Multi Path Research Articles

Multipath Dynamic Adaptive Streaming over HTTP Using Scalable Video Coding in Software Defined Networking

Dynamic Adaptive Streaming over HTTP (DASH) offers adaptive and dynamic multimedia streaming solutions to heterogeneous end systems. However, it still faces many challenges in determining an appropriate rate adaptation technique to provide the best quality of experience (QoE) to the end systems. Most of the suggested approaches rely on servers or client-side heuristics to improve multimedia streaming QoE. Moreover, using evolving technologies such as Software Defined Networking (SDN) that provide a network overview, combined with Multipath Transmission Control Protocol (MPTCP), can enhance the QoE of streaming multimedia media based on scalable video coding (SVC). Therefore, we enhance our previous work and propose a Dynamic Multi Path Finder (DMPF) scheduler that determines optimal techniques to enhance QoE. DMPF scheduler is a part of the DMPF Scheduler Module (DSM) which runs as an application over the SDN controller. The DMPF scheduler accommodates maximum client requests while providing the basic representation of the media requested. We evaluate our implementation on real network topology and explore how SVC layers should be transferred over network topology. We also test the scheduler for network bandwidth usage. Through extensive simulations, we show clear trade-offs between the number of accommodated requests and the quality of the streaming. We conclude that it is better to schedule the layers of a request into the same path as much as possible than into multiple paths. Furthermore, these result would help service providers optimize their services.

Robust Statistical Detection of GNSS Multipath Using Inter-Frequency C/N0 Differences

Multipath detection and mitigation are crucial issues for global navigation satellite system (GNSS) high-precision positioning. The multi-frequency carrier power-to-noise density ratio (C/N0)-based multipath detection technique has achieved good results in real-time static and low-dynamic applications, and shown better practicability because of the low computational load and the requirement for little additional hardware. However, the classic multipath detection method based on inter-frequency C/N0 differences directly employs the 3σ rule to determine the threshold without considering the distribution of detection statistics and their variation characteristics with elevation angle, and ignores the interference of outliers to the reference functions. A robust multipath detection method is proposed in this paper. The reference functions of C/N0 differences are fitted using least absolute deviation (LAD) to obtain more accurate nominal values. According to the skew characteristics of the detection statistics, a medcouple (MC)-based adjusted boxplot is employed to determine the threshold. The performance of the new detection method is verified in the multipath environments. The experimental results show that compared with the classic method, the new multipath detector has strong robustness and can respond more accurately to large changes in multipath (MP) combination values at most elevation angles. It is sensitive to short-delay multipath and diffraction, and is an important supplement to multipath detection techniques.

Q Learning Algorithm with Network Coding in Multi Path Transfer Protocol for Wireless Mesh Network

In a wireless mesh network, the network coding algorithm used to improve network efficiency. In this paper, we have implemented the Q-learning algorithm with CSMA/CA as in distributed co-ordination function along with multi-path transfer protocol (MPTP). The functioning of CSMA/CA is based on physical carrier sensing. Q-learning algorithm, along with network coding, is implemented to achieve better throughput. Our proposed method has used to reduce the packet loss and to minimize the end to end delay of the network communication. Also, it will improve the possibility of receiver buffer blocking.

Constrained Robust Unscented Kalman Filter for BDS Navigation in Dense Urban Areas

In dense urban environment, Multipath (MP) and Non-line-of-sight (NLOS) signals will degrade the performance of BeiDou Navigation Satellite System (BDS) Position, Velocity and Timing (PVT). In order to mitigate this negative impact, a Constrained Robust Unscented Kalman Filter (CRUKF) is implemented based on pseudorange/Doppler shift measurements. An equivalent weight function based on the innovation vector is constructed, which can overcome the problem of performance degrading of traditional robust methods caused by the inaccurate initial state vector. Then, the motion information, navigation direction and elevation, is included to further constrain the Robust UKF (RUKF) solution. The performance of CRUKF is analyzed using two real car tests in a dense building area, Tokyo. It is shown that there is a clear correlation between MP/NLOS errors and Position Dilution of Precision (PDOP), which seriously lows the positioning accuracy. Regarding the horizontal position, the Root Mean Square Error (RMSE) of CRUKF is 5.6 m, while those of Robust Iterative Least Square (RILS) and RUKF are 15.3 m and 6.4 m, respectively. Similar improvements are presented in vertical position and velocity and hence show the superior positioning performance of CRUKF. In addition, without sensor aiding or coupling, CRUKF can be suitable for real-time application of low-cost receiver.

Assessing the Positioning Performance Under the Effects of Strong Ionospheric Anomalies With Multi‐GNSS in Hong Kong

AbstractGlobal navigation satellite system (GNSS) precise positioning performance will be strongly affected under severe ionospheric anomaly conditions. The combination of multi‐GNSS can increase the available observations and improve the geometry of continuously tracked satellites. This paper focuses on assessing the positioning performance with the combination of Global Positioning System (GPS), Global'naya Navigatsionnaya Sputnikova Sistema (GLONASS), and BeiDou System (BDS) around the St. Patrick's Day geomagnetic storm (9–18 March) in 2015 in Hong Kong. The rate of total electron content (TEC) index (ROTI) indicates severe ionospheric anomalies before the superstorm, while it was absent during the main phase of the storm in Hong Kong. Furthermore, strong scintillation events on signal‐to‐noise ratio (SNR) and multipath (MP) observables are observed during ionospheric anomalies period. Then the performance of single‐point positioning (SPP) and precise point positioning (PPP) with multi‐GNSS is shown. The ionospheric scintillation events may reduce pseudorange accuracy but affect SPP performance a little in this study, while the PPP accuracy is vastly decreased due to the subsequent reconvergence caused by frequent cycle slip (CS). Compared to PPP solutions with GPS only, the accuracy is improved significantly with the combination of multi‐GNSS.

Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods

In ultra-wideband (UWB)-based wireless ranging or distance measurement, differentiation between line-of-sight (LOS), non-line-of-sight (NLOS), and multi-path (MP) conditions is important for precise indoor localization. This is because the accuracy of the reported measured distance in UWB ranging systems is directly affected by the measurement conditions (LOS, NLOS, or MP). However, the major contributions in the literature only address the binary classification between LOS and NLOS in UWB ranging systems. The MP condition is usually ignored. In fact, the MP condition also has a significant impact on the ranging errors of the UWB compared to the direct LOS measurement results. However, the magnitudes of the error contained in MP conditions are generally lower than completely blocked NLOS scenarios. This paper addresses machine learning techniques for identification of the three mentioned classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental dataset. The dataset was collected in different conditions in different scenarios in indoor environments. Using the collected real measurement data, we compared three machine learning (ML) classifiers, i.e., support vector machine (SVM), random forest (RF) based on an ensemble learning method, and multilayer perceptron (MLP) based on a deep artificial neural network, in terms of their performance. The results showed that applying ML methods in UWB ranging systems was effective in the identification of the above-three mentioned classes. Specifically, the overall accuracy reached up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it was 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we provide the publicly accessible experimental research data discussed in this paper at PUB (Publications at Bielefeld University). The evaluations of the three classifiers are conducted using the open-source Python machine learning library scikit-learn.

Analysis of the Relationship between Scintillation Parameters, Multipath and ROTI.

Global Navigation Satellite System (GNSS) operation can be affected by several environmental factors, of which ionospheric scintillation is one of the most significant. Scintillation is usually characterized by two indices, namely the amplitude scintillation index (S4) and phase scintillation index (σφ). However, these two indices can only be generated by specialized GNSS receivers, which are not widely available all around the world. To popularize the study of scintillation, this article proposes to use more accessible parameters, namely multipath (MP) and rate of change of total electron content index (ROTI), to characterize scintillation. Using GPS data obtained on six days in total from three stations, namely PRU2 and SAO0P located in Sao Paulo, Brazil and SNA0P located in Antarctica, respectively, both the time series plots and 2D maps were generated to investigate the relationship of scintillation indices (S4 and σφ) with MP and ROTI. To prevent the effect of the real multipath error, a 30-degree satellite elevation mask is applied to all the data. As the scintillation indices S4 and σφ have a sampling interval of 1 min, MP and ROTI are calculated with the same sampling interval for a more direct comparison. The results show that the structural similarity (SSIM) and correlation coefficient (CC) between parameters was greater than 0.7 for 70% of outputs. In addition, the variogram and cross-variogram are applied to investigate the spatial structure of the MP, ROTI, S4 and σφ in order to support the results of SSIM and CC. With outputs in three forms, promising spatial and temporal relationships between parameters was observed.

Optimal Multipath Conveyance with Improved Survivability for WSN’s In Challenging Location

The sensor networks with the capability of conveying information’s through the wireless medium in many circumstances are affected by the inner energy and the exterior atmospheric changes as the sensors are employed in the areas that are beyond human reach and left uncared. Since routing procedure seems as the main source of energy utilization in these types of networks formulated by the sensors and as the prevailing routing procedures considers the parametric optimization from the network perception alone eluding the atmospheric influences over the network from the exterior sources, makes the sensors obtuse to the sudden unexpected changes in the atmosphere. So this leads to failure in conveyance of information’s due to the failure in the routing and the fading of the path established. To manage such circumstance effectively the paper formulates a multi path conveyance with improved survivability for wireless-sensor-networks. The method takes into consideration the residual energy, depth and the dynamic changes in the atmosphere. The developed routing evades the information from passing over the hazardous area and forms a reliable conveyance by creating an atmosphere based on the monitoring competencies of the sensor network. The formulate method is experimentally evaluated using the network simulator-2 to acquire the vital enhancements in the life expectancy of the network framed and the delivery rate of the packets.

MIMU/Odometer Fusion with State Constraints for Vehicle Positioning during BeiDou Signal Outage: Testing and Results.

With the rapid development of autonomous vehicles, the demand for reliable positioning results is urgent. Currently, the ground vehicles heavily depend on the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) providing reliable and continuous navigation solutions. In dense urban areas, especially narrow streets with tall buildings, the GNSS signals are possibly blocked by the surrounding tall buildings, and under this condition, the geometry distribution of the in-view satellites is very poor, and the None-Line-Of-Sight (NLOS) and Multipath (MP) heavily affects the positioning accuracy. Further, the INS positioning errors will quickly diverge over time without the GNSS correction. Aiming at improving the position accuracy under signal challenging environment, in this paper, we developed an MIMU(Micro Inertial Measurement Unit)/Odometer integration system with vehicle state constraints (MO-C) for improving the vehicle positioning accuracy without GNSS. MIMU/Odometer integration model and the constrained measurements are given in detail. Several field tests were carried out for evaluating and assessing the MO-C system. The experiments were divided into two parts, firstly, field testing with data post-processing and real-time processing was carried out for fully assessing the performance of the MO-C system. Secondly, the MO-C was implemented in the BeiDou Satellite Navigation System (BDS)/integrated navigation system (INS) for evaluating the MO-C performance during the BDS signal outage. The MIMU standalone positioning accuracy was compared with that from the MIMU/Odometer integration (MO), MO-C and MIMU with constraints (M-C) for assessing the Odometer, and the influence of the constraint on the positioning errors reduction. The results showed that the latitude and longitude errors could be suppressed with Odometer assisting, and the height errors were suppressed while the state constraints were included.

APS: Adaptive Packet Spraying to Isolate Mix-Flows in Data Center Network

Modern data centers host diverse applications, which generate a mix of short flows with stringent latency requirement and long flows requiring large sustained throughput. To solve the problem of resource competition between the mixed flows, we propose an adaptive traffic isolation scheme APS. Based on the packet spraying scheme in the multipath transmission, APS dynamically separates long flows from short ones on different paths to provide the low latency for the short flows. Meanwhile, to resolve the out-of-order problem, APS limits the long flows to a few paths with Equal Cost Multi Path (ECMP). Experimental results of NS2 simulation and testbed implementation show that, APS reduces the average completion time for short flows by up to 60 percent and increases the throughputs for long flows by about 1.68x over the state-of-the-art multipath transmission schemes.

Multipath Mitigation in GNSS Positioning by the Dual-Path Compression Estimation

The combinations of triple-frequency carrier phase have been widely used for precise positioning in the global navigation satellite system (GNSS). However, the position accuracy of this system is negatively affected by multipath (MP) interference and highly kinematic problems. To bridge this gap, in this paper, a dual-path compression estimation algorithm based on generalized least absolute shrinkage and selection operator (LASSO) method with penalty terms is proposed to mitigate MP. We establish a linear observation model, taking into account triple-frequency carrier phase and Doppler shift. In order to improve the accuracy of the compression estimation, a small penalty term is added after the re-weighted L1 regularization. Accordingly, the linear observation model is transformed into the MP estimation model, with the weight matrix consisting of the carrier-to-noise ratio function and satellite elevation. We use the dual-path algorithm to solve the LASSO problem in the MP estimation model. QR decomposition and the specialized implementation strategy are applied to improve the dual-path LASSO with penalty (DPLP) algorithm. The results on real-world dataset show that the sparse estimation method proposed in this study can effectively reduce the carrier phase MP errors at triple-frequency so long as the number of MP-affected satellites is less than five (the visible satellites is eight-ten).

GNSS Integration in the Localization System of an Autonomous Vehicle Based on Particle Weighting

Autonomous vehicles leverage the data provided by a suite of sensors, combining measurements in order to provide precise and robust position estimation to localization and navigation systems. In this paper, an Adaptive Monte Carlo Localization algorithm is applied to an autonomous golf car, where data from wheel odometry, an inertial measurement unit, a Global Positioning System (GPS) and laser scanning is combined to estimate the pose of a vehicle in an outdoor environment. Monte Carlo Localization techniques allow the compensation of the technical flaws of different sensors by fusing the information delivered by each one. However, one of the main problems of fusing GPS data are sudden decreases of accuracy and sudden jumps on positions due to phenomenons like multi–path signal reception. In this paper, a particle weighting MCL model which integrates GPS measurements is proposed, and its performance is compared in several experiments with a particle generation approach when a GPS sensor suddenly provides erroneous data.

Mitigation of Short-Term Temporal Variations of Receiver Code Bias to Achieve Increased Success Rate of Ambiguity Resolution in PPP

Ambiguity resolution (AR) is critical for achieving a fast, high-precision solution in precise point positioning (PPP). In the standard uncombined PPP (S-UPPP) method, ionosphere-free code biases are superimposed by ambiguity and receiver clock offsets to be estimated. However, besides the time-constant part of the receiver code bias, the complex and time-varying term in receivers destroy the stability of ambiguities and degrade the performance of the UPPP AR. The variation of receiver code bias can be confirmed by the analysis in terms of ionospheric observables, code multipath (MP) of the Melbourne–Wübbena (MW) combination and the ionosphere-free combination. Therefore, the effect of receiver code biases should be rigorously mitigated. We introduce a modified UPPP (M-UPPP) method to reduce the effects of receiver code biases in ambiguities and to decouple the correlation between receiver clock parameters, code biases, and ambiguities parameters. An extra receiver code bias is set to isolate the code biases from ambiguities. The more stable ambiguities without code biases are expected to achieve a higher success rate of ambiguity resolution and a shortened convergence time. The variations of the receiver code biases, which are the unmodeled errors in measurement residuals of the S-UPPP method, can be estimated in the M-UPPP method. The maximum variation of the code biases is up to 16 ns within two-hour data. In the M-UPPP method, the averaged epoch residuals for code and phase measurements recover their zero-mean features. For the ambiguity-fixed solutions in the M-UPPP method, the convergence times are 14 and 43 min with 17.7% and 69.2% improvements compared to that in the S-UPPP method which are 17 and 90 min under the 68% and 95% confidence levels.

Analyzing the Satellite-Induced Code Bias Variation Characteristics for the BDS-3 Via a 40 m Dish Antenna.

The satellite-induced code bias variation of geostationary satellite orbit satellites and medium earth orbit satellites of the second-generation BeiDou Navigation Satellite System (BDS-2) exceeds 1 m, which severely affects the accuracy and stability of the ambiguity resolution and high-precision positioning. With the development of the third-generation BDS (BDS-3) with a new system design and new technology, analysis of the satellite-induced code variation characteristics of BDS-3 has become increasingly important. At present, many scholars have explored the satellite-induced code bias of BDS-3, but most of them focus on BDS-3 experimental satellites via normal geodetic antenna. Compared to normal geodetic antenna, the 40-m dish antenna from the National Time Service Center can accurately detect satellite-induced code variations with low noise and high gain. Thus, observational data from fifteen BDS-3 medium earth orbit satellites are collected with the B1I/B2b/B3I/B1C/B2a frequency bands on the day of year (DOY) 199–206 in 2019, the PRN numbers of which are C19/C20/C21/C22/C23/C24/C25/C26/C27/C28/C30/C32/C33 /C35/C37, via the 40 m dish antenna to analyze the code bias variation characteristics. The results show that the obvious satellite-induced elevation‑dependent code bias variations exist in the B1I/B2b/B3I/B1C/B2a frequency bands of C28, compared with other satellites. Similarly, the multipath (MP) combination of B3I has an obvious elevation‑dependent variation within a range of 0.1 m for C21/C24/C27/C28/C37 and elevation‑dependent variation of the B2a and B2b frequency bands also exists in most satellites with a range of 0.1 m. However, the MP combination values of some satellites are asymmetric with respect to elevation, which is different from BDS-2 satellites and especially obvious for BDS-3 satellites B1I and BIC frequency bands with elevation‑dependent variations of 0.2 m, indicating that the code bias variation is not uniquely related to elevation, especially for the B1I/BIC frequency bands. What’s more, the satellite-induced code bias variation of the BDS-3 satellites is greatly reduced compared with that of the BDS-2 satellites. In addition, the similar code bias variation appears at the Xia1 station with a normal geodetic antenna of B1I/B1C/B3I/B2a/B2b of C21, B3I/B2a/B2b of C24 and B2b of C28 among B1I/B1C/B3I/B2a/B2b of C21/C24/C27/C28/C37. The influence of the BDS-3 satellite-induced elevation‑dependent code bias on precision positioning and ambiguity fixing is worth further study using different antennas or receivers.

Unambiguous method for DLL BOC signals tracking

SummaryIn this paper, we propose an efficient method for eliminating ambiguity in the acquisition and tracking processes of binary offset carrier (BOC)‐modulated signals. Our contribution is realized in two stages. The first one is based on the use, at the receiver end, of optimized locally generated subcarrier sequences to get a completely unambiguous composite correlation function (CF). This first stage is used to initialize and control the second stage of the proposed method designed on the basis of a maximum likelihood algorithm (MLA) to mitigate, specifically, multipath (MP) effect. In fact, the idea is to combine, in this second stage, the received signal (affected by noise and MP) with a set of locally generated ideal BOC(m,n)‐modulated signals, which are delayed and weighted by the MLA‐estimated parameters of delays, amplitudes, and phases of all the MP signals, to obtain an unambiguous CF with better performance. The simulation results, based on MATLAB tool, have demonstrated that the proposed method effectively eliminates the ambiguity problem. Besides, a comparative study with several methods has shown that the proposed method grants better performance than the most popular techniques.

Improved routing in MANET with optimized multi path routing fine tuned with hybrid modeling

Routing in MANET is very challenging due to movement of nodes. Predicting the node position and routing based on predicted positions helps to establish routing path with much longevity. Most predictions approaches are based on the past locations of the node. In this work a node location prediction based on the temporal and spatial characteristics with respect to its neighborhood is applied to estimate the probable locations using a hybrid model. A multi path routing protocol based on estimated probability locations with path diversion at necessary places along path is proposed for improved routing performance without larger packet overhead.

Efficient Multipath Routing to Increase QoS by Link Estimation and Minimum Interference path in MANET'S

Many significant techniques have been developed for real time transmission to meet stringent QoS for various applications of wireless sensor network. However routing in MANET’s still a challenging issue to achieve QoS due to frequent topology changes and its dynamic nature. For majority MANET applications, on demand multi path data routing protocols have been modelled to transfer data in multiple routes to prove its effectiveness on link disjoints and high packet delivery ratio. However due to mobility and underlying medium, multiple routes are exposed to interference and low power unstable link quality which are essential to guarantee QoS in routing. In this paper we propose link quality estimation and minimum path interference based on Geographic adhoc on demand multi path distance vector (GAOMDV) an adaptation of AOMDV routing protocol. To ensure the reliability of MANET’s this adaptation is done by considering routing metrics. GAOMDV selects forwarding neighbour nodes based on link quality and minimum path interference across multiple paths and ensures high stable links between nodes. Extensive simulation is carried on discrete event simulation tool and performance of GAOMDV is analysed in terms of network QoS parameters.

Link Availability Based Routing Protocol for Mobile Ad-Hoc Networks based on Link Life Time Prediction

A self-configured MANET has a set of wireless devices. With no prior administration, the Mobile Nodes (MNs) communicate via the wireless links. The limited bandwidth, transmission errors, dynamic topology, energy constraints, and link stability fluctuations lead to node mobility. The links between nodes are unreliable and might break on account of such node mobility. Link breakage leads to the rerouting process at the sender node (where the link breakage occurs) or Source Node (SN). This work proposed Link availability based routing protocol (LBRP) for MANET to attain load balancing via the Multi-Path (MP) communication. The traffic data could completely pass through parallel multiple paths, which enhances the protocol efficiency. On weighing against the existing routing protocols (RPs), the LBRP chiefly considers frequent link failures and unpredictable change in topology. The simulation outcomes validate the proposed LBRP. The LBRP performs well when contrasted to the DSR, ZRP, and AODV in all the experiments. This work would serve as the groundwork for examining and optimizing certain network protocols, and could help in designing algorithms for transport control and medium access.

Efficient Model Points Selection in Insurance by Parallel Global Optimization Using Multi CPU and Multi GPU

In the insurance sector, Asset Liability Management refers to the joint management of the assets and liabilities of a company. The liabilities mainly consist of the policies portfolios of the insurance company, which usually contain a large amount of policies. In the article, the authors mainly develop a highly efficient automatic generation of model points portfolios to represent much larger real policies portfolios. The obtained model points portfolio must retain the market risk properties of the initial portfolio. For this purpose, the authors propose a risk measure that incorporates the uncertain evolution of interest rates to the portfolios of life insurance policies, following Ferri (Optimal model points portfolio in life, 2019, arXiv:1808.00866). This problem can be formulated as a minimization problem that has to be solved using global numerical optimization algorithms. The cost functional measures an appropriate distance between the original and the model point portfolios. In order to solve this problem in a reasonable computing time, sequential implementations become prohibitive, so that the authors speed up the computations by developing a high performance computing framework that uses hybrid architectures, which consist of multi CPUs together with accelerators (multi GPUs). Thus, in graphic processor units (GPUs) the evaluation of the cost function is parallelized, which requires a Monte Carlo method. For the optimization problem, the authors compare a metaheuristic stochastic differential evolution algorithm with a multi path variant of hybrid global optimization Basin Hopping algorithms, which combines Simulated Annealing with gradient local searchers (Ferreiro et al. in Appl Math Comput 356:282–298, 2019a). Both global optimizers are parallelized in a multi CPU together with a multi GPU setting.

CAPS: Coding-Based Adaptive Packet Spraying to Reduce Flow Completion Time in Data Center

Modern data-center applications generate a diverse mix of short and long flows with different performance requirements and weaknesses. The short flows are typically delay-sensitive but to suffer the head-of-line blocking and out-of-order problems. Recent solutions prioritize the short flows to meet their latency requirements, while damaging the throughput-sensitive long flows. To solve these problems, we design a Coding-based Adaptive Packet Spraying (CAPS) that effectively mitigates the negative impact of short and long flows on each other. To exploit the availability of multiple paths and avoid the head-of-line blocking, CAPS spreads the packets of short flows to all paths, while the long flows are limited to a few paths with Equal Cost Multi Path (ECMP). Meanwhile, to resolve the out-of-order problem with low overhead, CAPS encodes the short flows using forward error correction (FEC) technology and adjusts the coding redundancy according to the blocking probability. Moreover, since the coding efficiency decreases when the coding unit is too small or large, we demonstrate how to obtain the optimal size of coding unit. The coding layer is deployed between the TCP and IP layers, without any modifications on the existing TCP/IP protocols. The test results of NS2 simulation and small-scale testbed experiments show that CAPS significantly reduces the average flow completion time of short flows by ~30%–70% over the state-of-the-art multipath transmission schemes and achieves the high throughput for long flows with negligible traffic overhead.