Visible-light communication (VLC) is a promising method for indoor positioning. The received signal strength algorithm is the most widely used localization algorithm in visible-light positioning (VLP) systems. However, in a VLP system, the photodiode (PD) will have a small rotation angle during movement, which will result in a massive positioning error ignoring the angle. In this study, a three-dimensional (3D) indoor VLP system using an improved whale optimization algorithm (IWOA) is proposed to reduce the error caused by the PD rotation. Firstly, the model of the VLC system with the PD rotation angles is introduced. Secondly, a novel IWOA with an elite opposition-based learning strategy and Lévy flight strategy is proposed. The convergence speed and accuracy of the WOA are improved. Lastly, the IWOA algorithm is efficiently utilized to address the problem with the PD rotation in the indoor VLP system. Simulation results show that the average error of 3D positioning is 2.14 cm with no PD rotation. When the PD has a rotation angle, the average positioning error estimated by ignoring the rotation angle is 27.14 cm, while that estimated by considering the rotation angle is 7.85 cm. In the VLP system, the positioning error with the PD rotation angle is effectively reduced by the proposed algorithm, which can be applied in a variety of indoor location scenes.

Existing visible light positioning (VLP) algorithms typically require multiple LEDs to achieve high-precision positioning. However, in practice, the number of available LEDs is influenced by various factors such as the layout of LEDs and the field-of-view of the receiver, and these factors can significantly affect the coverage of a VLP algorithm. To address this issue, this paper proposes an inertial sensor-assisted single view geometry (ISA-SVG) algorithm, which can achieve accurate positioning as little as one LED. In particular, we first establish the projection transformation model between the actual point and its projection on the image plane. Then, a pose fusion model that effectively fuses the inertial sensor information and VLP information is established. Finally, the position of the receiver based on the pose fusion model is estimated. IAS-SVG significantly improves the usability and coverage performance of visible light positioning algorithms. Simulation results show that the coverage rate of the ISA-SVG algorithm is over 95%. Experimental results show that the proposed method can achieve an average positioning error as low as 4.70 cm.

Integrated visible light positioning and communication (VLPC) has been proposed to achieve both a high communication rate and accurate positioning. However, to integrate the visible light positioning (VLP) function, existing VLPC works typically suffer from a certain visible light communication (VLC) performance degradation. To address this issue, we propose an adaptive direct current-biased optical OFDM (ADCO-OFDM) scheme. In particular, we first design a novel waveform for ADCO-OFDM, which modulates the direct current (DC) bias of the VLC DCO-OFDM signal for positioning. Considering a practical system model with multi-LED and single photodetector (PD), the achievable rate of VLC and the Cramer-Rao Lower Bound (CRLB) for VLP are derived to analyze VLPC performance. Furthermore, to avoid the reverse effects of occlusions and obstacles, a joint LED selection and waveform design (J-LW) algorithm is proposed to optimize the VLPC performance. Specifically, the J-LW algorithm selects the most suitable LEDs for the VLPC system. Then, based on the selected LED subsets, a waveform optimization problem is formulated to simultaneously optimize communication and positioning precision. To solve the optimization problem, we prove the convexity of the optimization problem and solve it using convex optimization tools. Experiment results show that the proposed ADCO-OFDM can achieve up to 50% higher achievable rate and 72% lower CRLB values at most compared with the baseline models when the SNR is 22 dB.

The performance of multiple-input single-output (MISO) transmission is highly dependent on the accuracy of the channel state information (CSI) at the base station (BS), which necessitates precise CSI estimation and reliable feedback from the user equipment. However, the overhead of the CSI feedback occupies substantial uplink bandwidth resources. To alleviate the overhead, this paper proposes a novel task-based quantizer for uplink MISO visible light communication (VLC) systems. In particular, a hybrid radio frequency (RF)/VLC system is considered, where VLC links are mainly used for large-volume downlink transmissions and RF links are used for uplink CSI feedback. Since the RF bandwidth resources are limited, the CSI is quantified to reduce the uplink resource requirements, which, however, inevitably causes CSI estimation errors at the BS. To guarantee the CSI estimation accuracy while minimizing the RF resource cost, a task-based quantization scheme for channel estimation (TQ-CE) is proposed. In the TQ-CE, both the quantized codebook and the post-processing matrix are optimized to minimize the mean square error (MSE) of the channel estimation. Taking the minimum MSE as the target task, the TQ-CE leverages vector quantization (VQ) to generate a codebook, which is designed to reduce the feedback overhead without compromising the precision of the channel estimation. Then, an optimal closed-form solution of the post-processing matrix is derived based on the minimum mean square error (MMSE) criterion. The simulation results demonstrate that the proposed TQ-CE achieved $0.25\mathrm{Mbit}/\mathrm{s}$ and $0.62\mathrm{Mbit}/\mathrm{s}$ higher data rates compared with the conventional scalar quantization-based channel estimation (SQ-CE) schemes and vector quantization-based channel estimation (VQ-CE) schemes, respectively. Moreover, in terms of the feedback overhead, compared with the 18-bit SQ-CE, the 4-bit TQ-CE achieved a 22.2% reduction in uplink bits.

To solve the problem that plantar point cloud cannot be directly obtained in the digital twin foot model building system which simultaneously collect the 3D point cloud of foot and plantar pressure distribution data, a plantar point cloud repair method based on plantar pressure distribution data is proposed in this paper. First of all, the contour points of the foot point cloud and the plantar pressure distribution data are extracted, and then the two contours are matched by searching the optimal rotation and translation transformation, secondly, the plantar pressure distribution data is smoothed by using the mean and gaussian filters, then on the basis of the contour of the foot point cloud clipped the plantar pressure distribution data, finally, the plantar point cloud is optimized by constructing a mapping function to convert plantar pressure data into the height value of the plantar point cloud. Experimental results show that the proposed method is significantly better than the planar repair method.