The evolution toward 5G-Advanced and 6G networks necessitates high-accuracy indoor positioning for applications such as Internet of Things (IoT) and digital twins. However, existing technologies like ultrawideband (UWB) and Wi-Fi face challenges in cost, coverage, and accuracy, especially in multi-path environments. This paper proposes a novel framework for sub-meter-level indoor positioning using 5G picocell base stations (gNBs). First, a Semidefinite Programming-based Joint Array Calibration (SDP-JAC) algorithm mitigates gain-phase errors in compact arrays by reformulating array calibration as a covariance matrix structure-constrained optimization which is solved via semidefinite programming to avoid local optima. Second, a dual-stage phase compensation scheme integrates noise subspace projection with adaptive regularization to achieve globally optimal phase error estimates for multipath suppression. Finally, a Multi-Base-Station Weighted Fusion Localization (MBS-WFL) framework enhances positioning accuracy by employing a hierarchical scoring strategy to adaptively select the optimal gNB subset. Simulations demonstrate that the proposed method achieves a 90% cumulative probability of positioning errors below 1 meter at SNRs ≥ 0 dB, outperforming conventional algorithms and complying with 3GPP R17 standards. This work provides a deployable, cost-effective solution for 5G-Advanced integrated sensing and communication (ISAC) networks without requiring dedicated hardware.

In this paper, we investigate the near-field wideband terahertz (THz) massive multi-input multi-output (MIMO) integrated sensing and communication (ISAC) systems. Specifically, we propose an energy-efficient serial true-time-delay (TTD)-based reconfigurable antenna architecture for the dual-function base station (DFBS). This architecture enables DFBS to switch between modular and compact MIMO configurations by dynamically controlling the activation of antenna subarrays, corresponding to the sensing and communication stages, respectively. During the sensing stage, a modular MIMO architecture is deployed by deactivating several subarrays and all TTDs. This configuration utilizes fewer antennas to achieve a larger array aperture with enhanced energy efficiency. Moreover, we take advantage of beam squint effects at both the main and grating lobes to extend sensing range and enable rapid user sensing. During the communication stage, a compact MIMO configuration is employed by activating all antennas, and the TTD network is activated to mitigate the near-field beam squint effect. Based on the channel state information obtained during the sensing stage, we formulate a joint optimization problem of the hybrid analog/digital beamforming and TTD network time delays to maximize the system sum rate. To solve it, we first design analog beamforming and time delays through a serial-delay approach, followed by an alternating iterative optimization for digital beamforming. In addition, our proposed scheme accounts for the finite resolution and limited delay range of TTDs. Simulation results demonstrate the effectiveness of our designed antenna architecture and optimization scheme.

Robust power line detection from aerial images is an essential step for intelligent unmanned aerial vehicle (UAV) inspection. However, compared to regular object detection tasks, the generalization ability and accuracy of current methods are seriously insufficient due to the variability of power line scenes and the low proportion of power lines in the image. In this letter, we propose an object-based Markov random field with a discrete multineighborhood system (OMRF-DMNS) model, which can provide a solution for tackling power line detection challenges. First, the proposed method constructs a discrete multineighborhood system (DMNS) for the noncontact nodes. This not only greatly improves the antinoise ability of the model, but also lays the foundation for the transfer of context information among nodes under different receptive fields. Second, a multilevel logical model with multineighborhood joint reasoning (MLL-MNJR) is defined on the DMNS, which makes full use of the line features and location information to achieve semantic inference of nodes. Therefore, the OMRF-DMNS model can fully mine the multidimensional feature associations and context information among nodes, enabling it to effectively distinguish between power line segments and interference noises. Experimental results demonstrate that the proposed method significantly outperforms previous methods in different power line scenes.

Massive machine-type communications (mMTC) is a new key feature of 5G cellular and is expected to be further improved in future evolutions of the cellular standards. Data collection from machine-type communication devices (MTCDs), which can be achieved by various approaches, is important to operation of mMTC networks. This work studies data collection for mMTC networks enabled by unmanned aerial vehicle (UAV) stations moving in the air. Consider the limitation in battery lifetime at both the MTCDs and the UAV station, the UAV trajectory design problem is investigated from an energy efficiency perspective. In a generalized model where the target MTCDs are grouped into multiple clusters, the UAV station travels across the clusters and collect data from each cluster while hovering above the cluster. The corresponding MTCD clustering strategy, UAV hovering strategy and UAV flying strategy all have impacts on the energy consumption of the system, which results in a strongly coupled energy minimization problem that is difficult to solve. The sub-problems obtained through decomposition are decoupled in the proposed solution approach. Clustering of the MTCDs is done by a greedy learning clustering (GLC) algorithm. A novel modeling technique based on the idea of artificial energy map (AEM) is proposed to find the optimal hovering position within a cluster. The flying strategy that minimizes the energy consumption is equivalently transformed into a classic travelling salesman problem that is readily solved by the genetic algorithm (GA). Through alternating iterative optimization of the clustering and hovering strategies, the communication energy consumption and the UAV hovering energy consumption are monotonically decreasing until convergence.

Differential spatial modulation (DSM) is a novel multiple-input-multiple-output (MIMO) transmission technology that uses the transmit antenna index matrices to carry part of the information in non-coherent communication scenarios. However, when high-order modulation should be used for spectral efficiency considerations, the conventional use of constant modulus Phase Shift Keying (PSK) constellations in DSM would result in significant performance loss, whereas using non-constant modulus constellations such as Quadrature Amplitude Modulation (QAM) in DSM is very challenging because the peak signal power may grow without bound through the differential iterative processing. In this work, a generalized transmission scheme that uses non-constant modulus constellations for DSM signaling is investigated. By normalizing the power of all symbols in the previous transmit matrix when performing differential transmission, the Precoding Normalized DSM (PN-DSM) scheme which can adopt non-constant modulus constellations is proposed. In addition to the legacy QAM, another two non-constant modulus constellations in the literature, Amplitude Phase Shift Keying (APSK) and star-QAM, are considered for PN-DSM transmissions. It is revealed by numerical studies that, compared with the legacy QAM, APSK and star-QAM are more robust to the error propagation issue of the proposed PN-DSM scheme. In addition, because of the increased minimum distance in the constellation, the star-QAM slightly outperforms the APSK in terms of the average error rate performance.

Spatial modulation orthogonal frequency division multiplexing with subcarrier index modulation (ISM-OFDM) is a novel OFDM technique that conveys additional information bits by active antenna indices and grouped subcarrier indices. However, it takes a huge amount of resources to acquire the channel state information (CSI) at the receiver. In this paper, we propose two differential spatial frequency modulation with orthogonal frequency division multiplexing (DSFM-OFDM) systems, termed DSFM-OFDM-single (DSFM-OFDM-S) and DSFM-OFDM- multiple (DSFM-OFDM-M) over space-time-frequency, which completely avoid the need for CSI at the transmitter and receiver. In DSFM-OFDM, the transmitted symbols are modulated through ordinary signal modulation. And then part of some additional information bits are transmitted by selecting a permutation of the active antennas, whereas another part of it are employed to determine the subcarrier index matrices. Simulation results show that at low spectral efficiency, the performance of the proposed two schemes are better than differential spatial modulation (DSM) with the high SNR. Besides, the performance degradation of DSFM-OFDM-M and DSFM- OFDM-S over ISM-OFDM can be less than 6dB. And that the loss decreases as the number of receive antennas decreases.

At present, APD (Avalanche Photo Diode) arrays LIDAR (Light Detection and Ranging) has been broadly accepted as an important means to obtain 3D (Three Dimensional) data. A new method of a scalable adaptive N × N channel communication system is introduced in the paper, which is aimed at improving the transmission rate of APD array LIDAR data. This paper presents the research on multi-channel communication system based on FPGA (Field Programmable Gate Arrays) configuration, which can be realized multi-channel parallel data independent receiving and transmitting. The multi-channel transmission system not only improves the data bandwidth, but also improve the load capacity of the system. The feasibility of multi-channel communication is verified sufficiently in the paper.

The traditional ortho-rectification technique for remotely sensed imagery, which is performed on the basis of ground image processing platform, has been unable to meet the timeliness requirements. To solve this problem, this paper presents the research on ortho-rectification technique based on field programmable gate array (FPGA) platform, which can be implemented onboard and spaceborne for a real-time processing. Through comparing the correction accuracy and the consuming time of traditional ortho-rectification method with the proposed method based on FPGA, it is demonstrated that the proposed FPGA-based onboard orthorectification has great advantage over improving the image ortho-rectification speed and can reach the requirements of correction accuracy.

The lithosphere is the most stored reservoir of carbon on Earth. The study of the effects of rock weathering and in the atmosphere provides an important basis for accurate prediction of CO2in the atmosphere, which is essential for predicting global climate change. One of the main factors that has been neglected in the global carbon cycle study of carbonate weathering carbon sinks is the rate problem. In this paper, some published data were used to study the influence factors of karst carbon sink and the establishment of corrosion velocity model. Based on the data of 18 corrosion test sites in China, a corrosion rate model was fitted, and then the data predicted by the model were compared with the data provided paper of Liu Rui, which are close to the measured data. The validated model can provide a research method for the carbonate certification rate in different regions, and solve the problems that cannot be tested because of time and space constraints. At last, net sink of CO2in the atmosphere is calculated as 4.77×1014g·a−1(or 130 million tC·a−1) in the global surface carbonate certification, which is close to the datameasured by Liu Zaihua and Ichikuni.