Software Defined Wireless Networks Research Articles

OSWireless: Hiding specification complexity for zero-touch software-defined wireless networks

In the current practice of wireless engineering, to optimize wireless networks engineers usually need to grapple simultaneously with network modeling, algorithm and protocol design as well as their implementation on distributed edge nodes. This process is tedious and error prone. In this article we attempt to address this challenge by designing OSWireless, a new control plane for optimizing software-defined wireless networks. At the core of OSWireless is the virtualization of four control plane functionalities, including intent, mathematical, algorithmic and forwarding specifications, and then provide them as a service to network engineers. To this end, we design two new subplanes for the control plane: Wireless Network Abstraction Specification (WiNAS) Subplane and Optimization-as-a-Service (OaaS) Subplane. The former converts intent specifications defined using high-level Application Programming Interfaces (APIs) to the corresponding mathematical specifications, and the latter generates automatically operational (possibly distributed) algorithmic specifications. We prototype OSWireless and deploy it over NeXT, a newly developed software-defined experimentation testbed, and showcase the automated control program generation capability of OSWireless and the optimality of the resulting programs considering a variety of network control problems. We further test the applicability of OSWireless on UBSim, a newly-developed Python based simulator for integrated aerial-ground networks, considering location optimization of mobile nodes as an example. Through developing OSWireless, we hope to accelerate research towards future zero-touch software-defined wireless networks with reduced management complexity.

ПІДВИЩЕННЯ ЯКОСТІ СПРИЙНЯТТЯ ВІДЕО В БЕЗПРОВІДНИХ ПРОГРАМНО-КОНФІГУРОВАНИХ МЕРЕЖАХ З ВИКОРИСТАННЯМ МУЛЬТИПІДКЛЮЧЕННЯ

This article presents a method to enhance the quality of video streaming using multi-connection in software-defined wireless networks. The method utilizes bonding technology to combine various physical network adapters into a single integrated device, effectively utilizing network resources and ensuring high-quality video transmission. The study also includes an analysis of the use of the Peak Signal-to-Noise Ratio (PSNR) as a quality perception metric and the method's response to deteriorating communication channel parameters. The results indicate that an increase in packet loss leads to a decrease in PSNR values. This is explained by the fact that packet loss introduces errors or noise into the compressed signal, reducing its accuracy compared to the original. Specifically, packet loss can be caused by insufficient network bandwidth when the volume of data attempting to pass through the network exceeds its capacity. In such cases, packet loss can lead to a deterioration in the quality of the video stream. On the other hand, packet loss due to radio signal instability may result from external factors such as obstacles or interference, which can lead to packet loss even with sufficient network bandwidth. These losses can also impact the quality of the video stream and reduce PSNR values. During the study, it was observed that an equal percentage of packet losses caused by insufficient network bandwidth and losses due to radio signal instability have different effects on image perception quality. In general, the overall research results confirm the potential for significant improvement in the quality of service for wireless network users using the proposed method, without requiring additional network resource expenditures. This can have significant practical implications for advancing network technologies and ensuring high-quality video data transmission in modern wireless networks.

Assessment of SDN Controllers in Wireless Environment Using a Multi-Criteria Technique

Software-defined network (SDN) technology can offer wireless networks the advantages of simplified control and network management. This SDN subdomain technology is called the software-defined wireless network (SDWN). In this study, the performance of four controllers in an SDWN environment is assessed, since the controller is the most significant component of the entire network. Using the Mininet-WiFi platform, the performance of each controller is evaluated in terms of throughput, latency, jitter, and packet loss. Moreover, a multi-criteria evaluation is introduced and applied to provide a fair comparison between SDWNs. This study provides an appropriate configuration of SDWNs that is useful for network engineering and can be used for SDWNs performance optimization.

NeXT: Architecture, prototyping and measurement of a software-defined testing framework for integrated RF network simulation, experimentation and optimization

To support rigorous and repeatable experimental evaluation of wireless networked systems, the community has made significant efforts to develop experimentation platforms. However, existing platforms primarily focus on the data plane, i.e., the forwarding infrastructure, without explicitly considering the control plane. To fill this gap, in this work we develop NeXT, a software-defined playground with integrated wireless network simulation, experimentation and optimization capabilities. We first design the data plane, which integrates an event-driven broadband wireless network simulator called UBSim and a software-defined wireless network testing facility called RoboNet. We then design NeXT’s control plane, where a software toolchain is developed and deployed to support both traditional model-based optimization and new data-driven control techniques. We showcase the experimentation capability of NeXT considering a series of optimization and control problems in different wireless networks.

WOA-Based Robust Congestion Control Scheme with Two Kinds of Propagation Latencies and External Disturbance in Software-Defined Wireless Networks

This paper proposes a novel WOA-based robust control scheme with two kinds of propagation latencies and external disturbance implemented in Software-Defined Wireless Networks (SDWNs) to maximize overall throughput and enhance the stability of the global network. Firstly, an adjustment model developed using the Additive-Increase Multiplicative-Decrease (AIMD) adjustment scheme with propagation latency in device-to-device paths and a closed-loop congestion control model with propagation latency in device–controller pairs are proposed, and the effect of channel competition from neighboring forwarding devices is analyzed. Subsequently, a robust congestion control model with two kinds of propagation latencies and external disturbance is established. Then, a new WOA-based scheduling strategy that considers each individual whale as a specific scheduling plan to allocate appropriate sending rates at the source side is presented to maximize the global network throughput. Afterward, the sufficient conditions are derived using Lyapunov–Krasovskii functionals and formulated using Linear Matrix Inequalities (LMIs). Finally, a numerical simulation is conducted to verify the effectiveness of this proposed scheme.

Performance and Scalability Analysis of SDN-Based Large-Scale Wi-Fi Networks

The Software-Defined Networking (SDN) paradigm is one that is utilized frequently in data centers. Software-Defined Wireless Networking, often known as SDWN, refers to an environment in which concepts from SDN are implemented in wireless networks. The SDWN is struggling with challenges of scalability and performance as a result of the growing number of wireless networks in its coverage area. It is thought that SDN techniques, such as Mininet-Wi-Fi and Ryu Controller for wireless networks, can overcome the problems with scalability and performance. Existing Wi-Fi systems do not provide SDN execution to end clients, which is one reason why the capability of Wi-Fi is restricted on SDN architecture. Within the scope of this study, we analyzed Wi-Fi networks operating on SDN using the Transmission Control Protocol (TCP) and User Datagram Protocol (UDP). By utilizing a testbed consisting of Ryu Controller and Mininet-Wi-Fi, we were able to test Wi-Fi over SDN and evaluate its performance in addition to its scalability. When evaluating the performance of a network, we take into account a number of different metrics, such as bandwidth, round-trip time, and jitter. In order to assess the level of performance, the SDN-based Wi-Fi controller Ryu is linked to an increasing number of access points (1, 2, 3, and 4) as well as stations (10, 30, 50, and 100). The experimental findings obtained using Mininet-Wi-Fi indicate the scalability and dependability of the network performance provided by the SDN Wi-Fi network controller Ryu in an SDN environment. In addition, the round-trip time for TCP packets grows proportionally with the number of hops involved. A single access point is capable of simultaneously supporting up to fifty people at once.

Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWN.

Multicast communication technology is widely applied in wireless environments with a high device density. Traditional wireless network architectures have difficulty flexibly obtaining and maintaining global network state information and cannot quickly respond to network state changes, thus affecting the throughput, delay, and other QoS requirements of existing multicasting solutions. Therefore, this paper proposes a new multicast routing method based on multiagent deep reinforcement learning (MADRL-MR) in a software-defined wireless networking (SDWN) environment. First, SDWN technology is adopted to flexibly configure the network and obtain network state information in the form of traffic matrices representing global network links information, such as link bandwidth, delay, and packet loss rate. Second, the multicast routing problem is divided into multiple subproblems, which are solved through multiagent cooperation. To enable each agent to accurately understand the current network state and the status of multicast tree construction, the state space of each agent is designed based on the traffic and multicast tree status matrices, and the set of AP nodes in the network is used as the action space. A novel single-hop action strategy is designed, along with a reward function based on the four states that may occur during tree construction: progress, invalid, loop, and termination. Finally, a decentralized training approach is combined with transfer learning to enable each agent to quickly adapt to the dynamic changes of network link information and accelerate convergence. Simulation experiments show that MADRL-MR outperforms existing algorithms in terms of throughput, delay, packet loss rate, etc., and can establish more intelligent multicast routes. Code and model are available at https://github.com/GuetYe/MADRL-MR_code.

A neuro‐evolutionary approach for software defined wireless network traffic classification

AbstractAccurate network traffic classification is an essential and challenging issue for wireless network management and survivability. Existing network traffic classification algorithms, on the other hand, cannot meet the required specifications of real networks' in terms of user privacy control overhead, latency, and above all, classification speed. For wireless network traffic classification, machine learning‐based and hybrid optimization techniques have been deployed. This paper takes a software‐defined wireless network (SDWN) architecture for network traffic classification into account. Because the proposed scheme is perfectly contained within the network controller,the SDWN controller's higher processing capability, global visibility, and programmability can be used to achieve real‐time, adaptive, and precise traffic classification. In this paper, a neuro‐evolutionary approach is proposed in which the feed forward neural network (FFNN) is the base classifier and particle swarm optimization (PSO) is used to train the FFNN to accurately classify traffic while minimizing communication overhead between the controller and the SDWN switches. Simulation experiments were conducted by acquiring real‐world internet datasets to test the efficacy of the proposed scheme. The results and the state‐of‐the‐art comparisons show that the proposed approach has outperformed in terms of accuracy in wireless traffic classification.

Modified Multipath Transmission Control Protocol Using Logarithmic Increase Under Long-Term Evolution and Wi-Fi Networks

Multipath transmission control protocol (MPTCP) has been proposed for devices with multiple network interfaces. This idea seems to be impossible for user implementation at the beginning of its proposal. However, the emergence of smartphones with multiple network access allows this idea to be implemented. This paper provides both theoretical and empirical throughput analysis of MPTCP under long term evolution (LTE) and Wi-Fi networks. First, a new modified congestion control is presented as an enhancement of standard TCP Reno using logarithmic function. Second, the existing MPTCP approaches like linked increase algorithm (LIA) and balanced LIA (BALIA) are discussed briefly. Finally, the experimental results under long term evolution (LTE) and Wi-Fi under software defined wireless networking (SDWN) are given along with the analysis. The experimental result shows that the proposed congestion control achieves higher average congestion window size. The BALIA MPTCP is shown to have higher throughput than that of the LIA MPTCP.

WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping

Validating RF applications is traditionally time consuming, even for relatively simple systems. We developed the WISCA Software-Defined Radio Network (WISCANet) to accelerate the implementation and validation of radio applications over-the-air (OTA). WISCANet is a hardwareagnostic control software that automatically configures and controls a software-defined radio (SDR) network. By abstracting the hardware controls away from the user, WISCANet allows a non-expert user to deploy an OTA application by simply defining a baseband processing chain in a high level language. This technology reduces transition time between system design and OTA deployment, accelerates debugging and validation processes, and makes OTA experimentation more accessible to users that are not radio hardware experts. WISCANet emulates real-time RF operations, enabling users to perform real-time experiments without the typical restrictions on processing speed and hardware capabilities. WISCANet also supports multiple RF front-ends (RFFEs) per compute node, allowing sub-6 and mmWave systems to coexist on the same node. This coexistence enables simultaneous baseband processing that simplifies and enhances advanced algorithms and beyond-5G applications. In this study, we highlight the capabilities of WISCANet in several sub-6 and mmWave over-the-air demonstrations. The open source release of this software may be found on the WISCA GitHub page.

IRSML: An intelligent routing algorithm based on machine learning in software defined wireless networking

In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other well-known routing algorithms.

Centralized and Distributed Intrusion Detection for Resource-Constrained Wireless SDN Networks

Software-defined networking (SDN) was devised to simplify network management and automate infrastructure sharing in wired networks. These benefits motivated the application of SDN in resource-constrained wireless networks to leverage solutions for complex applications. However, some of the core SDN traits expose the networks to Denial-of-Service (DoS) attacks. There are proposals in the literature to detect DoS in wireless SDN networks; however, not without shortcomings: there is little focus on resource constraints, high detection rates have been reported mostly for small networks and the detection is disengaged from the identification of the type of attack or the attacker. Our work targets these shortcomings by introducing a lightweight, online change point detector to monitor performance metrics that are impacted when the network is under attack. A key novelty is that the proposed detector is able to operate in either centralized or distributed mode. The centralized detector has very high detection rates and can further distinguish the type of attack from a list of known attacks. In turn, the distributed detector can be useful to identify the nodes launching the attack. Our proposal is tested over IEEE 802.15.4 networks. The results show detection rates exceeding 96% in networks of 36 and 100 nodes and identification of the type of attack with a probability exceeding 89% when using the centralized approach.

Performance evaluation of SDWNs in cloud systems

The bursty traffic conditions and fast mobility of consumers induce significant delays in wireless networks. Taking the aforementioned factors into account, designing cost-effective wireless solutions with efficient resource usage and manageability is significantly challenging. Although cloud providers undeniably offer high-performance scalable wireless networks, delays may occur due to frequent packet losses as a result of weak signal strength, high mobility, and congestion. There is a slight difference between a wireless networking environment and typical wired networks, however, in practice, various variables exist, making it impossible to fully implement the core advantages of SDN such as rapid data processing, among others. To cope with such situations, we propose software-defined wireless networking (SDWN), a new means for wireless network virtualisation and programming capabilities. Moreover, to evaluate the proposed system, Mininet-WiFi in SDWN is used and conduct an experimental study in terms of packet transmission time and mobility visualisation over distance.

Optimizing Time-Sensitive Software-Defined Wireless Networks With Reinforcement Learning

Even though wireless networks are inevitable in mobile or infrastructure-less communication systems, such as vehicle-to-everything (V2X) infrastructure in automobile, precise formation control of unmanned vehicles (UVs), or other industries that employ ad hoc deployment of systems, operation and maintenance of network applications additionally impose time constraints on the wireless network. Such the requirement poses an immediate challenge to the time-sensitive aspects of devices, applications and network control, which has been addressed in the realm of time-sensitive networking (TSN). Meanwhile, software-defined networking (SDN) has successfully presented its efficiencies in ensuring quality of service for network traffic to accommodate many functions of network control and management. In this regard, we propose a traffic engineering solution based on reinforcement learning (RL) to implement TSN links with SDN over the wireless network, then optimize the quality of TSN links, and protect background traffic from TSN-enabled but SDN-supported traffic. We implemented SDN-based TSN on a real testbed, consisting of real nodes as single board computers (SBCs) and an SDN controller, and applied RL-based network control solution to the network. The empirical results are promising in that the jitter of time-constrained traffic is improved by 24.6 % and throughput of background traffic is increased by 6.5 %, compared to the manual configuration mode.

Использование SDR технологии для задач сетевого позиционирования. Формирование опорных сигналов LTE

An analysis of the state of the problem of building prototypes of fourth and fifth generation network positioning technology, using the approach of model-based design and software-defined radio SDR (Software-Defined Radio) according to open foreign sources showed the high actuality and relevance of this direction. At the same time, insufficient attention is paid to this area of research and development in native sources. This paper proposes the layout of the developed SDR demonstrator of network positioning technology. The task of developing the demonstrator is to substantiate technical solutions to improve the accuracy of determining the location of devices in modern and future communication networks in the absence of signals from the global navigation satellite system. The purpose of using SDR technology for network positioning tasks is to get the opportunity to improve the developed technical solutions in the process of their actual operation. Developed demonstrator includes the following subsystems: a subsystem of eNB base station prototype with known coordinates that implements the generation and transmission of reference signals of the LTE standard; prototype of the user equipment UE, which implements the reception and processing of the reference signals of the LTE standard, as well as the primary processing of measurements using the reference signals of the LTE standard; eNB base station prototype synchronization subsystem, which implements the distribution of a timestamp signal; the SDR demonstrator control subsystem, which implements the control of the procedures for collecting primary measurements of network positioning and their secondary processing with the resulting estimate of the coordinates of the user equipment UE. Also, this article describes the results of software-hardware implementation and experimental testing of the LTE reference signal generator on USRP SDR devices in Matlab environment. The procedure for generating reference signals is uniquely determined by the cell identifier, and the generator itself is an integral part of the layout of the SDR network positioning technology being developed. Experimental testing of the reference signal generator in laboratory conditions using objective control tools showed correct determination of the group identificator and cell identificator within group, which uniquely determine initialized cell identifier.

A User Prioritisation Algorithm for Horizontal Handover in Dense WLANs

This paper proposes an algorithm that enhances horizontal handover (HO) in dense wireless local area networks (WLANs), which is implemented in a software-defined wireless networking (SDWN)-based architecture. The algorithm considers the concept of user prioritisation, classifying the WLAN stations (STAs) into two categories representing high and low priorities respectively, and always attempts to guarantee the best quality of experience (QoE) to the high priority users. The architecture that implements the algorithm leverages the flexibility, programmability, and centralised nature of SDWN to efficiently manage the HO process. Moreover, the paper presents a performance evaluation campaign that demonstrates significant achievements against a state-of-the-art solution in terms of the provided QoE, throughput and delay. Finally, we discuss the importance of considering user prioritisation in a HO algorithm for dense WLANs.

5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network.

The COVID-19 outbreak has stimulated the digital transformation of antiquated healthcare system to a smart hospital, enabling the personalised and remote healthcare services. To augment the functionalities of these intelligent healthcare systems, 5G & B5G heterogeneous network has emerged as a robust and reliable solution. But the pivotal challenge for 5G & B5G connectivity solutions is to ensure flexible and agile service orchestration with acknowledged Quality of Experience (QoE). However, the existing radio access technology (RAT) selection strategies are incapacitated in terms of QoE provisioning and Quality of Service (QoS) maintenance. Therefore, an intelligent QoE aware RAT selection architecture based on software-defined wireless networking (SDWN) and edge computing has been proposed for 5G-enabled healthcare network. The proposed model leverages the principles of invalid action masking and multi-agent reinforcement learning to allow faster convergence to QoE optimised RAT selection policy. The analytical evaluation validates that the proposed scheme outperforms the other existing schemes in terms of enhancing personalised user-experience with efficient resource utilisation.

A Comprehensive Analysis of QoS in Wired and Wireless SDN Based on Mobile IP

Software-Defined Networking (SDN) is the new network paradigm whose primary focus is to create a dynamic, scalable and flexible network. It provides programmable functions for implementation of network configuration management. The demand for wireless network functionality is rising simultaneously. Mobility management for a large network is an issue in Internet Engineering Task Force (IETF). There are also a number of solutions to support mobility within the network. The Mobile Internet Protocol (MIP) is used for managing mobility. Furthermore, for the SDN platform, mobility adds roaming capability for mobile nodes in the software-defined wireless network (SDWN). In the wired scenario, SDN has different capabilities to deliver network services according to the fixed node. This study provides a quality of service (QoS) analysis in both SDN and SDWN. Mininet WiFi and RYU network emulator as a controller is used to implement the mobility API. The random walk model is applied as mobility functionality toward the final nodes. Moreover, several QoS measurement matrices are analyzed according to the network topology. At the end, round trip time (RTT), cumulative distributed function (CDF), packet loss and throughput are analyzed for quality of service comparable in the SDN and SDWN scenarios according to the MIP.

A centralized architecture for autonomic quality of experience oriented handover in dense networks

This paper presents an Optimised Handover (HO) Algorithm for Dense Wireless Local Area Networks (WLANs) based on a novel architecture of Software Defined Wireless Network (SDWN). The work has been designed to be effective in large network environments with a high density of Access Points (APs) and stations, which increase the chances of the Ping-Pong HO effect. Specifically, it considers Quality of Experience (QoE) by applying an optimised HO algorithm for WLANs, which relies on Fuzzy Logic Control Theory (FLCT) combined with Adaptive Hysteresis Values (AHVs). SDWN allows to monitor and manage the networks and to autonomously programme the APs through a centralized controller. The paper includes also a detailed performance analysis of the algorithm developed in an SDWN-based simulator implemented through OPNET. Specifically, our algorithm achieved promising performance results compared to the state of the art in terms of QoE, throughput, delay and reduction of the ping-pong HO effect.

Mobility models for next generation wireless mesh network

<span>Wireless mesh network (WMN) is a new trend in wireless communication promising greater flexibility, reliability, and performance over traditional wireless local area network (WLAN). Test bed analysis and emulation plays an essential role in valuation of software defined wireless network and node mobility is the prominent feature of next generation software defined wireless network. In this study, the mobility models employed for moving mobile stations in software defined wireless network are explored. Moreover, the importance of mobility model within software defined wireless mesh network for enhancing the performance through handover-based load balancing is analyzed. The mobility models for the next generation software defined wireless network are explored. Furthermore, we have presented the mobility models in the mininet-Wi-Fi test bed, and evaluated the performance of Gauss Marko’s mobility model.</span>