A Seamless Vertical Handoff Protocol for Enhancing the Performance of Data Services in Integrated UMTS/WLAN Network

The Next Generation Wireless Network (NGWN) is speculated to be a unified network composed of several existing wireless access networks such as Wireless Local Area Network (WLAN), Global System for Mobile (GSM), Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), and satellite network etc

A Seamless Vertical Handoff Protocol for Enhancing the Performance of Data Services in Integrated UMTS/WLAN Network

The Next Generation Wireless Network (NGWN) is speculated to be a unified network composed of several existing wireless access networks such as Wireless Local Area Network (WLAN), Global System for Mobile (GSM), Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), and satellite network etc

An Interference-Managed Hybrid Clustering Algorithm to Improve System Throughput

In the current smart era of 5G, cellular devices and mobile data have increased exponentially. The conventional network deployment and protocols do not fulfill the ever-increasing demand for mobile data traffic. Therefore, ultra-dense networks have widely been suggested in the recent literature. However, deploying an ultra-dense network (UDN) under macro cells leads to severe interference management challenges. Although various centralized and distributed clustering methods have been used in most research work, the issue of increased interference persists. This paper proposes a joint small cell power control algorithm (SPC) and interference-managed hybrid clustering (IMHC) scheme, to resolve the issue of co-tier and cross-tier interference in the small cell base station cluster tiers. The small cell base stations (SBSs) are categorized based on their respective transmitting power, as high-power SBSs (HSBSs) and low-power SBSs (LSBSs). The simulation results show that by implementing the IMHC algorithm for SBSs in a three-tier heterogeneous network, the system throughput is improved with reduced interference

A SIMPLE AND SEAMLESS VERTICAL HAN DOFF PROTOCOL TO ENHANCE THE PERFORMANCE OF DATA SERVICES IN INTEGRATED UMTS/WLAN NETWORK

The Next Generation Wireless Network (NGWN) is speculated to be a unified network composed of several existing wireless access networks such as Wireless Local Area Network (WLAN). Global System for Mobile (GSM). Universal Mobile Telecommunications System (UMTS). Worldwide lnteroperability for Microwave Access (WiMAX). and satellite network etc. The NGWN will permit the wireless client to roam across diverse integrated access networks while maintaining the session continuity. In order to maintain the session continuity in such an internetworking environment, the most important and challenging issue is to attain the seamless mobility. The literature demonstrated that when the wireless client switches the active data session between the heterogeneous wireless access networks, the loose coupling based mobility management techniques lack in providing the seamless mobility

A Clustered PD-NOMA in an Ultra-Dense Heterogeneous Network with Improved System Capacity and Throughput

In the current era of exponentially growing demand for user connectivity, spectral efficiency (SE), and high throughput, the performance goals have become even more challenging in ultra-dense 5G networks. The conventional orthogonal frequency division multiple access (OFDMA) tech-niques are mature but have not proven sufficient to address the growing user demand for high data rates and increased capacity. Therefore, to achieve an improved throughput in an ultra-dense 5G network with an expanded network capacity, the unified non-orthogonal multiple access (NOMA) technique is considered to be a more promising and effective solution. Throughput can be im-proved by implementing PD-NOMA, as the interference is managed with the successive inter-ference cancellation (SIC) technique, but the issue of increased complexity and capacity with compromised data rate persists. This study implements the clustered PD-NOMA algorithm to enhance user association and network performance by managing the users in clusters with fewer users per cluster with the implementation of the cooperative PD-NOMA within the clusters. In this study, we enhanced the user association in a network and ultimately improved the throughput, sum rate, and system capacity in an ultra-dense heterogeneous network (HetNet). By imple-menting the proposed clustered PD-NOMA scheme, the system throughput has improved by 23% when compared to the unified PD-NOMA scheme and 65% when compared to the OFDMA scheme with a varied number of randomly deployed users, along with an improvement in system capacity of 8% as compared to the unified PD-NOMA and almost 80% as compared to the conventional OFDMA scheme in a randomly deployed ultra-dense multi-tier heterogeneous network. Thus, we improved the network performance with the proposed algorithm and achieved increased capacity, throughput, and sum rate by outperforming the unified PD-NOMA scheme in an ultra-dense heterogeneous network

Clustering Approaches for Efficient Radio Resource Management in Heterogeneous Networks

5G telecommunication industry promises to manage and accomplish the massive data traffic and growing network requirement complexities in heterogeneous networks (HetNets). HetNets are K-tier networks and are expected to be seamlessly connected networks with robust services for users anywhere at any time. In near future, the significance of 5G/B5G cellular networks; in both indoor and outdoor environments will be greater than before and it would add up to an exhaustive level. However, as a result of the increased density of networks, a rise in interference within these ultra-dense networks (UDN) will have an alarming impact on throughput, interference and latency.  To ensure high throughput with reduced interference in UDNs a clustered architecture is required. A HetNet with clustered approach enables the network to mitigate interference effectively and achieve efficient radio resource management (RRM). In this paper, we analyzed different clustering classifications and existing clustering techniques that are used for proficient radio resource management. The centralized clustering techniques and decentralized clustering techniques are analyzed and as a result, it is assumed that improved performance can be achieved by emphasizing on hybrid clustering approaches. In addition to this, performed a thoughtful review of existing hybrid clustering techniques to achieve improved throughput and mitigate interference in dense heterogeneous networks.  Our analysis shows that improved radio resource management and increased throughput in HetNets is achieved by applying hybrid clustering techniques with reduced inter and intra tier interference.

Non-Manual Gesture Recognition: Pakistan Sign Language Database and Pilot Study

Individuals with limited hearing and speech rely on sign language as a fundamental nonverbal mode of communication. It communicates using hand signs, yet the complexity of this mode of expression extends beyond hand movements. Body language and facial expressions are also important in delivering the entire information. While manual (hand movements) and non-manual (facial expressions and body movements) gestures in sign language are important for communication, this field of research has not been substantially investigated, owing to a lack of comprehensive datasets. The current study presents a novel dataset that includes both manual and non-manual gestures in the context of Pakistan Sign Language (PSL). This newly produced dataset consists of. MP4 format films containing seven unique motions involving emotive facial expressions and accompanying hand signs. The dataset was recorded by 100 people. Aside from sign language identification, the dataset opens up possibilities for other applications such as facial expressions, facial feature detection, gender and age classification. In this current study, we evaluated our newly developed dataset for facial expression assessment (non-manual gestures) by YOLO-Face detection methodology successfully extracts faces as Regions of Interest (RoI), with an astounding 90.89% accuracy and an average loss of 0.34. Furthermore, we have used Transfer Learning (TL) using VGG16 architecture to classify seven basic facial expressions and succeeded with 100% accuracy. In summary, our study produced two different datasets, one with manual and non-manual sign language gestures, the second with Asian faces to find seven basic facial expressions. With both the dataset, our validation techniques found promising results

Non-Manual Gesture Recognition: Pakistan Sign Language Database and Pilot Study

Individuals with limited hearing and speech rely on sign language as a fundamental nonverbal mode of communication. It communicates using hand signs, yet the complexity of this mode of expression extends beyond hand movements. Body language and facial expressions are also important in delivering the entire information. While manual (hand movements) and non-manual (facial expressions and body movements) gestures in sign language are important for communication, this field of research has not been substantially investigated, owing to a lack of comprehensive datasets. The current study presents a novel dataset that includes both manual and non-manual gestures in the context of Pakistan Sign Language (PSL). This newly produced dataset consists of. MP4 format films containing seven unique motions involving emotive facial expressions and accompanying hand signs. The dataset was recorded by 100 people. Aside from sign language identification, the dataset opens up possibilities for other applications such as facial expressions, facial feature detection, gender and age classification. In this current study, we evaluated our newly developed dataset for facial expression assessment (non-manual gestures) by YOLO-Face detection methodology successfully extracts faces as Regions of Interest (RoI), with an astounding 90.89% accuracy and an average loss of 0.34. Furthermore, we have used Transfer Learning (TL) using VGG16 architecture to classify seven basic facial expressions and succeeded with 100% accuracy. In summary, our study produced two different datasets, one with manual and non-manual sign language gestures, the second with Asian faces to find seven basic facial expressions. With both the dataset, our validation techniques found promising results

Interpretation of Expressions through Hand Signs Using Deep Learning Techniques

It is a challenging task to interpret sign language automatically, as it comprises high-level vision features to accurately understand and interpret the meaning of the signer or vice versa. In the current study, we automatically distinguish hand signs and classify seven basic gestures representing symbolic emotions or expressions like happy, sad, neutral, disgust, scared, anger, and surprise. Convolutional Neural Network is a famous method for classifications using vision-based deep learning; here in the current study, proposed transfer learning using a well-known architecture of VGG16 to speed up the convergence and improve accuracy by using pre-trained weights. We obtained a high accuracy of 99.98% of the proposed architecture with a minimal and low-quality data set of 455 images collected by 65 individuals for seven hand gesture classes. Further, compared the performance of VGG16 architecture with two different optimizers, SGD, and Adam, along with some more architectures of AlexNet, LeNet05, and ResNet50.