5G Wireless Communication Networks Research Articles

Encapsulation of Energy Efficient, Clustering Algorithm and Spectrum Sensing for Cognitive Radio Based Internet of Things Networks

Since the two decade the Internet of Things (IoT) plays an important role in the field of communication technology. Out of maximum of IoT devices are battery operated. So there should be energy efficient devices that can operate more functions with less power consumption. The main power constraints in the IoT devices are in the communication like spectrum selection, data transmission, etc. To perform communication between the two nodes the spectrum must be available. But as spectrums are limited and a lot of data is to be transmitted there may the issue of spectrum scarcity. The dynamic spectrum access is done using Cognitive Radio (CR) technology that can overcome spectrum scarcity issue. This paper gives the research work on energy efficient, clustering algorithm and spectrum sensing for CR based IoT networks in terms of the methods, merits, demerits and implementation. For efficiency in the spectrum sensing and energy consumption in 5G wireless communication network and data transfer between the IoT devices this study is essential. The biblometric analysis is shown by using VOSviewer to visualize the bibliometric information and the result as an analysis of ROC curve for Rayleigh and Rician channel is plotted using Matlab.

Genetic Algorithm for Mode Selection in Device-to-Device (D2D) Communication for 5G Cellular Networks

The widespread use of smart devices and mobile applications is leading to a massive growth of wireless data traffic. With the rapidly growing of the customers’ data traffic demand, improving the system capacity and increasing the user throughput have become essential concerns for the future fifth-generation (5G) wireless communication network. In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular spectrum and all communications take place through the base stations (BS) and core network. Device-to-Device (D2D) communication refers to a technology that enables devices to communicate directly with each other, without sending data to the base station and the core network. This technology has the potential to improve system performance, enhance the user experience, increase spectral efficiency, reduce the terminal transmitting power, reduce the burden of the cellular network, and reduce end to end latency. In D2D communication user equipment’s (UEs) are enabled to select among different modes of communication which are defined based on the frequency resource sharing. Dedicated mode where D2D devices directly transmit by using dedicated resources. Reuse mode where D2D devices reuse some resources of the cellular network. Outband mode where D2D communication uses unlicensed spectrum (e.g. the free 2.4 GHz Industrial Scientific and Medical (ISM) band or the 38 GHz millimetre wave band) where cellular communication does not take place. Cellular mode where the D2D communication is relayed via gNode B (gNB) and it is treated as cellular users. In this work, the target was to reach the optimal mode selection policy and genetic algorithm method was used with the objective of maximizing the total fitness function. Optimal mode selection policy was presented and analysed amongst cellular, dedicated, reused and outband mode. In the present study of mode selection issues in D2D enabled networks, genetic algorithm was proposed for the case when the cellular user equipment (UE) moves in the network. Quality of service (QoS) parameters, mobility parameters and Analytic Hierarchy Process (AHP) method were used to define the mode selection algorithm. To evaluate the performance of the proposed genetic algorithm, a study of the convergence of the algorithm and the signal-to-interference plus noise ratio (SINR) was done.

Retracted: High-Concurrency Big Data Precision Marketing and Advertising Recommendation under 5G Wireless Communication Network Environment

Retracted: High-Concurrency Big Data Precision Marketing and Advertising Recommendation under 5G Wireless Communication Network Environment

Performance analysis of multiuser massive MIMO with multi-antenna users: Asymptotic data rate and its application

Massive multiple-input multiple-output (MIMO) systems have become an essential feature in the fifth-generation (5G) wireless communication networks. Due to the commercialization of 5G mobile communication devices, the number of multi-antenna devices have increased over the years. Therefore, a precoding scheme based on multi-antenna users is an inevitable feature that needs to be analyzed in massive MIMO systems. The asymptotic ergodic sum-rate analysis of block diagonalization (BD) based multiuser MIMO systems is performed in this study. Based on the random matrix theory results derived from the massive base station antenna characteristics, the singular values of the block diagonalized channel are obtained asymptotically, and thereby, the closed-form expression for the BD based sum-rate is derived. The asymptotic sum-rate performance gap between the BD and the zero-forcing beamforming is then analyzed. Moreover, from the derived asymptotic sum-rate, the optimal number of users needed to maximize the ergodic sum-rate is proposed. Simulation results show that the derived asymptotic analyses are closely aligned to the sum-rate obtained by Monte Carlo simulations. In addition, the simulation results prove that the maximum ergodic sum-rate can be achieved by using the proposed number of users.

Non-Destructive Imaging of Defects Using Non-Cooperative 5G Millimeter-Wave Signals.

Recent developments in fifth-generation (5G) wireless communications networks are creating an increasingly crowded electromagnetic environment at microwave (3-30 GHz) and millimeter-wave (30-300 GHz) frequencies. Radiation at these bands can provide non-destructive testing of defects and shielded structures using non-ionizing signals. In an actual building setting where 5G millimeter-wave communications signals are present, passive imaging of the radiation that is propagating through a wall defect can take place by means of interferometric processing without emitting additional signals in an already-crowded spectrum. We investigate the use of millimeter-wave interferometric imaging of defects in building walls and shielded structures by capturing the transmission of 5G millimeter-wave signals through the defects. We experimentally explore the ability to image defects by capturing the transmission of 38 GHz signals through materials using a 24-element interferometric receiving array.

Selected Aspects of Non orthogonal Multiple Access for Future Wireless Communications

In this paper overview of recent selected works that deal with novel directions in which Non orthogonal multiple access (NOMA) research is progressing is presented. These include the cell-free NOMA, deep learning extensions and optimizations of NOMA, energy optimization and task offloading with mobile-edge computing, NOMA and physical layer security, as well as virtualization, centralized-RAN aspects. All these are hot issues towards deployments of NOMA in the designs of beyond 5G and 6th generation (6G) wireless communication networks. Even though 3rd Generation Partnership Project (3GPP) has not yet made the decision regarding which NOMA techniques should be adopted, it seems like researchers already indicate clearly that NOMA has important place in the future network deployments based on ultra-density, novel 5G use-cases (massive machine type communications, ultra-reliable low latency communications). This paper highlights the most promising directions for NOMA research. The paper is summarized with necessary steps that are required to get NOMA into practical usage.

Study of Uplink Resource Allocation for 5G IoT Services by Using Reinforcement Learning

<p>In order to support real time IoT services, the ultra Reliable and Low Latency Communications (uRLLC) was proposed in 5G wireless communication network. Different from the grant based access in 4G, the grant free technique is proposed in 5G to reduce the random access delay of uRLLC-required applications. This paper proposes the dedicated resource for exclusive access of individual UE and the shared resource pool for the contention of multiple UEs by adopting the reinforcement learning approach. The objective of this paper is to accomplish the uplink successful rate above 99.9% under certain transmission error probability. The proposed Prediction based Hybrid Resource Allocation (PHRA) scheme allocates the access resource in a heuristic manner by referring to the activity of UEs. The dedicated resource is mainly allocated to the high activity UEs and the initial transmission of UEs with medium activity while the shared resource pool is allocated for the re-transmission of medium activity UEs and low activity UEs by using the reinforcement learning model. The burst traffic model was applied during the exhaustive experiments. And the simulation results show that the proposed scheme achieves higher uplink packet delivery ratio and more effective resource utilization than the other schemes.</p> <p> </p>

GCWCN: 6G-Based Global Coverage Wireless Communication Network Architecture

At present, the world is experiencing a significant transformation from the fifth generation (5G) wireless communication network to the sixth generation (6G) wireless communication network. An important goal is to achieve seamless global coverage of wireless signals. The combination of space network, air network and ground network has been proved to be effective, but the maritime network has not been taken into account. Therefore, how to effectively achieve the global coverage of wireless networks is a challenging topic. This paper first compares the performance of 5G and 6G, and analyzes the possibility of a wireless communication network with global coverage based on 6G technology. After that, we respectively analyze the typical characteristics of space network, air network, terrestrial network and maritime network, and then lead to the key enabling technologies to combine them. Based on the typical characteristics of the above heterogeneous networks and combined with the enabling technologies, we try to propose a 6Gsupported space-air-ground-sea integrated network (SAGSIN) architecture, to achieve seamless global wireless signal coverage. In the end, we rationally analyze a series of challenges and difficulties that may be faced in implementing the architecture.

Intelligent Reflection Enabling Technologies for Integrated and Green Internet-of-Everything Beyond 5G: Communication, Sensing, and Security

Internet-of-Everything (IoE) has gradually been recognized as an integral part of future wireless networks. In IoE, there can be an ultra-massive number of smart devices of various types to be served, imposing multi-dimensional requirements on wireless communication, sensing, and security. In this article, we provide a tutorial overview of the promising intelligent reflection communication (IRC) technologies, including reconfigurable intelligent surface (RIS) and ambient backscatter communication (AmBC), to support the requirements of IoE applications beyond the fifth-generation (5G) wireless communication network. Specifically, we elaborate on the benefits of IRC-assisted IoE in the context of the space-air-ground integrated communications and green communications, which are regarded as key features of supporting future IoE application from society and industries. Furthermore, we envision that the IRC-assisted communication and sensing can mutually benefit each other and articulate multiple ways of enhancing the security in IoE by the IRC. Numerical results help illustrate the importance of the IRC in unfavorable secrecy environments. Finally, open research issues and challenges about the IRC-assisted IoE are presented.

Oblique nanomachining of gallium arsenide explained using AFM experiments and MD simulations

Gallium Arsenide (GaAs) continues to remain a material of significant importance due to being a preferred semiconductor substrate for the growth of quantum dots (QDs) and GaAs-based quantum devices used widely in fifth-generation (5G) wireless communication networks. In this paper, we explored aspects of oblique nanomachining to investigate the improvement in the machining quality as well as to understand plasticity and transport phenomena in GaAs using atomic scale machining experiments and simulations. We studied the influence of the direction vector of the cutting tip (e.g. tip alignment) during the surface generation process in GaAs. We noticed a novel observation that when the AFM tip's cutting edge presented two acute angles (i.e., 30° angles each) between the cutting face and the cutting direction (which can be regarded as an oblique cutting condition), the cutting configuration involved early avalanche of dislocations compared to other tip configurations (e.g., orthogonal cutting). Orthogonal cutting involved the least coefficient of friction but the highest specific cutting energy compared to oblique cutting. High-resolution transmission electron microscopy (HRTEM) examination revealed that the shuffle-set slip on the {1 1 1} slip system due to the 〈1 1 0〉 type dislocation paves the way for plasticity during nanometric cutting of GaAs. Overall, a particular condition of oblique cutting was inferred to be the best for nanofabrication of high-quality wafers using an AFM.

A novel beamforming technique using mmWave antenna arrays for 5G wireless communication networks

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) and beamforming technologies are most likely the integral parts and the key enablers of 5G-and-Beyond (5G-B) wireless communication systems. From that perspective, this manuscript proposes an efficient digital beamforming technique for 5G applications. The suggested approach is formulated based on a reliable sampling technique that effectively samples the interference-plus-noise covariance matrix (IPNCM) to construct an efficient projection matrix (PM). The proposed technique is therefore named interference-plus-noise PM (IPNPM) beamformer. Next, a compact-size rectangular microstrip antenna operating at 49.3 GHz having 1.7 GHz potential bandwidth is designed using CST microwave studio. The designed antenna is subsequently used to model a 32-element uniform linear array (ULA) representing a typical 5G base station. The elements of the modeled ULA are then manually fed with the complex-weights generated from the proposed beamformer to produce high-power beams and deep nulls toward the desired and undesired locations, respectively. To further improve the beamformer performance in terms of minimizing radiating power within undesired regions, an iterative sidelobe level (SLL) suppression technique is developed and integrated with the proposed beamformer. The conducted theoretical analysis justifies that the IPNPM beamformer requires less arithmetic operations than the well-known beamforming approaches. To reveal the benefits of the proposed technique, the array radiation pattern based on the proposed beamformer is plotted and compared with those for the three widely used approaches. An intensive Monte Carlo simulation is also carried out in which the IPNPM beamformer performance is systemically compared with the popular approaches based on the output signal to interference-plus-noise ratio (SINR) criterion. The achieved results demonstrate that the proposed approach is superior and surpasses the rival algorithms in terms of generating deeper nulls, enhanced output SINR, and lower execution time. It is also demonstrated that, unlike the existing SLL reduction techniques, the newly developed SLL suppressor reduces the SLLs of the proposed beamformer to a predefined threshold value (i.e., −24 dB) after few iterations.

Deep neural network based resource allocation in D2D wireless networks

The increased complexity of future 5G wireless communication networks presents a fundamental issue for optimal resource allocation. This continuous, constrained optimal control problem must be solved in real-time since the power allocation should be consistent with the instantly evolving channel state. This paper emphasizes the application of deep learning to develop solutions for radio resource allocation problems in multiple-input multiple-output systems. We introduce a supervised deep neural network model combined with particle swarm optimization to address the issue using heuristic-generated data. We train the model and evaluate its ability to anticipate resource allocation solutions accurately. The simulation result indicates that the trained DNN-based model can deliver the nearoptimal solution.

Energy Efficiency Based Load Balancing Optimization Routing Protocol In 5G Wireless Communication Networks

A significant study area in cloud computing that still requires attention is how to distribute the workload among virtual machines and resources. Main goal of this research is to develop an efficient cloud load balancing approach, improve response time, decrease readiness time, maximise source utilisation, and decrease activity rejection time. This research propose novel technique in load balancing based network optimization using routing protocol for 5G wireless communication networks. the network load balancing has been carried out using cloud based software defined multi-objective optimization routing protocol. then the network security has been enhanced by data classification utilizing deep belief Boltzmann NN. Experimental analysis has been carried out based on load balancing and security data classification in terms of throughput, packet delivery ratio, energy efficiency, latency, accuracy, precision, recall.

CEAR: A cooperation based energy aware reward scheme for next generation green cognitive radio networks

Cognitive Radio (CR) networks are envisioned as a key empowering technology of the fifth-generation (5G) wireless communication networks, which solves the major issues of 5G, like high-speed data transmission, seamless connectivity, and increased demand for mobile data. Another significant characteristic of the 5G network is green communications, as energy consumption from the communication field is predicted to rise remarkably by the year 2030. In this work, we are concerned about energy-related issues and propose a cooperation-based energy-aware reward scheme (CEAR) for next-generation green CR networks. The proposed CEAR scheme is based on the antenna and temporal diversity of the primary users (PUs). For providing the service to the PUs, the users of another network called cognitive users (CUs) work as a cooperative relay node, and, in return, they get more spectrum access opportunities as a reward from the primary network. The CUs with delay-tolerant data packets take a cooperative decision by recognizing the availability and traffic load of PUs, channel state information, and data transmission requirements. We utilize the optimal stopping protocol for solving the decision-making problem and use the backward induction method to obtain the optimal cooperative solution. The simulation results reveal notable enhancements in energy efficiency (EE) of CUs compared with other cooperative schemes. The proposed CEAR scheme is more energy-efficient for ultra-dense network deployment because results show that the CU’s EE, spectral efficiency (SE), and throughput improved with the increase of PUs.

Optimization technique based on cluster head selection algorithm for 5G-enabled IoMT smart healthcare framework for industry

Abstract Internet of medical things (IoMT) communication has become an increasingly important component of 5G wireless communication networks in healthcare as a result of the rapid proliferation of IoMT devices. Under current network architecture, widespread access to IoMT devices causes system overload and low energy efficiency. 5G-based IoMT systems aim to protect healthcare infrastructure and medical device functionality for longer. Therefore, using energy-efficient communication protocols is essential for enhancing QoS in IoMT systems. Several methods have been developed recently to improve IoMT QoS; however, clustering is more popular because it provides energy efficiency for medical applications. The primary drawback of the existing clustering technique is that their communication model does not take into account the chance of packet loss, which results in unreliable communication and drains the energy of medical nodes. In this study, we concentrated on designing a clustering model named Whale optimized weighted fuzzy-based cluster head selection algorithm to facilitate successful communication for IoMT-based systems. The experimental study shows that the proposed strategy performs better in terms of QoS than compared approaches. Inferring from this, the proposed method not only reduces energy consumption levels of 5G-based IoMT systems but also uniformly distributes cluster-head over a network to improve QoS.

Wireless Self-Powered High-Performance Integrated Nanostructured-Gas-Sensor Network for Future Smart Homes

The accelerated evolution of communication platforms including Internet of Things (IoT) and the 5th generation (5G) wireless communication network makes it possible to build intelligent gas sensor networks to monitor the chemical safety of our surroundings and personal health in real time. However, this application scenario requires a very challenging combination of characteristics of gas sensors including small formfactor, low-cost, ultra-low power consumption, superior sensitivity, and high intelligence. Herein, we demonstrate self-powered integrated nanostructured-gas-sensor (SINGOR) systems and a wirelessly connected SINGOR network for the first time. The SINGOR features ultra-high sensitivity to H2, formaldehyde, toluene, and acetone with the record low limits of detection (LOD) of 10 ppb, 2 ppb, 1 ppb and 1 ppb, respectively, obtained under room temperature sensor operation. Therefore, ultra-low power consumption of 68.6 μW at sensor level and 1.71 mW at the system level has been achieved. As the result, an individual SINGOR system can be self-driven by indoor light with a Si solar cell. Furthermore, each SINGOR consists of an array of nanostructured sensors powered by machine learning algorithm and thus a SINGOR has the capability of gas pattern recognition and classification. And multiple SINGOR systems are wirelessly connected together to form a sensor network for smart home application demonstrations. Intriguingly, the sensor network has successfully demonstrated flammable gas leakage detection and alarm function when they are deployed in multiple rooms. And when the SINGOR network is deployed in one single room, it can achieve gas leakage localization with satisfactory precision. These successes open up the possibility of using nanostructured-gas-sensor network for wide range applications including smart home, smart building and future smart city.

Coordinated rate splitting and power allocation in energy-spectral efficiency tradeoff-based multicell networks

In order to support the energy efficiency (EE) and spectral efficiency (SE) in beyond 5G (B5G) wireless communication networks, we propose a flexible tradeoff design of these key performance metrics to obtain simultaneous EE and SE performance enhancement compared to conventional investigations where EE and SE are optimized independently. In this paper, a multi-objective optimization problem (MOOP) in the coordinated rate splitting (Co-RS) based multicell network is formulated to jointly optimize the private power, the coordinated common power and the common rate, by which both network EE and SE can be simultaneously achieved under the consideration of estimated channel error. To make the formulated MOOP tractable, we first transform the independent EE and SE maximization MOOP into a EE and SE tradeoff based single-objective optimization problem (SOOP) equivalently by introducing a flexible weight parameter. Then, fractional optimization is used to handle the multi-ratio objective function, by which an alternative optimization (AO) based sub-optimal solution is proposed to optimize the private power allocation, the joint common power and the spitted rate allocation iteratively with guaranteed convergence. Numerical results have demonstrated that the proposed Co-RS framework is able to achieve flexible EE-SE tradeoff in the multicell network, where better tradeoff has been achieved compared to the related benchmarks.

Fractal geometry-based chakra-shaped microstrip patch antenna array for vehicular communications under 5G environments

ABSTRACT In this paper, fractal geometry-based two-printed antenna structures are proposed for vehicular communication in 5G wireless communication networks. Initially, a 48 × 39 mm2 single element fractal patch antenna is designed and tested which shows dual-band resonance at around 28.1 and 32.5 GHz. In order to improve different antenna characteristic number of fractal radiating elements have been increased to four and constructed an array of patches such that it can be used for multiple applications in the field of vehicular communications. The proposed array model is fabricated on a Rogers RT duroid 5880tm material of size 115 × 82 mm2 and tested using Agilent Technologies N5247A VNA. The measured results confirm the validity of the designed model for intended applications. The proposed array antenna operates at distinct quintuple bands in the frequency range of 25–36 GHz for vehicular communication within the proposed lower fifth generation (5G) channel. The proposed array configuration shows a good peak gain of 6.72 dB within the operating bands.

Development Status of 5G Wireless Communication Technology and Network Security

Development Status of 5G Wireless Communication Technology and Network Security

Recent trends for 5G in vitro exposure systems dosimetry and setup

5G wireless communications networks are being massively studied and developed. This new technology introduces new operating frequencies and employs smart adaptative antennas that allow focusing electromagnetic waves in specific directions. Bioelectromagnetic investigations to assess exposure and interactions with living entities of these new 5G signals are carried out. This contribution aims to present current trends on exposure systems designs for bioelectromagnetic studies with associated computational and experimental dosimetry. The extension to 5G signals of classical exposure systems such as transverse electromagnetic cells (TEM cells) and new innovative designs are discussed.