Link Bandwidth Research Articles

A novel energy efficient QoS secure routing algorithm for WSNs

Quality of Service (QoS) routing protocol is a hot topic in the research field of wireless sensor networks (WSNs). However, the task of identifying an optimal path that simultaneously meets multiple QoS constraints is acknowledged as an NP-hard problem, with its complexity intensifying in proportion to the network’s nodal count. Therefore, a novel heuristic multi-objective trust routing method, the Levy Chaos Adaptive Snake Optimization-based Multi-Trust Routing Method (LCASO-MTRM), is proposed, aiming to enhance link bandwidth while simultaneously reducing latency, packet loss, and energy consumption. The proposed method incorporates innovative chaos and adaptive operators within the LCASO framework. The chaos operator enhances population diversity, expands the solution space, and accelerates the search process. Meanwhile, the adaptive operator improves convergence, enhances robustness, and effectively prevents stagnation. Additionally, this paper introduces a novel multi-objective QoS routing model that integrates a link trust mechanism, allowing for a more accurate assessment of link trust levels and a precise reflection of the current link status. The effectiveness of LCASO-MTRM is demonstrated through simulation comparisons with the Improved Particle Swarm Optimization (IPSO), Improved Artificial Bee Colony Algorithm (IABC), and Cloned Whale Optimization Algorithm (CWOA). Simulation results demonstrate that LCASO-MTRM significantly reduces energy consumption by 49.53%, latency by 22.56%, and packet loss by 40.21%, while increasing bandwidth by 6.13%, outperforming the other algorithms.

Cryo-VCSELs Operated at 2.8 K and 40 K With Record Bandwidth, Power, and Linearity for Optical Data Links in Quantum Computing

Cryo-VCSELs Operated at 2.8 K and 40 K With Record Bandwidth, Power, and Linearity for Optical Data Links in Quantum Computing

Multi-Path Data Transmission System Based on 5G Communication Technology

Due to the current problems of 5G(5th Generation Mobile Communication Technology) networks such as discontinuous coverage, poor coverage performance, and insufficient uplink rate, the real-time transmission performance of 5G networks has been greatly affected, and cannot meet the growing needs of high-speed communication and applications based on the Internet of Things. This paper aims to improve the transmission effect of 5G communication technology by improving the algorithm. By analyzing the problems of existing 5G bandwidth estimation algorithm, this paper proposes a Westwood-based coupling adaptive bandwidth estimation algorithm, and describes the algorithm ideas involved in the algorithm point-by-point. Moreover, this paper uses adaptive bandwidth estimation algorithm to update the slow start threshold in the fast retransmission phase, which makes the congestion window size of path transmission more reasonable. Simulation results show that the congestion control algorithm proposed in this paper improves the accuracy of link bandwidth estimation to a certain extent, improves the link congestion situation and improves the throughput of multipath transmission links. This article can be applied to the algorithm in multipath data transmission to further improve data processing efficiency and processing speed.Therefore, the improved method in this paper can be used in the practice of 5G communication to further improve the efficiency of 5G communication.

Comparative assessment and performance analysis of interference mitigation techniques for co‐existent non‐geostationary and geostationary satellites

SummaryIn recent years, technological developments with user demands, reduced production, and launch costs have rapidly increased the number of Low Earth Orbit (LEO) satellites in space. Since LEO satellites use the same frequency band as existing Geostationary Earth Orbit (GEO) satellites, the interference coordination between the two satellite networks is vital. In order to minimize the co‐existent interference between these satellite networks, studies perform on different interference mitigation strategies. In this paper, analysis and comparative assessment of these interference mitigation techniques are presented for the co‐existent Non‐Geostationary Earth (NGEO) and GEO systems. More specifically, power control (PC) and spatial isolation‐based link adaptation (SILA) techniques are studied comparatively for the performance evaluation. It is shown that the communication link bandwidth is more efficiently utilized in the SILA technique when compared with the PC technique. Moreover, the multi‐objective optimization problem (MOP) approach in the SILA technique is demonstrated to be more effective when compared with the single‐objective optimization problem (SOP) approach used in the PC technique as the simultaneous prioritizing objective functions outperforms single prioritization. Finally, it is shown that when the PC technique is applied together with the SILA technique, the exclusive angle (EA) can be reduced up to 8% for 100 Mbps, and 8.5% for 200 Mbps transmission bit rates in different operational scenarios. The presented performance evaluation in this paper may help the satellite operator or decision‐maker gain insights on which mitigation technique can be used in the case of a co‐existent interference.

Wavelength Dependence of Modal Bandwidth of Multimode Fibers for High Data Rate Transmission and Its Implications

Vertical-cavity surface-emitting laser (VCSEL)-based transmission over multimode fiber (MMF) has achieved data rates of 100G per lane and is progressing towards 200G per lane. Recently, high-data-rate MMFs derived from OM3 and OM4 have been proposed. These fibers exhibit higher effective modal bandwidths at 910 nm, leading to a different wavelength dependence compared to conventional OM3 and OM4 MMFs. Understanding the wavelength dependence of these fibers is crucial to address their utilization in a broader range of applications. Through Monte Carlo simulations, we have obtained the low-end boundary of the effective modal bandwidths (EMBs) for these fibers, revealing capability improvements over the existing OM3 and OM4. The high-data-rate OM4 performs the same as or better than OM5 from 840 nm to 920 nm, while also showing a high bandwidth for the 850–870 nm wavelength window, favoring VCSELs with center wavelengths shifted toward 860 nm. We also obtained the link bandwidth, which includes both modal bandwidth and chromatic dispersion contributions, and the transmission reaches for various types of transceivers. We find that for both high-data-rate OM3 and high-data-rate OM4, the link bandwidth stays above the value at 850 nm until around 910 nm, delivering a similar transmission performance from 850 to 910 nm without declining towards longer wavelengths, unlike the standard OM3 and OM4. This characteristic favors a wider range of wavelength choices for VCSELs and enables optimal deployments for various applications.

Robust H∞ Static Output Feedback Control for TCP/AQM Routers Based on LMI Optimization

This paper proposes a new static output feedback control method to address the congestion control problem in transmission control protocol networks using active queue management routers. Based on linear matrix inequality optimization, this method determines a static output feedback control law to minimize the norm of the transfer function between the controlled queue length of the buffer and the exogenous disturbance affecting the available link bandwidth. A linear matrix inequality formulation is presented as a sufficient condition to guarantee the closed-loop system’s asymptotic stability while maintaining disturbance rejection within a specified level, regardless of round-trip time delays. The proposed robust static output feedback control eliminates the need to measure or estimate all system states, thus simplifying practical implementation. The effectiveness of the proposed design method is demonstrated by applying it in a practical process, as illustrated through a numerical example.

MPR-QUIC: Multi-path partially reliable transmission for priority and deadline-aware video streaming

Video streaming has dominated Internet traffic over the past few years, spurring innovations in transport protocols. The QUIC protocol has advantages over TCP, such as faster connection setup and alleviating head-of-line blocking. Multi-path transport protocols like Multipath QUIC (MPQUIC) have been proposed to aggregate the bandwidth of multiple links and provide reliable transmission in poor network conditions. However, reliable transmission incurs unnecessary retransmission costs for MPQUIC, resulting in deteriorating performance, especially in real-time video streaming. Partially reliable transmission, which supports both reliable and unreliable delivery, may perform better by trading off data reliability and timeliness. In this paper, we introduce MPR-QUIC, a multi-path partially reliable transmission protocol for QUIC. Based on MPQUIC, MPR-QUIC extends unreliable transmission to provide partially reliable transmission over multiple paths. Specific schedulers are designed in MPR-QUIC based on priority and deadline, respectively, for video streaming optimization. Video frames with high priority are transmitted first since frames with low priority cannot be decoded before their arrival. Additionally, to alleviate rebuffering and freezing of the video, as many frames as possible should be delivered before the deadline. We evaluate MPR-QUIC experimentally on a testbed and in emulations. Results show that the rebuffer time of MPR-QUIC is significantly decreased by 60% to 80% when compared to state-of-the-art multi-path transmission solutions. The completion ratio of transmitted data blocks is increased by almost 100%.

Orchid: enhancing HPC interconnection networks through infrequent topology reconfiguration

Interconnection networks are key components of high-performance computing (HPC) systems. As HPC evolves towards the exascale era, providing sufficient bisection bandwidth between computing node pairs through oversubscription in traditional networks becomes prohibitively expensive and impractical. Over the past decade, several architectures leveraging optical circuit switches (OCSs) for dynamic link bandwidth allocation have gained traction. These architectures require frequent network topology reconfiguration to adapt to changing traffic demands. However, practical implementation remains hampered by the long reconfiguration delays inherent in OCS technology. We propose Orchid, an architecture that leverages OCSs to achieve infrequent topology reconfigurations, effectively addressing the problem of long reconfiguration delays. A key innovation of Orchid is its ability to extract stable traffic matrices from historical data. This functionality guides the reconfiguration of the topology without the need for adjustments with each traffic matrix, thereby enabling the sharing of OCS overhead over an extended timeframe. Furthermore, Orchid addresses potential congestion arising from unexpected traffic through the joint design of OCS configuration and routing, ensuring an even distribution of traffic across global links. Extensive experiments using real HPC application traces and synthetic traffic demonstrate that Orchid achieves significant performance improvements compared to existing HPC interconnection networks. Specifically, Orchid reduces packet delay by at least 3× and enhances throughput by up to 60%.

Topology-aware Parallel Joins

We study the design and analysis of parallel join algorithms in a topology-aware computational model. In this model, the network is modeled as a directed graph, where each edge is associated with a cost function that depends on the data transferred between the two endpoints and the link bandwidth. The computation proceeds in synchronous rounds and the cost of each round is measured as the maximum cost over all the edges in the network. Our main result is an asymptotically optimal join algorithm over symmetric tree topologies. The algorithm generalizes prior topology-aware protocols for set intersection and cartesian product to a binary join over an arbitrary input distribution with possible data skew.

ER-OCN: Toward efficient network routing in ocean city based on deep reinforcement learning

Smart ocean cities are crucial to future city development, and offshore networks are an essential part of smart ocean cities. Nowadays, communication in offshore networks is becoming increasingly important. However, the offshore networks are highly heterogeneous, with low reliability, narrow bandwidth, and dramatic dynamics. Offshore networks are more challenging to manage than traditional terrestrial networks, and the standard quality of service routing faces significant challenges. Currently, artificial intelligence provides a way to meet the requirements. This paper proposes ER-OCN (Efficient Routing in Ocean City Networks), a two-stage routing optimization scheme driven by artificial intelligence to cope with the complex offshore networks environment. The first stage will calculate the flow paths based on Lagrangian relaxation. Besides, the ocean environment is under dramatic dynamics, leading to unpredictable network topology or link bandwidth changes. Thus, the second stage deployed a deep reinforcement learning algorithm to monitor the environment in real-time and update the routing strategy when needed. We conducted experiments based on actual network topology and traffic data sets of offshore platforms’ network systems. The results prove that our scheme improves communication cost performance by 36.61% compared to traditional methods.

Digital twin-assisted service function chaining in multi-domain computing power networks with multi-agent reinforcement learning

The emerging computing power network (CPN) is believed to undergo the paradigm reformation of network function virtualization (NFV) and service function chaining (SFC). It is prerequisite to explore the performance upper bound of NFV-assisted CPN before truly deploying the NFV and SFC technologies onto physical networks. Inspired by the application of digital twin (DT) in the industry and due to its advantage in synchronizing physical objects with their virtual replicas, we propose to use the DT to assist the SFC deployment in the multi-domain CPN, with the aid of multi-agent deep deterministic policy gradient (MADDPG) framework. First, we build a dynamic SFC mapping problem in the virtual twin network layer, by modeling the computing power, link bandwidth, delay performance and the VNF ordering as DT objects and constraints, to jointly optimize the energy consumption, end-to-end delay and the VNF re-deploying cost. Then, the prioritized experience replay and re-parameterization trick-empowered centralized training and decentralized execution MADDPG framework is utilized to learn the SFC deployment, by taking each domain controller as one agent. Finally, numerical experiments are carried out to validate the effectiveness of MADDPG in the cross-domain SFC deployment. For performance verification, the deployment success rate, number of crossed domains, energy consumption, end-to-end latency and load balancing degree are all taken as metrics, to show the performance of MADDPG compared to other learning frameworks.

Enhancing the performance of LiFi communication with OSTBC, QAM, and OFDM: High-capacity, low-complexity transceiver design

Light fidelity (LiFi) is one of the most promising areas within wireless optical communication system. It provides many benefits, including high capacity, improved security and enhanced cost-effectiveness. However, there is still challenges which are link blockages, attenuation, interference and bandwidth restriction of modulation techniques. Orthogonal space–time block coding (OSTBC) offers a suitable solution to address link blockage by providing an alternative communication channel between the transmitter and the receiver. OSTBC has more tolerate to the practical channel impact limitation such as attenuation and interference that may occur in overlapping regions. In addition, the use of the OSTBC technique enhances the system’s ability to transfer data as the transmission rate is duplicated by the number of transmitters and receivers. This paper proposes the utilisation of a LiFi transceiver-based OSTBC that employs quadrature amplitude modulation (QAM) in conjunction with orthogonal frequency-division multiplexing (OFDM). Co-simulation tools have been done by integrated Matlab with Optisystem, were used in the design of LiFi transceiver with all assumption required. The findings reveal the capability of LiFi transceiver of transmitting rate up to 15 Gbps with maintaining a satisfactory bit error rate (BER) of 10−4 over a link range of 200 m evenin the presence of realistic channel effects. Moreover, the LiFi transceiver’s structure used does not incorporate optical amplifier components, resulting in reduced computational complexity compared to conventional LiFi transceivers. The proposed system proved its progress over the previous work under the influence of the same input effects.

Design and Analysis of a Bandwidth Aware Adaptive Multipath N-Channel Routing Protocol for 5G Internet of Things (IoT)

Large numbers of mobile wireless nodes that can move randomly and join or leave the network at any moment make up mobile ad-hoc networks. A significant number of messages are delivered during information exchange in populated regions because of the Internet of Things' (IoT) exponential increase in connected devices. Congestion can increase transmission latency and packet loss by causing congestion. More network size, increased network traffic, and high mobility that necessitate dynamic topology make this problem worse. An adaptive Multipath Multichannel Energy Efficient (AMMEE) routing strategy is proposed in this study, in which route selection strategies depend on forecasted energy consumption per packet, available bandwidth, queue length, and channel utilization. While multichannel uses a channel-ideal assignment process to lessen network collisions, multipath offers various paths and balances network strain. The link bandwidth is split up into a few sub-channels in the multichannel mechanism. To reduce network collisions, several source nodes simultaneously access the channel bandwidth. The cooperative multipath multichannel technique offers several paths from a single source or from several sources to the destination without colliding or becoming congested. The AMMEE routing approach is the basis for path selection. A load- and bandwidth-aware routing mechanism in the proposed AMMEE chooses the path based on node energy and forecasts their lifetime, which improves network dependability. The outcome demonstrates a comparative analysis of various multichannel medium access control (MMAC) techniques, including Parallel Rendezvous Multi Channel Medium Access Protocol (PRMMAC), Quality of Service Ad hoc On Demand Multipath Distance Vector (QoS-AOMDV), Q-learning-based Multipath Routing (QMR), and Topological Change Adaptive Ad hoc On-demand Multipath Distance Vector (TA-AOMDV) and the proposed AMMEE method. The results show that the AMMEE approach outperforms alternative systems. Doi: 10.28991/ESJ-2024-08-01-018 Full Text: PDF

Model and analysis of the data readout architecture for the ITS3 ALICE Inner Tracker System

The ALICE collaboration is developing the Inner Tracker System 3 (ITS3), a novel detector that exploits the stitching technique to construct single-die monolithic pixel sensors of up-to 266 mm × 93 mm. ITS3 requires all hits from a particle flux of 5.75 MHz/cm2 to be transmitted on-chip to one of the sensor edges. This on-chip readout must balance the resources of power consumption, dead area, and readout performance by tuning different design parameters such as on-chip memories or on-chip links bandwidth. For doing so, a model of the ITS3 on-chip readout architecture will be presented. This model combines an accurate simulation of the particle flux fluctuations expected in ALICE with a clock-accurate digital readout simulator, combining high accuracy with fast code in terms of simulation speed.

Enabling efficient routing for traffic engineering in SDN with Deep Reinforcement Learning

Traffic Engineering (TE) is applied to optimize network transmission efficiency by managing the routing of complicated traffic. Emerging Deep Reinforcement Learning (DRL) and Software-Defined Networking (SDN) provide flexible ability for traffic management and congestion control. However, existing methods either cannot reroute the network-wide traffic in an accurate way or encounter too much calculation latency due to the inference of mathematical optimization techniques. In this paper, we propose a flexible and link-reconfigurable TE solution called EfficientTE that effectively adjusts the traffic routing based on real-time traffic demands. By analyzing the characteristics of network topology and traffic, EfficientTE selects a few links that are critical for congestion in the network. Then, we propose the idea of virtual capacity that helps the DRL algorithm adjust to different link bandwidths. Based on the traffic demand and topology information collected by the SDN controller, the DRL algorithm is used to dynamically adjust the virtual capacity of the critical links to reshape the network. To ensure network performance with low disturbance, we selectively reroute the Top-K critical flows using the weighted K-shortest path algorithm, while forwarding the major flows with default rerouting. Experiments show that EfficientTE optimizes maximum link utilization and outperforms existing TE solutions by improving the load-balancing performance ratio by at least 6.13%, 18.78%, 16.20%, and 22.81% respectively in four network topologies.

Research on Power Service Route Planning Scheme Based on SDN Architecture and Reinforcement Learning Algorithm

The power communication network carries various power services to ensure the safe operation of the power network, among which, the relay protection service is the most important service. Reasonable planning of the service route can improve the effectiveness and reliability of data transmission in the power communication network, thereby ensuring the reliable operation of the power grid. This paper constructs a route planning architecture for the power communication network based on a software-defined network. On this basis, parameters such as the power service and network-carrying service status are defined. With the goal of minimizing network risk variance and considering link bandwidth utilization and overload constraints of relay protection services, a service route allocation problem has been raised. To solve this problem, a power service route planning scheme based on a reinforcement learning algorithm is proposed. This algorithm uses the state–action–reward–state–action (SARSA) algorithm to complete service route planning. The simulation results show that using the route planning scheme proposed in this paper can avoid the overload of relay protection services, reduce network risk variance, and effectively balance network risk.

A novel low-latency scheduling approach of TSN for multi-link rate networking

Time Sensitive Networks (TSN), as an important representative of deterministic networks, provide low-latency and highly reliable communication services for the growing network applications that have strict requirements. Cyclic Queuing and Forwarding (CQF) is a well-known mechanism proposed by IEEE 802.1Qch for low-latency flow control of time-sensitive networks. It achieves bounded end-to-end delay and jitter transmission through a set of queues without complicated queue gating. However, most of the current work overlooks the widespread existence of multi-link rate networks in LANs and WANs, and the single-cycle CQF is unable to adjust different link rates, resulting in low bandwidth utilization and high latency. In this paper, we propose a novel scheduling approach named Multi-Cycle CQF (MCCQF) to solve the transmission problem in multi-link rate networks, aiming to reduce deterministic end-to-end delay and improve link bandwidth utilization. In addition, we formulate the scheduling constraints, being of guiding significance for designing the transmission of multi-link-rate networks, and we design an online scheduling algorithm based on it. We compare the proposed scheme with the single-cycle CQF online scheduling algorithm in hierarchical multi-link-rate networking scenarios, and the evaluation shows that our algorithm achieves better end-to-end ultra-low latency (38.9% reduction) with a smaller schedulability gap compared with single-cycle CQF. And we also improved the schedulability based on MCCQF by utilizing internal offset.

TCP BBR-n: Increased throughput for wireless-AC networks

Google proposed a new TCP congestion control algorithm (CCA), Bottleneck Bandwidth and Round-trip propagation time (BBR) which has opened up new dimensions in congestion control. BBR tries to operate near Kleinrock’s operating point to avoid excessive queue formation at the bottleneck and to use the link bandwidth optimally. BBR creates a model of the network path by measuring the bottleneck bandwidth and minimum round-trip time (RTT) to maximize the delivery rate and minimize latency. BBR v2 is an updated version of BBR which addresses many shortcomings of the original BBR (BBR v1) such as interprotocol fairness, RTT fairness, and excessive retransmissions. However, BBR v2 has certain limitations in its operation in IEEE 802.11ac (Wi-Fi 5) networks. The default BBR v2 limits the throughput of Wi-Fi 5 and an increased latency has been observed. This is because the Wi-Fi 5 frame aggregation logic is underutilized and fewer frames are being sent to the Wi-Fi 5 interface. In this paper, we have proposed BBR-n (BBR new) which provides better throughput than the generic BBR v2 in the Wi-Fi 5 networks. Real-time experiments were performed over a physical testbed using Flent to confirm that BBR-n achieves over double throughput as compared to generic BBR v2 and reduced latency in networks as compared to pure loss-based variants such as Cubic and Reno.

Learning From Images: Proactive Caching With Parallel Convolutional Neural Networks

With the continuous trend of data explosion, delivering packets from data servers to end users causes increased stress on both the fronthaul and backhaul traffic of mobile networks. To mitigate this problem, caching popular content closer to the end-users has emerged as an effective method for reducing network congestion and improving user experience. To find the optimal locations for content caching, many conventional approaches construct various Mixed Integer Linear Programming (MILP) models. However, such methods may fail to support online decision making due to the inherent curse of dimensionality. In this paper, a novel framework for proactive caching is proposed. This framework merges model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image. For parallel training and simple design purposes, the proposed MILP model is first decomposed into a number of sub-problems and, then, Convolutional Neural Networks (CNNs) are trained to predict content caching locations of these sub-problems. Furthermore, since the MILP model decomposition neglects the network resources (such as caching space and link bandwidth) competition among sub-problems, the CNNs' outputs have the risk to be infeasible solutions. Therefore, two algorithms are provided: the first uses predictions from CNNs as an extra constraint to reduce the number of decision variables; the second employs CNNs' outputs to accelerate local search. Numerical results show that the proposed scheme can reduce 71.6% computation time, whose computation time reaches around 28.9 ms, with only 0.8% additional performance cost compared to the MILP solution, which provides high quality decision making in pseudo real-time.

Maximum Throughput First Access Point Selection Scheme for Multi-Rate Software-Defined Wireless Network

As the number of devices increases, many businesses deploy multiple access points (APs) in neighboring areas to provide Internet service. In such networks, a mobile station (MS) often has multiple APs within its coverage area, and deciding which AP to associate with becomes a significant issue that can influence network performance. Software Defined Network (SDN) has recently become a popular technology for making intelligent mobile station assignment decisions for APs. In a multi-rate Wi-Fi network, the actual bandwidth of high-rate links can be reduced to that of low-rate links. In this paper, we propose a Maximum Throughput First (MTF)AP selection scheme, which is a refinement of existing Mininet Wi-Fi mechanisms. MTF considers the parameters of actual throughput achieved by the access point and the number of associated stations as selection metrics when making the association decision. The simulation results show that MTF provides better performance, especially in multi-rate environments.