Machine Learning Based Classifiers for QoE Prediction Framework in Video Streaming over 5G Wireless Networks

Recently,the combination of video services and 5G networks have been gaining attention in the wireless communication realm.With the brisk advancement in 5G network usage and the massive popularity of threedimensional video streaming,the quality of experience(QoE)of video in 5G systems has been receiving overwhelming significance from both customers and service provider ends.Therefore,effectively categorizing QoE-aware video streaming is imperative for achieving greater client satisfaction.This work makes the following contribution:First,a simulation platform based on NS-3 is introduced to analyze and improve the performance of video services.The simulation is formulated to offer real-time measurements,saving the expensive expenses associated with real-world equipment.Second,A valuable framework for QoE-aware video streaming categorization is introduced in 5G networks based on machine learning(ML)by incorporating the hyperparameter tuning(HPT)principle.It implements an enhanced hyperparameter tuning(EHPT)ensemble and decision tree(DT)classifier for video streaming categorization.The performance of the ML approach is assessed by considering precision,accuracy,recall,and computation time metrics for manifesting the superiority of these classifiers regarding video streaming categorization.This paper demonstrates that our ML classifiers achieve QoE prediction accuracy of 92.59%for(EHPT)ensemble and 87.037%for decision tree(DT)classifiers.

Optimal Resource Allocation for NOMA Wireless Networks

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user accesses.Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user signals.Although its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power domain.In contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum utilization.However,it will increase the complexity of its receiver.Different power allocation techniques will have a direct impact on the system’s throughput.As a result,in order to boost the system capacity,an efficient power allocation mechanism must be investigated.This research developed an efficient technique based on conjugate gradient to solve the problem of downlink power distribution.The major goal is to maximize the users’maximum weighted sum rate.The suggested algorithm’s most notable feature is that it converges to the global optimal solution.When compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

Blockchain Enabled Metaheuristic Cluster Based Routing Model for Wireless Networks

With recent advancements made in wireless communication techniques,wireless sensors have become an essential component in both data collection as well as tracking applications.Wireless Sensor Network(WSN)is an integral part of Internet of Things(IoT)and it encounters different kinds of security issues.Blockchain is designed as a game changer for highly secure and effective digital society.So,the current research paper focuses on the design of Metaheuristic-based Clustering with Routing Protocol for Blockchain-enabled WSN abbreviated as MCRP-BWSN.The proposed MCRP-BWSN technique aims at deriving a shared memory scheme using blockchain technology and determine the optimal paths to reach the destination in clustered WSN.In MCRP-BWSN technique,Chimp Optimization Algorithm(COA)-based clustering technique is designed to elect a proper set of Cluster Heads(CHs)and organize the selected clusters.In addition,Horse Optimization Algorithm(HOA)-based routing technique is also presented to optimally select the routes based onfitness function.Besides,HOA-based routing technique utilizes blockchain technology to avail the shared mem-ory among nodes in the network.Sensor nodes are treated as coins whereas the ownership handles the sensor nodes and Base Station(BS).In order to validate the enhanced performance of the proposed MCRP-BWSN technique,a wide range of simulations was conducted and the results were examined under different measures.Based on the performance exhibited in simulation outcomes,the pro-posed MCRP-BWSN technique has been established as a promising candidate over other existing techniques.

Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,etc.At the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks.This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless networks.The proposed IM-EECNL technique involves two major processes namely node localization and clustering.Firstly,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the nodes.Secondly,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the network.Besides,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and load.The performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

Energy-Efficient Routing Protocol with Multi-Hop Fuzzy Logic for Wireless Networks

A Wireless Sensor Network(WSN)becomes a newer type of real-time embedded device that can be utilized for a wide range of applications that make regular networking which appears impracticable.Concerning the energy produc-tion of the nodes,WSN has major issues that may influence the stability of the system.As a result,constructing WSN requires devising protocols and standards that make the most use of constrained capacity,especially the energy resources.WSN faces some issues with increased power utilization and an on going devel-opment due to the uneven energy usage between the nodes.Clustering has proven to be a more effective strategy in this series.In the proposed work,a hybrid meth-od is used for reducing the energy consumption among CHs.A Fuzzy Logic-based clustering protocol FLUC(unequally clustered)and Fuzzy Clustering with Energy-Efficient Routing Protocol(FCERP)are used.A Fuzzy Clustering with Energy Efficient Routing Protocol(FCERP)reduces the WSN power usage and increases the lifespan of the network.FCERP has created a novel cluster-based fuzzy routing mechanism that uses a limit value to combine the clustering and multi-hop routing capabilities.The technique creates uneven groups by using fuz-zy logic with a competitive range to choose the Cluster Head(CH).The input variables include the distance of the nodes from the ground station,concentra-tions,and remaining energy.The proposed FLUC-FCERP reduces the power usage and improves the lifetime of the network compared with the existing algorithms.

MULTIPLE ATTRIBUTE NETWORK SELECTION ALGORITHM BASED ON AHP AND SYNERGETIC THEORY FOR HETEROGENEOUS WIRELESS NETWORKS

It is a hot issue in communication research field to select the best network for Heterogeneous Wireless Networks(HWNs),and it is also a difficult problem to reduce the handoff number of vertical handoff.In order to solve this problem,the paper proposes a multiple attribute network selection algorithm based on Analytic Hierarchy Process(AHP)and synergetic theory.The algorithm applies synergetics to network selection,considering the candidate network as a compound system composed of multiple attribute subsystems,and combines the subsystem order degree with AHP weight to obtain entropy of the compound system,which is opposite the synergy degree of a network system.The greater the synergy degree,the better the network performance.The algorithm takes not only the coordination of objective attributes but also Quality of Service(QoS)requirements into consideration,ensuring that users select the network with overall good performance.The simulation results show that the proposed algorithm can effectively reduce the handoff number and provide uses with satisfactory QoS according to different services.

Game theory-based attack and defense analysis in virtual wireless networks with jammers and eavesdroppers

Dynamic spectrum sharing and cognitive radio networks were proposed to enhance the Radio Frequency(RF)spectrum utilization.However,there are several challenges to realize them in real systems,such as sensing uncertainty causing issues to licensed users,business models for licensed service providers.Wireless virtualization is regarded as a technology that leverages service level agreements to sublease unused or underutilized RF spectrum that addresses aforementioned issues and helps to significantly enhance the utilization of the RF spectrum,offer improved coverage and capacity of networks,enhance network security and reduce energy consumption.With wireless virtualization,wireless networks'physical substrate is shared and reconfigured dynamically between virtual wireless networks through Mobile Virtual Network Operations(MVNOs).Wireless virtualization with dynamic configurable features of Wireless Infrastructure Providers(WIPs),virtualized wireless networks are vulnerable to a multitude of attacks,including jamming attacks and eavesdropping attacks.This paper investigates a means of defense through the employment of coalition game theory when jammers try to degrade the signal quality of legitimate users,and eavesdroppers aim to reduce secrecy rates.Specifically,we consider a virtual wireless network where MVNO users'job is to improve their Signal to Interference plus Noise Ratio(SINR)while the jammers target to collectively enhance their Jammer Received Signal Strength(JRSS),and an eavesdropper's goal is to reduce the overall secrecy rate.Numerical results have demonstrated that the proposed game strategies are effective(in terms of data rate,secrecy rate and latency)against such attackers compared to the traditional approaches.

Scope of machine learning applications for addressing the challenges in next-generation wireless networks

The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users.Due to this popularity,there has been a huge rise in mobile data volume,applications,types of services,and number of customers.Furthermore,due to the COVID-19 pandemic,the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home.This massive increase in demand for multi-class services has posed numerous challenges to wireless network frameworks.The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic,such as real-time live streaming of videos,audios,text,images etc.,at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers.Next-generation wireless networks(NGWNs,i.e.5G networks and beyond)are being developed to accommodate the service qualities mentioned above and many more.However,achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers.It requires the analysis of a huge volume of network data(structured and unstructured)received or collected from heterogeneous devices,applications,services,and customers and the effective and dynamic management of network parameters based on this analysis in real time.In the ever-increasing network heterogeneity and complexity,machine learning(ML)techniques may become an efficient tool for effectively managing these issues.In recent days,the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain.This study discusses current wireless network research,brief discussions on ML methods that can be effectively applied to the wireless networking domain,some tools available to support and customise efficient mobile system design,and some unresolved issues for future research directions.

Shortest Link Scheduling in Wireless Networks with Oblivious Power Control

Link scheduling has always been a fundamental problem in wireless networks for its direct impacts on the performance of wireless networks such as throughput capacity,transmission delay,lifetime,etc.Existing work is mainly established under graphbased models,which are not only impractical but also incorrect due to the essentially fading characteristics of signals.In this paper,we study the shortest link scheduling problem under two more realistic models,namely the signal to interference plus noise ratio(SINR)model and the Rayleigh fading model.We propose a centralized square-based scheduling algorithm(CSSA)with oblivious power control under the SINR model and prove its correctness under both the SINR model and the Rayleigh fading model.Furthermore,we extend CSSA and propose a distributed square-based scheduling algorithm(DSSA).Note that DSSA adopts CSMA/CA so that a wireless node can compete for the wireless channel before starting its communication.We also show theoretical analysis and conduct extensive simulations to exhibit the correctness and efficiency of our algorithms.

Deployment of Polar Codes for Mission-Critical Machine-Type Communication Over Wireless Networks

Mission critical Machine-type Communication(mcMTC),also referred to as Ultra-reliable Low Latency Communication(URLLC),has become a research hotspot.It is primarily characterized by communication that provides ultra-high reliability and very low latency to concurrently transmit short commands to a massive number of connected devices.While the reduction in physical(PHY)layer overhead and improvement in channel coding techniques are pivotal in reducing latency and improving reliability,the current wireless standards dedicated to support mcMTC rely heavily on adopting the bottom layers of general-purpose wireless standards and customizing only the upper layers.The mcMTC has a significant technical impact on the design of all layers of the communication protocol stack.In this paper,an innovative bottom-up approach has been proposed for mcMTC applications through PHY layer targeted at improving the transmission reliability by implementing ultra-reliable channel coding scheme in the PHY layer of IEEE 802.11a standard bearing in mind short packet transmission system.To achieve this aim,we analyzed and compared the channel coding performance of convolutional codes(CCs),low-density parity-check(LDPC)codes,and polar codes in wireless network on the condition of short data packet transmission.The Viterbi decoding algorithm(VA),logarithmic belief propagation(Log-BP)algorithm,and cyclic redundancy check(CRC)successive cancellation list(SCL)(CRC-SCL)decoding algorithm were adopted to CC,LDPC codes,and polar codes,respectively.Consequently,a new PHY layer for mcMTC has been proposed.The reliability of the proposed approach has been validated by simulation in terms of Bit error rate(BER)and packet error rate(PER)vs.signal-to-noise ratio(SNR).The simulation results demonstrate that the reliability of IEEE 802.11a standard has been significantly improved to be at PER=10−5 or even better with the implementation of polar codes.The results also show that the general-purpose wireless networks are prominent inproviding short packet mcMTC with the modification needed.

Multi-Source Spinal Coding for Coded Caching Multicast Transmissions in Wireless Networks

Recently,coded caching has been treated as a promising technique to alleviate the traffic burden in wireless networks.To support high efficient coded caching multicast transmissions,the time-varying heterogeneous channel conditions need to be considered.In this paper,a practical and novel multi-source spinal coding(MSSC)scheme is developed for coded caching multicast transmissions under heterogeneous channel conditions.By exploring joint design of network coding and spinal coding(SC),MSSC can achieve unequal link rates in multicast transmissions for different users.Moreover,by leveraging the rateless feature of SC in our design,MSSC can well adapt the link rates of all users in multicast transmissions without any feedback of time-varying channel conditions.A maximum likelihood(ML)based decoding process for MSSC is also developed,which can achieve a linear complexity with respect to the user number in the multicast transmission.Simulation results validate the effectiveness of the MSSC scheme.Compared to the existing scheme,the sum rate of MSSC in multicast transmissions is improved by about 20%.When applying MSSC in coded caching systems,the total transmission time can be reduced by up to 48% for time-varying channels.

An Improved Handoff Algorithm for Heterogeneous Wireless Networks

Heterogeneous Wireless Network is currently a major area of focus in communication engineering.But the important issue in recent communication is the approachability to the wireless networks while maintaining the quality of service.Today,all the wireless access networks are working in tandem to keep the users always connected to the internet cloud that matches the price affordability and performance goals.In order to achieve seamless connectivity,due consideration has to be given to handoff precision and a smaller number of handoffs.Several researchers have used heuristic approaches to solve this issue.In the present work,a hybrid intelligent algorithm has been suggested for vertical handoff decisions.This hybrid intelligent algorithm is based on dual optimization approach which uses“Particle Swarm Optimization(PSO)”and“Mobile Robustness Optimization(MRO)”techniques for improving the quality of services.This approach performs well even in the failure network conditions and gives the best results in terms of connectivity.The results at the last has been compared with the conventional techniques and it has been observed that the proposed methodology outperforms the existing one.

Towards Interference-Aware ZigBee Transmissions in Heterogeneous Wireless Networks

Cross-technology interference(CTI)from diverse wireless networks such as ZigBee,Bluetooth,and Wi-Fi has become a severe problem in the 2.4 GHz Industrial Scientific and Medical(ISM)band.Especially,low power and lossy networks are vulnerable to the signal interferences from other aggressive wireless networks when they perform low power operations to conserve the energy consumption.This paper presents CoSense,which accurately detects ZigBee signals with a reliable signal correlation scheme in the presence of the CTI.The key concept of CoSense is to reduce false wake-ups of low power listening(LPL)by identifying the pre-defined ZigBee signatures.Our scheme is robust in the coexistence environment of diverse wireless technologies since the signal correlation works well in bad wireless channel conditions.It achieves standard compliance and transparency without any hardware and firmware changes.We have implemented CoSense on the Universal Software Radio Peripheral(USRP)platform to verify its feasibility.The experimental exploration reveals that CoSense significantly reduces the false-positive and false-negative rate under typical setting and the additional overhead is negligible.The results show that our scheme saves much energy by up to 63%in dynamic network interference scenarios where low-power ZigBee transmissions are overwhelmed by strong Wi-Fi signal interferences.

Toward Optimal Cost-Energy Management Green Framework for Sustainable Future Wireless Networks

The design of green cellular networking according to the trafc arrivals has the capability to reduce the overall energy consumption to a cluster in a cost-effective way.The cell zooming approach has appealed much attention that adaptively ofoads the BS load demands adjusting the transmit power based on the trafc intensity and green energy availability.Besides,the researchers are focused on implementing renewable energy resources,which are considered the most attractive practices in designing energy-efcient wireless networks over the long term in a cost-efcient way in the existing infrastructure.The utilization of available solar can be adapted to acquire cost-effective and reliable power supply to the BSs,especially that sunlight is free,available everywhere,and a good alternative energy option for the remote areas.Nevertheless,planning a photovoltaic scheme necessitates viability assessment to avoid poor power supply,particularly for BSs.Therefore,cellular operators need to consider both technical and economic factors before the implementation of solar-powered BSs.This paper proposed the usercentric cell zooming policy of solar-powered cellular base stations taking into account the optimal technical criteria obtained by the HOMER software tool.The results have shown that the proposed system can provide operational expenditure(OPEX)savings of up to 47%.In addition,the efcient allocation of resource blocks(RBs)under the cell zooming technique attain remarkable energy-saving performance yielding up to 27%.

Collaborative Multiple Access And Energy-Efficient Resource Allocation in Distributed Maritime Wireless Networks

With the rapid increasing of maritime activities, maritime wireless networks(MWNs) with high reliability, high energy efficiency, and low delay are required. However, the centralized networking with fixed resource scheduling is not suitable for MWNs due to the special environment. In this paper,we introduce the collaborative relay communication in distributed MWNs to improve the link reliability, and propose an orthogonal time-frequency resource block reservation based multiple access(RRMA) scheme for both one-hop direct link and two-hop collaborative relay link to reduce the interference. To further improve the network performance, we formulate an energy efficiency(EE) maximization resource allocation problem and solve it by an iterative algorithm based on the Dinkelbach method. Finally, numerical results are provided to investigate the proposed RRMA scheme and resource allocation algorithm, showing that the low outage probability and transmission delay can be attained by the proposed RRMA scheme. Moreover,the proposed resource allocation algorithm is capable of achieving high EE in distributed MWNs.

Efficiency Effect of Obstacle Margin on Line-of-Sight in Wireless Networks

Line-of-sight clarity and assurance are essential because they are considered the golden rule in wireless network planning,allowing the direct propagation path to connect the transmitter and receiver and retain the strength of the signal to be received.Despite the increasing literature on the line of sight with different scenarios,no comprehensive study focuses on the multiplicity of parameters and basic concepts that must be taken into account when studying such a topic as it affects the results and their accuracy.Therefore,this research aims to find limited values that ensure that the signal reaches the future efficiently and enhances the accuracy of these values’results.We have designed MATLAB simulation and programming programs by Visual Basic.NET for a semi-realistic communication system.It includes all the basic parameters of this system,taking into account the environment’s diversity and the characteristics of the obstacle between the transmitting station and the receiving station.Then we verified the correctness of the system’s work.Moreover,we begin by analyzing and studying multiple and branching cases to achieve the goal.We get several values from the results,which are finite values,which are a useful reference for engineers and designers of wireless networks.

A Joint Algorithm for Resource Allocation in D2D 5G Wireless Networks

With the rapid development of Internet technology,users have an increasing demand for data.The continuous popularization of traffic-intensive applications such as high-definition video,3D visualization,and cloud computing has promoted the rapid evolution of the communications industry.In order to cope with the huge traffic demand of today’s users,5G networks must be fast,flexible,reliable and sustainable.Based on these research backgrounds,the academic community has proposed D2D communication.The main feature of D2D communication is that it enables direct communication between devices,thereby effectively improve resource utilization and reduce the dependence on base stations,so it can effectively improve the throughput of multimedia data.One of the most considerable factor which affects the performance of D2D communication is the co-channel interference which results due to the multiplexing of multiple D2D user using the same channel resource of the cellular user.To solve this problem,this paper proposes a joint algorithm time scheduling and power control.The main idea is to effectively maximize the number of allocated resources in each scheduling period with satisfied quality of service requirements.The constraint problem is decomposed into time scheduling and power control subproblems.The power control subproblem has the characteristics of mixed-integer linear programming of NP-hard.Therefore,we proposed a gradual power control method.The time scheduling subproblem belongs to the NP-hard problem having convex-cordinality,therefore,we proposed a heuristic scheme to optimize resource allocation.Simulation results show that the proposed algorithm effectively improved the resource allocation and overcome the co-channel interference as compared with existing algorithms.

TCP/IPsec Compatibility in Wireless Networks

Transmission Control Protocol (TCP) was originally designed for wired networks. Many compatibility problems exist when it is applied to wireless environments, and hence it needs to undergo modifications. Currently, there are several TCP performance enhancement techniques for application of TCP in wireless environments, but all of them conflict with IPsec. This paper not only analyzes these conflicts, but also proposes 4 solutions: replacing IPsec with Transport Layer Security/Secure Socket Layer (TLS/SSL) protocol, adopting the extended Encapsulating Security Payload (ESP) protocol, segmenting the TCP route, and modifying the end-to-end protection mode of IPsec. It also presents their strengths and weaknesses.

Scheduling Policies for Federated Learning in Wireless Networks: An Overview

Due to the increasing need for massive data analysis and machine learning model training at the network edge, as well as the rising concerns about data privacy, a new distrib?uted training framework called federated learning (FL) has emerged and attracted much at?tention from both academia and industry. In FL, participating devices iteratively update the local models based on their own data and contribute to the global training by uploading mod?el updates until the training converges. Therefore, the computation capabilities of mobile de?vices can be utilized and the data privacy can be preserved. However, deploying FL in re?source-constrained wireless networks encounters several challenges, including the limited energy of mobile devices, weak onboard computing capability, and scarce wireless band?width. To address these challenges, recent solutions have been proposed to maximize the convergence rate or minimize the energy consumption under heterogeneous constraints. In this overview, we first introduce the backgrounds and fundamentals of FL. Then, the key challenges in deploying FL in wireless networks are discussed, and several existing solu?tions are reviewed. Finally, we highlight the open issues and future research directions in FL scheduling.

Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

Artificial intelligence(AI)techniques have received significant attention among research communities in the field of networking,image processing,natural language processing,robotics,etc.At the same time,a major problem in wireless sensor networks(WSN)is node localization,which aims to identify the exact position of the sensor nodes(SN)using the known position of several anchor nodes.WSN comprises a massive number of SNs and records the position of the nodes,which becomes a tedious process.Besides,the SNs might be subjected to node mobility and the position alters with time.So,a precise node localization(NL)manner is required for determining the location of the SNs.In this view,this paper presents a new quantum bird migration optimizer-based NL(QBMA-NL)technique for WSN.The goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes.The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season.In addition,an objective function is derived based on the received signal strength indicator(RSSI)and Euclidean distance from the known to unknown SNs.For demonstrating the improved performance of the QBMA-NL technique,a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.