Improve Network Throughput Research Articles

ALBRL: Automatic Load-Balancing Architecture Based on Reinforcement Learning in Software-Defined Networking

Due to the rapid development of network communication technology and the significant increase in network terminal equipment, the application of new network architecture software-defined networking (SDN) combined with reinforcement learning in network traffic scheduling has become an important focus of research. Because of network traffic transmission variability and complexity, the traditional reinforcement-learning algorithms in SDN face problems such as slow convergence rates and unbalanced loads. The problems seriously affect network performance, resulting in network link congestion and the low efficiency of inter-stream bandwidth allocation. This paper proposes an automatic load-balancing architecture based on reinforcement learning (ALBRL) in SDN. In this architecture, we design a load-balancing optimization model in high-load traffic scenarios and adapt the improved Deep Deterministic Policy Gradient (DDPG) algorithm to find a near-optimal path between network hosts. The proposed ALBRL uses the sampling method of updating the experience pool with the SumTree structure to improve the random extraction strategy of the empirical-playback mechanism in DDPG. It extracts a more meaningful experience for network updating with greater probability, which can effectively improve the convergence rate. The experiment results show that the proposed ALBRL has a faster training speed than existing reinforcement-learning algorithms and significantly improves network throughput.

Resource Allocation for Intelligent Reflecting Surface Assisted Wireless Powered IoT Systems With Power Splitting

This paper proposes a new transmission policy for intelligent reflecting surface (IRS) empowered wireless powered internet of things systems. Particularly, an energy station (ES) wirelessly charges for multiple IoT devices during downlink wireless energy transfer (WET) and then these devices deliver their own message to an access point (AP) during uplink wireless information transfer (WIT). Also, an IRS is deployed to improve energy harvesting and data transmission capabilities. To enhance self-sustainability of the IRS, the IRS harvests energy from the ES based on the harvest-then-transmit protocol. In this paper, we maximize the sum throughput via optimizing the phase shifts of the IRS, the transfer time scheduling as well as the power splitting ratio. Due to the non-convexity of the formulated problem, we divide the problem into two sub-problems, each of which can be handled separately. Then, we adopt an alternating optimization (AO) algorithm with the semidefinite programming (SDP) relaxation. Also, we consider a special case where the circuit power consumption of IoT devices can be neglected. In this case, we derive a closed form solution for the optimal transmission time slots, power allocation and phase shift by the Lagrange dual method. Finally, numerical evaluations validate effectiveness of the proposed scheme, which significantly benefits from the IRS in improving network throughput.

An improved evaporation rate water cycle algorithm for energy-efficient routing protocol in WSNs

PurposeThe paper aims to introduce an efficient routing algorithm for wireless sensor networks (WSNs). It proposes an improved evaporation rate water cycle (improved ER-WC) algorithm and outlining the systems performance in improving the energy efficiency of WSNs. The proposed technique mainly analyzes the clustering problem of WSNs when huge tasks are performed.Design/methodology/approachThis proposed improved ER-WC algorithm is used for analyzing various factors such as network cluster-head (CH) energy, CH location and CH density in improved ER-WCA. The proposed study will solve the energy efficiency and improve network throughput in WSNs.FindingsThis proposed work provides optimal clustering method for Fuzzy C-means (FCM) where efficiency is improved in WSNs. Empirical evaluations are conducted to find network lifespan, network throughput, total network residual energy and network stabilization.Research limitations/implicationsThe proposed improved ER-WC algorithm has some implications when different energy levels of node are used in WSNs.Practical implicationsThis research work analyzes the nodes’ energy and throughput by selecting correct CHs in intra-cluster communication. It can possibly analyze the factors such as CH location, network CH energy and CH density.Originality/valueThis proposed research work proves to be performing better for improving the network throughput and increases energy efficiency for WSNs.

A router architecture with dual input and dual output channels for Networks-on-Chip

Networks-on-Chip (NoCs) are the most viable and scalable solution for connecting thousands of processing cores, and are emerging as the interconnect infrastructure for future Chip-Multiprocessors. Latency and throughput are the primary metrics for on-chip network performance, while chip area and power budgets are increasingly dominated by interconnect networks. Hence, it is critical to achieve higher performance gains at a lower cost. Previous research has proposed a router architecture that forwards packets through idle bidirectional channels to improve network performance, but it introduces significant area and power overheads. In this paper, we propose DIDO: a router architecture with dual input and dual output channels. First, the dual output channels provide extra flexibility for forwarding packets and reduce packet contention. Then, the redesigned input ports and crossbar shorten the pipeline and reduce the crossbar overhead. Finally, the removal of the virtual channel allocator allows DIDO to operate at higher frequencies. Simulation results indicate that DIDO reduces average packet latency by up to 44.8% and improves network throughput by up to 37.8% compared to the baseline router at the same frequency. Synthesis results indicate that the DIDO’s area and power overhead are comparable to the baseline router, while the maximum frequency is boosted by 18%.

An Adaptive CMT-SCTP Scheme: A Reinforcement Learning Approach

With the continuous increase of end users and types of services, the scale of the network has shown explosive growth, which has brought tremendous pressure and challenges to network data transmission. How to achieve high-quality data transmission has become a core issue. Single-path transmission has been difficult to meet the above requirements. The concurrent multipath transfer extension for stream control transmission protocol (CMT-SCTP), which supports multipath and independent data streams, can solve this problem. However, the current transmission path assessment scheme has too large granularity to make full use of the resources of the transition zone. Most studies ignore the different requirements of different services, a single transmission strategy, and the lack of an intelligent dynamic adjustment mechanism. Therefore, we designed a QCMT(Q-learning based CMT-SCTP) scheduling method. This method considers the multi-dimensional characteristics of the path and the characteristic preferences of different services, periodically evaluates and trains the reinforcement learning model for service adaptation, and makes scheduling decisions dynamically. Experimental results show that dynamic scheduling based on path parameters and service preferences can reduce message delay and improve network throughput.

Performance Analysis of Cooperative Multiple Access With Minimum Interference Range for Large Scale Wireless Networks

Cooperative communication techniques are widely used in large-scale wireless networks for improving network throughput. This is achieved through the creation of transmission diversity by forming multiple cooperative transmission links which involve several relaying nodes as helpers for an end-to-end data transmission. Consequently, link interference range will inevitably be enlarged, which will adversely hamper the diversity created and offset the network throughput improvement as a result. This problem has not been properly addressed by most cooperative communication protocols reported so far. In this paper, a novel protocol that performs cooperative medium access control as well as minimizes the interference range, namely MIR-CMAC, is proposed to directly address the enlarged link interference range problem introduced by cooperative communication. Specifically, functions like channel reservation, cooperative priority differentiation and contention for relay selection are collectively enabled in MIR-CMAC to reduce channel access collision and select the suitable helper node that can maximize the integrated link data rate and minimize the link interference concerned. In addition, a full interference model is established to evaluate the effect of interference across the whole process from channel access to packet transmission and derive a new network throughput analysis method for MIR-CMAC. Both numerical and simulation results demonstrate the effectiveness of MIR-CMAC and its performance improvements over other protocols.

Reinforcement Learning-Based Power Control for MACA-Based Underwater MAC Protocol

A major amount of the energy of battery-powered sensors is spent during packet transmissions. This issue has led to the development of power-control-based multiple-access collision avoidance (MACA) protocols that can reduce the packet transmission power and conserve energy. However, the reduction in transmission power renders the packets susceptible to collisions. To reduce these collisions while maintaining high energy efficiency, we propose a power control protocol that utilizes reinforcement learning to choose the optimal transmission power. The total reward is determined by the occurrence of a collision, amount of transmission power used, frequency of DATA packet retransmissions, and update of the interference range. A key feature of the proposed protocol is that it enables sensors to prevent collisions without any prior knowledge of interferences, thus eliminating the need for additional signaling. Further, simulation results reveal that compared with benchmark protocols such as collision avoidance power control media access control (MAC), two-level power control MAC, and MACA-based power control MAC protocols, the proposed protocol improves network throughput while minimizing the network energy consumption and collisions per packet. The efficacy of the proposed protocol is demonstrated through results obtained using varying average traffic loads.

Reinforcement learning-based joint self-optimisation method for the fuzzy logic handover algorithm in 5G HetNets

5G heterogeneous networks (HetNets) can provide higher network coverage and system capacity to the user by deploying massive small base stations (BSs) within the 4G macrosystem. However, the large-scale deployment of small BSs significantly increases the complexity and workload of network maintenance and optimisation. The current handover (HO) triggering mechanism A3 event was designed only for mobility management in the macrosystem. Directly implementing A3 in 5G-HetNets may degrade the user mobility robustness. Motivated by the concept of self-organisation networks (SON), this study developed a self-optimisation triggering mechanism to enable automated network maintenance and enhance user mobility robustness in 5G-HetNets. The proposed method integrates the advantages of subtractive clustering and Q-learning frameworks into the conventional fuzzy logic-based HO algorithm (FLHA). Subtractive clustering is first adopted to generate a membership function (MF) for the FLHA to enable FLHA with the self-configuration feature. Subsequently, Q-learning is utilised to learn the optimal HO policy from the environment as fuzzy rules that empower the FLHA with a self-optimisation function. The FLHA with SON functionality also overcomes the limitations of the conventional FLHA that must rely heavily on professional experience to design. The simulation results show that the proposed self-optimisation FLHA can effectively generate MF and fuzzy rules for the FLHA. The proposed approach can minimise the HO, ping-pong HO, and HO failure ratios while improving network throughput and latency by comparing with conventional triggering mechanisms.

On the validity of numerical simulations for control-theoretic AQM schemes in computer networks

Active Queue Management (AQM) schemes complement Transmission Control Protocol (TCP) congestion-control mechanisms in reducing queuing delay, packet loss, and in improving network throughput. This paper presents a detailed mathematical review of the control-theoretic AQM feedback structure. We highlight and clarify the misconceptions regarding its usage, and furthermore, we verify and validate the numerical time-domain and event-domain simulations of existing AQM schemes using the MATLAB-Simulink platform and the NS-3 simulator platform respectively. Finally, conclusions and some future research directions are briefly discussed.

Power and Rate Adaptive Pushing Over Fading Channels

Proactive caching is capable of reducing access latency and improving network throughput, thereby attracting attention from both industry and academia. However, the energy efficiency (EE) of proactive caching over fading channels has not been well studied yet. In this paper, we aim at presenting an energy-efficient content pushing policy by carefully adapting the transmit rate and power in pushing and on-demand delivery phases, while assuring delivery delay constraints. To this end, the average delay, delay-outage probability, and EE are analyzed for a general adaptive pushing policy based on the saddle point approximation. According to these results, we formulate EE maximization problems under average delay and delay-outage constraints, respectively, for two special types of adaptive pushing policies, namely, opportunistic pushing and water-filling-based pushing. Due to the high complexity of the grid search for solving the formulated problems, suboptimal algorithms are presented based on gradient descent and golden section search methods. Moreover, we mathematically derive the scaling property and numerically obtain request probability and delivery delay thresholds for content pushing. Simulations show that the presented pushing policies achieve higher EE than on-demand transmissions, especially when the content item is popular and the tolerable delivery delay is small.

RHNE-MAC: Random Handshake MAC Protocol Based on Nash Equilibrium for Underwater Wireless Sensor Networks

Underwater wireless sensor networks (UWSNs) have a long propagation delay; thus, when multiple nodes access the channel at the same time, the nodes cannot perceive the channel status in time, which causes access conflicts between nodes. To reduce multinode access conflicts and improve network throughput, a random handshake MAC protocol based on Nash equilibrium (RHNE-MAC) is proposed. In the RTS phase, RHNE-MAC regards nodes sending control packets to channels as competitive behavior and an incomplete information game. This paper firstly discusses the collision probability of two nodes sending data to the master node at the same time when the nodes are evenly distributed. Subsequently, according to the collision probability and the size of the network, the mathematical expectation concept is added to the payment function. Then, it uses the Nash equilibrium theory to solve the game, which allows the network to dynamically adjust the probability of nodes requesting access to a channel according to the number of nodes, thereby minimizing the problem of multinode access conflicts. Finally, the master node schedules the data transmission for each node according to the successful receipt of control packet information and propagation delay information of the network; therefore, multiple nodes can transmit data after a handshake period. The simulation results show that the throughput is increased by approximately 12% compared with prescheduling-based MAC and by 207% compared with TDMA; moreover, the end-to-end delay also has better performance.

A survey of 5G millimeter wave, massive multiple‐input multiple‐output, and vehicle‐to‐vehicle channel measurements and models

SummaryThere is no doubt that the world evolves nowadays in the digital communications era. This opens the horizon to research and investment in new promising technologies like 5G and beyond cellular networks that are able to increase the subscribers' data rates in all communication systems. The aim of 5G and beyond cellular networks is to provide anywhere and anytime connectivity. By combining the enormous available bandwidth in millimeter wave (mmWave) frequencies with high multiplexing gains achieved by large antenna arrays in multiple‐input multiple‐output (MIMO) systems, these technologies can significantly improve network throughput, increase network power, boost energy and spectral efficiency, and enhance network coverage. Furthermore, ultra‐reliable vehicular communication and high capacity would be essential features of communication networks beyond 5G. Fortunately, when vehicles are moving on the road and communicating with each other, the vehicular communication issue becomes more challenging. Among the several new technologies expected to play significant roles in the 5G and beyond systems, this paper focused on summarizing the state‐of‐the‐art mmWave, vehicle‐to‐vehicle (V2V), and massive MIMO measurements and models. We introduce a detailed overview of each of the three technologies individually. Besides, novel taxonomies for the 5G channel measurements, objectives, and model types are provided. We expect that mmWave‐based massive MIMO technology enables real‐time channel estimation and ultra‐precise beamforming, and thus enables better service coverage. Finally, challenges and future research directions for 5G channel measurements and models are presented.

An IoT-Based Water Level Detection System Enabling Fuzzy Logic Control and Optical Fiber Sensor

The usage of wireless sensors has become widespread for the collection of data for various Internet of Things (IoT) products. Specific wireless sensors use optical fiber technology as transmission media and lightwave signals as carriers, showing the advantages of antielectromagnetic interference, high sensitivity, and strong reliability. Hence, their application in IoT systems becomes a research hotspot. In this article, multiple optical fiber sensors are constructed as an IoT detection system, and a Transmission Control Protocol (TCP)/Internet Protocol (IP) communication stack is used for the sensor module. Furthermore, design of gateway module, data server, and monitoring module is established in order to run the data server in the Windows system and communicate across the network segments. Furthermore, the optical fiber sensor is connected to the development board with WiFi, meanwhile considering the optical fiber wireless network’s congestion problem. The fuzzy logic concept is introduced from the perspective of cache occupancy, and a fiber sensor’s network congestion control algorithm is proposed. In the experiment, the IoT detection system with multiple optical fiber sensors is used for water level detection, and the sensor’s real-time data detected by the User Interface (UI) are consistent with the feedback results. The proposed method is also compared with the SenTCP algorithm and the CODA algorithm, and it was observed that the proposed network congestion control algorithm based on the fuzzy logic can improve network throughput and reduce the network data packet loss.

Coordinated 5G Network Slicing: How Constructive Interference Can Boost Network Throughput

Radio access network (RAN) slicing is a virtualization technology that partitions radio resources into multiple autonomous virtual networks. Since RAN slicing can be tailored to provide diverse performance requirements, it will be pivotal to achieve the high-throughput and low-latency communications that next-generation (5G) systems have long yearned for. To this end, effective RAN slicing algorithms must (i) partition radio resources so as to leverage coordination among multiple base stations and thus boost network throughput; and (ii) reduce interference across different slices to guarantee slice isolation and avoid performance degradation. The ultimate goal of this paper is to design RAN slicing algorithms that address the above two requirements. First, we show that the RAN slicing problem can be formulated as a 0-1 Quadratic Programming problem, and we prove its NP-hardness. Second, we propose an optimal solution for small-scale 5G network deployments, and we present three approximation algorithms to make the optimization problem tractable when the network size increases. We first analyze the performance of our algorithms through simulations, and then demonstrate their performance through experiments on a standard-compliant LTE testbed with 2 base stations and 6 smartphones. Our results show that not only do our algorithms efficiently partition RAN resources, but also improve network throughput by 27% and increase by 2× the signal-to-interference-plus-noise ratio.

R-SplitC: Collision minimization for cellular communication in unlicensed spectrum

The 3rd generation partnership project (3GPP) has standardized 5G new radio in unlicensed spectrum (NR-U) that uses a wide unlicensed spectrum as an alternative solution to the insufficient bandwidth problem of the existing NR. NR-U has a listen-before-talk (LBT) operation similar to the carrier sense multiple access with collision avoidance (CSMA/CA) operation of Wi-Fi. It allows nodes to transmit only after LBT success. NR-U suffers from the collision issue because its channel access mechanism is similar to that of Wi-Fi. Wi-Fi solves the collision problem through the request-to-send/clear-to-send (RTS/CTS) mechanism. However, NR-U has no way of solving the collision problem. As a result, NR-U suffers severe performance degradation due to collisions as the number of contending nodes increases. In this paper, we propose to use an extended and split reservation signal (RS) for reservation in NR-U that consists of front RS and rear RS and design a new collision minimization scheme, termed R-SplitC, that contains two components: new RS structure and contention window size (CWS) control. The new RS structure helps to minimize collisions in NR-U transmissions, and CWS control works to protect the performance of other communication technologies such as Wi-Fi. We mathematically analyze and evaluate the performance of our scheme and confirm that R-SplitC improves network throughput by up to 99.3% compared to the baseline RS scheme without degrading Wi-Fi performance.

Multi-Agent Reinforcement Learning Based 3D Trajectory Design in Aerial-Terrestrial Wireless Caching Networks

This paper investigates a dynamic 3D trajectory design of multiple cache-enabled unmanned aerial vehicles (UAVs) in a wireless device-to-device (D2D) caching network with the goal of maximizing the long-term network throughput. By storing popular content at the nearby mobile user devices, D2D caching is an efficient method to improve network throughput and alleviate backhaul burden. With the attractive features of high mobility and flexible deployment, UAVs have recently attracted significant attention as cache-enabled flying base stations. The use of cache-enabled UAVs opens up the possibility of tracking the mobility pattern of the corresponding users and serving them under limited cache storage capacity. However, it is challenging to determine the optimal UAV trajectory due to the dynamic environment with frequently changing network topology and the coexistence of aerial and terrestrial caching nodes. In response, we propose a novel multi-agent reinforcement learning based framework to determine the optimal 3D trajectory of each UAV in a distributed manner without a central coordinator. In the proposed method, multiple UAVs can cooperatively make flight decisions by sharing the gained experiences within a certain proximity to each other. Simulation results reveal that our algorithm outperforms the traditional single- and multi-agent Q-learning algorithms. This work confirms the feasibility and effectiveness of cache-enabled UAVs which serve as an important complement to terrestrial D2D caching nodes.

Information-Priority-Oriented Adaptive MAC Protocol for High Dynamic UAV Network

In recent years, unmanned aerial vehicles (UAVs) have attracted wide attention in both military and civilian fields because of their versatility, flexibility, and low-cost characteristics. In this letter, a UAV network architecture oriented to the virtual cloud processing center (VCPC) is considered, where the highly dynamic UAV network can transmit real-time data from sensors on the ground as well as data observed by the UAV nodes to the VCPC. Due to the high dynamic characteristics of UAVs, the topology of networks and traffic load within the coverage of the VCPC change frequently, especially when the priority of the information is different, and it is necessary to design a novel media access control (MAC) protocol to reduce the transmission delay of high priority and improve throughput of UAV networks. Therefore, an information priority oriented adaptive (IPOA-MAC) protocol is presented in this letter for high dynamic UAV networks. The relative length of control channel interval and service channel interval can be adaptively adjusted to improve network throughput in high dynamic application scenarios. Also, priority flag bits are set in data to ensure real-time transmission of high-priority information. Finally, the simulation results show the proposed IPOA-MAC can improve network performance effectively than other existing protocols.

A dynamic source routing protocol based on path reliability and link monitoring repair.

The two most essential factors for mobile self-organizing networks applicable to drones are reliability and stability. In harsh communication environments, such as mountainous regions and natural disasters, the use of satellites and terrestrial communication stations has severe time delays due to the high speed of UAVs, resulting in frequent communication interruptions with UAVs. Therefore, UAVs need to establish self-organizing networks for communication and information sharing. High-speed movement will lead to rapid changes in the network topology, resulting in established links being in an unstable connection state and even frequent routing errors, thus preventing the establishment of stable communication links. In order to improve the communication quality of UAVs under high-speed movement, we propose a dynamic source routing protocol based on path reliability and monitoring repair mechanism (DSR-PM). The model performs data transmission by filtering the best reliability path. The link state information is monitored during transmission and broken links are repaired in time to ensure the communication stability and reliability of the links and improve the data transmission efficiency. We simulated the approach in NS2 software and the simulation results show that the DSR-PM protocol effectively reduces parameters such as overhead, packet loss and delay, improves network throughput, and provides better communication performance.

: Multi-Dimensional Spatial Reuse Enhancement for Directional Millimeter-Wave Wireless Networks

Millimeter wave (mmWave) wireless networks are envisioned to bring a very high degree of spatial reuse, i.e., multiple links can operate simultaneously without interference. The vision, however, is becoming doubtful, as recent studies found that non-negligible interference exists due to imperfect beam patterns. In this paper, we extensively measure the spatial reuse issue in a dense 60 GHz mmWave network consisting of multiple access points (AP) and users. Our measurement quantifies the impact of interference on network performance and finds that the existing prediction based on interference-resolving approaches are insufficient. Motivated by the findings, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDSR</i> , which enhances the spatial reuse in 60 GHz mmWave networks. Instead of relying on interference prediction, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDSR</i> takes a new measurement principle of building a conflict graph that implicitly takes into account the impact of both beam imperfection and reflections. Using the conflict graph, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDSR</i> improves the spatial reuse from three dimensions: AP association, user scheduling, and beam selection, which can determine the optimal AP-user-beam combination and minimize interference in each scheduling cycle. We prototype and evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDSR</i> on the testbed using commodity mmWave radios. The evaluation results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDSR</i> improves network throughput by multi-folds compared with the state-of-the-art one.

An EAOW algorithm to optimize the throughput in ad-hoc network in emergency communication environment

For improving the throughput of wireless ad-hoc networks in emergency communication environments, this paper proposes an enhanced adaptive on-demand weighted clustering algorithm. The clustering algorithm is very important in heterogeneous wireless ad-hoc networks, but the widely used AOW algorithm has certain limitations. Combined with the emergency communication environment, this article first analyzes the importance of clustering algorithm in improving network throughput, and secondly introduces and analyzes the two shortcomings of the AOW algorithm. On this basis, an enhanced adaptive on-demand weighted clustering algorithm (EAOW) is proposed. In the EAOW algorithm, each weight factor is dimensionlessly processed, and the properties of the nodes are added to the heterogeneous network. The simulation results show that compared with the AOW algorithm, the EAOW algorithm has an obvious progress in the number of cluster heads and the network throughput.