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Analysis of Variance (ANOVA) is a popular method to infer, based on the sampled data, whether the true means of a set of subpopulations differ from each other. The variance of conditional expectation (VOCE) is the variance of these effects in sub-populations, and this is estimated by sampling a sub-population of size n k , for each sub-population labeled k, and by sampling K such sub-populations in the experiment. For the general case of varying n k , it is unknown what the variance of the VOCE estimator is, though it is known for the special case n k = n , n ≥ 2 for all k ∈ 1 , 2 , ... , K as derived in the literature. The following derivation settles the former question and is of value in situations where sampling has constraints or only a skewed sampling budget is available. Our first application is with regard to the decision of whether samples from pilot simulation can be included in the regular simulation to estimate VOCE. The second application is an estimation technique where the estimate of optimal inner-loop size n * can be updated throughout the duration of simulation. We demonstrate with these 2 applications where we observe a 20% reduction in the variance of the VOCE estimate, when the proposed method is applied. [ABSTRACT FROM AUTHOR]

With the capability for single-receiver AR (Ambiguity Resolution), real-time GNSS (Global Navigation Satellite System) satellite integer clock has been the research hotspot of GNSS community in the past decade. Robust ambiguity datum is one prerequisite for high-quality integer clock. In this paper, with undifferenced AR model derived, a robust ambiguity datum definition method is proposed to isolate the signal biases from the integer ambiguity. Firstly, based on the ambiguity graph, the ambiguity datum is selected with the minimum spanning tree algorithm. Secondly, the ambiguity datum is transferred to the subsequent epochs through signal bias connection. Moreover, additional datum ambiguities are introduced to accommodate the arising of receiver and satellite in real-time circumstance. With the combination of ambiguity graph and signal bias, the efficient definition and smooth transition of ambiguity datum are assured for the real-time processing. To validate the approach, simulated real-time integer clock estimation for GPS/Galileo/BDS-3 satellites are conducted with one-month (January 2022) data from about 100 global IGS (International GNSS Service) network stations. Experiments show that the proposed robust ambiguity datum can produce accurate real-time integer clock. The average WL (Wide-Lane) and NL (Narrow-Lane) fixed rates exceed 99 % and 95 %, respectively, for most of the GPS/Galileo satellites. While comparable WL fixed rate is achieved for the BDS-3 satellites, the NL fixed rate is about 5 % lower as a result of poor orbit quality and imperfect measurement model. With eclipsing satellites excluded, the standard deviations between real-time integer clock and post-processed reference clock reach 0.018 ns and 0.016 ns for the GPS and Galileo constellations, respectively, which are 28 % and 24 % smaller than those of corresponding float clocks. In contrast, the standard deviations of float and integer clocks are similar for the BDS-3 constellation. Further improvements on the background models, including unconservative force modeling, satellite attitude modeling and antenna calibration are required to achieve ultimate accuracy, especially for the BDS-3 satellites. [ABSTRACT FROM AUTHOR]

On considering modern society, the wireless communication system plays a most significant role. This system has kept evolving and deployed into a wireless system of Fifth Generation (5G). One of the significant factors of the 5G system has utilized Machine Learning (ML) as well as Artificial Intelligence (AI) for the wireless network. Each of the building block and components of the wireless system, which is familiar and involves one or other ML/AI techniques are required. Here, the 5G generation has been used as the digital technology as well as run over higher radio frequencies. Further, the development of new techniques as well as the advanced features over the 5G network has raised some issues for the networking operators. ML is regarded as the AI that is accepted to unlock the capability of difficult large-scale issues over traditional Multiple-Input Multiple-Output (MIMO) systems. Today's wireless system has incomplete the MIMO system that has become common in recent years due to the increased potential in both energy efficiency and spectrum efficiency at a significant rate. AI-dependent ML has resolved the issues and then offers more energy efficiency and throughput in the 5G system. Hence, an effective artificial intelligence-based solution is introduced for resolving the aforementioned challenges to execute an efficient MIMO system in this proposal. In this work, the operations that are carried out for developing an efficient MIMO communication system are channel estimation, spectrum sensing of channels, fault/anomaly detection, resource allocation, and edge computation offloading. For performing all these functions, a deep learning method called Adaptive Autoencoder-Based Dilated DNN with Attention Mechanism (AADD-AM) is implemented. The parameters of the designed model are optimized with the help of Modified Update of Ant Lion and Horse herd Optimization (MU-ALHO) to achieve an accurate and effective outcome. The performance of the suggested efficient MIMO model is validated by comparing it with various performance metrics. Throughout the result analysis, the accuracy and sensitivity rate of the designed model is 94.86% and 95.18%. Therefore, it is revealed that the recommended model achieves high-speed wireless communication in a wide range of applications. [ABSTRACT FROM AUTHOR]

Owing to its success in 4G, Orthogonal Frequency Division Multiplexing (OFDM) made its contenting presence in 5G communications. To accommodate massive 5G data, it would be quite opportune to maintain significant reduction in overhead, while also ensuring reliable symbol detection at the receiver. For instance, reducing pilot symbol transmissions required for OFDM channel estimation, still retaining the efficacy of symbol detection is propitious. In this regard, contemporary compressive sensing framework with sparse recovery approaches such as Orthogonal Matching Pursuit (OMP) and Sparse Bayesian Learning (SBL) have been widely investigated in the literature. However study on approaches such as Least Absolute Shrinkage and Selection Operator (LASSO), Split Augmented Lagrangian Shrinkage Algorithm (SALSA) and corresponding investigations with experimentally acquired data frames are very less explored. In this work, by modifying the group LASSO optimization, a sparse recovery approach is proposed for OFDM channel estimation, particularly for a severely less pilot scenario. A non-coherent OFDM wireless link is developed using two Universal Software Radio Peripheral (USRP) devices, and several complex baseband OFDM frames are extracted in an indoor wireless lab environment for different receiver gains. Sparse recovery approaches are incorporated in two scenarios, namely number of pilots less than and severely less than channel length. The results of Mean Square Error (MSE) and Bit Error Rate (BER) versus receiver gain, show that the proposed approach outperforms all the other considered approaches. Particularly, while estimating CIR of length 20 with the proposed approach, employing 17 pilots resulted in BER of 0.0050, which degraded to only 0.0099 in a 5 pilot scenario, thus accommodating severe reduction in pilot overhead. [ABSTRACT FROM AUTHOR]

Summary: The non‐orthogonal multiple access (NOMA) is highly capable of serving multiple users at similar times and frequencies. The power allocation (PA) is widely considered as a main factor in NOMA for efficient communication. Here, the application of multiple‐input multiple‐output (MIMO) is added to NOMA to fulfill demands of enriched spectral efficiencies and extra user data. In this research, the Ladybug Beetle Honey Badger Optimization (LBHBO) is proposed for efficient PA in MIMO‐NOMA. Initially, the received signals from the user are modulated for amplitude and frequency. Then, user grouping is conducted by fuzzy local information c‐means (FLICM) clustering followed by using PA done by proposed LBHBO. This power is then moved to the transmitter and then to the channel estimation process. Moreover, cyclic prefix (CP) removal is carried out that tends to discrete Fourier transform (DFT). Finally, quadrature amplitude modulation (QAM) demodulation is performed for reallocated output. Furthermore, LBHBO is formed by combining Ladybug Beetle Optimization (LBO) and Honey Badger Algorithm (HBA). The performance offered by the LBHBO‐PA with maximal values with energy efficiency (EE) of 25.38 Mbits/s, sum rate of 1.29 Mbits/s, and achievable rate of 100.47 Mbits/s. [ABSTRACT FROM AUTHOR]

A small set of pilots in underwater acoustics OFDM (UWA-OFDM) systems tends to be insufficient for recovering the channel impulse response (CIR). This may result from the fast changes in the propagation environment and the requirement for high transmission data rates. A previous work, namely PE, considered the received subcarriers whose distances to their closest constellation points below the predetermined threshold T as potential pilots. However, extracting these pilots at the same time gives no chance to use some of them to facilitate searching for others. Fixing threshold T is also another limitation for searching pilot candidates. This paper proposes a reliable pilot search (RPS) method that consists of a multi-iterations pilot searching process with an adaptive threshold T a to improve the estimator in UWA-OFDM. The pilot search process gradually extracts reliable pilots at each iteration based on the threshold T a and evaluates them by performing channel estimation. Our method is compared to the MMSE and PE estimators on a wide range of settings, including the number of channel taps, pilot spacing, and various modulation schemes (i.e., MPSK and MQAM). The experimental results show that the RPS method often outperforms the MMSE and PE methods in terms of bit error rate (BER). [ABSTRACT FROM AUTHOR]

Summary: In the context of Reconfigurable Intelligent Surface (RIS) assisted millimeter wave communications within the Multiple Input Multiple Output (MIMO) system, Channel Estimation (CE) poses a significant challenge due to signal sparsity and blockages between transmitters and receivers. Consequently, communication faces difficulties without a direct line‐of‐sight path, leading to increased pilot overhead. To address these obstacles, this research introduces a channel estimation approach for RIS‐aided millimeter wave communication employing the Multidimensional Runge Kutta Orthogonal Matching Pursuit (MRKOMP) algorithm in MIMO systems. The proposed method aims to enhance network coverage and channel capacity for efficient data transmission. By utilizing the multiscale cumulative residual distribution entropy function, the method establishes an indirect path when the line‐of‐sight is obstructed. Furthermore, the multidimensional orthogonal matching pursuit methodology calculates sparsity levels and facilitates sparse recovery. Optimization of the weight parameter through the Runge Kutta optimizer effectively eliminates sparsity, resulting in improved spectral efficiency and RIS gain. Simulation results demonstrate a significant 99.9% enhancement in network capacity and spectral efficiency compared to existing methods. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) can provide unprecedented spectral efficiency gains and excellent ability to manipulate electromagnetic waves. This article considered a RIS‐assisted multiuser multiple‐input multiple‐output (MIMO) downlink system, where the beamforming at the base station and RIS are jointly designed to maximize the sum‐rate. For the large dimension scenario and high‐rank beamforming matrix, the accurate deterministic approximations from random matrix theory are then utilized to simplify the RIS‐assisted MIMO systems. The asymptotical signal‐to‐interference‐plus‐noise ratio values obtained through random matrix theory is infinitely close to the theoretical limits calculated by accurately iteration. And the performance of the proposed algorithm computed via the sharing second‐order channel statistics matches that of the RIS algorithm with sharing full channel state information asymptotically. The deterministic approximations are instrumental to get improvement into the structure of the optimal beamforming and to reduce the implementation complexity in large‐scale MIMO system. Numerical simulations results are provided to evaluate and verify the accuracy of the asymptotic results obtained from the proposed algorithm in the finite system regime. With the complex operation process of large dimension matrix reducing to the deterministic approximations, a lower computational complexity can be obtained compared with other methods. [ABSTRACT FROM AUTHOR]

Compressive sampling matching pursuit (CoSaMP), as a conventional algorithm requiring system sparsity and sensitive to step size, was improved in this paper by approximating the sparsity with adaptive variable step size. In the proposed algorithm (CoSaMP with variable step size abbreviated as Vss-CoSaMP), the idea of approximating sparsity with adaptive step size was borrowed from the sparsity adaptive matching pursuit (SAMP) algorithm to determine the sparsity for the CoSaMP algorithm. The applicability of the CoSaMP algorithm was therefore expanded considerably. On this basis, a step size reduction was added as the iteration termination condition of an orthogonal frequency division multiplexing (OFDM) system. An adaptive variable step size algorithm was then put forward to address the CoSaMP algorithm's sensitivity to step size. It could realize the required precision at different initial step sizes. A simulation was carried out to analyze the influence of pilot number and step size in an OFDM system on the algorithm. The algorithms, including SAMP, CoSaMP, and Vss-CoSaMP, were compared with two sparse channels, revealing that the Vss-CoSaMP algorithm overcame the problem of the CoSaMP algorithm, that is, the impossibility to forecast the channel sparsity. With the adaptive step size, the proposed algorithm could reach and achieve better accuracy than the CoSaMP algorithm. Additionally, the proposed algorithm was superior over the SAMP algorithm in terms of reconstruction, mean square error (MSE), and bit error ratio (BER). [ABSTRACT FROM AUTHOR]

Summary The channel plays a significant role as the multipath in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems. The precoding‐based channel estimation algorithms estimate the channel capacity at the receiver node and they provide efficient channel estimates under worst case channel scenarios. To estimate the channel capacity in the mobile node, semi‐blind scenario is a crucial task in Multi User (MU) MIMO systems. Therefore, a semi blind sparse channel estimation algorithm is demonstrated in the MU‐MIMO system to provide better performance. In the transmitter side, the signal modulation is performed with the help of Quadrature Phase Shift Keying (QPSK), and the modulated signal is given to Pulse Shaping Algorithm (PSA) to reduce the inter‐carrier interference as well as inter symbol interference. At each transmitter, the Inverse Fast Fourier Transforms (IFFT) technique is used for mapping the symbols and then the mapped signal is send to the receiver. In the receiver side, the FFT, pulse reshaping, and QPSK demodulation blocks are serially connected and perform inverse operations of the transmitter in the receiver. Then, the sparse channel is estimated at the receiver by using the semi blind sparse algorithm, where the parameters are optimized by using the Hybridized Drosophila with Virus Colony Search Optimization (HD‐VCSO). The objective function of the developed sparse channel estimation is the reduction of Minimum Mean Square Error (MMSE) in the channel. The performance of the developed sparse channel estimation algorithm is analyzed through different conventional algorithms to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the remarkable progress in wireless communication systems due to the escalating demand for higher data rates, improved reliability, and increased energy efficiency. In this regard, Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technology, enhancing spectral efficiency and accommodating multiple users concurrently within the same time and frequency resources. Simultaneously, the energy harvesting has surfaced as a sustainable solution, converting ambient environmental energy into usable electrical power for operating communication nodes. This paper proposes a cooperative NOMA transmission scheme integrating energy harvesting and utilizing Least Squares (LS) channel estimation for precise Channel State Information (CSI) acquisition. The objective is to establish an optimal communication path from source to destination. Relay selection methods: Optimal Relay Selection (ORS) and Max-Min Relay Selection (MMRS), are compared, focusing on their impact on the system performance. The analysis considers the influence of the number of relays and power allocation factor on the system, with a specific emphasis on the outage probability expressions. Comparative analysis between the cooperative-NOMA and the traditional cooperative relaying without NOMA reveals the superior performance of the cooperative-NOMA. Additionally, the ORS scheme outperforms MMRS in terms of the outage performance. [ABSTRACT FROM AUTHOR]

Introduction: Efficient NTDs elimination strategies require effective surveillance and targeted interventions. Traditional methods are costly and time-consuming, often failing to cover entire populations in case of movement restrictions. To address these challenges, a morbidity image-based surveillance system is being developed. This innovative approach which leverages the smartphone technology aims at simultaneous surveillance of multiple NTDs, enhancing cost-efficiency, reliability, and community involvement, particularly in areas with movement constraints. Moreover, it holds promise for post-elimination surveillance. Methodology: The pilot of this method will be conducted across three states in southern Nigeria. It will target people affected by Neglected Tropical Diseases and members of their communities. The new surveillance method will be introduced to target communities in the selected states through community stakeholder's advocacy meetings and awareness campaigns. The pilot which is set to span eighteen months, entails sensitizing NTDs-affected individuals and community members using signposts, posters, and handbills, to capture photos of NTDs manifestations upon notice using smartphones. These images, along with pertinent demographic information, will be transmitted to a dedicated server through WhatsApp or Telegram accounts. The received images will be reviewed and organized at backend and then forwarded to a panel of experts for identification and annotation to specific NTDs. Data generated, along with geocoordinate information, will be used to create NTDs morbidity hotspot maps using ArcGIS. Accompanying metadata will be used to generate geographic and demographic distributions of various NTDs identified. To protect privacy, people will be encouraged to send manifestation photos of the affected body part only without any identifiable features. Evaluation protocol: NTDs prevalence data obtained using conventional surveillance methods from both the pilot and selected control states during the pilot period will be compared with data from the CIMS-NTDs method to determine its effectiveness. Expected results and conclusion: It is expected that an effective, privacy-conscious, population inclusive new method for NTDs surveillance, with the potential to yield real-time data for the identification of morbidity hotspots and distribution patterns of NTDs will be established. The results will provide insights into the effectiveness of the new surveillance method in comparison to traditional approaches, potentially advancing NTDs elimination strategies. [ABSTRACT FROM AUTHOR]

Summary: Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm‐based channel estimation in OFDM (DiWGAN‐Hyb AOA‐COA‐MIMO‐OFDM) is proposed in this manuscript. In OFDM, there is a non‐stationary channel physical appearance during channel estimation (CE). Therefore in this work, Hyb AOA‐COA is employed to enhance the DiWGAN weight parameters. The proposed DiWGAN‐Hyb AOA‐COA‐MIMO‐OFDM technique is executed in network simulator (NS2) tool. The proposed technique attains lower computational cost 99.67%, 92.34%, and 97.45%; lesser bit error rate 98.33%, 83.12%, and 88.96%; and lesser mean square error 93.15%, 79.90%, and 92.88% compared with existing methods, like MIMO‐OFDM system using deep neural network and MN‐based improved AMO model (DNN‐IAMO‐MIMO‐OFDM), MIMO‐OFDM systems using the deep learning and optimization (RBFNN‐PSO‐MIMO‐OFDM), and MIMO‐OFDM systems using hybrid neural network (HNN‐CSI‐MIMO‐OFDM) respectively. [ABSTRACT FROM AUTHOR]

Objectives: This pilot study primarily aimed to detect the adherence as well as the effect size required to estimate the actual sample size needed for a larger scale study to compare and evaluate the effectiveness of two extracorporeal shock wave therapy (ESWT) protocols along, with a physical therapy program in reducing pain and improving function among patients suffering from plantar fasciitis. The study also aimed to report the effects of the ESWT protocols used on pain and function. Methods: A total of 26 participants took part in the study, including 17 females and 9 males. The average age of the participants was 34 years with a body mass index (BMI) of 23 kg/m2. Participants were divided into three equal groups; Group A received ESWT at a frequency of 15 Hz and intensity of 3, Group B received ESWT at a frequency of 10 Hz and intensity of 4, while Group C underwent the selected physical therapy program along with sham shock wave therapy as a control. Pain levels were assessed using the Visual Analog Scale (VAS) while functional improvements were evaluated using the Foot Function Index (FFI). Data was collected prior to treatment, after three sessions and at the end of six weeks (after six sessions). Results: The three groups were well matched, and the results revealed high adherence rates (90%, 90% and 80% respectively). Results also indicated reductions in pain levels and improvements in function for both intervention groups when compared to the control group. Group A demonstrated better outcomes compared to Group B while Group C showed relatively less improvement. Conclusion: The study concluded a high adherence rate for the three groups as well as a small effect size detected of 0.282 that would suggest a total of 123 participants to be required to replicate the study on a larger scale. With regards to the findings of this pilot, the combination of ESWT and a targeted physical therapy program revealed a possible effective therapeutic approach for plantar fasciitis, with a higher frequency potentially yielding more favourable results. [ABSTRACT FROM AUTHOR]

Orthogonal chirp division multiplexing (OCDM) offers a promising modulation technology for shallow water underwater acoustic (UWA) communication systems due to multipath fading resistance and Doppler resistance. To handle the various channel distortions and interferences, obtaining accurate channel state information is vital for robust and efficient shallow water UWA communication. In recent years, deep learning has attracted widespread attention in the communication field, providing a new way to improve the performance of physical layer communication systems. In this paper, the pilot-based channel estimation is transformed into a matrix completion problem, which is mathematically equivalent to the image super-resolution problem arising in the field of image processing. Simulation results show that the deep learning-based method can improve the channel distortion, outperforming the equalization performed by traditional estimator, the performance of Bit Error Rate is improved by 2.5 dB compared to the MMSE method in OCDM system. At the 7.5 to 20 dB region, it achieves better bit error rate performance than OFDM systems, and the bit error rate is reduced by approximately 53% compared to OFDM when the SNR value is 20, which is very useful in shallow water UWA channels with multipath extension and severe time-varying characteristics. [ABSTRACT FROM AUTHOR]

Several state-of-the-art convolutional neural networks (CNNs)-based methods are available for image denoising tasks. CNNs are typically trained using the backpropagation algorithm, which requires all operations in the network to be differentiable. Most CNN operations satisfy this requirement and can be applied to backpropagation-based training algorithms. However, some transforms, including wavelet transform, which is useful for speeding up CNN computations as well as performing multi-resolution analysis, are not strictly differentiable. This paper addresses this challenge by proposing a wavelet-like transform that is differentiable. This new design is, in fact, a new CNN architecture named semi-wavelet, specific edge convolutional neural network (SW/SE-CNN), consisting of three newly designed layers. The first layer is a Semi-Wavelet (SW)-based layer which is a differential down-sampling operator for wavelet approximation. That is, the SW layer converts the input image into four channels. Three of these channels are estimations of the vertical, horizontal, and diagonal edges of the original image; and the fourth channel is a down-sampled version of it. The second proposed layer, called Semi-Wavelet Inverse (SWI), is to restore the original image by using the four SW output channels. Additionally, a specific edge extractor (SE), as another new layer, is designed on the basis of the well-known Sobel operator to extract specific edges of the image. The reason behind proposing the SE layer is to provide more edge information for the network; and the motive for including the SW layer is to speed the network up as well as multi-resolution analysis. Then, the new SW/SE-CNN architecture is implemented for Gaussian image denoising. The experimental results show that the new SW/SE-CNN outperformed the state-of-the-art methods for Gaussian image denoising based on the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) measurements for grayscale as well as color images. [ABSTRACT FROM AUTHOR]

Simultaneous transmit and receive wireless communications have been highlighted for their potential to double the spectral efficiency. However, it is necessary to mitigate self-interference (SI). Considering both the SI channel and remote transmission (RT) channel need to be estimated before equalizing the received signal, we propose two adaptive algorithms for linear and nonlinear self-interference cancellation (SIC), based on a multi-layered joint channel estimator structure. The proposed algorithms estimate the RT channel while performing SIC, and the multi-layered structure ensures improved performance across various interference-to-signal ratios. The M-estimate function enhances the robustness of the algorithm, allowing it to converge even when affected by impulsive noise. For nonlinear SIC, this paper introduces an adaptive algorithm based on generalized Hammerstein polynomial basis functions. The simulation results indicate that this approach achieves a better convergence speed and normalized mean squared difference compared to existing SIC methods, leading to a lower system bit error rate. [ABSTRACT FROM AUTHOR]

Channel estimation in Underwater Acoustic Communication (UAC) faces significant challenges due to the non-Gaussian, impulsive noise in ocean environments and the inherent high dimensionality of the estimation task. This paper introduces a robust channel estimation algorithm by solving an l 1 − l 1 optimization problem via the Alternating Direction Method of Multipliers (ADMM), effectively exploiting channel sparsity and addressing impulsive noise outliers. A non-monotone backtracking line search strategy is also developed to improve the convergence behavior. The proposed algorithm is low in complexity and has robust performance. Simulation results show that it exhibits a small performance deterioration of less than 1 dB for Channel Impulse Response (CIR) estimation in impulsive noise environments, nearly matching its performance under Additive White Gaussian Noise (AWGN) conditions. For Delay-Doppler (DD) doubly spread channel estimation, it maintains Bit Error Rate (BER) performance comparable to using ground truth channel information in both AWGN and impulsive noise environments. At-sea experimental validations for channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems further underscore the fast convergence speed and high estimation accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, we propose a joint waveform based on universal filtered multicarrier (UFMC) chirp for radar jamming and communication joint systems. Modulation of radar jamming chirp signals and communication signals on different subcarrier groups in the UFMC sub-band is used to achieve the waveform design. The jamming signal in the waveform contains a frequency shift coefficient that depends on the delay time, which can effectively improve the anti-frequency hopping ability and enhance the overall jamming efficiency. Simultaneously jamming signals can provide assistance in channel estimation and equalization of communication, improving the information transmission quality of communication subsystems. We concluded through reasonable trade-off analysis that the combined weight of radar jamming and communication is closely related to the overall performance of the waveform. The simulation results show that the proposed UFMC chirp synthesized waveform has good jamming and communication performance. Software defined radio (SDR) simulation experiments demonstrated the effectiveness of this method in practical environments. [ABSTRACT FROM AUTHOR]

The presented pilot for the Synthesis Product for Ocean Time Series (SPOTS) includes data from 12 fixed ship-based time-series programs. The related stations represent unique open-ocean and coastal marine environments within the Atlantic Ocean, Pacific Ocean, Mediterranean Sea, Nordic Seas, and Caribbean Sea. The focus of the pilot has been placed on biogeochemical essential ocean variables: dissolved oxygen, dissolved inorganic nutrients, inorganic carbon (pH, total alkalinity, dissolved inorganic carbon, and partial pressure of CO 2), particulate matter, and dissolved organic carbon. The time series used include a variety of temporal resolutions (monthly, seasonal, or irregular), time ranges (10–36 years), and bottom depths (80–6000 m), with the oldest samples dating back to 1983 and the most recent one corresponding to 2021. Besides having been harmonized into the same format (semantics, ancillary data, units), the data were subjected to a qualitative assessment in which the applied methods were evaluated and categorized. The most recently applied methods of the time-series programs usually follow the recommendations outlined by the Bermuda Time Series Workshop report (Lorenzoni and Benway, 2013), which is used as the main reference for "method recommendations by prevalent initiatives in the field". However, measurements of dissolved oxygen and pH, in particular, still show room for improvement. Additional data quality descriptors include precision and accuracy estimates, indicators for data variability, and offsets compared to a reference and widely recognized data product for the global ocean: the GLobal Ocean Data Analysis Project (GLODAP). Generally, these descriptors indicate a high level of continuity in measurement quality within time-series programs and a good consistency with the GLODAP data product, even though robust comparisons to the latter are limited. The data are available as (i) a merged comma-separated file that is compliant with the World Ocean Circulation Experiment (WOCE) exchange format and (ii) a format dependent on user queries via the Environmental Research Division's Data Access Program (ERDDAP) server of the Global Ocean Observing System (GOOS). The pilot increases the data utility, findability, accessibility, interoperability, and reusability following the FAIR philosophy, enhancing the readiness of biogeochemical time series. It facilitates a variety of applications that benefit from the collective value of biogeochemical time-series observations and forms the basis for a sustained time-series living data product, SPOTS, complementing relevant products for the global interior ocean carbon data (GLobal Ocean Data Analysis Project), global surface ocean carbon data (Surface Ocean CO 2 Atlas; SOCAT), and global interior and surface methane and nitrous oxide data (MarinE MethanE and NiTrous Oxide product). Aside from the actual data compilation, the pilot project produced suggestions for reporting metadata, implementing quality control measures, and making estimations about uncertainty. These recommendations aim to encourage the community to adopt more consistent and uniform practices for analysis and reporting and to update these practices regularly. The detailed recommendations, links to the original time-series programs, the original data, their documentation, and related efforts are available on the SPOTS website. This site also provides access to the data product (DOI: 10.26008/1912/bco-dmo.896862.2, Lange et al., 2024) and ancillary data. [ABSTRACT FROM AUTHOR]

For the channel estimation of broadband OFDM system assisted by IRS, most of the current researches are based on the full pilot setting on a single symbol, and excessive cascaded channel coefficients will lead to large pilot overhead. This paper proposed a two-stage channel estimation scheme based on block sparse in time-angle domain. Firstly, by analyzing the common sparse characteristics of the channel in the time domain and the angular domain, this paper transformed the cascaded channel matrix into a block sparse representation in the time domain-angular domain, and converted the channel estimation problem into a block sparse matrix recovery problem. Secondly, this paper proposed a two-stage sparse channel estimation scheme to sparsely recover the cascaded channel considering the transmission pilot limitation and the time-angle domain block sparse characteristics. Simulation results show that compared to the other three benchmark schemes, the proposed scheme can achieve better channel estimation performance with fewer pilots, effectively reducing the pilot overhead and achieving higher estimation accuracy. [ABSTRACT FROM AUTHOR]

In this letter, an optimal calibration factor for mutual information effective signal‐to‐interference‐plus‐noise ratio (SINR) mapping (MIESM) scheme in fifth‐generation new radio (5G NR) is provided. By applying the optimal calibration factor to the MIESM scheme, the estimation accuracy of the effective SINR can be improved. The optimal calibration factor minimizes the mean squared error (MSE) of the difference between the estimated effective SINR and the required SNR under additive white Gaussian noise (AWGN) at a block error rate (BLER) of 10%. Simulation results show that the optimized MIESM scheme outperforms the non‐optimized MIESM scheme. [ABSTRACT FROM AUTHOR]

Underwater signal processing is primarily based on sound waves because of the unique properties of water. However, the slow speed and limited bandwidth of sound introduce numerous challenges, including pronounced time-varying characteristics and significant multipath effects. This paper explores a channel estimation method utilizing superimposed training sequences. Compared with conventional schemes, this method offers higher spectral efficiency and better adaptability to time-varying channels owing to its temporal traversal. To ensure success in this scheme, it is crucial to obtain time-varying channel estimation and data detection at low SNRs given that superimposed training sequences consume power resources. To achieve this goal, we initially employ coarse channel estimation utilizing superimposed training sequences. Subsequently, we employ approximate message passing algorithms based on the estimated channels for data detection, followed by iterative channel estimation and equalization based on estimated symbols. We devise an approximate message passing channel estimation method grounded on a Gaussian mixture model and refine its hyperparameters through the expectation maximization algorithm. Then, we refine the channel information based on time correlation by employing an autoregressive hidden Markov model. Lastly, we perform numerical simulations of communication systems by utilizing a time-varying channel toolbox to simulate time-varying channels, and we validate the feasibility of the proposed communication system using experimental field data. [ABSTRACT FROM AUTHOR]

High-accuracy positioning plays an important role in future 5G-Advanced cellular networks. However, it is quite challenging to utilize 5G signals to estimate positioning parameters accurately under multipath environments. To establish a high-precision real-time location system (RTLS), which is suitable for indoor scenarios, advanced parameter estimation techniques with low computational complexity should be developed. This paper proposes a modified subspace-based time-of-arrival (TOA) estimation algorithm for 5G signals with ultra-high accuracy and reduced complexity under multipath environments. A novel smoothing scheme termed as grouped subcarrier smoothing (GSS) is proposed to reduce the dimension of the covariance matrix involved in typical subspace-based methods. Then, the computational complexity of the corresponding eigenspace calculation is reduced. The TOA estimation performance is enhanced by constructing multiple snapshots of the estimated channel frequency response vector in the frequency domain. Finally, the super-resolution TOA estimate can be obtained using only one orthogonal frequency division multiplexing (OFDM) symbol. Simulation results show that the proposed method achieves centimeter-level TOA estimation accuracy under multipath channels when the signal-to-noise ratio (SNR) is high. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-channel speech separation method based on spatial estimation via sparse Bayesian inference (SBI) and nonnegative matrix factorization (NMF). The spatial information estimated by traditional compressed sensing (CS) models is insufficient when two microphones receive limited columns of mixed signals. Considering the sparsity of peak values in the cross-correlation spectrum between two received signals, the proposed method builds a new CS model based on cross-correlation spectrum and applies SBI algorithm to solve this model to improve the estimation accuracy of spatial information. Combined the spatial information with the spectral features decomposed by NMF, NMF coefficient matrix masks belonging to individual source are generated for pre-separation. To mitigate retained potential interference components, a post-separation processing stage is designed using an expectation maximization (EM) algorithm based on a Gaussian mixture model (GMM). The estimated spatial information and binary time–frequency masks are used for parameter initialization of the EM algorithm. The experimental results using real-world speech data show that the proposed method can achieve better separation performance compared to various existing methods. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, multiple input multiple outputs with orthogonal frequency division multiplexing (MIMO‐OFDM) provide better communication performance that can be applied to the fast‐growing Internet of Things (IoTs). In underwater IoT, information fades away rapidly due to varying water conditions. Therefore, the MIMO model can be applied to the OFDM acoustic system, enabling high‐speed data transmission without affecting the channel effectively. However, detecting the underwater signal and estimating the channel is highly necessary for enhancing underwater acoustic communication (UWAC). Recently, many techniques have been introduced for effectively performing signal detection and channel estimation. However, those techniques face high time complexity due to increased channel interferences and noises during data transmission. Hence, this article brings a novel technique for SD and CE for the UWAC‐IoT‐enabled MIMO‐OFDM system. An adaptive recursive least square (ARLS) technique is proposed in this study for CE that aids in evaluating the acoustic channel parameters effectively. For performing SD, a bi‐directional deep pelican convolutional neural network (BDPCNN) technique is introduced to ensure the presence and absence of signals at the receiver end. The proposed method is analyzed via the MATLAB platform, and the performances are analyzed under different water types like turbid water, coastal water, clear ocean water, and pure seawater. Different performance metrics like bit error rate (BER), mean square error (MSE), energy efficiency (EE), and time complexity are analyzed with different existing techniques. The experimental section obtains the BER of 0.0086, 0.013, 0.017, and 0.021 for turbid, coastal, clear, and pure seawater, respectively. [ABSTRACT FROM AUTHOR]

Summary: The future generation of wireless communications systems uses the generalized frequency division multiplexing (GFDM) due to its viable candidate waveform to perform multiple user scheduling. It is a non‐orthogonal waveform susceptible to intercarrier and intersymbol interference (ISI); still, it offers flexible pulse shaping that enhances the efficiency of user scheduling. Here, to accomplish spatial diversity, the multiple‐input multiple‐output (MIMO) is incorporated with GFDM to enhance the performance; there is also an extra inter‐antenna interference problem that limits the model's performance. The issue concerning inter‐antenna interference can be resolved by adopting pilot‐based information transfer. The detection of a signal at the receiver based on the minimum mean square error has the issue of computational complexity while enhancing the estimation quality of a detector. Hence, a low‐complexity channel estimation technique is proposed in this research using the V‐degree polynomial‐based channel estimation technique, wherein the cubic order computation is reduced to square order. Also, the proposed adaptive pulse shaping technique, wherein the filter coefficient is optimized using the gazelle optimization algorithm (GOA) to provide optimal pulse shaping filter parameters by employing bit error rate (BER) as the objective function. The performance of a proposed V‐degree polynomial‐based channel estimation is analyzed based on various assessment measures like BER and MSE and acquired the minimal values of 5.75E‐05 and 2.07E‐05, respectively. [ABSTRACT FROM AUTHOR]

A digital coding metasurface is a platform connecting the digital space and electromagnetic wave space, and has therefore gained much attention due to its intriguing value in reshaping wireless channels and realizing new communication architectures. Correspondingly, there is an urgent need for electromagnetic information theory that reveals the upper limit of communication capacity and supports the accurate design of metasurface-based communication systems. To this end, we propose a macroscopic model and a statistical model of the digital coding metasurface. The macroscopic model uniformly accommodates both digital and electromagnetic aspects of the meta-atoms and predicts all possible scattered fields of the digital coding metasurface based on a small number of simulations or measurements. Full-wave simulations and experimental results show that the macroscopic model is feasible and accurate. A statistical model is further proposed to correlate the mutual coupling between meta-atoms with covariance and to calculate the entropy of the equivalent currents of digital coding metasurface. These two models can help reconfigurable intelligent surfaces achieve more accurate beamforming and channel estimation, and thus improve signal power and coverage. Moreover, the models will encourage the creation of a precoding codebook in metasurface-based direct digital modulation systems, with the aim of approaching the upper limit of channel capacity. With these two models, the concepts of current space and current entropy, as well as the analysis of information loss from the coding space to wave space, is established for the first time, helping to bridge the gap between the digital world and the physical world, and advancing developments of electromagnetic information theory and new-architecture wireless systems. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of velocity distribution is the fundamental information hydraulic engineers need to estimate an alluvial channel's discharge and stage characteristics. This study examines the Tsallis entropy approach for estimating streamwise velocity patterns in open channels. Entropy, which quantifies system uncertainty, has been applied in hydraulic research to account for variables such as shear strength, silt content, and flow velocities. However, its applicability to non-uniform channel sections remains unexplored. In the current work, the velocities estimation under various experimental conditions was calculated using the Tsallis entropy approach, wherein the random variable employed for constructing the velocity estimations was the time-averaged normalised velocity. This study considered two experimental conditions: (1) channels with attached spurs under seepage and non-seepage conditions and (2) channels with a 31-degree bank slope with and without an upstream pit. The velocity pattern obtained closely corresponds to the experimental data, exhibiting significant accuracy. However, it should be noted that the accuracy of the velocity pattern is slightly diminished in the region near the spur field when the y / D value is below 0.3. The difference can be caused by factors such as the area's non-uniform cross-section, sediment interaction along the bed, and secondary currents, which ultimately affect the velocity profile. [ABSTRACT FROM AUTHOR]

A matched filtering (SMF) channel estimation method based on sequence pilots can solve the problem of difficulty and high complexity in channel estimation for existing orthogonal time-frequency-space (OTFS) modulation systems in terms of peak to average ratio and fractional Doppler channels. This algorithm first combines the sequence pilot and data into a frame, and obtains the number of paths, delay, and integer Doppler based on the correlation of the sequence. Then, it estimates the fractional Doppler tap and channel gain through matched filtering to obtain channel state information. Finally, the channel gain and initial phase are updated through channel features. The simulation results show that this method improves the peak to average ratio and channel estimation performance compared to channel estimation based on embedded pulse pilots. Compared to traditional sequence pilots, this method can estimate fractional Doppler taps and provide more accurate channel state information. This channel estimation method is more universal. [ABSTRACT FROM AUTHOR]

To tackle massive data, subsampling is a practical approach to select the more informative data points. However, when responses are expensive to measure, developing efficient subsampling schemes is challenging, and an optimal sampling approach under measurement constraints was developed to meet this challenge. This method uses the inverses of optimal sampling probabilities to reweight the objective function, which assigns smaller weights to the more important data points. Thus, the estimation efficiency of the resulting estimator can be improved. In this paper, we propose an unweighted estimating procedure based on optimal subsamples to obtain a more efficient estimator. We obtain the unconditional asymptotic distribution of the estimator via martingale techniques without conditioning on the pilot estimate, which has been less investigated in the existing subsampling literature. Both asymptotic results and numerical results show that the unweighted estimator is more efficient in parameter estimation. [ABSTRACT FROM AUTHOR]

The orthogonal frequency division multiplexing (OFDM) system applies coherent demodulation to achieve high spectral efficiency at a bandwidth cost by the pilot tones. Considering the statistical property of the down-link channels to the users, it can be found that there is an opportunity to reduce the pilot number in the conventional designs while maintaining the same signal demodulation performances. The design philosophy involves utilizing the difference of the channel coherent bandwidths (CCBs) by allocating data to appropriate positions upon the fact that different CCBs can tolerate different minimized pilot spacing. The proposed design allows each user's equipment's data not to exceed its CCB with the sparser pilots. The theoretical analysis is carried out based on the concept of channel frequency response using linear interpolation with channel estimation employing the least squares (LS) method. The gain of the proposed method is demonstrated in terms of the ergodic capacities and confirmed by the simulations. [ABSTRACT FROM AUTHOR]

Multi-user detection techniques are crucial in minimizing the impact of Multi-Access Interference in Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) systems. It is suggested to combine channel estimation with multi-user identification to get rid of different interferences and lower the Bit Error Rate. In this manuscript, Channel Estimation and Data Detection for 5G MIMO Communication in Fading Channels based on Recalling Enhanced Recurrent Neural Network are proposed. The suggested method prioritizes fast communication and strives to provide high reliability even when there is just a small amount of user equipment per base station. The study of the multipath Rayleigh fading channel in MIMO-OFDM systems has a difficulty with frequency selective channel estimate. The primary air interface for 4G and 5G broadband wireless communications is MIMO-OFDM. With its emphasis on the network's active users and its ability to reduce interference, MIMO-OFDM offers further advantages in multi-user identification. When the activity factor is unknown, the deep learning technique is used to identify the active users in the multi-access system. With a high data rate, the RERNN classifier is utilized to evaluate the unknown activity component. The BS receiver scans for active users and deciphers their transmissions. Both pilot and data symbols are used in activity detection. A unique pilot allocation strategy that reduces the common relation of the measurement matrix is proposed for optimum pilot placement. The sparse channel assessment is eventually verified, and the computational cost is decreased, by selecting the best pilot patterns with the aid of improved Mexican axolotl optimization algorithm. The CE and decoder techniques both regulate the impact of sounds and MAI. It accurately decodes the received signal without channel issues using Python implementation. Here the performance metrics like of Symbol Error Rate, Bit Error Rate, Uplink Sum Rate, Downlink Sum Rate, Normalized CE Error and Mean Square Error. The suggested approach demonstrates a reduction in BER by 23.34%, 17.02%, and 32.21%, as well as a reduction in SER by 21.09%, 12.32%, and 19.08%, when compared to conventional methodologies. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is an important research topic in the field of wireless communication and mobile computing, as it can effectively decrease the latency and energy consumption due to the trade‐off between the communication and computing, where some intensive computing tasks can be offloaded to computational access points (CAPs), especially when the wireless transmission channel is in good condition. This article studies how to intelligently allocate the computing capability and wireless bandwidth among users for a cache‐aided multi‐terminal multi‐CAP MEC network with non‐ideal channel estimation, where there are N$$ N $$ mobile terminals and M$$ M $$ CAPs in the network. Each terminal has some tasks that need to be computed in a fast and efficient way. For such a system, we first design the system by jointly considering the computing capability and wireless bandwidth allocation, where the computing and communication delay is used as the performance of metric. To optimize the system performance, we then employ deep deterministic policy gradient to learn an effective strategy on the allocation of computing capability and wireless bandwidth, in order to decrease the system delay as much as possible. Simulations are finally conducted to show the superiority of the proposed studies in this article, especially about the advantages from cache. [ABSTRACT FROM AUTHOR]

Summary: To provide the increasing demand for wireless technologies, high capacity with lower latency is vital. New emerging technologies like 5G, millimeter wave, THz, and so forth are able to characterize such complex fading and shadowed environments. Spatial diversity is one of the techniques to minimize fading in the wireless communication channel. The performance of the wireless communication system can be further optimized by using estimators at the receivers. The influence of imperfect channel estimation on the performance over shadowed Beaulieu‐Xie (SBX) fading channels with L‐maximal ratio combining (MRC) diversity is studied. The probability density function (PDF) of the received output signal‐to‐noise ratio (SNR) with L‐MRC over SBX fading channels is derived. Further PDF of the received output SNR, outage probability, and average bit error rate (ABER) using the derived PDF is evaluated for L‐MRC diversity with channel estimation error over SBX fading channels. ABER expression for Gray‐coded rectangular quadrature amplitude modulation over the SBX channel is also evaluated. The study also includes how the system performs under the effect of different shadowing and fading parameters. [ABSTRACT FROM AUTHOR]

Summary: Several input high‐data‐rate transmissions over broadband wireless channels are possible using multiple input multiple output (MIMO) systems paired with orthogonal frequency division multiplexing (OFDM) technology. Channel estimation is an essential technique and a necessary component of MIMO‐OFDM systems. However, the noise will be there in MIMO‐OFDM due to the environment. As a result, the wireless system performs degrades in terms of bit error rate (BER). The suggested method offers a better pilot pattern strategy for MIMO‐OFDM and an efficient power allocation to address this issue. The binomial distribution‐based grey wolf optimization (BDGWO) algorithm is proposed to identify the optimal pilot patterns. The power is then adaptively distributed to each transmit antenna to increase the spectral efficiency and maximum channel capacity through an adaptive neuro‐fuzzy inference system with a sigmoid membership function (SMFANFIS). The best pilot patterns in PDGSIP (pilot design with generalized shift invariant property) were determined using the BDGWO algorithm based on the binomial distribution. According to the simulation results, the proposed BDGWO established pilot design with generalized shift invariant property (BDGWO‐DGSIP) achieves higher performance compared other existing approaches such as PDGSIP, TPDGSIP, and LS in terms of NMSE, BER, and SER. Compared to the PDGSIP technique, the proposed PDGSIP‐BDGWO system minimizes NMSE at 10%, BER at about 12%, and SER at 15%. [ABSTRACT FROM AUTHOR]

For wireless communication systems with large-scale multiple-input–multiple-output array in the presence of an active eavesdropper, the eavesdropper masquerades as a legitimate user sending the identical pilot sequence to manipulate channel estimation in the uplink training phase, which misleads precoding at the transmitter. If such pilot spoofing attack is not handled effectively, it will not only reduce system capacity, but also cause much signal power leak to the spoofer, and so it seriously threatens the security transmission in wireless communication. Nevertheless, how to detect such potential pilot spoofing and recover the contaminated channel plays a significant role in designing combating schemes and ensuring security transmission. In this paper, the impact of pilot spoofing attack on the system security capacity is investigated, and it is shown that pilot spoofing attack will degrade system capacity greatly and the transmitted information will be intercepted by the spoofer. Then, a novel detection scheme based on pilot-random-block-encryption is proposed, through which not only the detection probability on the potential pilot spoofing can be improved greatly, but also the channel state information of the legal user and the spoofer are obtained. Simulation results show that the detection probability of the proposed detection scheme reaches 93% even the transmit power of pilot spoofing is as low as − 10 dB, which is much greater than other existing schemes, and the system security capacity is also enhanced with the proposed channel estimation strategy. [ABSTRACT FROM AUTHOR]

The study of the strong ground motion is of utmost relevance because the amplitude of seismic waves and their frequency content could severely damage structures. As both the amplitude and the frequency content directly depend on the seismic source, a proper description and simulations of the earthquakes' rupture process are required. This means that realistic source models should incorporate a heterogeneous distribution of rupture parameters that generates self-arrested ruptures. One of these models is a heterogeneous energy-based (HE-B) method, which can describe the kinematic rupture process based on the distribution of residual energy ( E res ). This parameter defines zones in faults where the accumulated energy is larger than the dissipated energy. In this context, this study presents the spatial variations of radiated energy, corner frequency, and stress drop at far-field distances as a consequence of the heterogeneous distribution of positive residual energy. It is found that the rupture of asperities, determined by large values of E res , strongly shifts the frequency content and generates a Doppler effect of the frequency content. That is, the location in the far-field in direction where the asperity is being ruptured generates traveling waves characterized by an increase of the observed corner frequency, which corresponds to the corner frequency measured by the observer. This implies that different station measures different frequency content implying different estimations of the source parameters. Besides, the variability of the observed corner frequency could break the scaling between the corner frequency and the magnitude. Nevertheless, it is also found that the average observed corner frequency, when considering all the points or stations, is almost the same as that obtained for the seismic source. A similar property is found for radiated energy and stress drop. These results show that the ground motion at a given location varies depending on the heterogeneities of the section of the fault being ruptured. [ABSTRACT FROM AUTHOR]

This paper presented the performance of physical layer security of permutation index (PI) with modulating bits-based orthogonal frequency division multiplexing differential chaos shift keying system (PIM-OFDMDCSK), the objective behind PIM-OFDM-DCSK is to improve and increase the information rate, spectral, and energy efficiencies. In PIM-OFDM-DCSK, the (M) subcarriers, each carry (n+1) bits. The n bits are index bits utilized to choose one permutation of the chaotic reference. On the receiver, after OFDM demodulation, the correlation is made between the permuted versions of the chaotic reference and the received bearing sequence of data. Then the received bits are assessed by determining the maximum correlator outputs via which the n bits are detected. Then the maximum output correlator sign is utilized to detect the modulating bit. The performance of the PIM-OFDM-DCSK is tested under additive white gaussian noise and multipath fading channel. Furthermore, the bit error rate is derived based on the wiretap system for the proposed PIM-OFDM-DCSK and compared with other DCSK modulations. The results show that the proposed PIM-OFDM-DCSK scheme is promising and competitive compared with other DCSK system, and the best BER performance is as an increase in the value of n and a decrease in the value of β. [ABSTRACT FROM AUTHOR]

Summary: Two fundamental aspects of wireless communication, which make it challenging and interesting, are fading and interference. Multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) techniques when utilized together effectively combat fading and interference thereby achieving the requirement of high data rate and high performance. In this paper, a 2 × 1 MIMO‐OFDM baseband model of the physical layer according to IEEE 802.16.1 standard employing 256 subcarriers capable of transmitting 192 Mbps in a 21‐MHz bandwidth frequency selective fading channel is designed. All essential blocks of the communication system like channel coding, modulation, time synchronization, frequency synchronization, channel estimation, residual carrier frequency offset (CFO) estimation, and channel decoding are covered at the algorithm level. A two‐step time synchronization method is used in this work to avoid any ambiguity in frame detection. A long preamble, consisting of three short preambles, is designed to aid in time synchronization, frequency synchronization, and channel estimation. To evaluate the performance of the proposed design, simulations were carried out in MATLAB‐SIMULINK. [ABSTRACT FROM AUTHOR]

This paper proposes a novel machine learning (ML) assisted low‐latency low density parity check (LDPC) coded adaptive modulation (AM) system, where short block‐length LDPC codes are used. Conventional adaptive modulation and coding (AMC) system includes fixed look‐up table method, which is also called inner loop link adaptation (ILLA) and outer loop link adaptation (OLLA). For ILLA, the adaptive capability is achieved by switching the modulation and coding modes based on a look‐up table using signal‐to‐noise ratio (SNR) thresholds at the target bit error rate (BER), while OLLA builds upon the ILLA method by dynamically adjusting the SNR thresholds to further optimize the system performance. Although both improve the system overall throughput by switching between different transmission modes, there is still a gap to optimal performance as the BER is comparatively far away from the target BER. Machine learning (ML) is a promising solution in solving various classification problems. In this work, the supervised learning based k‐nearest neighbours (KNN) algorithm is invoked for choosing the optimum transmission mode based on the training data and the instantaneous SNR. This work focuses on the low‐latency communications scenarios, where short block‐length LDPC codes are utilized. On the other hand, given the short block‐length constraint, we propose to artificially generate the training data to train our ML assisted AMC scheme. The simulation results show that the proposed ML‐LDPC‐AMC scheme can achieve a higher throughput than the ILLA system while maintaining the target BER. Compared with OLLA, the proposed scheme can maintain the target BER while the OLLA fails to maintain the target BER when the block length is short. In addition, when considering the channel estimation errors, the performance of the proposed ML‐LDPC‐AMC maintains the target BER, while the ILLA system's BER performance can be higher than the target BER. [ABSTRACT FROM AUTHOR]

Quantum Parameter Estimation (QPE) is commonly led using quantum probe states for the characterization of quantum systems. For these purposes, Quantum Fisher Information (QFI) plays a crucial role by imposing a lower bound for the parametric estimation of quantum channels. Several schemes for obtaining QFI lower bounds have been proposed, particularly for Pauli channels regarding qubits. Those schemes commonly employ either the individual channel, multiple copies of it, or arrangements including communication architectures. The present work aims to propose an architecture involving path superposition and causal indefinite order in superposition. Thus, by controlling the symmetry balance of this superposition, it reaches notable improvements in quantum parameter estimation. The proposed architecture has been tested to find the best possible QPE bounds for a representative and emblematic set of Pauli channels. Further, for the most reluctant channels, it was revisited testing the architecture again under a primary path superposition (using double teleportation) and also using entangled probe states to recombine their outputs with the original undisturbed state. Notable outcomes practically near zero were found for the QPE bounds, stating a hierarchy between the approaches, but anyway reaching a perfect theoretical QPE, particularly for the last path superposition including the proposed architecture. [ABSTRACT FROM AUTHOR]

An emerging challenge of integrated communication and sensing is the extraction of weak sensing signals transmitted through an unknown non-stationary channel. In this work, we propose a weak signal extraction method with weak measurement. Taking advantage of time division multiplexing, we preliminarily estimate the channel via adjustable finite impulse response filter, further suppressing the interfering signal caused by background noises via spectrum shift. By subsequently using the time-varying phase estimation method via weak measurement, the real-time detection of weak signals in the non-stationary channel is achieved. We demonstrate via theoretical analysis and confirmatory experiment that our method is able to amplify the phase shift, to suppress technical noise and to improve detection resolution limit, while proving robust against light source fluctuations, initial phase differences and detector saturation. The method hence enables weak sensing signal extraction with a low signal-to-noise ratio non-stationary channel. Furthermore, we interface our measurement method to squeezed light sources, offering the possibility of surpassing standard quantum limit. Integrated communication and sensing require high performance, sensing precision and preliminary knowledge of the channel, posing challenges to sensing weak signals from non-stationary channels. In this work, the authors demonstrate a weak signal extraction method with weak measurement, achieving weak signal sensing through a non-stationary channel. [ABSTRACT FROM AUTHOR]

The estimate of precipitation from satellite measurements is an indirect estimate if compared to rain gauges or disdrometer measurements, but it has the advantage of complete coverage over oceans, mountainous regions, and sparsely populated areas where other sources of precipitation data (e.g., weather radar) are unavailable or unreliable. Among the satellite-based precipitation estimates, geostationary (GEO) data ensure the highest spatial and temporal resolution. At the same time, the IR/VIS channels deployed on GEO satellites have lower capabilities than microwave (MW) channels in characterizing the cloud structure. Machine learning (ML) techniques can be considered a powerful tool to overcome the limitations related to the physical relationship between IR/VIS channels and precipitation estimation. This study describes the development of a convolutional neural network (U-Net) to retrieve the precipitation rate using IR measurements only from the Meteosat Second Generation (MSG) satellite. Its performances are evaluated through a comparison with H SAF and NASA operational products (e.g., H60B or H03B and IMERG-E, respectively), of which the algorithms are based on different principles. The results highlight a lower error in precipitation rate estimates for the U-Net with respect to the other products but also some issues in correctly estimating the more intense precipitation (>5 mmh−1). On the other hand, the precipitation detection capabilities of the U-Net outperform the H SAF products for lower precipitation rate, while IMERG-E shows the best performance regardless of the precipitation regime. Furthermore, the U-Net is able to account for and correct the parallax displacement that affects the measurement as the satellite viewing angle increases. [ABSTRACT FROM AUTHOR]

Grant-free random access (RA) utilizing massive multiple-input multiple-output (MIMO) technology has attracted considerable attention in recent years due to its potential to enhance spectral efficiency. This paper introduces an innovative and advanced approach for the joint detection of users and estimation of channels in grant-free RA. The approach incorporates two distinct preamble structures: the single orthogonal preamble (SOP) and the concatenated orthogonal preamble (COP). The proposed algorithms make full use of the inherent quasiorthogonal characteristic of massive MIMO, thereby enabling the accurate estimation of user channels while effectively avoiding collisions in the preambles. As a result, these algorithms generate highly precise estimations of user channels. To substantiate the effectiveness of the proposed algorithms, this paper provides an extensive theoretical analysis and presents a comprehensive set of experimental results. These findings offer robust evidence for the efficacy of the algorithms in substantially bolstering the performance of grant-free RA. Additionally, we have conducted further research and analysis, which has led to additional insights and refinements in our proposed approach. Moreover, the experimental results validate the statistical significance and reliability of the performance enhancements achieved by these algorithms. Moreover, the proposed approach exhibits robustness in scenarios with different levels of user density and varying channel conditions. Through a thorough analysis of these scenarios, we showcase the versatility and applicability of our algorithms in real-world environments. [ABSTRACT FROM AUTHOR]