Path Bandwidth Research Articles

Reduced Complexity Sequential Digital Predistortion Technique for 5G Applications

Wireless communication infrastructure is a key enabling technology for smart cities. This paper investigates a novel technique to enhance the performance of 5G base stations by addressing the compensation of nonlinear distortions caused by radiofrequency power amplifiers. For this purpose, a sequential digital predistortion approach that uses twin nonlinear two-box structure along with reduced sampling rates in the feedback path is proposed to implement a linearization system. Such a system is shown to have a correction bandwidth that exceeds the bandwidth of the feedback path. This is achieved by synthesizing the predistortion function in two successive characterization iterations. Both characterizations use the same hardware, which has a reduced sampling rate in the feedback path. Hence, the proposed predistorter scheme does not require any additional hardware compared to standard schemes. Moreover, coarse delay alignment is performed while identifying the memory polynomial function in order to further reduce the computational complexity of the proposed system. Experimental results using an inverse Class-F power amplifier demonstrate the ability of the proposed predistorter to achieve a correction bandwidth of 100 MHz with a feedback sampling rate as low as 25 MSa/s.

Modeling aperiodic sampling to increase the signal processing bandwidth of the sampling analog optical path

The paper discusses methods of aperiodic optical pulsed signal sampling in photonic analog-digital systems. We describe numerical modeling of aperiodic sampling and a method for reconstructing a spectrally sparse signal from aperiodicly selected samples. The described method is based on minimizing the l1 norm of the spectral representation of the reconstructed signal. Numerical simulations show that using aperiodic sampling, it is possible to reconstruct a spectrally sparse signal in the 5 GHz band using a sampling rate of 500 MS/s. The work also shows that using window functions it is possible to improve the quality of signal reconstruction from pseudo-random samples.

High-Speed Rotorcraft Pitch Axis Response Type Investigation

This paper presents a systematic investigation of high-speed rotorcraft pitch-axis response types, command models, and handling qualities specifications. The investigation was done using two future vertical lift-relevant rotorcraft configurations—a lift offset coaxial helicopter with a pusher propeller and a tiltrotor. Five response types were investigated, consisting of a pitch rate-command/attitude-hold response type typically used for rotorcraft, a pitch rate-command/attitude-hold response type using a higher-order command model based on the conventional airplane pitch rate transfer function, a normal acceleration command/angle-of-attack hold response type, a flight path rate command/flight path hold response type, and a "blended" flight path rate command response type which varies the command model bandwidth based on stick input size. Designs of varying levels of pitch attitude bandwidth, flight path bandwidth, control anticipation parameter, and pitch attitude dropback were evaluated in a piloted simulation experiment conducted at the Penn State Flight Simulator facility using two high-speed mission task elements. The results of the piloted simulation suggest that both the pitch attitude bandwidth and the pitch attitude dropback requirements must be met for Level 1 handling qualities. A set of recommended specifications and associated updated level boundaries is provided in the Appendix.

Efficient congestion control scheme based on caching strategy in NDN

As a pioneering network architecture, Named Data Networking (NDN) leverages the content-centric model and connectionless transmission mode to enhance network capacity. Despite the emergence of numerous congestion control protocols aimed at improving NDN transmission efficiency, most of these approaches implicitly assume that each interest packet elicits one single data packet, which significantly restricts network performance. To address this issue, we introduce C3NDN, a Congestion Control scheme that leverages caching strategy in NDN. C3NDN formulates a One-Interest-Multiple-Data model to improve network efficiency, and employs a probabilistic caching strategy to cache popular content at important nodes and optimize the in-network caching characteristic of NDN. Furthermore, C3NDN incorporates a congestion control algorithm based on the One-Interest-Multiple-Data model, which considers the bandwidth and delay information of the transmission path. Additionally, C3NDN implements a marking method that takes full advantage of node cache and reduces transmission time. Extensive experiments have been conducted to show the performance of C3NDN, and the results demonstrate that C3NDN can reduce the transmission time by up to 41.68% when compared with the other congestion control schemes.

ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows

Information-centric networking (ICN) is a promising solution that can meet the challenges of IP-based networks. ICN adopts the solution of separating name and address to solve the dual semantics problem of IP addresses, which has natural advantages in supporting mobility. These mobility support methods in ICN have realized route optimization to a certain extent. In fact, due to these methods not considering the real-time network environment, such as the bandwidth of the switching path and the characteristics of different mobile data flows, these approaches adopt the same routing optimization solution for all data flows generated by mobile users, resulting in a single switching path. The bandwidth of a single switching path may not meet the transmission requirements of mobile data flows, which will greatly affect the performance of mobile users. Therefore, how to efficiently allocate switching paths for multiple mobile data flows generated by mobile users is an urgent problem in ICN mobility support. In this paper, we first propose a mobility support method for dynamically allocating mobile data flows (DAFM) based on the ICN mobility support architecture and model the mobile data flow allocation problem as a late-binding node selection problem. We then propose a double-constraints heuristic algorithm (DCHA) based on the Moser algorithm. By simultaneously adjusting the bandwidth resource constraints of the switching path and the space resource constraints of the Mobility Flow Table (MFT), we achieve late-binding node selection to minimize average packet delay. Finally, experimental results show that our proposal can achieve mobility support. Under the guarantee of a low-latency Name Resolution System (NRS), our method outperforms other methods in terms of average packet delay, packet loss rate, and handover delay.

A congestion control scheme based on path recovery for smart grid communication

In order to solve the problem that the original path forwarding traffic caused by congestion that cannot be recovered in time in Smart Grid Communication, a congestion control scheme RFCC (Router Forwarding Congestion Control) based on path recovery is proposed, which not only supports the receiving end combined with router nodes to relieve congestion, it can also solve the problem of low transmission performance caused by not being able to restore the original path in time after the traffic is transferred. Active queue management technology is used to detect congestion and notify downflow nodes (users and routers) in time, which trigger downflow users to adjust the request sending window and intermediate routers to transfer packets. In addition, the original path traffic is restored in time through the path recovery policy. Firstly, the path traffic is recovered according to the congestion mark carried by the subsequent content packets. Secondly, the transmission frequency of the probe packets is calculated according to the information such as the packet queue length of routers and the available bandwidth of the congested path, and the probe packets are periodically sent to the congested path, and the traffic is sent to the congested path in time. The alternate path is transferred back to the original path. This paper implements the scheme in NS-3, and extensive experiments show that the proposed scheme outperforms the comparison schemes, which not only higher and more stable throughput can be obtained, but also shorter and more stable transmission delay can be obtained. In addition, the optimal path transmission volume and link utilization are improved.

Coupled CUBIC Congestion Control for MPTCP in Broadband Networks

Recently, multipath transmission control protocol (MPTCP) was standardized so that data can be transmitted through multiple paths to utilize all available path bandwidths. However, when high-speed long-distance networks are included in MPTCP paths, the traffic transmission performance of MPTCP is severely deteriorated, especially in case the multiple paths’ characteristics are heavily asymmetric. In order to alleviate this problem, we propose a “Coupled CUBIC congestion control” that adopts TCP CUBIC on a large bandwidth-delay product (BDP) path in a linked increase manner for maintaining fairness with an ordinary TCP traversing the same bottleneck path. To verify the performance excellence of the proposed algorithm, we implemented the Coupled CUBIC Congestion Control into Linux kernels by modifying the legacy MPTCP linked-increases algorithm (LIA) congestion control source code. We constructed asymmetric heterogeneous network testbeds mixed with large and small BDP paths and compared the performances of LIA and Coupled CUBIC by experiments. Experimental results show that the proposed Coupled CUBIC utilizes almost over 80% of the bandwidth resource in the high BDP path, while the LIA utilizes only less than 20% of the bandwidth for the same path. It was confirmed that the resource utilization and traffic transmission performance have been greatly improved by using the proposed Coupled CUBIC in high-speed multipath networks, as well as maintaining MPTCP fairness with competing single-path CUBIC or Reno TCP flows.

Improving the routing performance in optical networks by an optimised BFS algorithm

The procedure of routing and wavelength assignment became a limiting factor for new applications in Optical Networks (ONs) due to the faster dynamic and greater unpredictability of service requests. The need for quick service recovery in the event of failures is also a crucial aspect which depends on this procedure. The present work proposes a new assignment of alternative routes able to comply to any data service request in ONs. The assignment procedure is based on the distinction by low correlation between paths, on the available bandwidth of different paths and on the latency for the establishment of communication between network nodes. In such context, an improvement of the Breadth-First Search (BFS) algorithm is proposed and tested with parameters from a real-world system, resulting in a good performance to find alternative routes and to classify them according to the best score obtainable, independently of the source or destiny nodes.

A Novel Strategy for Computing Routing Paths for Software-Defined Networks Based on MOCell Optimization

Software-defined networking (SDN) is the fastest growing and most widely deployed network infrastructure due to its adaptability to new networking technologies and intelligent applications. SDN simplifies network management and control by separating the control plane from the data plane. The SDN controller performs the routing process using the traditional shortest path approach to obtain end-to-end paths. This process usually does not consider the nodes’ capacity and may cause network congestion and delays, affecting flow performance. Therefore, we evaluate the most conventional routing criteria in the SDN scenario based on Dijkstra’s algorithm and compare the found paths with our proposal based on a cellular genetic algorithm for multi-objective optimization (MOCell). We compare our proposal with another multi-objective evolutionary algorithm based on decomposition (MOEA/D) for benchmark purposes. We evaluate various network parameters such as bandwidth, delay, and packet loss to find the optimal end-to-end path. We consider a large-scale inter-domain SDN scenario. The simulation results show that our proposed method can improve the performance of data streams with TCP traffic by up to 54% over the traditional routing method of the shortest path and by 33% for the highest bandwidth path. When transmitting a constant data stream using the UDP protocol, the throughput of the MOCell method is more than 1.65% and 9.77% for the respective paths.

Voyager: Revisiting Available Bandwidth Estimation With a New Class of Methods—Decreasing- Chirp-Train Methods

The available bandwidth (ABW) of a network path is a crucial metric for various applications, such as traffic engineering, congestion control, multimedia streaming, and path selection in software-defined wide-area networks (SDWAN). In recent years, a new class of measurement methods have been proposed to estimate the available bandwidth, decreasing-chirp-train methods. However, the performance and limitations of this new class of methods are neither well studied nor fairly compared beyond simulation studies. In this work, we implement Voyager, a modular framework that allows us to conduct a fair and thorough comparison of how a variety of modern bandwidth estimation methods perform under different network paths and traffic conditions. We shed light on the characteristics and limitations of the internal algorithms of various methods, and propose two new methods. We investigate the impact of various bottleneck types and traffic types, and we explore the performance of these methods on high speed links where interrupt coalescence can cause measurement noise. We finally test Voyager on long-distance Internet links and with live traffic and report our findings.

2.9 K VCSEL demonstrates 100 Gbps PAM-4 optical data transmission

A cryogenic packaged vertical-cavity-surface-emitting laser (VCSEL) with microwave input and coupled with OM4 fiber for optical output was developed and demonstrated laser operation from 295 to 2.9 K. Record 2.9 K optical data links of 100 Gbps pulsed amplitude modulation 4-level have been demonstrated with an energy per bit ∼153.4 fJ/bit when TDECQ = 3.79 dB. The energy/bit is limited by the link path bandwidth resulted from the input cable loss to 2.9 K VCSEL and can be further improved. Thus, the VCSEL optical data link is proven to be a viable solution for cryogenic communication and computing.

Rapid and green detection of manganese in electrolytes by ultraviolet-visible spectrometry to control pollutant discharge.

Controlling the manganese (Mn2+) concentration is important to product quality in the electrolytic manganese industry. Conventional methods for detecting Mn2+ are complex and reagent-consuming, which makes them slow and polluting. It is of great significance to develop a new fast and environmentally friendly method to quantify Mn2+ in electrolyte. The characteristic ultraviolet-visible (UV-vis) absorbance at 401 nm of Mn2+ will vary linearly with the Mn2+ concentration after data correction. Adjusting the pH, calibrating the spectral bandwidth (SBW) and optical path length (OPL), and subtracting the interference from coexisting substances by linear interpolation improve the measuring accuracy. Mn2+ concentration can be determined by direct fast UV-vis absorbance measurement at characteristic peaks without using harmful reagents which facilitates such measurement to be applicated as on-line detection method for electrolytic manganese industry. The method developed in this study will help achieve the goal of improving the detection speed while cutting back on pollutant discharge from concentration analysis process in electrolytic manganese industry.

ReSQ: Reinforcement Learning-Based Queue Allocation in Software-Defined Queuing Framework

With the evolution of 5G networks, the demand for Ultra-Reliable Low Latency Communications (URLLC) services is increasing. Software-Defined Networking (SDN) offers flexible network management to prioritize URLLC services coexisting with best-effort traffic. Utilizing the network programmability feature of SDN, Software-Defined Queueing (SDQ) framework selects the optimal output port queue on forwarding devices and routing path for incoming traffic flows to provide deterministic Quality of Service (QoS) support required for URLLC traffic. However, in the existing SDQ framework, the selections of optimal queue and path are done manually by observing the traffic type of each incoming flow, the available bandwidth of each potential routing path, and the status of output port queues of each forwarding device on each potential routing path. The static allocations of path and queue for each flow are inefficient to provide a deterministic QoS guarantee for a high volume of incoming traffic which is typical in 5G networks. The limited buffer availability on the forwarding devices is another constraint regarding optimal queue allocation that ensures an end-to-end (E2E) delay guarantee. To address these challenges, in this paper, we extend the SDQ framework by automating queue management with a reinforcement learning (RL)-based approach. The proposed queue management approach considers diverse QoS demands as well as a limited buffer on the forwarding devices and performs prioritized queue allocation. Our approach also includes a hash-based flow grouping to handle a high volume of traffic having diverse latency demands and a path selection mechanism based on available bandwidth and hop count. The simulation result shows that the proposed scheme ReSQ reduces the QoS violation ratio by 10.45% as compared to the baseline scheme that selects queues randomly.

WITHDRAWN: Fiber optics routing enhancement based heuristic searching technique

Optical network is the corner stone for the high data rate/throughput applications due to the wide bandwidth allowed in optical channel. Additionally, optical network tackled the delay problem since data is reformed into equivalent light and transmitted light speed to the destinations e.g. 3 × 10 8 m/h. Data can travel in optical domain over the light-path in high rate approaching to tens of Gigabits. Routing of data in optical domain is controlled by the wavelength division multiplexing (WDM) scheme in which allotting each wavelength at the ingress of optical fiber with its suitable bandwidth. Bandwidth of optical path is being optimized in order to utilize the fiber resources efficiently using Artificial Neural Network (ANN). In this paper Artificial Bee Colony (ABC) algorithm is used for optimizing the performance of ANN which is predicting the wavelength of the optical path.

STCC: A SDN‐oriented TCP congestion control mechanism for datacenter network

Due to characteristics of high bandwidth and low latency, datacenter networks ensure tremendous data could be transmitted in an efficient way. However, in many-to-one transmission scenarios, high concurrency of TCP flows aggravates network congestion and causes overflows in switches, seriously impairing network performance. To solve the problem, a TCP congestion control mechanism based on software-defined networking (STCC) is proposed. Without any modification in TCP stack, STCC monitors network performance through centralized control and global network view of SDN, employs a routing algorithm based on the minimum path bandwidth utilization rate to forward packets and uses different methods to adjust congestion windows of senders so that network congestion can be greatly mitigated. An experiment platform is built to carry out simulation tests for evaluating STCC, and the results show that under the same network conditions, STCC effectively reduces the number of retransmission timeout of senders and noticeably raises network throughput, compared with other congestion control algorithms.

SAFAR: Simulated Annealing-Based Flow Allocation Rules for Industrial Networks

In this article, we address the problem of optimal flow allocation in the context of industrial networks. The problem is known to be computationally hard, which makes it difficult to solve for large networks. Moreover, industrial networks, such as power grids, often have unique characteristics, for example, integral flows and delay-symmetric path requirements, which further constrain the problem. To address these challenges, we propose the Simulated Annealing-based Flow Allocation Rules (SAFAR) scheme. At a high level, SAFAR identifies the near-optimal paths satisfying the bandwidth demands of a subset of flows by iteratively considering several configurations. The optimality criteria herein corresponds to maximizing the number of flows for which bandwidth paths are obtained, as well as improving the relative fairness in link utilization arising out of the process. Subsequently, SAFAR assigns a fraction of bandwidth (slice) to the remaining flows. Finally, SAFAR generates appropriate forwarding rules for the switches. Results of performance evaluation using real-life and synthetically-generated networks reveal that SAFAR can allocate up to 99% flows, which is an improvement of up to 9.6% over a contemporary greedy scheme. Moreover, SAFAR can improve the flow allocation fairness relatively by up to 37%. Finally, we also use a network of Open vSwitch switches to verify the end-to-end functionality of SAFAR.

Single-beam laser device for measuring the surface slope for landing a helicopter

A temporal method is proposed for estimating the slope of a landing site for a helicopter at night. The width of a pulse reflected from a surface under nonstationary irradiation is used as an informational parameter for estimating the surface slope angle. The receiver-amplification path bandwidth and the surface slope determination algorithm are validated with implementation of this method. A possible block scheme for implementing this method is proposed.

Stateful-TCP—A New Approach to Accelerate TCP Slow-Start

The Transmission Control Protocol (TCP) is one of the pillars of the Internet. Therefore extensive research has been conducted to improve its performance, primarily via optimizing TCP's congestion control algorithm. In this work, we show that besides congestion control, TCP's Slow-Start algorithm is increasingly becoming a bottleneck in modern high-speed networks. To tackle the problem, we propose a different approach called Stateful-TCP, where path bandwidth estimated in a previous flow is used to instantly ramp up the transmission rate of the subsequent flow to the same destination. This eliminates the need for bandwidth probing in conventional Slow-Start, enabling TCP to efficiently utilize the available path bandwidth right from the beginning. We applied Stateful-TCP to Linux's default TCP implementation - Cubic, to form S-Cubic and evaluated its performance via extensive emulations and Internet experiments. Results from independent Internet experiments using over 1,000 end-user clients showed that S-Cubic could reduce FCT by 37.5% and increase throughput by over 50% compared to Cubic. As opposed to an entirely new TCP design, Stateful-TCP is designed to complement congestion control and thus could potentially be applied to many of the existing TCP variants, as well as incorporated into future TCP designs.

Optimized Preference-Aware Multi-Path Video Streaming with Scalable Video Coding

Most client hosts are equipped with multiple network interfaces (e.g., WiFi and cellular networks). Simultaneous access of multiple interfaces can significantly improve the users’ quality of experience (QoE) in video streaming. An intuitive approach to achieve it is to use Multi-path TCP (MPTCP). However, the deployment of MPTCP, especially with link preference, requires OS kernel update at both the client and server side, and a vast amount of commercial content providers do not support MPTCP. Thus, in this paper, we realize a multi-path video streaming algorithm in the application layer instead, by considering Scalable Video Coding (SVC), where each layer of every chunk can be fetched from only one of the orthogonal paths. We formulate the quality decisions of video chunks subject to the available bandwidth of the different paths, chunk deadlines, and link preferences as an optimization problem. The objective is to to optimize a QoE metric that maintains a tradeoff between maximizing the playback rate of every chunk and ensuring fairness among chunks. The proposed metric prefers to use bandwidth of the links to optimize a concave utility function of the chunk quality. Even though the formulation is a non-convex discrete optimization, we provide a quadratic complexity algorithm which is shown to be optimal in some special cases. We further propose an online algorithm where several challenges including bandwidth prediction errors, are addressed. Extensive emulated experiments in a real testbed with real traces of public dataset reveal the robustness of our scheme and demonstrate its significant performance improvement compared to other multi-path algorithms.

Hidden Markov Model-based Load Balancing in Data Center Networks

AbstractModern data centers provide multiple parallel paths for end-to-end communications. Recent studies have been done on how to allocate rational paths for data flows to increase the throughput of data center networks. A centralized load balancing algorithm can improve the rationality of the path selection by using path bandwidth information. However, to ensure the accuracy of the information, current centralized load balancing algorithms monitor all the link bandwidth information in the path to determine the path bandwidth. Due to the excessive link bandwidth information monitored by the controller, however, much time is consumed, which is unacceptable for modern data centers. This paper proposes an algorithm called hidden Markov Model-based Load Balancing (HMMLB). HMMLB utilizes the hidden Markov Model (HMM) to select paths for data flows with fewer monitored links, less time cost, and approximate the same network throughput rate as a traditional centralized load balancing algorithm. To generate HMMLB, this research first turns the problem of path selection into an HMM problem. Secondly, deploying traditional centralized load balancing algorithms in the data center topology to collect training data. Finally, training the HMM with the collected data. Through simulation experiments, this paper verifies HMMLB’s effectiveness.