Multi Path Research Articles

A Novel Design for Joint Collaborative NOMA Transmission with a Two–Hop Multi–Path UE Aggregation Mechanism

A Novel Design for Joint Collaborative NOMA Transmission with a Two–Hop Multi–Path UE Aggregation Mechanism

A multi path routing protocol with efficient energy consumption in IoT applications real time traffic

The extensive utilization of IoT applications leads to the aggregation of a substantial volume of data, presenting a crucial challenge in terms of data routing within these networks. RPL intentionally surpasses the limitations sometimes observed in low-power and lossy networks, which are particularly prevalent in IoT networks. The RPL protocol is designed specifically for static networks that do not involve mobility or topological changes. The RPL protocol guarantees continuous connectivity between nodes and mitigates the risk of data loss in stationary IoT applications that do not involve mobility or alterations in network configuration. The article utilizes a mobility aid technology known as network performance stability using the intelligent routing protocol (nPSIR), which expands upon RPL. The Mobility Support Entity (nPSIR) facilitates the displacement of all nodes, with the exception of the root node, and ensures uninterrupted connection during mobility. Moreover, it deals with the situation where there is a physical barrier between two interconnected nodes in a changing environment. In order to achieve this objective, it employs a dynamic trickle timer that operates within two distinct ranges. Furthermore, it utilizes a neighbor link quality table, a mechanism for selecting the most beneficial parent node in the event of migration, a measure of confidence, the identification of crucial regions, and a blacklist. Multiple simulations validate that nPSIR effectively decreases hand-off delay and improves packet delivery, despite the minor drawbacks of increased signaling costs and power consumption. The delivery ratio decreases the quantity of lost data packets and surpasses both RPL as a responsive protocol and mRPL as a proactive protocol in relation to mobility.

Data rates transmission estimation for passive multi path light EPON/GPON networks performance signature with multi point services

Data rates transmission estimation for passive multi path light EPON/GPON networks performance signature with multi point services

Global–local feature learning for fine-grained food classification based on Swin Transformer

Separable object parts, such as the head and tail in a bird, are vital for fine-grained visual classifications. For those objects without separable parts, the classification task relies only on local and global textural image features. Although the Swin Transformer architecture was proposed to efficiently capture both local and global visual features, it still exhibits a bias towards global features. Therefore, our goal is to enhance the local feature learning capability of the Swin Transformer by adding four new modules of the Local Feature Extraction Network (L-FEN), Convolution Patch-Merging (CP), Multi-Path (MP), and Multi-View (MV). The L-FEN enhances the Swin transformer with the improved local feature capture. The CP is a localized and hierarchical adaptation of the Swin’s Patch Merging technique. The MP method integrates features across various Swin stages to accentuate local details. Meanwhile, the MV Swin transformer block supersedes traditional Swin blocks with those incorporating varied receptive fields, ensuring a broader scope of local feature capture. Our enhanced architecture, named Global–Local Swin Transformer (GL-Swin), is applied to solve a fine-grained food classification task. On three major food datasets: ISIA Food-500 UEC Food-256, and Food-101, our GL-Swin achieved accuracies of 66.75%, 85.78%, and 92.93% respectively, consistently outperforming other leading methods.

GNSS direct position estimation-inspired positioning with pseudorange correlogram for urban navigation

Multipath (MP) reception has been among the main issues for accurate and reliable positioning in urban environments. It has been shown to introduce positioning errors of up to tens of meters for conventional two-step (2SP) receivers. The direct position estimation (DPE) has been introduced as a more robust positioning algorithm compared to the conventional two-step (2SP) receivers in terms of MP mitigation. However, its high computational load prevents DPE from real-time positioning for commercial receivers. Thus, we present a novel grid-based maximum likelihood estimation (MLE) algorithm based on DPE by making use of pseudorange measurements to obtain the correlogram on a predefined searching space. Unlike DPE, which performs correlations at the intermediate frequency (IF) level, correlations are done by directly comparing the code phase of each candidate position, velocity, and timing with the incoming pseudorange. This way, the proposed method retains MP mitigation properties from DPE through the use of MLE from DPE and allows for a significantly reduced computational load compared to DPE. The proposed method was tested with both open-sourced datasets collected in urban environments as well as IF simulation data, and its performance is evaluated against a 2SP receiver. Results show that the proposed method manages to acquire the MP mitigation capability of DPE and outperforms 2SP by up to around 23% in the tested urban datasets and 91% in the simulation data, at a much-reduced computational time. The resilience of our proposed method against MP and NLOS could even potentially offer applications in geodetic networks, where robust estimators are traditionally employed to counteract outliers.

Evaluation of ML-based classification algorithms for GNSS signals in ocean environment

Abstract In the maritime environment, multipath interference exhibits a significantly pronounced influence, resulting in GNSS system performance degradation. Enhancing system performance involves the identification and elimination of NLOS signals. This study focuses on the analysis of multipath data induced by sea waves, collected off the coast of Kakinada Sea (16.98° N, 82.29° E), to categorize signals as Line-of-Sight (LOS), Non-Line-of-Sight (NLOS) and Multipath (MP). A machine learning (ML) approach is employed to identify the presence of LOS, NLOS and MP signals in a coastal environment, both before and after the advancement of tidal waves. In the proposed approach, ML algorithms are trained using 3 key parameters namely elevation angle, signal strength and pseudorange residuals. This study involves the implementation of 14 prominent supervised classification algorithms and their accuracies and computational times are compared. The results due to GPS (L1) and IRNSS (L5 and S1) are considered. Decision Tree and its ensemble function AdaBoost, exhibited exceptional performance of accuracy (99.99 %) and computational time (0.45 s).

QFS-RPL: mobility and energy aware multi path routing protocol for the internet of mobile things data transfer infrastructures

QFS-RPL: mobility and energy aware multi path routing protocol for the internet of mobile things data transfer infrastructures

An efficient reconfigurable geographic routing congestion control algorithm for wireless sensor networks

<span lang="EN-US">In recent times, huge data is transferred from source to destination through multi path in wireless sensor networks (WSNs). Due to this more congestion occurs in the communication path. Hence, original data will be lost and delay problems arise at receiver end. The above-mentioned drawbacks can be overcome by the proposed efficient reconfigurable geographic routing congestion control (RgRCC) algorithm for wireless sensor networks. the proposed algorithm efficiently finds the node’s congestion status with the help queue length’s threshold level along with its change rate. Apart from this, the proposed algorithm re-routes the communication path to avoid congestion and enhances the strength of scalability of data communication in WSNs. The proposed algorithm frequently updates the distance between the nodes and by-pass routing holes, common for geographical routing. when the nodes are at the edge of the hole, it will create congestion between the nodes in WSNs. Apart from this, more nodes sink due to congestion. it can be reduced with the help of the proposed RgRCC algorithm. As per the simulation analysis, the proposed work indicates improved performance in comparison to conventional algorithm. By effectively identifying the data congestion in WSNs with high scalability rate as compared to conventional methods</span>

An Improved Carrier-Smoothing Code Algorithm for BDS Satellites with SICB

Carrier Smoothing Code (CSC), as a low-pass filter, has been widely used in GNSS positioning processing to reduce pseudorange noise via carrier phases. However, current CSC methods do not consider the systematic bias between the code and carrier phase observation, also known as Satellite-induced Code Bias (SICB). SICB has been identified in the BDS-2 and the bias will reduce the accuracy or reliability of the CSC. To confront bias, an improved CSC algorithm is proposed by considering SICB for GEO, IGSO, and MEO satellites in BDS constellations. The correction model of SICB for IGSO/MEO satellites is established by using a 0.1-degree interval piecewise weighted least squares Third-order Curve Fitting Method (TOCFM). The Variational Mode Decomposition combined with Wavelet Transform (VMD-WT) is proposed to establish the correction model of SICB for the GEO satellite. To verify the proposed method, the SICB model was established by collecting 30 Multi-GNSS Experiment (MGEX) BDS stations in different seasons of a year, in which the BDS data of ALIC, KRGG, KOUR, GCGO, GAMG, and SGOC stations were selected for 11 consecutive days to verify the effectiveness of the algorithm. The results show that there is obvious SICB in the BDS-2 Multipath (MP) combination, but the SICB in the BDS-3 MP is smaller and can be ignored. Compared with the modeling in the references, TOCFM is more suitable for IGSO/MEO SICB modeling, especially for the SICB correction at low elevation angles. After the VMD-WT correction, the Root Mean Square Error (RMSE) of SICB of B1I, B2I, and B3I in GEO satellites is reduced by 53.35%, 63.50%, and 64.71% respectively. Moreover, we carried out ionosphere-free Single Point Positioning (IF SPP), Ionosphere-free CSC SPP (IF CSC SPP), CSC single point positioning with the IGSO/MEO SICB Correction based on the TOCFA Method (IGSO/MEO SICB CSC), and CSC single point positioning with the IGSO/MEO/GEO SICB correction based on VMD-WT and TOCFA (IGSO/MEO/GEO SICB CSC), respectively. Compared to IF SPP, the average improvement of the IGSO/MEO/GEO SICB CSC algorithm in the north, east, and up directions was 24.42%, 27.94%, and 24.98%, respectively, and the average reduction in 3D RMSE is 24.54%. Compared with IF CSC SPP, the average improvement of IGSO/MEO/GEO SICB CSC is 7.03%, 6.50%, and 10.48% in the north, east, and up directions, respectively, while the average reduction in 3D RMSE was 9.86%. IGSO/MEO SICB mainly improves the U direction positioning accuracy, and GEO SICB mainly improves the E and U direction positioning accuracy. After the IGSO/MEO/GEO SICB correction, the overall improvement was about 10% and positioning improved to a certain extent.

A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length

A smartphone equipped with a Global Navigation Satellite System (GNSS) module can generate positional information for location-based services. However, GNSS signals are susceptible to fragility, multipath (MP), and Non-Line-Of-Sight (NLOS) interference, which can lead to a degradation in the accuracy of GNSS positioning on smartphones. Due to limitations in the smartphone’s antenna, GNSS signal strength is typically lower. Moreover, in urban areas, where smartphones rely on GNSS, MP and NLOS signals are the primary factors impeding accurate positioning. In this paper, with the goal of enhancing both the accuracy and robustness of smartphone GNSS positioning, we propose two methods. Firstly, an optimized particle filter method employing a Krill Herd Algorithm (KHA) is suggested for the integration of GNSS and Pedestrian Dead Reckoning (PDR). Secondly, a probabilistic approach is presented to identify faulty GNSS measurements using step distance information obtained from the PDR. Experimental tests were conducted using smartphones to evaluate the performance of the proposed method. The results demonstrate that both the KHA and fault detection methods effectively enhance the performance of integrated PDR and GNSS.

Channel State Information Based Equalizer Design for Frequency Selective Channels

Abstract—Equalizer design is an extremely critical aspect for wireless networks. This paper presents an approach combining the decision feedback mechanism and successive signal detection to equalize frequency selective channel effects for signals traversing different run-lengths. In this approach, the errors on comparison between the transmitted signal and received signal are fed to the equalizer to adjust the tap weights. Still the irreversible nature of inter symbol interference is a huge challenge due to multi path propagation mechanisms in wireless channels. It is a common observation that signals traversing a smaller path reach the receiver earlier compared to the multi-path component traversing a longer path. Assuming similar shadowing effects, it is seen that fading effects make it difficult to accurately receive long distance MPCs, thereby degrading the BER performance. It has been shown that the proposed system attains almost similar BER performance irrespective of the shadowing effect or run length of the MPCs. Keywords— Multi Path Component (MPC), Inter Symbol Interference (ISI), Adaptive Equalizer, Channel State Information (CSI), , Bit Error Rate (BER), Probability of Error

Blocking aware offline survivable path provisioning of connection requests in elastic optical networks

Survivability of Elastic Optical Networks (EONs) from a link failure is an important problem. Shared Backup Path Protection (SBPP) is widely used for protection against a link failure. Backup Spectrum Reservation with Multi Path Protection (BSR-MPP) was introduced to increase backup spectrum sharing by reserving backup spectrum and employing protection with multiple backup paths. We propose a new polynomial-time strategy, Blocking Aware Survivable Path Provisioning (BASPP) that shares backup resources like SBPP and BSR-MPP but can reduce request blocking when opportunity of sharing resources does not exist. Since we address the offline problem of Survivable Routing, Modulation and Spectrum Allocation in EONs, we also propose an Integer Linear Program formulation. Results show appreciable reduction in bandwidth blocking ratio by using BASPP over existing strategies.

A proposal on multi-agent static path planning strategy for minimizing radiation dose

To minimize the cumulative radiation dose, various path-finding approaches for single agent have been proposed. However, for emergence situations such as nuclear power plant accident, these methods cannot be effectively utilized for evacuating a large number of workers because no multi-agent method is valid to conduct the mission. In this study, a novel algorithm for solving the multi-agent path-finding problem is proposed using the conflict-based search approach and the objective function redefined in terms of the cumulative radiation dose. The proposed method can find multi paths that all agents arrive at the destinations with reducing the overall radiation dose. To verify the proposed method, three problems were defined. In the single-agent problem, the objective function proposed in this study reduces the cumulative dose by 82% compared with that of the shortest distance algorithm in experiment environment of this study. It was also verified in the two multi-agent problems that multi paths with minimized the overall radiation dose, in which all agents can reach the destination without collision, can be found. The method proposed in this study will contribute to establishing evacuation plans for improving the safety of workers in radiation-related facilities.

An improved satellite-induced code bias correction model for BDS un-GEO satellites in consideration of elevation and azimuth angles

The satellite-induced code bias (SICB) resulting in code-phase divergences of nearly 1.0 m has been observed in the code measurements of BeiDou navigation satellite system (BDS). Since the SICB is proved to be elevation- and frequency-dependent, several SICB correction models have been proposed to remove the systematic bias, but most of them only consider elevation angles, overlooking the influence of azimuth angles, which would limit the accuracy of the correction models, especially for Medium Earth Orbit (MEO) satellites. To deal with this problem, we proposed an improved SICB piecewise correction model as a function of elevation and azimuth angles and estimated parameters for each BDS un-GEO (Geostationary Earth Orbit) satellite and each available frequency with node-separation of 1°. The epoch-difference method is employed to remove the effect of stable terms, such as phase-ambiguities, receiver- and satellite-dependent hardware delays. Moreover, to reduce the effect of code noise and multipath at low-elevation conditions, the threshold of elevation angle for the improved model is determined based on the statistics of Multipath (MP) combinations. Besides, weight-average algorithm is used to obtain the optimal MP deviations and improve the accuracy of proposed correction model in consideration of signal-to-noise (SNR). The experiment results with real GNSS data show that the SICB models are more suitable for BDS-2 MEO satellites than Inclined Geosynchronous Orbit (IGSO) satellites. The code measurements on B1 signal are more susceptible to the SICB than that on B3 signal. After applying the improved SICB correction models, the RMS errors of MP series are significantly reduced for BDS-2 un-GEO satellites. However, for BDS-3 satellites, only centimeter-level SICB deviations can be detected for all frequency-bands, and the RMS of MP variations have slight changes after applying the SICB corrections, indicating that the systematic bias in MP combinations is not obvious, especially for BDS-3 MEO satellites.

Improving the Accuracy of Vehicle Position in an Urban Environment Using the Outlier Mitigation Algorithm Based on GNSS Multi-Position Clustering

In this paper, we propose a multi-position cluster-based weighted position estimation method that minimizes the influence of multipath (MP)/non-line-of-sight (NLOS) signals using a global navigation satellite system (GNSS) receiver. The proposed method is suitable for positioning passenger cars, particularly in urban driving environments. Density-based spatial clustering of applications with noise (DBSCAN)-based clustering is performed by generating multi-position data through a subset of observable satellites and analyzing the density characteristics of position data generated by line-of-sight (LOS) satellite signals from the generated multi-position data. To estimate the optimal position through clustered data, we propose a method by constructing a weighted model through Doppler-based velocity measurements, which is robust to MP delay signals compared to code-based pseudorange measurements. In addition, to prevent unnecessary clustering points from having weights, the predicted range is selected based on the nonholonomic constraint of the vehicle. The proposed algorithm was quantitatively validated by selecting a region in an actual urban environment where the MP/NLOS error could occur significantly. It was confirmed that the accuracy of vehicle position was improved by approximately 24% by the proposed method compared to existing positioning solutions.

An Improved Doppler-Aided Smoothing Code Algorithm for BeiDou-2/BeiDou-3 un-Geostationary Earth Orbit Satellites in Consideration of Satellite Code Bias

The extensive use of carrier-aided smoothing code (CSC) filters has led to a reduction in the noise level of raw code measurements in GNSS positioning and navigation applications. However, the existing CSC technique is sensitive to the changes in the integer ambiguity, and then the smoothing procedure needs to be restarted in the presence of cycle slips. As the Doppler shift is instantaneously observed and immune to cycle slips, the Doppler-aided smoothing code (DSC) algorithm would be more promising in a challenged environment. Based on the Hatch filter, an optimal DSC approach is proposed with the principle of minimum variance. Meanwhile, to inhibit the effect of the integral cumulative error of the Doppler, a balance factor is adopted to adjust the contributions of raw code and DSC. The noise level of code observable is not only affected by thermal noise, but also limited by systematic bias. Satellite code bias (SCB) was identified in the raw code observable on each frequency for each BDS-2 satellite. By minimizing the sum of the absolute value of residuals, the polynomial segment fitting algorithm as a function of elevation angles is applied to establish the SCB correction model based on epoch-differenced multipath (MP) deviations. Finally, different types of experiments demonstrate the validity and efficiency of the refined DSC filter with SCB corrections on each available frequency for BDS un-GEO satellites.

The Extraction and Characterization of Pseudorange Multipath Based on BDS-3 Multi-Frequency Observations.

Global Navigation Satellite System (GNSS) observations are subject to various errors during their propagation process. A reasonable correction of these errors can improve the positioning, navigation, and timing (PNT) service capability. The impact of multipaths on pseudorange observations can reach a decimeters or even meters level. However, their mechanism is complex and there is currently no universally accepted high-precision correction model. The correlation between the pseudorange multipaths (MP) of BDS-2 satellites and satellite elevation has been confirmed, while there have been fewer analyses of the MP characteristics for different frequencies of BDS-3 satellites. The broadcasting of multi-frequency observations in BDS-3 should theoretically make the extracted MP more accurate compared to traditional methods. Based on this, in this contribution, a multi-frequency MP extraction algorithm based on the least squares principle is proposed, which can simultaneously eliminate the influence of higher-order ionospheric delay. The analytical expression for only eliminating first-order ionospheric delay is successfully derived. Subsequently, the characteristics of the MPs extracted from different frequency combinations and the impact of combination noise on the extraction accuracy are discussed. The influence of second-order ionospheric delay on the MPs for each frequency under different combination noises, as well as the periodic behavior exhibited in long-term observations of the BDS-3 medium earth orbit (MEO) and inclined geosynchronous orbit (IGSO) satellites, are also analyzed. Finally, the correlations between the MPs of each frequency of BDS satellite and elevation are quantitatively analyzed based on observations from 35 stations. Overall, this work has positive implications for the study of the MP characteristics of BDS-3 and subsequent modeling efforts.

Performance Improvement of Packet Delay in Advanced Public Transportation System Using Multi Path UDP on Cellular Environment

Performance Improvement of Packet Delay in Advanced Public Transportation System Using Multi Path UDP on Cellular Environment

Link quality and energy efficient optimal simplified cluster based routing scheme to enhance lifetime for wireless body area networks

Monitoring of patient’s health in the medical industry can be enabled using wireless body area networks (WBANs), which are already used for various purposes, including assisting in human safety. It is imperative to use better power management strategies since the body sensors are small and the battery cannot hold a charge for a long time. Due to the vast amounts of information generated by medical sensors, resource-constrained networks face a significant challenge when guaranteeing the specified quality of service (QoS). Moreover, the WBAN regularly meets the primary hassle of QoS degradation because of congestion WBAN structure can easily compromise heterogeneous and complex networks. Either inappropriate data collection or using energy effectively to transmit medical data without the expense of travel and length has become an important one. To address this issue, the present research work ‘Link Quality and Energy Efficient Optimal Clustering-Multipath (LEOC-MP)’ scheme tries to explore an answer. The main goals of the LEOC-MP (Optimal Link Quality and Energy Efficient Optimal Clustering-Multipath) system are to guarantee node-to-node link quality, lengthen network life, and compute high-performing cluster heads to guarantee reliable multi path data transfer. This work was executed in three phases. First, an optimal simplified clustering technique for data collection from body sensors using an improved pelican optimization (ICO) algorithm is introduced. Next, multiple design constraints for node rank computation, energy efficiency, link quality, path loss, distance, and delay are used. Besides, an Auto-Regressive Probabilistic Neural Network (AR-PNN) is introduced to optimize those design constraints and compute the cluster head (CH) of each cluster. Multipath firing is then performed using a moderated puffer-fish optimization (MPO) algorithm that finds the closest optimal and shortest node to transmit optimal drug data. The work is simulated using an NS-3 environment, and the results are obtained. The outcome of this work is analyzed with existing methodologies, and the results prove that the present work consistently outperforms the existing methodologies.

Clutter Suppression Algorithm with Joint Intrinsic Clutter Motion Errors Calibration and Off-Grid Effects Mitigation in Airborne Passive Radars

In an airborne passive radar, multipath (MP) clutter, which is caused by MP signals contained in the contaminated reference signal, degrades the space-time adaptive processing (STAP) performance. The MP clutter suppression algorithm before STAP can mitigate the influence of impure reference signals. However, the performances of the existing MP clutter suppression methods deteriorate when the intrinsic clutter motion (ICM) exists because the sparse model of MP clutter is disturbed. To eliminate the impacts of ICM on MP clutter suppression, a joint optimization algorithm is developed for airborne passive radar. Firstly, the sparse model of MP clutter is modified by taking ICM fluctuation into account. Subsequently, the joint optimization function of the ICM fluctuation and MP clutter profile is derived. Finally, based on the local search technique, MP clutter is suppressed with ICM error calibration and off-grid effects mitigation. A range of simulations verify the reliability and superiority of the proposed method.