Adaptive Knn-based algorithm for network selection in next-generation networks

Nowadays, mobile users are equipped with multi-mode terminals allowing them to connect to different radio access technologies like WLAN, 3G (HSPA and HSPA+), and Long term evolution (LTE) each at a time. In this context, the challenge of the next-generation networks is to achieve the Always Best Connected (ABC) concept. To this end, solving the problem of selecting the most suitable radio access technology (RAT) from the list of available RAT is at the heart of the next-generation systems. The decision process is called access network selection and it depends on several parameters, such as quality of service, mobility, cost of each RAT, energy consumption, battery life, etc. Several methods and approaches have been proposed to solve the network selection problem with the fundamental objective which is to offer the best QoS to the users and to maximize the usability of the networks without affecting the users’ experience. In this paper, we propose an adaptive KNN (K nearest neighbour) based algorithm to solve the network selection problem, the proposed solution has a low computation complexity with a high level of veracity is compared with the well-known MADM methods.

Interconnection In the Convergence Era (IP-BASED Interconnection Regulation Challenges)

The IP age of networks and services is increasing very quickly. Starting from its VoIP, NGN, and IMS to today's IP services landscape, which includes fixed and mobile high-definition (HD) voice, HD video calling, unified communications (UC), video conferencing, and telepresence, cooperation, instant messaging (IM), and the rich communication suite (RCS). Convergence is one of the most significant current trends in the ICT sector. It has changed the way services are delivered and obscured the distinction between fixed and mobile services. The transition to Next-Generation Networks (NGN) is the most important next phase in the ICT sector's convergence-driven evolution. In the realms of NGN, VoIP, and IMS, there is no PSTN equivalent; instead, there are "IP islands " that locate the total value of an IP service. The next basic step is to expand the arrive of those services across a totally interconnected cross-network premise in order to maximize their overall esteem among their target audiences. The liberalization of infrastructure services depends on managing the interconnection interface between the competitive and regulated sectors. These paper outlines regulation issues raised by IP-based interconnection and examine the current practices and procedural ways that being developed to address these concerns.

Contradiction Management in Intent-driven Cognitive Autonomous RAN

<div>Intent Based Networks (IBNs) are mainly used to transform a user's intent into network configuration, operation, and maintenance strategies. IBN is a prominent feature for designing the AI-enabled next generation networks. In this paper we propose intent-driven orchestration of cognitive autonomous RAN for managing network control parameters.</div><div>Our proposed design enables the Controller to detect and remove contradiction, which may arise from a single intent, in the runtime. We also provide a brief overview of existing standards on this aspect and standardization impact of our research to show that it conforms with the worldwide mobile network management standardization efforts.</div>

Contradiction Management in Intent-driven Cognitive Autonomous RAN

<div>Intent Based Networks (IBNs) are mainly used to transform a user's intent into network configuration, operation, and maintenance strategies. IBN is a prominent feature for designing the AI-enabled next generation networks. In this paper we propose intent-driven orchestration of cognitive autonomous RAN for managing network control parameters.</div><div>Our proposed design enables the Controller to detect and remove contradiction, which may arise from a single intent, in the runtime. We also provide a brief overview of existing standards on this aspect and standardization impact of our research to show that it conforms with the worldwide mobile network management standardization efforts.</div>

Addressing Overfitting Problem in Deep Learning-Based Solutions for Next Generation Data-Driven Networks

Next-generation networks are data-driven by design but face uncertainty due to various changing user group patterns and the hybrid nature of infrastructures running these systems. Meanwhile, the amount of data gathered in the computer system is increasing. How to classify and process the massive data to reduce the amount of data transmission in the network is a very worthy problem. Recent research uses deep learning to propose solutions for these and related issues. However, deep learning faces problems like overfitting that may undermine the effectiveness of its applications in solving different network problems. This paper considers the overfitting problem of convolutional neural network (CNN) models in practical applications. An algorithm for maximum pooling dropout and weight attenuation is proposed to avoid overfitting. First, design the maximum value pooling dropout in the pooling layer of the model to sparse the neurons and then introduce the regularization based on weight attenuation to reduce the complexity of the model when the gradient of the loss function is calculated by backpropagation. Theoretical analysis and experiments show that the proposed method can effectively avoid overfitting and can reduce the error rate of data set classification by more than 10% on average than other methods. The proposed method can improve the quality of different deep learning-based solutions designed for data management and processing in next-generation networks.

Democratizing Global Healthcare through Scalable, Emergent (Beyond-the-Mobile) Wireless Technologies (Preprint)

UNSTRUCTURED Advances in mobile phone technologies coupled with availability of modern wireless networks is beginning to have a marked impact on digital health through the growing array of apps and connected devices. This said limited deployment outside of developed nations will require additional approaches in order to collectively reach the some 8 billion people on earth. Another consideration for development of digital health centered around mobile devices lies in the need for pairing steps, firmware updates and a variety of user-inputs which can increase friction for the patient. An alternate, so called ‘Beyond the Mobile’ (BTM) approach where medicaments, devices, and health services communicate directly to the cloud offers an attractive means to expand and fully realize our connected health utopia. In addition to offering highly personalized experiences, such approaches could address cost, security, and convenience concerns associated with smartphone based systems, translating to improved engagement and adherence rates with patients. Furthermore, by connecting these so-called Internet of Medical Things (IoMT) instruments through next generation networks, it offers the potential to reach patients with acute needs in non-urban regions of developing nations. Herein we outline how deployment of BTM technologies through Low Power Wide Area Networks (LPWAN) could offer a scalable means to democratize digital health and contribute to improved patient outcomes globally