Type Of Communication Research Articles

A Review of Ilya Utekhin, Chto takoe vizualnaya antropologiya: putevoditel po klassike etnograficheskogo kino [What Is Visual Anthropology: А Guidebook on the Classics of Ethnographic Cinema]. St Petersburg: Poryadok slov, 2018, 352 pp.

This is a review of anthropologist Ilya Utekhin’s What Is Visual Anthropology: A Guidebook on the Classic of Ethnographic Cinema. On the example of classical ethnographic films of the 20th century, the monograph considers the phenomenon of ethnographic cinema as an important part of visual anthropology. The book’s main intent is to show how outstanding projects that shaped visual anthropology as a discipline posed key questions for anthropology as a whole. These include nonverbal communication, the socialization and association of personality types with the dominant ethos of culture, the form and function of ritualized aggression in tribal society, obsession and communication with other beings, psycho-pathology, boundaries of normality, arrangement of specialized social institutes, boundaries of human beings and human projections in relations between human beings and animals. The review focuses on the key problems of developing visual anthropology as a discipline and how they were reflected in films.

Improved Resource Allocation in 5G MTC Networks

Effective resource allocation has always been one of the serious challenges in wireless communication. A considerable number of machine type communication (MTC) devices in 5G with variable quality of service (QoS) aggravates this challenge even further. Existing Resource allocation schemes in MTC are usually considering signal to noise ratio (SNR), which provides preference to MTC devices based on distance rather than their QoS requirements. This paper proposes a resource allocation scheme with dynamic priorities for MTC devices with multiple radio access technologies (RATs). The proposed resource allocation scheme has two main parts namely medium access and resource allocation. The medium access leverages the broadcast nature of wireless signal and MTC devices’ wait time to assign priorities using capillary band in a secure and integral way. At resource allocation, SNR, total induced transmission delay, and transmission-awaiting MTC devices are used to assign resources in the cellular band. The rumination of two-staged dynamic priorities in the proposed scheduling scheme brings significant performance improvements in outage and success probabilities. Compared to SNR-based schemes, the proposed mechanism performs well by expressively improving the outage and success probability by 20% and 30%, respectively.

State Machine with Tracking Tree and Traffic Allocation Scheme Based on Cumulative Entropy for Satellite Network

Satellite network is regarded as an important component of 5th Generation (5G). New deployment scenarios have been designed in form of satellite network fused 5G terrestrial network. Service requests in Non terrestrial networks (NTN) are noncentralized in the time and space domain, which cause services modelling uncertain and lack of balance between latency and bandwidth. Targeted at those bottlenecks, a Satellite service model based on State machine with tracking tree (SM-SMTT) and traffic allocation scheme base on cumulative entropy is proposed. Enhanced mobile broadband service, low latency service and massive machine type communications in satellite will be modelled by SM-SMTT. Cumulative entropy is introduced as the criterion to allocate traffic. According to the lower bound from cumulative entropy, the balance of latency and bandwidth is achieved in different deployment scenarios. The simulation in OPNET, demonstrations the new scheme has promoted the performance of delay and bandwidth utilization by 37.8% and 12.0%.

Energy-Efficient Edge Offloading in Heterogeneous Industrial IoT Networks for Factory of Future

The ultra-reliable and low latency communication (URLLC) and massive machine type communication (mMTC) in 5G are envisioned to support intelligent automation in the heterogeneous Factory of Future (FoF) networks, and Mobile-edge computing (MEC) is considered to be a promising system for enabling real-time task processing at the edge of the network. In the future factory, production machines, and environmental monitoring devices will be endowed with the wireless connecting for mobility. These devices are deployed for running complicated real-time tasks. To make such mission-critical tasks being processed in time, parts of the tasks should be completed with the assistance of the edge server or even the cloud. In this work, we jointly investigate the partial task offloading, computation, and communication (licensed and unlicensed) resource allocation problem in the trade-off between overall power consumption and quality of service (QoS) satisfaction. A 2-tier MEC-cloud framework is provided, wherein the IoT mobile devices (MDs) are able to partition the tasks into segments and offload them to the MEC and the cloud server. Considering the limits of communication and computation resources, we proposed a mechanism call 5G and NR-U opportunity-cost-based offloading algorithm (5G/NR-U OCBOA) to optimize resource allocation. Within the mechanism, there are two proposed algorithms, 5G OCBOA is for the licensed-only case, and NR-U OCBOA dedicates on unlicensed one. We iteratively perform the two algorithms to get the final solution. The simulation results show that our low-complexity algorithms almost outperform the other benchmark greedy algorithms. The proposed algorithm is up to 59.3% MD blocking probability less, up to 58.7% power saving gain, and up to 47.6% more QoS gain.

QUALITY OF SERVICE ASSESSMENT IN LTE NETWORKS WITH A LIMITED NUMBER OF USERS

Narrow Band Internet of Things (NB-IoT) is the latest cellular radio access technology that was based on LTE technology and implemented as part of the 3GPP 3GPP 3GPP for Low-power Wide-area Network (LPWAN). The paper considers the features of NB-IoT standards based on the characteristics of the physical and channel layers of NB-IoT technology based on release 14-15 of the 3GPP group. The aim of this work is also to build and study a model for serving heterogeneous traffic in an isolated cell of the LTE standard, which supports the functionality of the Internet of Things IoT (Internet of Things), in particular eMTC (enhanced Machine Type Communication). The constructed mathematical model takes into account the heterogeneity of the arrival of requests and their dependence on the number of users of cellular services. Depending on the number of sources, incoming requests are described by Poisson or Engset models. The studied model makes it possible to dynamically allocate resources that have LTE.

Power Saving Techniques for 5G and Beyond

Energy efficiency is one of the key performance indicators in 5G New Radio (NR) networks targeted to support diversified use cases including enhanced mobile broadband (eMBB), massive machine type communications (mMTC) and ultra-reliable and low latency communications (URLLC). Trade-offs have to be carefully considered between energy efficiency and other performance aspects such as latency, throughput, connection densities and reliability. Energy efficiency is important for both user equipment (UE) side and base station side. On UE side, UE battery life has great impact on user experience. It is challenging to improve UE experience in other performance aspects without affecting battery life of 5G handsets. On the base station side, efficient network implementation is critical in both environmental and operation cost standpoints. To adapt different requirements and trade-offs, the 5G NR standard is designed to have great flexibility on network operation modes. This paper provides an overview on power saving techniques supported by 5G NR standards according to the current 5G standardization progress. It provides the 5G evolution path of the power saving techniques from the first release of 5G standard to the future beyond-5G releases. In addition to the existing standardized techniques, some major development trends of green communication and the future potential enhancements expected in the beyond-5G standards are discussed.

5G TECHNOLOGIES AND BUSINESS ASPECTS: HIGH-LEVEL POINT OF VIEW

This paper covers technological and business aspects of 5G mobile networks at their initial targeted year for acceptance, 2020. It gives a high level view beyond 5G, toward the next generation which is referred to here as 6G. The 5G introduces New Radio (NR) in sub-6 GHz bands and also in mm Wave bands above 24 GHz. The paper analyzes 5G novelties such as network function virtualization and network softwarization, which means that 5G Next Generation Core and 5G NR access network are built by using different functions in split user and control planes. That provides the basis for the network slicing approach, with three main service types defined for the 5G era: enhanced Mobile Broadband (eMBB), massive Machine Type Communication (mMTC) and Ultra-Reliable Low-Latency Communica­tion (URLLC), which may be provided via separate network slices as logically separated network partitions. Consid­ering the 5G business aspects, the paper states that the initial 5G driver is eMBB, then follows mMTC, and last to be deployed is the most demanding one, the URLLC. Finally, with many 5G network functions, also exposed to the third parties such as Over-The-Top (OTT) service providers, with constantly increasing volume of traffic and number of connected devices, this paper also includes future use of Machine Learning and generally Artificial Intelligence (AI) is becoming necessity for further expansion of the mobile world beyond 5G, toward the 6G in 2030s.

Machine learning based cell association for mMTC 5G communication networks

With the advent of 5G communication networks, the number of devices on the core 5G network significantly increases.A5Gnetwork is a cloud native, massively connected internet of things (IoT) platform with a huge number of devices hosted on the network now known as massive machine type communication (mMTC). As ultra-low latency is pivotal in developing 5G communication, a proper cell association scheme is now required to meet the load and traffic needs of the new network, opposed to older cell association schemes which were based only on the reference signal received power (RSRP). This paper proposes an unsupervised machine learning algorithm, namely hidden Markov model (HMM) learning on the network's telemetry data, which is used to learn network parameters and select the best eNodeB for cell association. The proposed model uses an HMM learning followed by decoding for selecting the optimal cell for association.

Low-Complexity Multi-User Detection Based on Gradient Information for Uplink Grant-Free NOMA

Massive machine type communication (mMTC) serves an irreplaceable role in the development process of the Internet of Things (IoT). Because of its characteristics of massive connection and sporadic transmission, compressed sensing (CS) has been applied in joint user activity and data detection in the uplink grant-free non-orthogonal multiple access (NOMA) system. In previous work, greedy iterative-based multi-user detection (MUD) algorithms were developed in mMTC scenarios because of the computational benefit and competitive performance. However, conventional greedy iterative-based MUD algorithms still suffer from high computational complexity due to the process of large-size matrix inversion with the accession of massive devices into the system. In this paper, gradient information is used to address this problem. A low-complexity gradient descent-based gradient pursuit MUD (GDGP-MUD) algorithm is proposed, which uses the gradient information of error function in the process of iteration as a new updating direction, instead of the matrix inversion process. Then, a multi-step quasi-Newton MUD (MSQN-MUD) algorithm is proposed to improve the precision of detection while maintaining low complexity. In the algorithm, high-order information in the process of adjacent iteration is used effectively to update data values more accurately. Moreover, the convergence and complexity analysis of both algorithms are derived. The analysis shows that both proposed algorithms have lower computational consumption than most of the state-of-the-art greedy-based MUD algorithms. It is worth noting that in comparison to most existing CS-based MUD algorithms, the two proposed algorithms do not require the exact user sparsity level and, thus, reduce the dependence on prior knowledge. The numerical experiments demonstrate that the proposed algorithms have better real-time performance than existing greedy-based MUD algorithms with similar symbol error rate performance.

5G is Real: Evaluating the Compliance of the 3GPP 5G New Radio System With the ITU IMT-2020 Requirements

The 3rd Generation Partnership Project (3GPP) submitted the 5G New Radio (NR) system specifications to International Telecommunication Union (ITU) as a candidate fifth generation (5G) mobile communication system (formally denoted as IMT-2020 systems). As part of the submission, 3GPP provided a self-evaluation for the compliance of 5G NR systems with the ITU defined IMT-2020 performance requirements. This paper considers the defined 5G use case families, Ultra Reliable Low-Latency Communication (URLLC), massive Machine Type Communication (mMTC) and enhanced Mobile Broadband (eMBB), and provides an independent evaluation of the compliance of the 3GPP 5G NR self-evaluation simulations with the IMT-2020 performance requirements for connection density, reliability, and spectral efficiency for future mobile broadband and emerging IoT applications. Independent evaluation indeed shows the compliance of the 3GPP 5G NR system with the ITU IMT-2020 performance requirements for all parameters evaluated by simulations.

Cluster-based authentication for machine type communication in LTE network using elliptic curve cryptography

Cluster-based authentication for machine type communication in LTE network using elliptic curve cryptography

Martingales-Based ALOHA-Type Grant-Free Access Algorithms for Multi-Channel Networks With mMTC/URLLC Terminals Co-Existence

As a simple single-phase transmission strategy, grant-free access is believed to be an effective way to guarantee the stringent quality of service (QoS) requirements for ultra-reliable low-latency communications (URLLCs). However, unless a theory-based fine evaluation on dynamic delay, we cannot hope to overcome the natural defects of random access and so effectively utilize the time-frequency resources. In this paper, we propose a novel multi-channel ALOHA-type (M-ALOHA) grant-free access algorithm for heterogeneous machine type communication (MTC) networks with URLLC-type terminals and delay-tolerant massive MTC (mMTC)-type terminals co-existence. Firstly, we construct a statistical service model characterizing the transmission rate of each terminal with joint consideration of the features of M-ALOHA access scheme, short packet transmissions and frequency-selective fading channel. Then, taking the great advantages of service-martingales theory in random queuing analysis, we present an ingenious delay analysis and obtain the martingales-based formulation of delay-bound violation probability, where the sporadic feature of MTC traffic arrival is carefully addressed. Finally, the M-ALOHA algorithm is formulated as a system throughput maximization problem subject to martingales-based statistical delay-QoS and the total bandwidth of system. The problem is solved by the proposed bi-objective multi-variable-grey wolf optimizer (BOMV-GWO) algorithm. As a result, we obtain the access probability for each terminal and the optimal parameters for the system design, including the number of sub-channels, the bandwidth for each sub-channel and the packets transmission rate. Simulation results demonstrate that the performance of our M-ALOHA algorithm is favorable.

Machine learning based cell association for mMTC 5G communication networks

With the advent of 5G communication networks, the number of devices on the core 5G network significantly increases.A5Gnetwork is a cloud native, massively connected internet of things (IoT) platform with a huge number of devices hosted on the network now known as massive machine type communication (mMTC). As ultra-low latency is pivotal in developing 5G communication, a proper cell association scheme is now required to meet the load and traffic needs of the new network, opposed to older cell association schemes which were based only on the reference signal received power (RSRP). This paper proposes an unsupervised machine learning algorithm, namely hidden Markov model (HMM) learning on the network's telemetry data, which is used to learn network parameters and select the best eNodeB for cell association. The proposed model uses an HMM learning followed by decoding for selecting the optimal cell for association.

Analytical Modeling of Radio Network Performance for 5G (Non-Standalone) and It’s Network Connectivity

The traffic demand and prediction for the next decade would be mostly affiliated with the Internet of Things (IoT). Various challenges with mobile communication industry will be faced as the demand in high capacity, multi mobile devices (users) connected to the network, uplink power consumption on User Equipment (UE), and its effect on the life span of mobile phone. The major features of 5G as per user experience on the network are Ultra-Reliable Low Latency Communication (URLLC), Internet of Things (IoT), sustaining high rate Enhanced Mobile Broadband (eMBB), and connection density Massive Machine Type Communication (mMTC). This research work focuses on Non-Standalone (NSA) 5G New Radio (NR) early deployment on eMBB for achieving the required throughput. The 5G performance requirement is higher than 4G, which includes the capacity to support user experience downlink throughput with target value of 1 Gbps, millisecond-level of end-to-end latency, and high connection density of 1 million per square kilometer. Optimization is a vast topic, and this paper discusses the problems faced by users latching on 5G NSA network on the downlink and 4G Network on the uplink and suggests its solution.

Advanced Energy-Efficient Computation Offloading Using Deep Reinforcement Learning in MTC Edge Computing

Mobile edge computing (MEC) supports the internet of things (IoT) by leveraging computation offloading. It minimizes the delay and consequently reduces the energy consumption of the IoT devices. However, the consideration of static communication mode in most of the recent work, despite varying network dynamics and resource diversity, is the main limitation. An energy-efficient computation offloading method using deep reinforcement learning (DRL) is proposed. Both delay-tolerant and non-delay tolerant scenarios are considered using capillary machine type communication (MTC). Depending upon the type of service, an intelligent MTC edge server using DRL decides either process the incoming request at the MTC edge server or sends it to the cloud server. To control communication, we draft a markov decision problem (MDP). This minimizes the long-term power consumption of the system. The formulation of the optimization problem is considered under the constraint of computing power resources and delays. Simulation results delineate the significant performance gain of 12% in computation offloading through the proposed DRL approach. The effectiveness and superiority of the proposed model are compared with other baselines and are demonstrated numerically.

Cluster-based authentication for machine type communication in LTE network using elliptic curve cryptography

Machine type communication (MTC) is a significant approach for communication in the long-term evaluation (LTE) networks. This paper intends to introduce a new cluster-based authentication model for MTC in the LTE network. At first, the nodes in LTE network are subjected under the clustering process via k-means approach. However, for the selection of optimal cluster head this paper establishes a novel hybrid approach termed as wolf insisted Jaya algorithm (WI-JA) that hybrid the concept of both Jaya algorithm (JA) and grey wolf optimisation (GWO) algorithms. As a novelty, in this work, all the selected cluster head involves the records of all the remaining nodes exists in network, and this will be handled via block chain technology. Finally, in the authentication phase, authenticate to the chosen cluster head via elliptic curve cryptography (ECC). The performance of the presented framework was evaluated and proved over other conventional models with respect to certain measures.

On the Opportunities and Challenges of NOMA-Based Fog Radio Access Networks: An Overview

Future generations of wireless networks are expected to provide new services with an unprecedented level of diverse and stringent requirements. Fog Radio Access Network (FRAN) and Non-Orthogonal Multiple Access (NOMA) have emerged as complimentary enablers to meet such requirements. On the one hand, FRAN architecture is designed to reduce the delay caused by the fronthaul link by pushing control and storage to the network edge. On the other hand, in addition to increasing the spectral and energy efficiency and the number of connected devices, NOMA has the potential to improve network latency. This paper overviews the joint benefits of enabling NOMA schemes in an FRAN architecture, by means of examining the applicability and adequateness of the NOMA-based FRAN features in achieving specific objectives of next generation of mobile networks, mainly those related to enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable low latency communication (URLLC). The paper further depicts the challenges and future research directions that must be addressed in order to meet such opportunities.

IEEE Access Special Section Editorial: New Waveform Design and Air-Interface for Future Heterogeneous Network Towards 5G

Unprecedented levels of spectral and energy efficiency are expected from next-generation wireless networks to achieve ubiquitous communications between anybody, anything, and at any time. Fifth-generation (5G) radio access technology is expected to take a huge leap compared to the previous radio generations by supporting cognitive radio, machine type communication, and the Internet of Things, besides traditional mobile broadband access. The necessity of supporting new services targeted by 5G has made researchers to question the suitability of the orthogonal frequency-division multiplexing (OFDM) waveform as adopted in Long-Term Evolution (LTE). However, the demerits of OFDM are recognized to be its large out-of-band emissions, which affect the coexistence of asynchronous services or devices, as well as its sensitivity to phase noise, Doppler frequency shift, and large peak-to-average power ratio (PAPR). Significant research effort is currently being spent on the design and analysis of alternative solutions aimed at overcoming the inefficiencies of present waveforms. Candidate multicarrier solutions rely on different degrees of filtering to obtain lower sidelobes than OFDM, thus improving robustness to asynchronous transmission. For example, in filter bank multicarrier (FBMC) approaches, universal filter multicarrier (UFMC), and generalized frequency-division multiplexing (GFDM). Even though the mentioned solutions offer undeniable advantages in terms of spectrum containment, their suitability as 5G waveforms when considering complexity constraints, as well as realistic impairments, is still disputable. In particular, the extension to multiple-input multiple-output (MIMO) antenna transmission with multiple streams seems rather cumbersome. Thus, this will lay down an interdisciplinary research agenda that combines energy and time-efficient spectrum schemes into waveform design.

Multi-Dimensional Affinity Propagation Clustering Applying a Machine Learning in 5G-Cellular V2X

Cellular systems are facing the ever-increasing demand for vehicular communication aimed at applications such as advanced driving assistance and ultimately fully autonomous driving. Cellular Vehicle to Anything (C-V2X) has become more applicable with the release of the first sets of 5G (5 th Generation) system specifications. The highly capable 5G systems will therefore support even a larger number of moving objects. This study aims to present a sophisticated clustering mechanism that enables cellular systems to accommodate a massive number of moving Machine Type Communication (MTC) objects with a minimum set of connections while maintaining system scalability. Specifically, we proposed Normalized Multi Dimension-Affinity Propagation Clustering (NMDP-APC) scheme and applied it for Vehicular Ad hoc Network (VANET) clustering. For VANET clustering formation, our study employed Machine Learning (ML) to determine the granularity, i.e., the size and span of clusters desirable for use in dynamic motion environments. The study achieved a sufficient level of prediction accuracy with fewer training data through a learned prediction function based on the selected key criteria. This paper also proposes a system sequence designed with a series of procedures fully compliant with C-V2X systems. We demonstrated substantial simulations and numerical experiments with theoretical analysis, specifically applying soft-margin-based Support Vector Machine (SVM) algorithm. The simulation results confirmed that the granularity parameter we applied fairly controls the size of VANET clusters although vehicles are in motion and that the prediction performance has been adjusted through controlling of key SVM parameters.

A Systematic Dynamic Key Agreement Mechanism for LTE Advanced Networks in the Internet of Things

Of late, Session Initiation Protocol (SIP) has become one of the popular signaling protocols especially for the multimedia communication system. Various protocols have been proposed by researchers to ensure access independence, authentication, and Key Agreement security characteristic. With the extensive growth of cellular networks, mobile traffic connected with the advancements of the wireless communication channel. In this scenario, Machine Type Communication (MTC) plays a crucial role in line with Long Term Evaluation-Advanced Networks as their communication happened between Machine to machine without human intervention. In order to reach Mobile Type Transmission (MTT) security condition, the access verification process required to pursue the verification and Key Agreement protocol. Moreover, the development of Group premised communication and individual authentication mechanisms to every Machine Type Communication device (MTTD) would lead to signal-congestion in real-time networking scenarios. Jinguo et al. proposed a Group-Based Verification and Key-Agreement protocol with dynamically updating policy for mutual authentication. Especially, they chose an asynchronous secret shared key merged to work with Diffie-Hellman protocol for establishing disjoint verification and session-key establishment across LTE Advanced Networks. However, the DH algorithm could not provide message integrity to upgrade the security feature namely integrity. In this paper, the algorithms Advanced Encryption Standard (AES) in addition to Elliptic Curve Diffie-Hellman (ECDH) can be integrated called an Elliptic Curve Digital Signature Algorithm (ECDSA)” which addresses verification and integrity.