fog and cloud. The results show that compared to the traditional two-layer architecture in an un-capacitated design approach, energy savings in renewable energy are up to 60% with high workload services. Compared to the data centre powered by non-renewable energy, the model can achieve up to 30% energy savings.

For the problem of geomagnetic navigation in the case of autonomous underwater vehicle (AUV) without a prior geomagnetic information, this paper proposes a geomagnetic navigation algorithm based on deep reinforcement learning. Using the correlation between the geomagnetic parameters and the navigation trajectory, the Deep Q Network is used for choosing the navigation direction by multiple geomagnetic components, and the relative position is estimated by the difference between the geomagnetic parameters of the current position and the target position. Under the influence of the above navigation model, the multiple geomagnetic component gradually approaches the target value with the movement of the aircraft, thereby achieving the navigation purpose. By comparing with the evolutionary algorithm (EA), it is proved that the DQN algorithm have better convergence ability.