High-speed Scenario Research Articles

Lowering the PAPR of the optical OTFS-based 6G radio with a hybrid PTS-PSO genetic approach

Orthogonal Time Frequency Space (OTFS) is regarded as one of the best contenders for sixth-generation (6G) radio systems due to its ability to obtain excellent performance in high-speed scenarios. Peak-to-average power ratio (PAPR) significantly impacts and degrades the efficiency of the power amplifier used in OTFS-based 6G systems. The proposed article presents a genetic hybrid algorithm, Partial transmission sequence-particle swarm optimization (PTS-PSO), obtaining an optimal PAPR performance with low computation complexity. The PSO generates the best phase factor compared to the conventional phase generation method of PTS, making PTS-PSO more effective. The impacts such as PAPR, bit error rate (BER), power spectral density (PSD), and complexity of the proposed PTS-PSO are compared with the conventional selective mapping (SLM) and PTS methods for 64, 256, and 512 OTFS sub-carriers. The projected PTS-PSO attained a PAPR and PSD gain of 1–6 dB and − 540 while preserving the BER performance. The experimental results demonstrate that the projected PTS-PSO outperforms the conventional SLM and PTS algorithms with trivial intricacy.

Enhancing RODNet detection in complex road environments based on ESM and ISM methods

In autonomous driving, accurately identifying traffic targets is crucial for ensuring the safe and reliable operation of autonomous vehicles. Millimeter-wave radar, known for its low cost, long detection range, and excellent performance under various weather conditions. Deep learning algorithms, particularly the radar object detection network (RODNet), have been effectively applied to radar target detection by analyzing the range-azimuth (RA) heatmaps that capture complex target features. However, the low angular resolution of radar RA heatmaps, combined with the high sensitivity of millimeter-wave radar to metal objects, makes adjacent targets prone to misdetection and increases the likelihood of misclassification of target types due to metal reflections from road obstacles. To address these issues, this paper proposes an innovative extension suppression method to enhance RA heatmaps, reducing interference between adjacent targets and significantly improving target resolution. Additionally, the paper incorporates Gaussian filtering, peak detection, and amplitude suppression algorithms to design an interference suppression method, accurately identifying and mitigating strong reflections from non-target regions, thereby improving detection efficiency in complex environments. The effectiveness and superiority of these methods have been fully validated, with AP improvements of 18% in overlapping scenarios, 2% in metal obstacle scenarios, and around 10% in high-speed scenarios compared to the latest methods.

Towards sustainable high-speed cruising: Optimizing energy efficiency of plug-in hybrid electric vehicle via intelligent pulse-and-glide strategy

Conventional pulse-and-glide (PnG) strategies expose issues including poor coordination with energy management systems (EMSs), discomfort and low levels of automation, this paper innovatively addresses these challenges by the comprehensive considerations of energy economy, dynamic performance, and ride comfort, proposing an intelligent pulse-and-glide (I-PnG) strategy tailored for plug-in hybrid electric vehicle (PHEV) configurations. The I-PnG strategy is aimed at optimizing economic cruising in high-speed scenarios; however, it demonstrates applicability across diverse conditions without deterioration in performance. I-PnG is a data-driven autonomous driving solution developed through a deep Q network (DQN), featuring two distinct modes: ECO and SPORT, the former prioritizes energy economy, while the latter emphasizes dynamic performance enhancement. The results indicate that our proposed strategy significantly optimizes energy management compared to the baseline methods. Specifically, during variable-speed cruising, I-PnG ECO achieves a maximum energy efficiency improvement of 16.13 % and a cost reduction of 7.74 %, while I-PnG SPORT exhibits a maximum energy efficiency improvement of 13.37 % and a cost reduction of 3.92 %. Under the Highway Fuel Economy Test (HWFET), I-PnG ECO and I-PnG SPORT further improve energy efficiency by up to 22.94 % and 19.04 %, respectively, with corresponding cost savings of 18.47 % and 13.35 %.

Research on Obstacle Avoidance Strategy of Automated Heavy Vehicle Platoon in High-Speed Scenarios

Amidst the rapid progression of the road transport industry, the safety and efficiency of heavy-vehicle platoons have garnered significant attention. The study tackles the challenge of obstacle avoidance presented by vehicles owing to their considerable mass, delayed response times, and line-of-sight impediments, by introducing a cooperative obstacle avoidance system for heavy-vehicle platoons based on deep reinforcement learning. The system comprises three primary modules: perception, decision-making, and control. Initially, the perception module acquires real-time environmental data. Subsequently, the decision-making module formulates obstacle avoidance decisions based on the acquired data. Specifically, it implements a two-stage braking obstacle avoidance strategy under low collision risk scenarios, while employing a fifth-degree polynomial for planning and tracking obstacle avoidance paths under high collision risk conditions suitable for steering maneuvers. The control module utilizes the local multi-agent deep deterministic policy gradient (LADDPG) algorithm to train the heavy-vehicle platoon agents, ensuring the formation’s maintenance while mitigating collisions with other vehicles and obstacles. The effectiveness of the proposed system is substantiated through simulation experiments, demonstrating its adaptability to various traffic conditions, selection of suitable obstacle avoidance strategies, and significant enhancement of obstacle avoidance performance and heavy-vehicle platoon stability.

Semantic-empowered visible light communication for image transmission based on deep convolutional generative adversarial network

In this paper, a semantic communication-based scheme was proposed to tackle the optimization challenge of transmission efficiency and link stability in indoor visible light communication (VLC) systems utilizing light-emitting diodes for image transmission. The semantic model, established by deep convolutional generative adversarial network (DCGAN) and vector quantization method, can effectively extract the essential characteristics of images. In addition, indoor VLC channel models including line-of-sight (LOS) and non-line-of-sight (NLOS) links are established in a 5*5*3 room, while incorporating noise interference encountered during signal transmission into the training process of the semantic model to enhance its anti-interference capability. Besides, the performance disparity between the semantic VLC system and the conventional VLC system using Better Portable Graphics (BPG) is assessed across various modulation formats, transmission distances, and receiving locations. The simulation results demonstrate that the semantic VLC system effectively enhances link stability and achieves a signal-to-noise ratio (SNR) gain exceeding 6 dB. Additionally, the semantic VLC system achieves superior performance compared to the conventional VLC system employing convolutional code at a rate of 1/2, without incurring additional bits consumption for error correction. Moreover, the practicality of the semantic VLC system is experimentally validated on an indoor physical testbed online enabled by the field programmable gate array (FPGA) with up to 1 Gbps data rate. In comparison with existing schemes, the semantic VLC system effectively reduces communication overhead by 40% while maintaining similar received image quality. Furthermore, it significantly enlarges the communication coverage of the VLC system by a factor of two without necessitating any hardware modifications and ensures stable transmission of images in high-speed scenarios.

Waveform Design for the Integrated Sensing, Communication, and Simultaneous Wireless Information and Power Transfer System.

Next-generation communication systems demand the integration of sensing, communication, and power transfer (PT) capabilities, requiring high spectral efficiency, energy efficiency, and low cost while also necessitating robustness in high-speed scenarios. Integrated sensing and communication systems (ISACSs) exhibit the ability to simultaneously perform communication and sensing tasks using a single RF signal, while simultaneous wireless information and power transfer (SWIPT) systems can handle simultaneous information and energy transmission, and orthogonal time frequency space (OTFS) signals are adept at handling high Doppler scenarios. Combining the advantages of these three technologies, a novel cyclic prefix (CP) OTFS-based integrated simultaneous wireless sensing, communication, and power transfer system (ISWSCPTS) framework is proposed in this work. Within the ISWSCPTS, the CP-OTFS matched filter (MF)-based target detection and parameter estimation (MF-TDaPE) algorithm is proposed to endow the system with sensing capabilities. To enhance the system's sensing capability, a waveform design algorithm based on CP-OTFS ambiguity function shaping (AFS) is proposed, which is solved by an iterative method. Furthermore, to maximize the system's sensing performance under communication and PT quality of service (QoS) constraints, a semidefinite relaxation (SDR) beamforming design (SDR-BD) algorithm is proposed, which is solved using through the SDR technique. The simulation results demonstrate that the ISWSCPTS exhibits stronger parameter estimation performance in high-speed scenarios compared to orthogonal frequency division multiplexing (OFDM), the waveform designed by CP-OTFS AFS demonstrates superior interference resilience, and the beamforming designed by SDR-BD strikes a balance in the overall performance of the ISWSCPTS.

The effect of latency, speed and task on remote operation of vehicles

IntroductionAlthough self-driving vehicle technology has been developing rapidly in recent years, there are still many challenges left before full autonomy can possibly be achieved. Remote operation could facilitate the development of autonomous vehicles in a safe and efficient manner by putting a human in the loop without the need for the human to be physically present in the vehicle. In the current experiment, three aspects of remote driving have been investigated in a driving simulator to evaluate the effect of i) latency, ii) type of task to perform, and iii) speed on a number of performance measures. MethodThirty-one participants drove in simulated rural (high-speed) and urban (low-speed) scenarios. Five hazards were created for each scenario and the participants drove each scenario three times with different latencies (baseline, +100 ms, and +200 ms). The latency condition was masked for the participants. The hazards were designed with the intention of creating challenging traffic situations. For example, in hazard one (H1) a car parked next to the road activates their turn indicators and then cuts into the participant’s lane close in front of the ego vehicle, forcing the participant to either brake or veer. Latency, type of hazard, and scenarios (high- and low-speed) were all within participants’ variables. Objective simulator data collected included variables such as reaction time, post-encroachment time, speed variation, distance to hazard, collisions, etc. Subjective data was gathered through questionnaires between each of the balanced latency conditions to assess trust, perceived control, realism of scenarios, and workload etc. After the completed drive, participants were asked to rate in which order they believed they had been subjected to the different latencies. The participants were divided into two groups, experienced drivers and experienced gamers. ResultThe results of the simulator study show that for some of the hazards, but not all, there were significant differences in the latency conditions and there were interaction effects between participant groups and environment/speed. For example, in H1 the effect on the reaction time was significantly larger than the added latency. Overall, the experienced gamers drove with larger safety margins although they had not been told that the latency was varied. Speed, latency, and group characteristics were interacting in significant ways and affected performance measures. The subjective ratings show that participants experienced less control of the vehicle during higher latency conditions, even though they were not told in which order they had been subjected to the latency conditions. The separate tasks to perform were affected differently by the independent measures. The number of collisions was not affected by latency. ConclusionThere seems to be a certain level of adaptivity among the participants, although they were not told that the latency varied between scenarios, and they could also not guess in which order they drove with the different conditions. In some situations, they drove with larger safety margins, especially experienced gamers in the high-speed scenario. Moreover, the subjective ratings show that participants felt less in control of the vehicle at higher latencies without being able to pinpoint what it was that affected their driving.

Optimized DM-RS Configuration for Improved 5G New Radio Network Capacity and Performance

Network load in mobile networks is continuously growing, putting pressure on mobile operators to deliver target network performance and user experience. This paper focuses on network capacity improvement through increased spectral efficiency, which can be achieved with overhead reduction by optimizing the demodulation reference signal (DM-RS) configuration, which is one of the channels/reference signals with the largest contribution to overhead. In a high-speed scenario, the Doppler effect noticeably influences the temporal nature of the channel such that a channel interpolation process is required within a slot. However, increasing the number of symbols used for DM-RS has a negative impact on capacity. The Doppler effect was analyzed for various 5G NR configurations of operating frequency and subcarrier spacing (SCS), and various use cases were considered using user equipment (UE) speed as the main parameter. For suitable use cases, the DM-RS configuration was optimized in networks with live traffic. The impact of the configuration change on 5G/NR spectral efficiency, user experience and link adaptation performance was assessed through a deep-dive analysis of active measurements, available performance Management (PM) counters and key performance indicators. An optimized DM-RS configuration is proposed, and it is demonstrated to achieve gains of 5–15%, depending on the metric used, use case analyzed, network load, traffic mix and other relevant network characteristics such as topology and clutter type.

High-Speed Surface Property Recognition with a 140 GHz Frequency

In the field of integrated sensing and communication, there is a growing need for advanced environmental perception. The terahertz (THz) frequency band, significant for ultra-high-speed data connections, shows promise in environmental sensing, particularly in detecting surface textures crucial for autonomous systems’ decision-making. However, traditional numerical methods for parameter estimation in these environments struggle with accuracy, speed, and stability, especially in high-speed scenarios like vehicle-to-everything communications. This study introduces a deep learning approach for identifying surface roughness using a 140-GHz setup tailored for such conditions. A high-speed data acquisition system was developed to mimic real-world scenarios, and a diverse set of rough surface samples was prepared for realistic high-speed datasets to train the models. The model was trained and validated in three challenging scenarios: random occlusions, sparse data, and narrow-angle observations. The results demonstrate the method’s effectiveness in high-speed conditions, suggesting terahertz frequencies’ potential in future sensing and communication applications.

Analysis of mobility robustness optimization in ultra-dense heterogeneous networks

Stable and reliable wireless connections during the user equipment's mobility are the critical issue in future mobile communication networks especially during UE's high-speed scenarios over dense heterogeneous networks (HetNets). Thus, setting an accurate value for the handover control parameters (HCPs) (i.e., time-to-trigger (TTT) and handover margin (HOM)) at different speed scenarios is required for system performance. This study proposes six different systems and each one employs different HCP settings using various mobile speed scenarios over a HetNet. Several performance metrics (i.e., received signal reference power (RSRP), handover ping-pong (HOPP), radio link failure (RLF), handover probability (HOP), handover interruption time (HOIT), and handover failure (HOF)) were used as key performance indicators (KPIs) for systems evaluations. Furthermore, 20 users were evaluated in this study using a 40 ms measurement interval. The results show a different impacts of each system on the performance of the deployed HetNet. However, System 1 (lowest TTT & moderate HOM) was the best system in term of RSRP and RLF but on the expense of HOPP. Furthermore, assigning high level of the TTT and HOM (i.e., TTT >1000 ms and HOM >8 dB) leads to 0 HOPP but high RLFs. Moreover, the best system performance in term of HOP, HOIT, and HOF were System 6 (highest TTT & high HOM) with 0.06 %, 3 %, and 0.004 %, respectively. System 4 represents the best trading off system between HOPP and RLF due to the proper configuration of the HCPs. However, the aims of evaluating several systems based on fixed HCPs over several mobile speed scenarios were to see the behaviors of the systems performance when different setting values are applied. Furthermore, this study proposes a HO self-optimization algorithm for auto-tuning the HCPs. Weighted function algorithm along with a trigger timer is used for reducing unnecessary HOs while RLF minimized by setting a trigger timer under a certain conditions when the RSRP of the serving base station (BS) goes below the received signal strength indicators (RSSI). The proposed algorithm shows a significant improvement in term of HOPP, RLF, HOP, and HOF compared to System 4 and FLC algorithm. The RSRP of the proposed algorithm is kept within acceptable range. Thus, accurate setting values for the HCPs are significant to keep the tradeoff between RLF and HOPP at the minimum level.

UAV-Borne Mapping Algorithms for Low-Altitude and High-Speed Drone Applications.

This article presents an analysis of current state-of-the-art sensors and how these sensors work with several mapping algorithms for UAV (Unmanned Aerial Vehicle) applications, focusing on low-altitude and high-speed scenarios. A new experimental construct is created using highly realistic environments made possible by integrating the AirSim simulator with Google 3D maps models using the Cesium Tiles plugin. Experiments are conducted in this high-realism simulated environment to evaluate the performance of three distinct mapping algorithms: (1) Direct Sparse Odometry (DSO), (2) Stereo DSO (SDSO), and (3) DSO Lite (DSOL). Experimental results evaluate algorithms based on their measured geometric accuracy and computational speed. The results provide valuable insights into the strengths and limitations of each algorithm. Findings quantify compromises in UAV algorithm selection, allowing researchers to find the mapping solution best suited to their application, which often requires a compromise between computational performance and the density and accuracy of geometric map estimates. Results indicate that for UAVs with restrictive computing resources, DSOL is the best option. For systems with payload capacity and modest compute resources, SDSO is the best option. If only one camera is available, DSO is the option to choose for applications that require dense mapping results.

Exploring the potential of smartphone MEMS sensors for cost-effective rotating machinery speed estimation

PurposeThe purpose of this study is to explore the use of smartphone-embedded microelectro-mechanical sensors (MEMS) for accurately estimating rotating machinery speed, crucial for various condition monitoring tasks. Rotating machinery (RM) serves a crucial role in diverse applications, necessitating accurate speed estimation essential for condition monitoring (CM) tasks such as vibration analysis, efficiency evaluation and predictive assessment.Design/methodology/approachThis research explores the utilization of MEMS embedded in smartphones to economically estimate RM speed. A series of experiments were conducted across three test setups, comparing smartphone-based speed estimation to traditional methods. Rigorous testing spanned various dimensions, including scenarios of limited data availability, diverse speed applications and different smartphone placements on RM surfaces.FindingsThe methodology demonstrated exceptional performance across low and high-speed contexts. Smartphones-MEMS accurately estimated speed regardless of their placement on surfaces like metal and fiber, presenting promising outcomes with a mere 6 RPM maximum error. Statistical analysis, using a two-sample t-test, compared smartphone-derived speed outcomes with those from a tachometer and high-quality (HQ) data acquisition system.Research limitations/implicationsThe research limitations include the need for further investigation into smartphone sensor calibration and accuracy in extremely high-speed scenarios. Future research could focus on refining these aspects.Social implicationsThe societal impact is substantial, offering cost-effective CM across various industries and encouraging further exploration of MEMS-based vibration monitoring.Originality/valueThis research showcases an innovative approach using smartphone-embedded MEMS for RM speed estimation. The study’s multidimensional testing highlights its originality in addressing scenarios with limited data and varied speed applications.

The trajectory of carbon emissions and terrestrial carbon sinks at the provincial level in China

Global greenhouse gas emission, major factor driving climate change, has been increasing since nineteenth century. STIRPAT and CEVSA models were performed to estimate the carbon emission peaks and terrestrial ecosystem carbon sinks at the provincial level in China, respectively. Utilizing the growth characteristics and the peak time criteria for the period 1997–2019, the patterns of energy consumption and CO2 emissions from 30 Chinese provinces are categorized into four groups: (i) one-stage increase (5 provinces), (ii) two-stage increase (10 provinces), (iii) maximum around 2013 (13 provinces), and (iv) maximum around 2017 (2 provinces). According to the STIRPAT model, the anticipated time of peak CO2 emissions for Beijing from the third group is ~ 2025 in both business-as-usual and high-speed scenarios. For Xinjiang Uygur autonomous region from the first group and Zhejiang province from the second group, the expected peak time is 2025 to 2030. Shaanxi province from the fourth group is likely to reach carbon emission peak before 2030. The inventory-based estimate of China’s terrestrial carbon sink is ~ 266.2 Tg C/a during the period 1982–2015, offsetting 18.3% of contemporary CO2 emissions. The province-level CO2 emissions, peak emissions and terrestrial carbon sinks estimates presented here are significant for those concerned with carbon neutrality.

VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows.

Different from visible cameras which record intensity images frame by frame, the biologically inspired event camera produces a stream of asynchronous and sparse events with much lower latency. In practice, visible cameras can better perceive texture details and slow motion, while event cameras can be free from motion blurs and have a larger dynamic range which enables them to work well under fast motion and low illumination (LI). Therefore, the two sensors can cooperate with each other to achieve more reliable object tracking. In this work, we propose a large-scale Visible-Event benchmark (termed VisEvent) due to the lack of a realistic and scaled dataset for this task. Our dataset consists of 820 video pairs captured under LI, high speed, and background clutter scenarios, and it is divided into a training and a testing subset, each of which contains 500 and 320 videos, respectively. Based on VisEvent, we transform the event flows into event images and construct more than 30 baseline methods by extending current single-modality trackers into dual-modality versions. More importantly, we further build a simple but effective tracking algorithm by proposing a cross-modality transformer, to achieve more effective feature fusion between visible and event data. Extensive experiments on the proposed VisEvent dataset, FE108, COESOT, and two simulated datasets (i.e., OTB-DVS and VOT-DVS), validated the effectiveness of our model. The dataset and source code have been released on: https://github.com/wangxiao5791509/VisEvent_SOT_Benchmark.

Derivation And Applications of The Lorentz Transformation

The Lorentz transformation is a foundational mathematical construction that has revolutionized people’s comprehension of space, time, and motion within the realm of special relativity. Crafted through the collaborative insights of Lorentz and Einstein, it offers a profound perspective on the intricate fabric of the universe. By delving into the intricate interplay between space and time, the Lorentz transformation transcends traditional Newtonian notions, providing a groundbreaking framework to grasp the behavior of matter and energy in the cosmos. This paper embarks on a meticulous exploration, deriving the Lorentz transformation equations from first principles. It subsequently delves into a captivating journey through its multifaceted applications. From unraveling the mysteries of high-energy particle interactions to enhancing the accuracy of global navigation systems, the reach of the Lorentz transformation extends across diverse scientific and technological domains. The applications include elucidating counterintuitive effects like time dilation and length contraction, elucidating the mass-energy equivalence principle, and facilitating precise measurements in high-speed scenarios like medical imaging. Moreover, the Lorentz transformation guides people’s understanding of electromagnetic fields and underpins advancements in nuclear energy and aerospace technology.

Fuzzy compensation of model predictive control for PMSMs with external disturbances

In some high-speed scenarios, encoders may prove ineffective in providing real-time feedback, thereby increasing the installation and maintenance costs of Permanent Magnet Synchronous Motors (PMSMs). Consequently, the development of sensorless algorithms for medium and high-speed scenarios will enhance the practical applicability of PMSMs. In contrast to the angle estimation scheme for a rotated coordinate system, model predictive control (MPC) commences from the stationary coordinate system to explore Lyapunov stability and directly derive speed and angle based on the stability condition. The primary advantage of this approach lies in its ability to mitigate the accumulation of observation angle iteration errors, while simultaneously simplifying stability conditions. Furthermore, it is important to note that uncertain factors will significantly impede the performance of PMSMs. Subsequently, various fuzzy schemes are introduced to mitigate the impact of uncertain factors in the sensorless mode, and a rigorous proof is provided for the relationship between the fuzzy error and current error. Furthermore, a comprehensive analysis of controller stability conditions is conducted. Ultimately, experimental results demonstrate that the proposed fuzzy compensations effectively counteract external disturbances.

A High-Performance Pixel-Level Fully Pipelined Hardware Accelerator for Neural Networks.

The design of convolutional neural network (CNN) hardware accelerators based on a single computing engine (CE) architecture or multi-CE architecture has received widespread attention in recent years. Although this kind of hardware accelerator has advantages in hardware platform deployment flexibility and development cycle, it is still limited in resource utilization and data throughput. When processing large feature maps, the speed can usually only reach 10 frames/s, which does not meet the requirements of application scenarios, such as autonomous driving and radar detection. To solve the above problems, this article proposes a full pipeline hardware accelerator design based on pixel. By pixel-by-pixel strategy, the concept of the layer is downplayed, and the generation method of each pixel of the output feature map (Ofmap) can be optimized. To pipeline the entire computing system, we expand each layer of the neural network into hardware, eliminating the buffers between layers and maximizing the effect of complete connectivity across the entire network. This approach has yielded excellent performance. Besides that, as the pixel data stream is a fundamental paradigm in image processing, our fully pipelined hardware accelerator is universal for various CNNs (MobileNetV1, MobileNetV2 and FashionNet) in computer vision. As an example, the accelerator for MobileNetV1 achieves a speed of 4205.50 frames/s and a throughput of 4787.15 GOP/s at 211 MHz, with an output latency of 0.60 ms per image. This extremely shorts processing time and opens the door for AI's application in high-speed scenarios.

A LiDAR-IMU-GNSS fused mapping method for large-scale and high-speed scenarios

In the field of autonomous navigation and surveying, constructing large-scale and globally consistent environmental maps in real-time is a critical problem. In this case, the algorithm faces many challenges, such as high-speed motion, GNSS-denied tunnels and open fields with few features. To solve the aforementioned problems, this paper proposes a mapping method that fuses LiDAR, IMU and GNSS information simultaneously. The proposed method mainly includes two parts: LiDAR-IMU joint optimization and LIO-GNSS joint optimization. In the LiDAR-IMU joint optimization, we realize a tightly-coupled LiDAR inertial odometry (LIO) and propose a new degradation processing mechanism that enables more robust mapping in degraded environments, such as tunnels and open fields. Furthermore, we propose a new constraint method based on ground points to solve the drift problem in the vertical direction. This method maintains a local map based on ground grids, providing more effective constraints for the vertical direction. In the LIO-GNSS joint optimization, we use the factor graph optimization(FGO) algorithm based on sliding window to realize the fusion of LIO and GNSS data, so as to achieve real-time, no drift and global consistency mapping. We conducted experiments in many complex environments with challenges such as high-speed motion, LiDAR degradation, GNSS-denied scenarios and so on. While most open-source algorithms struggled to produce satisfactory mapping results or even failed, our algorithm proved to be robust and achieved the best mapping results.

OTFS-IM Modulation Based on Four-Dimensional Spherical Code in Air-to-Ground Communication

Unmanned aerial vehicles (UAVs) have been widely utilized for their various advantages. However, UAVs exhibit high mobility and energy storage restrictions in some applications, which can compromise the quality and reliability of communication links. This is a challenge that future aircraft and low-orbit aircraft will inevitably encounter. To effectively address the issue of dynamic Doppler spread in air-to-ground communication, this paper creatively introduces four-dimensional spherical code modulation into the orthogonal time–frequency space with an index modulation (OTFS-IM) system. The fundamental concept of the four-dimensional spherical code is elaborated in detail. Multiple resource symbols can be jointly used to increase the modulation dimension, thereby achieving a larger minimum Euclidean distance between constellation points. Furthermore, detailed analysis is conducted on the bit error rate (BER) and the peak-to-average-power ratio (PAPR) expressions of the proposed system to evaluate its performance and provide theoretical guidance. The proposed scheme not only adapts well to high-speed scenarios but also achieves better power consumption efficiency. The simulation results demonstrate that our proposed scheme outperforms conventional methods. Its robustness and generalization ability are also validated.

EFFECTS OF WIND SPEED AND MOUNTING TYPE ON PV MODULE IN UNBALANCED DISTRIBUTION SYSTEMS

This paper assesses the effects of wind speed and mounting type on the performance of photovoltaic (PV) modules in the three phase unbalanced IEEE 34 node distribution system. The study was conducted in OpenDSS considering ZIP load model and residential load shape. The module temperature was calculated considering the wind speed and mounting type of the PV panel. The impact of wind speed on PV has been analyzed using three different wind data sets. Furthermore, free standing and flat roof mounting types were considered to evaluate the effect of mounting configuration. It was found that integrating PV into the distribution system reduced substation demand and energy losses. Results also show that the PV produced more power in high wind speed scenarios than in low wind speed scenarios. Regarding the mounting configuration, the PV incorporated with free standing configuration generated more power than the flat roof mounting type.