Utilization Of 5G Networks in Current Scenario

The fifth-generation (5G) mobile network system is the next huge thing in the world of mobile communication. With the rapid development of wireless communication network, It is expected that a fifth-generation network system will provide seamlessly higher data transfer speeds and various capabilities. 5G has evolved in such a way that it can be beneficial for each and every individual who is using it by giving them an ultimate experience. In this article we give a brief overview on working of the electromagnetic spectrum of 5G and its various applications and at the end, the overall opportunities arise in the 5G network system on the basis of their applications. Keywords: 5G Communication, Network, working, speed, Application, Evolution, MIMO,3GPP

4G to 5G Network Evolution: Advantages and Differences

Starting from the emergence of 1st Generation network (1G), wireless mobile communications have been undergoing an evolution - from 2nd Generation (2G), 3rd Generation (3G), 4th Generation (4G) networks to 5th Generation network (5G) at present. The fifth era is only a continuation of the ongoing evolution as it is still in the research phase and is also the basis for further development of industries and the society in general. The paper presents and compares the fourth and fifth generation of wireless mobile communications, focusing on the differences and progress in terms of data transmission rate, capacity, architecture, technology and applied multiple-technique approaches and services provided.

OPC UA TSN: a next-generation network for Industry 4.0 and IIoT

Purpose The purpose of this paper is to describe how emerging open standards are replacing traditional industrial networks. Current industrial Ethernet networks are not interoperable; thus, limiting the potential capabilities for the Industrial Internet of Things (IIoT). There is no forthcoming new generation fieldbus standard to integrate into the IIoT and Industry 4.0 revolution. The open platform communications unified architecture (OPC UA) time-sensitive networking (TSN) is a potential vendor-independent successor technology for the factory network. The OPC UA is a data exchange standard for industrial communication, and TSN is an Institute of Electrical and Electronics Engineers standard for Ethernet that supports real-time behaviour. The merging of these open standard solutions can facilitate cross-vendor interoperability for Industry 4.0 and IIoT products. Design/methodology/approach A brief review of the history of the fieldbus standards is presented, which highlights the shortcomings for current industrial systems in meeting converged traffic solutions. An experimental system for the OPC UA TSN is described to demonstrate an approach to developing a three-layer factory network system with an emphasis on the field layer. Findings From the multitude of existing industrial network schemes, there is a convergence pathway in solutions based on TSN Ethernet and OPC UA. At the field level, basic timing measurements in this paper show that the OPC UA TSN can meet the basic critical timing requirements for a fieldbus network. Originality/value This paper uniquely focuses on the specific fieldbus standards elements of industrial networks evolution and traces the developments from the early history to the current developing integration in IIoT context.

Ubiquitous Networks: A Need of Future World of Things

With an exponential increase in the number of applications and user demand, it is essential to respond to the query of users with fast services and networks used. This is possible only by the use of ubiquitous networks supporting mass media communications. The integration of advanced technologies such as Communication, Navigation and Sensing Services (CONASENSE) and Human Bond Communications (HBC) takes care of sensing, services, data, speed, cooperation, content, and cost of communication. The combination of Data, Technology, and Media used for intelligent computation and communication over the internet could serve the purpose, and that’s the urgent demand of growing networks marching towards a fusion of IoT and 5G leading to 6G. IoT with 5G will be the backbone of networks in the future generation network, adding the concept of virtualization at Anytime, Anywhere, Anything, and Anybody. The definition of ubiquitous technology considers it networked, wireless and mobile, to connect a more significant number of users and the world around them. The ubiquitous network connects the D2D, M2M, D2M and uses the ICT and Cloud-based technology to mitigate the QoS parameters. The paper’s primary contribution is the proposal of 6G enabling technologies and use cases to demonstrate the need and integration of various prime techniques as IoT++5G++Cloud++AI/ML. The technology road map and proposed C6-WISDOM model illustrate the fundamentals of enabling future ubiquitous networks (6G). It also focuses on the critical requirements of 6G technology in support of ubiquitous networks and identifies the present technologies integrated to provide vertical sustainable wireless networking solutions.

Design of Low Power Cam Memory Cell for the Next Generation Network Processors

In the modern world, high performance embedded applications in the field of multimedia, networking, and imaging are increasing day by day. These applications require high performance and more complex out-of-order superscalar processor. These complex dynamic instructions scheduling superscalar processors need higher levels of on-chip integration designs which are often associated with power dissipation. These out-of-order superscalar processors achieve higher performance compared to other processors by simultaneous fetching, decoding and execution for multiple instructions in out-of-order that are used in the next generation network processors. The main data path resources of the processor use CAM+RAM structure which is the major power consuming unit in the overall out-of-order processor design. The proposed new design of CAM+RAM with power-gating technique reduces the overall average power consumption compared to the conventional design without any significant impact on their performance.

LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals

Electroencephalography (EEG) signals collected from human brains have generally been used to diagnose diseases. Moreover, EEG signals can be used in several areas such as emotion recognition, driving fatigue detection. This work presents a new emotion recognition model by using EEG signals. The primary aim of this model is to present a highly accurate emotion recognition framework by using both a hand-crafted feature generation and a deep classifier. The presented framework uses a multilevel fused feature generation network. This network has three primary phases, which are tunable Q-factor wavelet transform (TQWT), statistical feature generation, and nonlinear textural feature generation phases. TQWT is applied to the EEG data for decomposing signals into different sub-bands and create a multilevel feature generation network. In the nonlinear feature generation, an S-box of the LED block cipher is utilized to create a pattern, which is named as Led-Pattern. Moreover, statistical feature extraction is processed using the widely used statistical moments. The proposed LED pattern and statistical feature extraction functions are applied to 18 TQWT sub-bands and an original EEG signal. Therefore, the proposed hand-crafted learning model is named LEDPatNet19. To select the most informative features, ReliefF and iterative Chi2 (RFIChi2) feature selector is deployed. The proposed model has been developed on the two EEG emotion datasets, which are GAMEEMO and DREAMER datasets. Our proposed hand-crafted learning network achieved 94.58%, 92.86%, and 94.44% classification accuracies for arousal, dominance, and valance cases of the DREAMER dataset. Furthermore, the best classification accuracy of the proposed model for the GAMEEMO dataset is equal to 99.29%. These results clearly illustrate the success of the proposed LEDPatNet19.

Research on the Generation and Design Method of Residential Community Scheme Based on Improving Design

Along with the urban renewal and development, the urban living environment has given rise to various problems that need to be solved. With an eye on the future development model of residential communities, an experimental preliminary design for the construction of architectural space, public space, and landscape space based on people’s actual needs is carried out in an attempt to alleviate the more urgent symbiotic relationship between people and urban environment. To this end, this paper proposes a planning and design generation framework for the constructed external spatial environment of building groups based on a recursive double-adversarial network model. Firstly, we extract the features of the constructed external spatial environment of the building group in depth and generate the expression feature map, which is used as a supervisory signal to generate an expression seed image of the constructed external spatial environment of the building group; then we use the generated seed image together with the constructed external spatial environment of the original target building group as the input to generate a feature-holding image as the output of the current frame, and the feature-holding image is also used as the input for the next. Finally, the seed image generation network and the feature-holding image generation network are recursively used to generate the next frame, and the video sequence of the expressions of the constructed external spatial environment of the building group with the same feature-holding expressions as the original input is recursively obtained several times. The experimental results on the building group database show that the proposed method can generate clear and natural video frames of the constructed external spatial environment of the building group, which can be gradually derived from the design of building units to the construction of the building group and penetrate into the planning and design of the external spatial environment in order to comprehensively improve the living environment of urban population and provide a design method and theoretical support for the design of future urban residential communities.

Connected Objects Geo-Localization Based on SS-RSRP of 5G Networks

The Global Positioning System (GPS) is not the only way to solve connected objects’ geo-localization problems; it is also possible to use the mobile network infrastructure to geo-locate objects connected to the network, using antennas and signals designed for voice and data transfer, such as the 5th generation network. 5G is considered as a least expensive solution because there is no specific equipment to set up. As long as the object is in an area covered by the network, it connects to the nearest 5G Micro-Cell (MC). Through exchange of signals with the MC node we can locate the object. Currently, this location is very fast with less than 5 s but not very precise because it depends on the number of MC antennas of the operator in question and their distance. This paper presents a novel technique to geo-locate connected object in a covered 5G area. We exploit the 5G SS-RSRP used for signal quality measurement, to estimate the distance between two Connected Objects (COs) in move and in a dense urban area. The overall goal is to present a new concept laying on the 5G SS-RSRP signalling. The proposed solution takes into consideration the Deterministic and the Stochastic effect of the received signals which is not treated by the previous works. The accuracy is optimum even after approaching to the distance of one meter which is not reached in previous works too. Our method can also be deployed in the upcoming 5G network since it relies on 5G signals itself. This work and that of Wang are both based on RSRP and give comparable theoretical complexities therefore comparable theoretical execution times as well. However, to obtain a reliable learning Wang requires a huge amount of data which makes it difficult to get a real time aspect of this algorithm. The use of RSRP and the elimination of the learning phase will give more chance to our work to achieve desired performances. Numerical results show the appropriateness of the proposed algorithms and good location accuracy of around one meter. The Cramer Rao Lower Bound derivations shows the robustness of the proposed estimator and consolidate the work.