Indoor positioning system based on visible light communication and location fingerprinting can achieve cm-level positioning accuracy, which has become an important candidate for high precision positioning. However, in the case of non-uniform and low-density location fingerprinting distribution, it will still lead to large errors and poor robustness. To solve this problem, an accurate visible light positioning (VLP) method based on location fingerprinting and meta-heuristic is proposed, and all possible cases of uneven distribution of location fingerprinting are considered by introducing the random selection rate. The effectiveness of the proposed VLP method is compared with other six VLP methods based on location fingerprinting. Simulation results show that when the signal-to-noise ratio is 20 dB, the average positioning error of the proposed VLP method is 0.51 cm, which is at least 80.23% lower than the other six VLP methods. Experimental results show that when the random selection rate is 50%, the average positioning error of the proposed VLP method is 3.11 cm, which is at least 41.87% lower than the other six VLP methods. The proposed VLP method can be applied to positioning scenarios with different location fingerprinting distributions. The research results can provide a new research idea for the fingerprinting weight calculation method.

TeraHertz} (THz) transmission technologies constitute a promising candidate for supporting ultra-broadband short-range next generation communications. Hence, we analyse the performance of unmanned aerial vehicle (UAV) in the THz networks. The coverage probability is derived as well as the area spectral efficiency (ASE) and a pair of Line-of-sight (LoS)/non-line-of-sight (NLoS) probability models, namely the macrocell LoS/NLoS probability model and picocell LoS/NLoS probability model are adopted. Furthermore, the lower-bound of the network performance are derived via homogeneous Poisson point process (HPPP) analysis, as well as the upper-bound. The simulation results match the analytical results well, which show that the coverage probability of the network first increases upon increasing the THz UAV BS density, and then decreases beyond the maximum. Given the severe path loss experienced by THz signals, a higher UAV density is required for a certain coverage probability than at lower carrier frequencies.

Terahertz (THz) systems have the benefit of high bandwidth and hence are capable of supporting ultra-high data rates, albeit at the cost of high pathloss. Hence they tend to harness high-gain beamforming. Therefore a joint hybrid 3D beamformer relying on sophisticated sensor-based beam training and channel estimation is proposed for Reconfigurable Intelligent Surface (RIS) aided THz Multi-user Massive Multiple Input Multiple Output (MIMO) systems. A novel joint subarray based THz base station (BS) architecture and the corresponding sub-RIS is proposed. The BS, RIS and receiver antenna arrays of the users are all Uniform Planar Arrays (UPAs). Moreover, the conditions of maintaining the orthogonality of the proposed joint architecture are derived in support of spatial multiplexing. The closed-form expressions of the near-field and far-field path-loss are also derived. The Ultra-wideband (UWB) sensors are integrated into the RIS and the user location information obtained by the UWB sensors is exploited for channel estimation. The optimal active and passive beamforming schemes are also derived. Moreover, Precise Beamforming Algorithm (PBA) for joint RIS phase shift and user equipment (UE) receiver beamforming is proposed, which further improves the beamforming accuracy by circumventing the performance limitations imposed by positioning errors. Our simulation results show that the proposed system significantly improves the spectral efficiency, despite its low complexity. Compared to the scheme operating without PBA, our proposed scheme increases the spectral efficiency on average by 10.41%, 10.17%, and 5.19% for the three farfield configurations, and by 5.05% and 3.95% for the two nearfield configurations, respectively. This makes our solution eminently suitable for delay-sensitive applications.

The weighted K-nearest neighbor (WKNN) algorithm is a commonly used fingerprint positioning, the difficulty of which lies in how to optimize the value of K to obtain the minimum positioning error. In this paper, we propose an adaptive residual weighted K-nearest neighbor (ARWKNN) fingerprint positioning algorithm based on visible light communication. Firstly, the target matches the fingerprints according to the received signal strength indication (RSSI) vector. Secondly, K is a dynamic value according to the matched RSSI residual. Simulation results show the ARWKNN algorithm presents a reduced average positioning error when compared with random forest (81.82%), extreme learning machine (83.93%), artificial neural network (86.06%), grid-independent least square (60.15%), self-adaptive WKNN (43.84%), WKNN (47.81%), and KNN (73.36%). These results were obtained when the signal-to-noise ratio was set to 20 dB, and Manhattan distance was used in a two-dimensional (2-D) space. The ARWKNN algorithm based on Clark distance and minimum maximum distance metrics produces the minimum average positioning error in 2-D and 3-D, respectively. Compared with self-adaptive WKNN (SAWKNN), WKNN and KNN algorithms, the ARWKNN algorithm achieves a significant reduction in the average positioning error while maintaining similar algorithm complexity.

Visible light positioning has become a new research hotspot in recent years. In this study, a new Gaussian model of hybrid noise and multipath reflection is established for noise interference in line‐of‐sight (LOS) communication and multipath reflection in non‐LOS (NLOS) communication. First, the likelihood function of the ranging error is established according to the Gaussian hybrid model, and the analytical expression of Cramer–Rao lower bound is derived. Second, the novel adaptive parameter particle swarm optimisation (AP‐PSO) algorithm is proposed to calculate the three‐dimensional (3D) coordinates of the target. In order to avoid falling into the local optimal solution, a novel particle adaptive mutation algorithm, namely AP‐PSO‐M algorithm is also proposed. Simulation results show that if the NLOS propagation probability is 0.3 and the swarm size is 20, compared with Newton–Raphson (NR), particle swarm optimisation (PSO), dissipative PSO (DPSO) and simulated annealing PSO (SA‐PSO) algorithms, the average positioning error based on AP‐PSO‐M algorithm can be reduced by 45.43, 38.98, 25.37 and 26.42%, respectively. If similar positioning accuracy is obtained, compared with PSO, DPSO and SA‐PSO algorithms, the average calculation time required by the AP‐PSO‐M algorithm can be reduced by 48.16, 30.24 and 26.14%, respectively.

This article proposes an indoor cooperative positioning system for a swarm of micro unmanned aerial vehicles (UAVs) based on visible light communication. The proposed cooperative positioning system is composed of light-emitting diodes (LEDs) installed on the ceiling and a group of micro UAVs. Each micro UAV is composed of a single LED and multiple photodiodes (PDs) with a uniform distribution. The cooperative positioning of a group of micro UAVs is achieved through the LEDs installed on the ceiling and the interactive positioning information shared among them. In order to solve the nonconvex optimization function constructed by cooperative localization, an improved bat algorithm based on adaptive parameters was designed. In the proposed cooperative localization scenario, the theoretical lower bound of the localization error, i.e., the Cramer–Rao lower bound, is precisely analyzed by mathematical method. Simulation and experimental results demonstrate that compared with the single PD receiving technology, the multi-PD receiving technology can significantly improve the positioning accuracy, while the cooperative positioning technology can significantly improve the positioning accuracy compared with the noncooperative positioning technology.