Heterogeneous Wireless Networks Research Articles

Fuzzy Based Network Controlled Vertical Handover Mechanism for Heterogeneous Wireless Network

In future next Generation wireless networks, it is expected to have multi-mode wireless interface devices with common IP core communication. The user will be having multiple option of radio access technology (RAT) for data and voice service. This multi-mode provides the terminal seamless mobility across the heterogeneous wireless network. Mobile terminals transferring their communication sessions from one RAT to other, such transferring ongoing call process is known as vertical handover. With the rise in popularity of multimode mobile devices, switching between networks will be in high demand for users to move seamlessly through the network, either because users were moving away from an access point or because the network currently does not provide quality. service (QoS), but in both situations, a better network should be available, so a good network selection algorithm should take into account the possibility of multiple users performing a handover simultaneously in the same area. The objectives of this paper are to develop a handover mechanism that Provides good quality of service to the end user.

Coded Distributed Gaussian Process Regression

In this letter, we propose a coded load balancing method for distributed Gaussian process regression over heterogeneous wireless networks, where users with diverse computational and communications capabilities may offload excessive training data onto a computationally stronger central server to reduce collaborative processing times. The offloaded data are transformed using random Fourier feature mapping and encoded with a random orthogonal matrix to prevent transmission of raw data. The proposed method is particularly applicable to compute-intensive applications, where users operate with large datasets.

Multi-criteria Handoff Decision making Algorithm for Seamless Mobility in Heterogenous Wireless Networks

Wireless networks have been through many transformations to provide Quality of Service (QoS) and seamless mobility. Handoff management plays a vital role in maintaining quality of service while roaming. The focus of the paper is to develop a handoff technique for optimized handoff decision making. Multi-Criteria Decision Making (MCDM) techniques are utilized for making decision where more than one contradicting parameter are involved. Fuzzy Analytic Hierarchy Process (FAHP) is used for weight calculation and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) decides the rank of available networks. The proposed technique utilizes six network parameters i.e., received signal strength, bandwidth, signal to interference & noise ratio, delay, packet loss, and bit error rate for weights calculation under different traffic classes and three decision makers i.e., network, mobile node, and user preference for optimized handoff. The results show that the proposed technique effectively classify the best available network in terms of their rank and reduces the number of unnecessary handoffs.

Joint user association and fair scheduling for dual connectivity heterogeneous networks

Heterogeneous small cell network is one of the most effective solutions to overcome spectrum scarcity for the next generation of mobile networks. Dual connectivity (DC) can improve the throughput for each individual user by allowing concurrent access to two heterogeneous radio networks. In this paper, we propose a joint user association and fair scheduling algorithm (JUAFS) to deal with the resource allocation and load balancing issues for DC heterogeneous small cell networks. Considering different coverage sizes, numbers of users, and quality of experience characteristics of heterogeneous cells, we present a proportional fair scheduling for user association among cells and utilize interference graph to minimize the transmission conflict probability. Simulation results show the performance improvement of the proposed algorithm in spectrum efficiency and fairness comparing to the existing schemes.

Access selection in heterogeneous wireless networks based on user preferences

Access selection is an important key in heterogeneous networks, and the design of a new algorithm for decision is not a trivial task. Different aspects must be taken into consideration while designing a new decision algorithm, including both users' requirements (in terms of resources, QoS, user's preferences), and operator policies that aim to maximise the utilisation of its network capacity and to deliver services with acceptable QoS levels for the largest number of customers. Thus, in this paper, we propose a new selection algorithm based on user's preferences. The comparison between the proposed scenarios is given based on several performance indicators. The results show the improvement achieved by increasing the resource utilisation and therefore the overall system capacity.

Multi-attribute Synergetic Decision-making Algorithm for 5G Integrated Heterogeneous Wireless Network

Multi-attribute Synergetic Decision-making Algorithm for 5G Integrated Heterogeneous Wireless Network

Handover decision algorithm for heterogeneous wireless network with approaches multi-attribute decision-making technique

Handover decision algorithm for heterogeneous wireless network with approaches multi-attribute decision-making technique

Access selection in heterogeneous wireless networks based on user preferences

Access selection in heterogeneous wireless networks based on user preferences

Handover decision algorithm for heterogeneous wireless network with approaches multi-attribute decision-making technique

Handover decision algorithm for heterogeneous wireless network with approaches multi-attribute decision-making technique

Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

In recent years, real-time video streaming has grown in popularity. The growing popularity of the Internet of Things (IoT) and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience (QoE) and performance objectives. Most researchers focused on Forward Error Correction (FEC) techniques when attempting to strike a balance between QoE and performance. However, as network capacity increases, the performance degrades, impacting the live visual experience. Recently, Deep Learning (DL) algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks. But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time. To address the previous challenge, this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics. The proposed framework contains modules such as Intelligent Content Extraction Module (ICEM), Channel Status Monitor (CSM), and Adaptive FEC (AFEC). This framework adopts the Cognitive Learning-based Scheduling (CLS) Module, which works on the deep Reinforced Gated Recurrent Networks (RGRN) principle and embeds them along with the FEC to achieve better performances. The complete framework was developed using the Objective Modular Network Testbed in C++ (OMNET++), Internet networking (INET), and Python 3.10, with Keras as the front end and Tensorflow 2.10 as the back end. With extensive experimentation, the proposed model outperforms the other existing intelligent models in terms of improving the QoE, minimizing the End-to-End Delay (EED), and maintaining the highest accuracy (98%) and a lower Root Mean Square Error (RMSE) value of 0.001.

Retraction Note: Maximizing quality of multimedia streaming services in heterogeneous wireless networks using optimal multi-server technique

Retraction Note: Maximizing quality of multimedia streaming services in heterogeneous wireless networks using optimal multi-server technique

Semi-Supervised Federated Learning Over Heterogeneous Wireless IoT Edge Networks: Framework and Algorithms

Federated learning (FL) is a promising paradigm for future sixth-generation wireless systems to underpin network edge intelligence for smart cities applications. However, most of the data collected by the Internet of Things devices in such applications is unlabeled, necessitating the use of semi-supervised learning. Existing studies have introduced solutions to run semi-supervised FL; however, they overlooked the inherent critical impacts of the wireless characteristics at the network edge. We fill this gap by proposing novel solutions to run semi-supervised FL over wireless network edge, considering the limited computation and communication resources and deadline constraints and realizing that unlabeled data can be automatically labeled during the training rounds to improve the performance of the global model. The problem is first formulated as an optimization problem followed by a two-phase solution. In the first phase, we propose a bisection-based algorithm to find the transmit power and local processing speed that optimally fit the new injected labeled data. In the second phase, we propose three algorithms to control the local updates and injected samples that meet the deadline constraint. We analyze the performance of each algorithm concerning the tradeoffs between learning performance, training time, and total energy consumption. Targeting two applications in smart cities, human activity recognition and object detection, we conduct extensive simulations using realistic federated data sets under nonindependent and identically distributed settings. Numerical results show that the proposed algorithms effectively utilize unlabeled samples while accounting for the characteristics of wireless edge networks in smart cities.

A networked multi-agent reinforcement learning approach for cooperative FemtoCaching assisted wireless heterogeneous networks

To meet the explosive growth of mobile traffic requirement in the 5th generation (5 G) mobile system, FemtoCaching at the network edge has been regarded as a promising technique for the 5 G mobile system. In this paper, we focus on studying the cooperative FemtoCaching problem in wireless heterogeneous networks (HetNets), which is aimed to minimize the overall fetching delays of all users. Owing to the NP-hardness of the problem, we formulate the cooperative FemtoCaching problem as a Networked Multi-Agent Reinforcement Learning (NMARL) problem and accordingly propose a Soft Attentional Networked Multi-Agent Actor-Critic (SAN-AC) Reinforcement Learning algorithm. Simulation results demonstrate that the proposed algorithm can significantly increase the overall performance compared with existing work.

NLADSS: Design of Connectivity as a Service (CaaS) Model using Node-Level Augmentation & Dynamic Sleep Scheduling for Heterogeneous Wireless Network Handoffs

NLADSS: Design of Connectivity as a Service (CaaS) Model using Node-Level Augmentation & Dynamic Sleep Scheduling for Heterogeneous Wireless Network Handoffs

A New Vertical Handover Prediction Method for Heterogeneous Wireless Networks

Long Term Evolution (LTE) is the fourth generation (4G) cellular network technology that provides improved performance that related to data rate, coverage and capacity compared to earlier cellular systems. The addition of many small cells in a heterogeneous network configuration provides a means to enhance capacity through extreme frequency reuse. The heterogeneous environment of different network technologies can provide high data rate and enhance multimedia services, but it is challenging to provide optimized handover (HO). In this paper, a new method is proposed to select the optimal network from available networks to which a UE may be handed over to achieve better QoS performance. The aggregation of multiple criteria for calculation of overall ranking of networks is obtained by Analytical Hierarchy Process (AHP) [1] and is combined with the history of previously visited cells and regression analysis of the Layer 1 (L1) and Layer 3 (L3) filtered Received Signal Strength (RSS) data for prediction of future values. The AHP is used to calculate the weights for the system attributes and to rank the available networks based on multiple criteria Multiple Attribute Decision Making (MADM). The sequence of visited cells is used in target network selection as it reduces frequent handover. The sequence of visited cells would be modeled as a Markov Chain. To assess RSS, beside L1, L3 filtering RSS prediction and smoothing is used to predict future signal samples to hasten the process of HO. RSS forecasting is used to predict handover necessity so as to reduce the handover delay. The results show that the number of handovers is reduced by up to 50% compared to the conventional AHP. The results also show that the threshold crossing rate and average duration of fades are reduced by 47% compared with the AHP. Handover delay is also reduced by up to 40 ms due to RSS forecasting.

Active Learning Assisted Admission and Bandwidth Management in HWN for Facilitating Differential QoS under Multicriteria Factors

Control of call admission and management of bandwidth are the two important functionalities to achieve higher call handling capacity in heterogeneous wireless networks (HWN). This work addresses the problem of supporting differential qualities of services (QoS) with adherence to multicriteria factors in addition to reducing call-dropping probability in HWN. Toward this end, learning-assisted admission and bandwidth management are proposed. The decision to control the call acceptance ratio and bandwidth allocation level is learned continuously based on current network dynamics and the differential QoS requirements of the current calls. This learning reduces the call drop probability and slippage in QoS for calls. The parameters employed for evaluation in the suggested approach for call admission control include call priority, service type, service delivery mode, bandwidth availability for scalable and nonscalable calls, QoS distortion rate, and call ratio.

A Dueling Deep Recurrent Q -Network Framework for Dynamic Multichannel Access in Heterogeneous Wireless Networks

This paper investigates a deep reinforcement learning algorithm based on dueling deep recurrent Q -network (Dueling DRQN) for dynamic multichannel access in heterogeneous wireless networks. Specifically, we consider the scenario that multiple heterogeneous users with different MAC protocols share multiple independent channels. The goal of the intelligent node is to learn a channel access strategy that achieves high throughput by making full use of the underutilized channels. Two key challenges for the intelligent node are (i) there is no prior knowledge of spectrum environment or the other nodes’ behaviors; (ii) the spectrum environment is partially observable, and the spectrum states have complex temporal dynamics. In order to overcome the aforementioned challenges, we first embed the long short-term memory layer (LSTM) into the deep Q -network (DQN) to aggregate historical observations and capture the underlying temporal feature in the heterogeneous networks. And second, we employ the dueling architecture to overcome the observability problem of dynamic environment in neural networks. Simulation results show that our approach can learn the optimal access policy in various heterogeneous networks and outperforms the state-of-the-art policies.

Vertical handoff technique using context transfer cross-layer P-SIP Scheme for IMS based LTE-WiMAX architecture

Next-generation heterogeneous wireless networks provide flexibility to access IP multimedia services (IMS) anywhere at any time and with always-on connectivity in various networks. IMS service allows to include various networks to provide seamless data services and helps to reduce the delay that occurred during the handoff process. Long-term evolution (LTE) is the fastest emerging mobile communication technology in the current networking field. We proposed an interworking architecture of LTE and Worldwide Interoperability for Microwave Access (WiMAX). The key goal is to provide seamless services in heterogeneous networks. We proposed an algorithm to reduce this IMS delay using Cross-layer and Context Transfer Prior –SIP (P-SIP) scheme using Media Independent Handover (MIH) services. Performance metrics of Integrated LTE- WiMAX architecture in terms of VHO latency, signaling overhead cost, and packet loss are analyzed

A novel approach for optimization of handover mechanism using metaheuristics algorithms

The new generation wireless system is an integrated global network, which provides many services for end users across the globe. The major requirement of end users is to allow the mobile user to roam seamlessly across the network. Heterogeneous Wireless Networks (HWNs) permit users with global devices to access the required service at any place and anywhere. The HWNs contain many Radio Access technology RATs such as GPRS (General Packet Radio Services), UMTS (Universal Mobile Telecommunication Service), Wi-Fi (Wireless Fidelity), Wi-Max (Worldwide Interoperability for Microwave Access), WLAN (Wireless Local Area Network) and transferring the ongoing call and providing seamless mobility can be achieved by Vertical Handover (VHO) mechanisms. The heterogeneous wireless networks (HWN) integrated with the network characteristics, like the bandwidth, speed support, cost, security, power consumption, delay, and range of communication are employed for obtaining better Quality of Services (QoS). For better mobility management, handover (HO) is the most significant aspect considered for solving the issues regarding mobility the designed Novel approach uses metaheuristics algorithms like grey wolf optimization (GWO) and Mayfly optimization (MFO) technique to provide a better service for end-user in terms of energy consumption, and handover delay, Call drop probability and throughput.

A Novel Analytical Framework Is Developed for Wireless Heterogeneous Networks for Video Streaming Applications

Due to bandwidth and time limits, delivering high-quality video streaming facility offers real-time wireless networks, but assuring quality of experience (QoE) is rather difficult. A network host can deliver its data stream via numerous different network pathways. At the same time, using multipath data transmission, where video streaming may be supplied through Internet protocols (IPs) as well as broadband with bidirectional communication between video sources and consumers, is one way to address this difficult problem. In this work, a novel framework is developed for wireless heterogeneous networks for video streaming applications using concurrent multipath transfer (CMT). The performance of frame-level delay can be enhanced for better video quality using this proposed method. The network congestion on end-to-end path utilised in CMT is not dependent of one another, and we operate under prior assumption that receiver’s announced window does not limit the sender. The analytical results show that the proposed framework outperforms existing methods in terms of performance with minimum retransmission delay.