5G Wireless Communication Research Articles

Dynamic thresholding logarithmic companding for PAPR reduction in MIMO-OFDM systems for 5G wireless communication

In order to advance 5G wireless communication technology, this paper presents a novel method for lowering the Peak-to-Average Power Ratio (PAPR) in Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) systems. It is called the Dynamic Thresholding-based μ-law (DT-μ-law) logarithmic companding technique, and it greatly improves the performance of the system by effectively broadcasting data streams on a single frequency across multiple channels. This technique's ability lies in compressing the amplitude of the transmitting signal beyond a specific threshold value before the parallel-to-serial conversion block in the MIMO-OFDM system. It employs the direct insertion of an offset value into the guard bits, rather than using a step function for the amplitude reduction process, which can reduce additional system complexity and data loss during signal expansion at the receiver end. This technique is more adequately suited for dynamic data rate shifting and interference within a limited threshold value. The dynamic thresholding value (α) is determined by calculating the median and standard deviation of the μ-law companding. This ensures the effective compression of higher amplitude signals at the transmitter, using μ-law log companding. To streamline the decompanding process at the receiver, an offset is introduced, facilitating a smoother transition and reducing complexity. The results demonstrate that the proposed DT- μ-law companding technique significantly improves PAPR reduction while maintaining low Bit Error Rates (BER) and high average spectral power efficiency. This efficiency is optimal when the μ value is maintained within the range of 4–4.5. Furthermore, it was observed that an increase in the μ value corresponds to a decrease in both PAPR value and BER. Specifically, at a signal-to-noise ratio of 15.2 dB, the BER stabilizes at 10−1. Compared to existing methods like the Max-Min Decomp-SLM approach, this technique offers lower Signal-to-Noise Ratio (SNR) values, thereby enhancing spectral efficiency and maximizing data capacity.

Impact of memory on beamforming optimization for DDPG-assisted RIS-multi-user system

Recently, reconfigurable intelligent surfaces (RIS) have garnered considerable attention as indispensable components driving the evolution of future 6G wireless communication systems. This heightened interest is primarily attributed to notable advancements in programmable meta-material fabrication, which enable the creation of highly versatile and adaptable surfaces. These surfaces, often referred to as intelligent reflecting arrays, represent a significant departure from the conventional capabilities of massive multiple-input multiple-output (MIMO) systems, thereby catalyzing the emergence of intelligent radio environments characterized by enhanced flexibility and efficiency. Our study is dedicated to the comprehensive exploration of coordinated design strategies that encompass both the transmission beamforming matrix at the base station and the phase shift matrix at the RIS. Leveraging recent breakthroughs in deep reinforcement learning (DRL), our approach harnesses the power of a Long Short-Term Memory (LSTM) based DRL algorithm. This algorithm orchestrates the joint design process through iterative interactions with the environment, strategically guided by predefined rewards operating within a continuous framework of states and actions. While recent research has underscored the efficacy of LSTM-based architectures in bolstering the learning capacity of reinforcement learning (RL) algorithms and simplifying the search process, our investigation reveals a nuanced insight. Contrary to prevailing trends, we find that the integration of memory into the Deep Deterministic Policy Gradient (DDPG) with LSTM (DDPG-LSTM) algorithm yields unexpected consequences, negatively impacting the system's overall performance. Through preliminary simulation results, we empirically demonstrate the adverse effect of memory on DDPG performance when applied to RIS systems. This finding not only sheds light on the complexities of optimizing 6G wireless communication systems but also underscores the importance of careful algorithmic design and parameter tuning in achieving desired outcomes in emerging technologies.

A high isolation wideband palm tree-shaped printed 4 × 4 MIMO antenna for 5G mm-waves applications

A high-isolation wideband MIMO (Multiple-Input Multiple-Output) antenna, shaped like a palm tree, is designed for 5G wireless communication purposes. The propounded MIMO antenna is formulated on an FR4 substrate, featuring a quartet of ports and adhering to a precise dimension of 45 × 50 × 0.8 mm3. A detailed analysis of the proposed high isolation wideband palm tree shaped MIMO antenna, considering its bandwidth, gain, isolation, radiation patterns, and diversity performance metrics such as diversity gain, channel capacity loss, mean effective gain, and total active reflection coefficient, is presented. The results obtained from simulations and measurements demonstrate good agreement, indicating the antenna’s effectiveness for intended applications. Indeed, an extensive bandwidth covering a range of 11.6 GHz from 23.4 GHz to 35 GHz is achieved by demonstrating superb reflection coefficient. Additionally, the design reports remarkable isolation and a peak gain of 12.4 dB, indicating its ability to enhance signal strength. One of the most notable features of this MIMO antenna is its coverage of multiple 5G bands, making it ideal for use in various geographical regions, including the USA and Canada (27.5–28.35 GHz, 37.0–37.6 GHz, and 37.6–40.0 GHz), China (24.75–27.5 GHz), Korea (28–39 GHz), Japan (27–29.5 GHz) and Sweden (26 GHz).

Data and Knowledge Dual-Driven Automatic Modulation Classification for 6G Wireless Communications

Data and Knowledge Dual-Driven Automatic Modulation Classification for 6G Wireless Communications

Integrated beamforming and trajectory optimization algorithm for RIS-assisted UAV system

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have garnered considerable research interest in the fields of 5G and 6G wireless communication due to their remarkable flexibility and cost-effectiveness. However, the inherent openness of wireless communication environments renders these technologies vulnerable to eavesdropping. This paper presents a penalty-based successive convex approximation algorithm and a minorize–maximization algorithm to optimize the transmission beamforming vector, RIS beamforming vector, and UAV–RIS trajectory. The objective of this study was to enhance the physical layer security performance of wireless communication systems using UAVs and RISs. Our simulation results demonstrate that the proposed technique achieves a higher security transmission rate compared to existing techniques.

Spectrum-efficient user grouping and resource allocation based on deep reinforcement learning for mmWave massive MIMO-NOMA systems

Millimeter-wave (mmWave) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is proven to be a primary technique for sixth-generation (6G) wireless communication networks. However, the great increase in users and antennas brings challenges for interference suppression and resource allocation for mmWave massive MIMO-NOMA systems. This study proposes a spectrum-efficient and fast convergence deep reinforcement learning (DRL)-based resource allocation framework to optimize user grouping and allocation of subchannel and power. First, an enhanced K-means grouping algorithm is proposed to reduce the multi-user interference and accelerate the convergence. Then, a dueling deep Q-network (DQN) structure is proposed to perform subchannel allocation, which further improves the convergence speed. Moreover, a deep deterministic policy gradient (DDPG)-based power resource allocation algorithm is designed to avoid the performance loss caused by power quantization and improve the system’s achievable sum-rate. The simulation results demonstrate that our proposed scheme outperforms other neural network-based algorithms in terms of convergence performance, and can achieve higher system capacity compared with the greedy algorithm, the random algorithm, the RNN algorithm, and the DoubleDQN algorithm.

Massive MIMO Precoding and Beamforming Techniques for Future Generation Communication System

Massive MIMO, which was initially suggested, is a critical technique for meeting the rapidly growing data demand of 5G networks. MIMO technology may provide the propagation channel with a greater degree of flexibility in terms of multiplexing gain or diversity gain. New disruptive technologies have been developed in response to the large increase in data traffic that the forthcoming 5G mobile communication system. This is a critical research area for 5G wireless communication since massive MIMO has the possible system to significantly boost throughput and data transmission charges, as well as residual energy and energy efficiency. As a result of three years of fast development in massive MIMO technology, spectrum efficiency has been enhanced to an unprecedented level by the combination of growing antennas and adopting an extremely duplex communication mechanism. It is achieved by modeling the interference signal first and then constructing an exactly opposite signal added to a pre-distorted signal to cancel it. Additionally, this article discusses precoding methods and energy efficiency.

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.

Cryogenic and ultrasonic-assisted micro-drilling of printed circuit boards using high-frequency-amplitude spindle

Printed circuit boards (PCBs) are key components in computers, 5G, 6G, and other wireless communication devices. On the other hand, the manufacture of micro-holes in PCBs faces severe challenges. Conventional micro-drilling methods often encounter challenges such as poor surface quality and limited tool life. To overcome these issues, a novel micro-drilling method called cryogenic and ultrasonic-assisted micro-drilling (CUAMD) has been developed. This study compared four drilling methods to investigate the benefits of cryogenic and ultrasonic-assisted drilling in hybrid machining processes: conventional micro-drilling (CMD), cryogenic-assisted micro-drilling (CAMD), ultrasonic-assisted micro-drilling (UAMD), and cryogenic and ultrasonic-assisted micro-drilling (CUAMD). The experimental results unequivocally indicate that the utilization of cryogenic-assisted drilling leads to a significant reduction in cutting heat during the machining process. This not only contributes to an improvement in machining accuracy but also exerts a positive influence on tool life and surface morphology. Ultrasonic-assisted drilling separates chips into smaller volumes through high-frequency-vibration, effectively reducing the cutting forces. The high frequency of 38.25 kHz and high amplitude vibration of 5.16 μm of the ultrasonic spindle induced vibrations on the drill bit. Liquid nitrogen with a pressure and temperature of 28 kPa and − 196 °C, respectively, was used in the cryogenic-assisted method. The superior machining performance of the CUAMD approach was evaluated by comparing it to various methods, including cutting forces, tool flank wear, entrance/exit hole morphology, and chip formation. The CUAMD method avoids chip entanglement, suppresses burrs, and reduces the maximum cutting force, maximum torque, and exit hole damage factor Fd by 47.7 %, 56.1 %, and 42.5 %, respectively, compared with CMD.

Ubiquitous learning models for 5G communication network utility maximization through utility-based service function chain deployment

The main problem of deploying service function chains in virtualized 5G networks is dealt with in wireless 5G communications and effective ubiquitous learning models. The aim is to ensure differentiated network performance for a wide range of services while maximizing the collaborative revenue of infrastructure operators and wireless virtual operators. To achieve this, a utility-based service function chain deployment strategy is introduced, tailored to the specific characteristics of Ubiquitous Learning based 5G-RAN-Architecture. Consideration is given to the virtual operator's maximum tolerable end-to-end latency, minimum service rate requirements, and the infrastructure operator's constraints on computing and link resources. It also looks at how different deployment scenarios for service function chains affect network performance and creates a utility model using a business framework. The ultimate objective is to optimize the collective revenue of infrastructure operators and virtual operators. The approach leverages genetic algorithms and Matlab's Linprog function for iterative problem-solving. The graph clearly shows that the SFC deployment algorithm in this study uses less infrastructure resources for Front Haul links. This implies that the algorithm successfully reduces the load on Front Haul lines, which in turn lowers the cost of SFC deployment and makes it easier to deploy more SFCs across the infrastructure. This work contributes to the evolution of 5G wireless communications and its seamless integration with ubiquitous learning models.

Enabling intrinsic intelligence, ubiquitous learning and blockchain empowerment for trust and reliability in 6G network evolution

This study investigates the 6th Generation (6G) wireless communication network as a vital component of Ubiquitous Learning, which makes it possible to build trustworthy relationships and accurate intelligence in a variety of contexts. The important significance of blockchain technology is recognized, and its potential to improve the performance of the 6G network is investigated. Blockchain technology powers Ubiquitous Learning as a decentralized tool. But in Ubiquitous Learning contexts, problems arise when varied communication settings, information asymmetry, and resource limits confront wireless-connected devices functioning as blockchain consensus nodes. To solve these difficulties, which are critical for Ubiquitous Learning, a blockchain sharding and incentive system is presented. The purpose of this technique is to improve the learning process in the 6G network by cultivating reliable intelligence. A sharding architecture that integrates a contract theory-based incentive mechanism and a realistic Byzantine fault tolerance mechanism, particularly tailored for Ubiquitous Learning settings, is described. This model analyses the useful Byzantine consensus mechanism and block broadcast characteristics in wireless networks. This study finds the best contract combination for both completely symmetric and asymmetric information scenarios, optimizing block rewards for customers that seek blockchain services.

Design of an ultra wideband THz reflectarray antenna based on orbital angular momentum

A Reflectarray antenna (RA) that can generate orbital angular momentum vortex waves in the THz band was proposed. First of all, a reflective phase-shifting element composed of a notched double-shaped ring and a single Matal structure was designed to form a radiation patch. The reflective phase-shifting element has the advantages of good linearity, high smoothness, and a large phase shift range, which is suitable for the subsequent production of reflective array antennas. Then, the phase distribution of the reflected wave of the OAM vortex wave carrying mode l=+1、+2、+3、+4 is given, and the size of each element is determined based on the reflected phase of each element. Using the proposed element, an OAM reflectarray antenna operating within the waveguide standard WR-5 (120GHz–220GHz) is designed. The optimal frequency band for antenna operation in mode l=+1 is 150GHz–190 GHz, and the maximum gain of the reflector array antenna is 19.4 dBi, and the minimum divergence angle of the antenna array is 5.2°, which has good directivity and beam radiation ability. The spiral structure of the OAM beam space generated by the RA in this paper is clearly visible, proving that the implementation of OAM vortex waves in the THz frequency band is feasible. This design also provides a new method for generating OAM vortex waves in wireless and microwave wireless communications. The proposed RA is suitable for 6G wireless communication systems.

Linear feedback coding scheme for multiple-access fading channels with degraded message sets.

Channel coding technology plays an important role in wireless communication systems, and it serves as a crucial mechanism to reduce interference during the transmission process. As the fifth-generation (5G) and sixth-generation (6G) wireless communication systems rapidly advance, requirements of the users on the quality and security of wireless service are increasing. To solve these problems, it calls for us to explore the new channel coding technologies. In this paper, a linear feedback coding scheme for fading multiple-access channels with degraded message sets (FMAC-DMS) is proposed. In this scheme, the transmitting beamforming and channel splitting are used to transform the channel with complex signals into scalar equivalent sub-channels. Then, the extended Schalkwijk-Kailath coding scheme (SK) is further applied to each sub-channel. The channel capacity, finite blocklength (FBL) sum-rate and FBL secrecy achievable sum-rate of the FMAC-DMS in single-input single-output (SISO) and multi-input single-output (MISO) cases are derived. Finally, we show that the proposed scheme not only provides a FBL coding solution but also guarantees physical layer security(PLS). The numerical and simulation results show the effectiveness of the proposed scheme as a channel coding solution. The study of this paper provides a new method to construct a practical FBL scheme for the FMAC-DMS.

The Evolution of Mobile Communication: A Comprehensive Survey on 5G Technology

This comprehensive survey meticulously navigates the evolution of mobile communication, focusing on the revolutionary strides introduced by 5G technology. The study elucidates primary objectives, research methodology, pivotal findings, and implications inherent in the trajectory of mobile communication. It aims for a detailed examination of the technological landscape, outlining features that distinguish 5G from its predecessors. The research methodology involves an exhaustive literature review, culminating in a comprehensive overview of 5G technology. Key findings highlight 5G's transformative capacities, spanning unparalleled data transfer speeds, low-latency communication, and applications like IoT, augmented reality, and autonomous vehicles. The study underscores 5G's far-reaching impact across various sectors. Serving as both a retrospective analysis and a valuable resource, the survey guides researchers, industry professionals, and policymakers through the intricate landscape of 5G technology. Addressing the imperative of accommodating increasing mobile data traffic, the 5G network strives for improved spectrum utilization, energy efficiency, and QoS parameters. Notable innovations include Massive MIMO, promising high spectral efficiency. The focus on efficacy, accuracy, and channel estimation techniques is evident. The article explores specific requirements of 5G wireless communication systems, addressing challenges faced by earlier mobile generations. The advent of 5G emerges as a pivotal solution, aiming for a 1000-fold capacity gain and assuring quality-of-service, higher spectral efficiency, ultra-reliable communication, and facilitating massive machinetype communication. Challenges in traffic load and resource consumption necessitate innovative approaches. The research reviews emerging wireless technologies, including an analysis of Massive MIMO as an efficient solution. The paper promises to be a valuable resource, contributing to the seamless development of state-of-the-art 5G and B5G networks, recognizing the importance of continued research and innovation in the dynamic landscape of mobile communication technologies.

A broadband dual-polarized antenna for 5G wireless communication systems

This article presents a broadband dual-polarized antenna for the 5G wireless communication technology. A ring resonator is chosen with the purpose of enhancing the axial ratio (AR) and impedance bandwidth. Partial ground plane is chosen with a slot placed beneath the feedline. The dimensions of the antenna are 50 mm × 50 mm × 1.6 mm, and it is fabricated on a dielectric substrate made of FR4 material. The variation of antenna polarization is achieved through the function of PIN diode. When the diode is in the ON state, the antenna functions as a wideband antenna that exhibits right hand circular polarization (RHCP). The frequency range covered by this system spans from 4.5 GHz to 7.5 GHz. Conversely, it provides linear polarization (LP) within a frequency range that spans from 4.3 GHz to 5.3 GHz. The metrics of the prototype antenna are measured and tested. The CST full wave simulator is utilized for conducting simulations.

A Survey of PAPR Techniques Based on Machine Learning.

Orthogonal Frequency Division Multiplexing (OFDM) is the modulation technology used in Fourth Generation (4G) and Fifth Generation (5G) wireless communication systems, and it will likely be essential to Sixth Generation (6G) wireless communication systems. However, OFDM introduces a high Peak to Average Power Ratio (PAPR) in the time domain due to constructive interference among multiple subcarriers, increasing the complexity and cost of the amplifiers and, consequently, the cost and complexity of 6G networks. Therefore, the development of new solutions to reduce the PAPR in OFDM systems is crucial to 6G networks. The application of Machine Learning (ML) has emerged as a promising avenue for tackling PAPR issues. Along this line, this paper presents a comprehensive review of PAPR optimization techniques with a focus on ML approaches. From this survey, it becomes clear that ML solutions offer customized optimization, effective search space navigation, and real-time adaptability. In light of the demands of evolving 6G networks, integration of ML is a necessity to propel advancements and meet increasing prerequisites. This integration not only presents possibilities for PAPR reduction but also calls for continued exploration to harness its potential and ensure efficient and reliable communication within 6G networks.

Metaheuristic Algorithms for 6G wireless communications: Recent advances and applications

The widespread distribution of intelligent applications and devices, coupled with the vast array of mobile data, technologies, and architectures within Sixth Generation (6G) networks, highlights the importance of optimization strategies. Concurrently, state-of-the-art Metaheuristic Algorithms (MHAs) have emerged as a promising optimization approach for wireless networks, bridging game theory and convex optimization domains. Falling within the domain of Artificial Intelligence (AI), MHAs draw their inspiration from Evolutionary Algorithms (EAs), which are rooted in the principle of natural selection, the principles of Swarm Intelligence (SI), and Trajectory-based Algorithms (TAs). In recent times, MHAs have been implemented in the 6G network. These simple solutions with limited capabilities have proven effective in solving complex, challenging and high-dimensional problems. Nevertheless, the literature has yet to study the MHAs comprehensively, especially within 6G networks. The main objective of this study is to investigate how to merge the 6G and MHAs domains. We begin by giving a comprehensive overview of MHAs, from fundamental to popular optimization methods. Then, we examine the utilization of MHAs to address the challenges in 6G, such as network security, resource allocation, spectrum management, edge computing, wireless caching, and others. Finally, we identify the current literature’s limitations and suggest avenues for further research directions.

Application research of 5G/6G cellular network in intelligent Transportation Internet of Things

This study paper's primary goal is to examine the developments and use cases of 5G/6G wireless communication technologies and the Internet of Things (IoT) in intelligent transportation. This paper will delve into the foundational aspects of IoT technology and its integration into intelligent transportation systems (ITS). It will conduct in-depth research on 5G and 6G wireless communication technologies and the critical IoT technologies that play a pivotal role in intelligent transportation. By analyzing these cutting-edge technologies, the paper highlights their significance and potential impact on transportation systems' efficiency, safety, and sustainability. This research will identify and elucidate the major applications of 5G and 6G wireless communication technologies in intelligent transportation. By comprehensively addressing these objectives, the essay will provide readers with an insightful understanding of the fundamental IoT technology, important 5G/6G wireless communication technologies, and their diverse application domains in intelligent transportation and smart cities. Through this exploration, the study aims to shed light on the transformative potential of these technologies in shaping the future of transportation systems and urban living.

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.

6G Wireless Communication Networks

This paper presents a comprehensive survey of security needs, applications, and current challenges and threats for 6G wireless communication networks. 6G technology has brought much attention to business entities and academia recently. The development of a relevant 6G wireless technology to meet the security issues and technical challenges of 5G networks as well as major system upgrades over previous wireless technologies are discussed. The importance of the comparative study is estimated for security issues and communication of devices like wireless devices and focuses on creative innovations that would offer the progression changes required for empowering 6G. First, the authors present the evolution and security threats landscape of wireless communication networks. Then they explore the potential applications of 6G networking technologies. Finally, this paper concludes with a detailed discussion of security issues, research challenges, and possible solutions in 6G that enable critical technologies.