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A scout's book of signs, signals and symbols
\"Boy Scouts cofounder and avid outdoorsman \"Uncle Dan\" Beard researched the secret languages of trappers, hobos, steamer pilots, and Native American tribes to compile this comprehensive resource of pictographs and other encoded communication symbols. First published nearly a century ago, this practical reference provides Scouts and other lovers of the outdoors with an ever-useful guide to following trails and interpreting their surroundings. Uncle Dan leads readers from basic directional signs to danger signals of land and sea, chalk and map signs of animals, symbols of the elements, celestial characters, and marks of the seasons and of time. He explains common gesture language, signal codes, flag signaling, animal tracking, and a host of other well-illustrated signs, signals, and symbols. This timeless manual provides valuable insights that will enrich the adventures of hunters, campers, backpackers, Scouts, and other wilderness enthusiasts\"-- Provided by publisher.
Book
Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments
The operational costs of the advanced Air Traffic Management (ATM) solutions are often prohibitive in low- and medium-sized airports. Therefore, new and complementary solutions are currently under research in order to take advantage of existing infrastructure and offer low-cost alternatives. The 5G signals are particularly attractive in an ATM context due to their promising potential in wireless positioning and sensing via Time-of-Arrival (ToA) and Angle-of-Arrival (AoA) algorithms. However, ToA and AoA methods are known to be highly sensitive to the presence of multipath and Non-Line-of-Sight (NLOS) scenarios. Yet, LOS detection in the context of 5G signals has been poorly addressed in the literature so far, to the best of the Authors’ knowledge. This paper focuses on LOS/NLOS detection methods for 5G signals by using both statistical/model-driven and data-driven/machine learning (ML) approaches and three challenging channel model classes widely used in 5G: namely Tapped Delay Line (TDL), Clustered Delay Line (CDL) and Winner II channel models. We show that, with simulated data, the ML-based detection can reach between 80% and 98% detection accuracy for TDL, CDL and Winner II channel models and that TDL is the most challenging in terms of LOS detection capabilities, as its richness of features is the lowest compared to CDL and Winner II channels. We also validate the findings through in-lab measurements with 5G signals and Yagi and 3D-vector antenna and show that measurement-based detection probabilities can reach 99–100% with a sufficient amount of training data and XGBoost or Random Forest classifiers.
Journal Article
A Segmented Sliding Window Reference Signal Reconstruction Method Based on Fuzzy C-Means
Reference signal reconstruction serves as a crucial technique for suppressing multipath interference and noise in the reference channel of passive radar. Aiming at the challenge of detecting Low-Slow-Small (LSS) targets using Digital Terrestrial Multimedia Broadcasting (DTMB) signals, this article proposes a novel segmented sliding window reference signal reconstruction method based on Fuzzy C-Means (FCM). By partitioning the reference signals based on the structure of DTMB signal frames, this approach compensates for frequency offset and sample rate deviation individually for each segment. Additionally, FCM clustering is utilized for symbol mapping reconstruction. Both simulation and experimental results show that the proposed method significantly suppresses constellation diagram divergence and phase rotation, increases the adaptive cancellation gain and signal-to-noise ratio (SNR), and in the meantime reduces the computation cost.
Journal Article
Experimental Study on LTE Mobile Network Performance Parameters for Controlled Drone Flights
This paper analyzes the quantitative quality parameters of a mobile communication network in a controlled drone logistic use-case scenario. Based on the analysis of standards and recommendations, the values of key performance indicators (KPIs) are set. As the main network-impacting parameters, reference signal received power (RSRP), reference signal received quality (RSRQ), and signal to interference and noise ratio (SINR) were selected. Uplink (UL), downlink (DL), and ping parameters were chosen as the secondary ones, as they indicate the quality of the link depending on primary parameters. The analysis is based on experimental measurements performed using a Latvian mobile operator’s “LMT” JSC infrastructure in a real-life scenario. To evaluate the altitude impact on the selected network parameters, the measurements were performed using a drone as transport for the following altitude values: 40, 60, 90, and 110 m. Network parameter measurements were implemented in automatic mode, allowing switching between LTE4–LTE2 standards, providing the opportunity for more complex analysis. Based on the analysis made, the recommendations for the future mobile networks employed in controlled drone flights should correspond to the following KPI and their values: −100 dBm for RSRP, −16 dB for RSRQ, −5 dB for SINR, 4096 kbps for downlink, 4096 kbps for uplink, and 50 ms for ping. Lastly, recommendations for a network coverage digital twin (DT) model with integrated KPIs are also provided.
Journal Article
Indoor Localization in Commercial 5G Environment with Single BS
As commercial 5G systems rapidly expand, indoor positioning using 5G signals holds great potential for serving a large number of users. In this paper, an effective fingerprint solution is proposed for indoor positioning with 5G signal base station by exploring the multi-beam property. Multi-beam channel state information (CSI) and multi-beam reference signal received power (RSRP) are used as the observations for fingerprinting. To assess the effectiveness of the proposed scheme, field tests were conducted across various indoor environments. The results showed that the positioning accuracy is improved by more than 45% compared with the single beam by using the multi-beam characteristics of the 5G signal. Based on the multi-beam RSRP, the proposed scheme can achieve a positioning error of 67% below 1 meter. In the awareness of the two typical indoor deployments of 5G systems, i.e., the digital indoor distribution (DID) and the distributed antenna system (DAS), the paper also compared the 5G positioning performance in these two scenarios. The field tests showed that, the multi-beam in DID has more features than in DAS, which lead to a better positioning performance than that in DAS.
Journal Article
Impact of Mobile Received Signal Strength (RSS) on Roaming and Non-roaming Mobile Subscribers
Mobile phones have transitioned from voice-centric devices to smart devices supporting functionalities like high-definition video and games, web browsers, radio reception, and video conferencing. Mobile phones are used in telemedicine, health monitoring applications, navigation tools, and gaming devices, among other applications. Given the above, Mobile broadband connectivity affects mobile access to the internet and voice communications. This paper assesses the impact of the Reference Signal Received Power (RSRP) and broadband connectivity around Covenant University. LTE, GSM, and HSPA mobile signal measurement campaigns were conducted around Covenant University in Ota, Ogun state, Nigeria. To investigate the best optimized mobile network for mobile subscribers on roaming services and subscriber's high performance and data rates. After the experiment, exploratory data analysis was used to visualize the best mobile network; GSM proved as stable than LTE and HSPA.
Journal Article
Automatic Removal of Physiological Artifacts in OPM-MEG: A Framework of Channel Attention Mechanism Based on Magnetic Reference Signal
The high spatiotemporal resolution of optically pumped magnetometers (OPMs) makes them an essential tool for functional brain imaging, enabling accurate recordings of neuronal activity. However, physiological signals such as eye blinks and cardiac activity overlap with neural magnetic signals in the frequency domain, resulting in contamination and creating challenges for the observation of brain activity and the study of neurological disorders. To address this problem, an automatic physiological artifact removal method based on OPM magnetic reference signals and a channel attention mechanism is proposed. The randomized dependence coefficient (RDC) is employed to evaluate the correlation between independent components and reference signals, enabling reliable recognition of artifact components and the construction of training and testing datasets. A channel attention mechanism is subsequently introduced, which fuses features from global average pooling (GAP) and global max pooling (GMP) layers through convolution to establish a data-driven automatic recognition model. The backbone network is further optimized to enhance performance. Experimental results demonstrate a strong correlation between the magnetic reference signals and artifact components, confirming the reliability of magnetic signals as artifact references for OPM-MEG. The proposed model achieves an artifact recognition accuracy of 98.52% and a macro-average score of 98.15%. After artifact removal, both the event-related field (ERF) responses and the signal-to-noise ratio (SNR) are significantly improved. Leveraging the flexible and modular characteristics of OPM-MEG, this study introduces an artifact recognition framework that integrates magnetic reference signals with an attention mechanism. This approach enables highly accurate automatic recognition and removal of OPM-MEG artifacts, paving the way for real-time, automated data analysis in both scientific research and clinical applications.
Journal Article
A Machine Learning Approach for 5G SINR Prediction
Artificial Intelligence (AI) and Machine Learning (ML) are envisaged to play key roles in 5G networks. Efficient radio resource management is of paramount importance for network operators. With the advent of newer technologies, infrastructure, and plans, spending significant radio resources on estimating channel conditions in mobile networks poses a challenge. Automating the process of predicting channel conditions can efficiently utilize resources. To this point, we propose an ML-based technique, i.e., an Artificial Neural Network (ANN) for predicting SINR (Signal-to-Interference-and-Noise-Ratio) in order to mitigate the radio resource usage in mobile networks. Radio resource scheduling is generally achieved on the basis of estimated channel conditions, i.e., SINR with the help of Sounding Reference Signals (SRS). The proposed Non-Linear Auto Regressive External/Exogenous (NARX)-based ANN aims to minimize the rate of sending SRS and achieves an accuracy of R = 0.87. This can lead to vacating up to 4% of the spectrum, improving bandwidth efficiency and decreasing uplink power consumption.
Journal Article
On the Design of Effective New Radio Sounding Reference Signal-Based Channel Estimation: Linear Regression with Channel Impulse Response Refinement
In this paper, we introduce a robust framework for linear regression–based channel estimation (CE) designed for multipath channel environments within a new radio (NR) sounding reference signal (SRS) system. The main contribution of this study is to show that integrating channel impulse response (CIR) refinement with existing CE schemes significantly improves CE performance in terms of normalized mean squared error (NMSE). Specifically, our approach employs thresholding-based CIR refinement to eliminate noise tap components effectively, discern the lengths of dominant tap elements, and augment linear regression–based CE’s efficacy. Specifically, it is shown that increasing the number of channel taps for threshold setting further enhances the performance of regression–based CE by leveraging CIR refinement. By utilizing an optimized threshold design, our results reveal close performance compared to both ideal tap information-based regression and the theoretical performance of linear minimum mean square error (LMMSE) estimation, whose findings are substantiated by numerical analyses employing our proposed polynomial regression–based channel estimation (PRCE) and DFT regression–based channel estimation (DRCE) schemes.
Journal Article
A dynamic handover scheme based on bidirectional VLC channel in multi-user attocell networks
Light fidelity (LiFi) would be a potential optical wireless communications technology for future mixed-spectrum 5G/6G indoor attocell network, which uses light-emitting diodes based as an optical antenna and offers Gbits capacity. For this scenario, a dynamic handover scheme based on bidirectional visible light communications (VLC) channel is proposed, which uses the uplink light sources to characterize terminal’s movement status for the mobility and make the handover decision in contrast to the unidirectional links. The uplink reference signal received power and the movement state are examined, and the created algorithm is executed in the bidirectional VLC test bed. The experimental results show that the proposed method will reduce the terminal’s handover rate by up to 45% and increase the optical network throughput by up to 26% at the experimental data rate compared with the skipping handover scheme for indoor LiFi network.
Journal Article