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The deployment of fifth-generation mobile network (5G), beyond 5G and sixth-generation mobile network platforms encounters challenges of blockage, interference, and path loss in radio mobile environments. Metasurfaces provide a promising solution to address these limitations. In this paper, we present a methodology for developing ultrathin flexible metasurface-based frequency selective surfaces (FSSs). Our approach combines thermal evaporation for metallic thin films with a macroscopic metasurface mask (something analogous to screen-printing but using thermal evaporation instead of inks). As a proof of concept, we fabricate a sub-6 GHz metasurface-based FSS using gold deposition on a flexible polyethylene terephthalate substrate. Experimental results are validated through numerical full-wave simulations using COMSOL Multiphysics and equivalent-circuit model simulations. The metasurface operates within the primary frequency band utilized in 5G networks (3–5 GHz), indicating its potential applicability across a wide range of flexible, conformal, and wearable devices. The fabricated FSS can be installed on surfaces of any shape, such as flat or curved windows, as well as on walls or other external surfaces. This methodology offers practical solutions for wireless communications and enhancing signal transmission in diverse environments. [ABSTRACT FROM AUTHOR]

Summary: In this study, we propose an inverse design approach based on a neural network for a novel multiport power divider (MP‐PD) with complex geometry. The inverse design approach is obtaining geometry from the desired physical performance to address the challenge of conventional methods. We develop a hybrid neural network model for this inverse design. The backbone architecture incorporates a bidirectional long short‐term memory module, a multihead self‐attention module, and convolutional modules. This hybrid neural network is employed to capture the feature of physical performance and learn the relationship between the geometric structure of the proposed MP‐PD and its corresponding physical performance. Consider the design of the power divider as an end‐to‐end methodology that directly maps design requirements to optimal geometric parameters. The neural network transfers the designed process into multiple‐input‐multiple‐output. We adopt the network model to successfully predict 20 geometric parameters of MP‐PDs for two distinct operating frequencies. The two operating frequencies are those utilized in real engineering applications, which are 3.5 GHz in the 5G band and 2.45 GHz in the trackside communication band. The predicted MP‐PD improves the return loss and bandwidth by 8.05 dB and 0.25 GHz, respectively, over the desired performance. The experiments and comparisons demonstrate the effectiveness and accuracy of our inverse design approach. The efficiency and flexibility of design are also significantly improved by the hybrid neural network model. [ABSTRACT FROM AUTHOR]

Summary: In order to make networks more adaptable and flexible, software‐defined networking (SDN) is an architecture that abstracts the many, easily distinct layers of a network. By enabling businesses and service providers to react swiftly to shifting business requirements, SDN aims to improve network control. SDN has become an important framework for Internet of Things (IoT) and 5G. Despite recent research endeavors focused on pinpointing constraints within SDN design components, various security attacks persist, including man‐in‐the‐middle attacks, host hijacking, ARP poisoning, and saturation attacks. Overcoming these limitations poses a challenge, necessitating robust security techniques to detect and counteract such attacks in SDN environments. This study is dedicated to developing a method for detecting and mitigating control plane attacks within Software Defined Network Environments utilizing Deep Learning Algorithms. The study presents a deep‐learning‐based approach to identifying malicious hosts within SDN networks, thus thwarting unauthorized access to the controller. Experimental results demonstrate the effectiveness of the proposed model in host classification, exhibiting high accuracy and performance compared to alternative approaches. [ABSTRACT FROM AUTHOR]

Summary: The rapid evolution of user equipment (UE) and 5G networks drives significant transformations, bringing technology closer to end‐users. Managing resources in densely crowded areas such as airports, train stations, and bus terminals poses challenges due to diverse user demands. Integrating mobile edge computing (MEC) and network function virtualization (NFV) becomes vital when the service provider's (SP) primary goal is maximizing profitability while maintaining service level agreement (SLA). Considering these challenges, our study addresses an online resource allocation problem in an MEC network where computing resources are limited, and the SP aims to boost profit by securely admitting more UE requests at each time slot. Each UE request arrival rate is unknown, and the requirement is specific resources with minimum cost and delay. The optimization problem objective is achieved by allocating resources to requests at the MEC network in appropriate cloudlets, utilizing abandoned instances, reutilizing idle and soft slice instances to shorten delay and reduce costs, and immediately scaling inappropriate instances, thus minimizing the instantiation of new instances. This paper proposes a deep reinforcement learning (DRL) method for request prediction and resource allocation to mitigate unnecessary resource waste. Simulation results demonstrate that the proposed approach effectively accepts network slice requests to maximize profit by leveraging resource availability, reutilizing instantiated resources, and upholding goodwill and SLA. Through extensive simulations, we show that our proposed DRL‐based approach outperforms other state‐of‐the‐art techniques, namely, MaxSR, DQN, and DDPG, by 76%, 33%, and 23%, respectively. [ABSTRACT FROM AUTHOR]

ABSTRACT Mobile edge computing (MEC) facilitates storage, cloud computing, and analysis capabilities near to the users in 5G communication systems. MEC and deep learning (DL) are combined in 5G networks to enable automated network management that provides resource allocation (RA), energy efficiency (EE), and adaptive security, thereby reducing computational costs and enhancing user services. A hybrid quantum‐classical convolutional neural network (HQCCNN) with simplicial attention network (SAN) is presented in the study that allocates appropriate resources for various users in the network. First, the green anaconda optimization (GAO) algorithm is used to optimize the objective function for effective RA. Consequently, the neural network receives the optimized objective functions to allocate resources. In the study, the suggested HQCCNN‐GAO model assesses the degree of need for every user and, based on those needs, allots resources to every user in the 5G network while preserving higher throughput and EE. Throughput, latency, mean square errors, processing time, bit error rates, and EE are used to measure the proposed model's efficiency. A few of the RA models that are now in use are contrasted with the outcomes of the suggested method. From the obtained outcomes, it is noticed that the suggested model provides a low latency of 0.08 s and a high throughput of 790 kbps for a range of network users. [ABSTRACT FROM AUTHOR]

ABSTRACT The potential benefits of 5G networks, such as faster data speeds and improved user experiences, come with a critical challenge—efficiently preserving energy in base stations (BSs). Addressing this challenge is crucial, necessitating a focus on maximizing the energy efficiency of these stations. BSs play a vital role in providing coverage and capacity by using different frequency bands adapting to diverse communication needs. The choice of a specific frequency band can impact network performance, data rates, coverage range, and signal propagation. This study emphasizes the crucial challenge of preserving energy in 5G BSs and underscores the significance of strategic frequency band selection for optimizing energy efficiency and network performance. For this, stochastic models are introduced for BSs, specifically the Markov and semi‐Markov models, which help choose frequency bands based on system traffic. Closed‐form solutions for steady‐state system size probabilities in these models are derived. A sensitivity analysis is conducted to assess power consumption in various scenarios. Furthermore, a comparison between analytical findings and simulation outcomes regarding power consumption is presented. The convergence of results in both methodologies emphasizes a significant level of agreement; that is, the introduced stochastic models provide insights and are validated by a close alignment between analytical and discrete‐event simulation outcomes. We have shown the behavior of power consumption with respect to three different distributions named deterministic, exponential, and hypo‐exponential. This research highlights the importance of strategic frequency band selection for 5G BSs to optimize energy efficiency and meet the demands of evolving communication networks. [ABSTRACT FROM AUTHOR]

Nonorthogonal multiple access (NOMA) waveforms need to be tested for their performance and effectiveness using partial transmit sequence (PTS) techniques and peak-to-average power ratio (PAPR) analysis. This is an important part of the advanced radio framework. This paper examines the PAPR features of NOMA systems using PTS with different sub-carrier configurations (64, 256, and 512). We examine BER, PSD, and PAPR distributions by modeling NOMA waveforms with PTS to understand the impact of different sub-carrier counts on signal complexity and efficiency. The findings shed light on how sub-carrier quantity affects PAPR statistics and offer guidance on the best way to design NOMA waveforms for improved spectral efficiency and reduced signal distortion. The simulation results show that by lowering the PAPR while maintaining the BER performance, the suggested system performs better than the traditional PAPR algorithm. [ABSTRACT FROM AUTHOR]

Vehicle-to-everything (V2X) communication is essential in 5G and upcoming networks as it enables seamless interaction between vehicles and infrastructure, ensuring the reliable transmission of critical and time-sensitive data. Challenges like unstable communication in highly mobile vehicular networks, limited channel state information, high transmission overhead, and significant communication costs hinder vehicle-to-vehicle (V2V) communication. To tackle these issues, a unified approach utilizing distributed deep reinforcement learning is proposed to enhance the overall network performance while meeting the quality of service (QoS), latency, and rate requirements. Recognizing the complexity of this NP-hard, non-convex problem, a machine learning framework based on the Markov decision process (MDP) is adopted for a robust strategy. This framework facilitates the formulation of a reward function and the selection of optimal actions with certainty. Furthermore, a spectrum-based allocation framework employing multi-agent deep reinforcement learning (MADRL) is confidently introduced. The deep deterministic policy gradient (DDPG) within this framework enables the exchange of historical data globally during the primary learning phase, effectively removing the need for signal interaction and manual intervention in optimizing system efficiency. The data transmission policy follows an augmented online policy scheme, known as the proximal online policy scheme (POPS), which confidently reduces the computational complexity during the learning process. The complexity is marginally adjusted using the clipping substitute technique with assurance in the learning phase. Simulation results validate that the proposed method outperforms existing decentralized systems in achieving a higher average data transmission rate and ensuring quality of service (QoS) satisfaction confidently. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANET) are revolutionizing intelligent transportation systems (ITS), and as a result, research on their security is becoming increasingly important. As the primary security concern for VANET, authentication security is still quite difficult to achieve. Consequently, the prior knowledge of VANET is covered in this survey before outlining the primary security concerns. To set itself apart from previous surveys, this study suggests security properties and challenges among VANET. Next, the essential and significant features of a secure VANET system, such as confidentiality and integrity of data, and the availability of network systems have been reported, the authenticity of nodes and messages, and the refusal to deny data once it has been transmitted is detailed. Later, it outlined the requirement of the ITS which makes the survey unique. More importantly, the report on the most recent developments in VANET concentrates on the authentication schemes that have been proposed recently. The security features and authentication resistance against attacks, along with the overhead and efficiency of these schemes, are thoroughly examined and contrasted. A detailed analysis of V2V, V2I, and V2X authentication is been reported. Various cryptographic schemes have been discussed along with some advanced techniques such as Blockchain and hybrid schemes. An overview of the integration of 5G/6G networks is documented. Applications of VANET have been discussed in detail along with some open challenges in VANET. In summary, this work reviews a few lessons learned and explores different possibilities for future research. [ABSTRACT FROM AUTHOR]

The study aims to investigate how much young Hungarian city dwellers would accept potential urban environment changes triggered by AVs. In the full-profile conjoint analysis, 1011 young people expressed their preferences through the evaluation of 18 printed cards in multiple steps during in-person data collection. As a result, the ‘city of the future’ most preferred by youngsters can be described with the help of the partial utility of attribute levels and the constant value. Their ideal city envisions roads exclusively for AVs with new lane allocations, no drones, pedestrian-only sidewalks equipped with sensors, and frequent 5G system to boost connectivity. [ABSTRACT FROM AUTHOR]

Summary: The future wireless communication networks (beyond 5G) are intended to provide seamless connectivity, higher data rates, and lower latency. The intelligent reflecting surface (IRS) promises extended network coverage. The non‐orthogonal multiple access (NOMA) technique provides higher spectral efficiency and supports a larger number of users. In general, the cell‐edge users far from the base station are affected by severe fading, which may result in an outage. To overcome this issue, we propose an IRS‐aided device‐to‐device (D2D)‐enabled two cells coordinated NOMA system in which a common cell‐edge user acts as D2D receiver. The cell‐edge user receives the signals from near users, IRSs, and both base stations to achieve better diversity. We analyze the outage probability of cell‐edge user, while all the fading channels are assumed Nakagami‐ m and obtain a closed‐form expression at higher signal‐to‐noise ratios. We develop an algorithm to find the ergodic capacity of the cell‐edge user. We validate the mathematical expressions with simulation results and see a substantial improvement in outage performance over the existing systems. [ABSTRACT FROM AUTHOR]

Summary: A simple planar wideband four‐port MIMO antenna of size 2.75휆l × 2.75휆l × 0.04휆l (휆l is the wavelength when fl is 24.26 GHz) with high isolation that offers the 24.26 to 30.2 GHz bandwidth covering the 5G FR‐2 spectrums is proposed in this work. Total fractional bandwidth of 21.8% has been achieved that covers the n257 (26.5 to 29.5 GHz), n258 (24.25 to 27.5 GHz), and n261 (27.5 to 28.35 GHz) 5G spectrums. Popular rectangular radiator‐based 4‐port MIMO antenna with symmetric stubs loaded parallel to feed line and tapered semi‐circular cut in the lower corners of patch is incorporated that exhibits an excellent isolation that is below −27 dB through the entire spectrum and the extreme realized separation is −40 dB. The projected MIMO radiator is premeditated on RT/duroid 5880 of 0.508 mm depth and permittivity (εr) of 2.2. The maximum radiation efficiency and gain of the proposed antenna are 93.07% and 6.48 dBi, respectively. The MIMO antenna parameters such as envelope correlation coefficient (ECC) were achieved lesser than 0.003, diversity gain (DG) was achieved around 10 dB, channel capacity loss (CCL) was achieved lower than 0.15 bits/s/Hz, and mean effective gain (MEG) was achieved between −3.6 dB and −3 dB throughout the impedance band, which exhibit the excellent MIMO properties, and thus, the projected antenna is appropriate for 5G/beyond 5G wireless technologies. [ABSTRACT FROM AUTHOR]

Summary: The present communication presents a novel compact microstrip line feed dual‐band four‐element MIMO (multiple input multiple output) antenna for 5G sub‐6GHz N79 and WiFi‐6E bands with a defined size of 48 × 48 × 1.6 mm3. The FR4 substrate used in the construction of the designated antenna has a thickness of 1.6 mm, a relative permittivity of 4.4, and loss tangent values of 0.02. The proposed MIMO architecture consists of four equilateral triangle‐shaped antenna elements on top and ground with inverted L‐shaped stubs on the substrate's bottom layer. The ground structure of the antenna is a connected ground and is formed by defected ground parts which are connected with inverted L‐shaped stubs. The orthogonal structure of the MIMO antenna elements, with four armed square stubs among them, enhanced the isolation between surrounding antennas. The proposed four‐port MIMO antenna is built and tested in to validate design, scattering, and radiation performance, as well as MIMO diversity features. The designed antenna has more than 15 dB isolation and operates at 4.75 GHz from 4.45 to 5.2 GHz (5G sub‐6 GHz N79 band) and 6.3 GHz from 5.95 to 7.15 GHz (WiFi‐6E band). Furthermore, the design provides omnidirectional patterns, high gain (6.5dBi) and efficiency (>95%), a low envelope correlation coefficient (<0.00001) and acceptable diversity gain (10 dB), a good total active refraction coefficient (−10 dB) and low channel capacity loss (0.03 bit/s/Hz), and a considerable mean effective gain (MEG = −3 dB) and MEG ratio (0 dB). Because the simulated and tested results match so well, the antenna is a good candidate for 5G sub‐6 GHz N79 band operations. [ABSTRACT FROM AUTHOR]

An antenna array having a size of 45 40 cm2 (5.7 5 2) and consisting of four pairs of printed U-shaped dipoles positioned above a metal reflector, for 5G Sub-6 GHz base station applications, is designed and tested. The array consists of eight excitation ports, one port for each dipole. Four parasitic square patches are etched on the bottom side of the dipole arms for producing radiations in 2.2 GHz and 3.8 GHz bands. The size of the reflector and height of the dipoles are optimized in order to enhance antenna gain up to 11.5 dB at 2.2 GHz and 14.5 dB at 3.8 GHz. Beam steering up to 20 is achieved, using phase shifted simultaneous excitation of different ports. The proposed antenna array not only fulfills 5G base station requirements but is also simple and compact as it only requires eight ports to achieve dual-band, high-gain and beam steering operation in a single design. It also offers a unique feature of dual-sector coverage per panel, which results in an increased coverage capacity of the base station without increasing the system resources. [ABSTRACT FROM AUTHOR]

ABSTRACT A compact eight‐port MIMO structure containing planar inverted‐F antennas (PIFAs) as radiating elements is designed for 5G cellular communication applications within the sub‐6 GHz band. The proposed antenna consists of four radiating elements that are placed at four corners on the same plane of geometry. The overall dimension of the ground plane is considered to be 59 mm × 120 mm to make it convenient for modern smart mobile handsets. Each radiating element has 2‐feeding plates, which are situated perpendicular to each other in orientation to make the arrangement cross‐polarized, which in turn exploits polarization diversity as well as spatial diversity among the combination of different radiating elements. The patches are structured by embedding rectangular slots in a meandered shape, and the ground plane is configured by introducing rectangular slots and narrow metallic strips. The prototype of the prescribed MIMO PIFA model has been fabricated and experimentally tested. It shows multi‐band operations (3.27–3.37, 4.16–4.35, 4.93–5.06, and 5.47–5.68 GHz) within the sub‐6 GHz spectrum. The isolation among various patches is observed within the range of −35 dB to −42 dB. The peak gain reaches around 8.1 dBi. The designed MIMO PIFA exhibits superior diversity performance by producing ECC ≤ 0.0006, DG ≈ 10 dB, CCL < 0.20 bits/Hz/s, and MEG ≤ −3 dB. Furthermore, the specific absorption rate (SAR) is evaluated according to the standard values of fat, skin, and muscle at 3.3 GHz. The prescribed model maintains acceptable SAR values for 1‐g and 10‐g tissues, which makes it suitable for smartphone applications. [ABSTRACT FROM AUTHOR]

The success of future 5G/6G communication relies heavily on efficient and low-latency wireless cellular device-to-device communication, which enhances network spectrum, energy efficiency, and data transfer rate. To achieve this, advanced technologies such as deep learning and swarm optimization are utilized to improve energy-efficient communication while considering latency. The fifth and beyond-generation networks require intelligent and efficient technologies to control and transfer data. Unmanned Aerial Vehicles (UAVs) are employed to establish high-transfer-rate data communication between devices using minimal energy consumption. This study also focuses on integrating UAV-based D2D communication with other advanced technologies, highlighting the significance of integration with possible solutions to enhance network performance. The development of 5G/6G communication technologies aims to meet the increasing demands of wireless networks, such as faster data rates, lower latency, improved reliability, and more excellent device and application support. However, the current network advantages need to be enhanced to meet the needs of the upcoming digital environment. Thus, to address these challenges, we propose a novel approach that utilizes optimized deep learning models in three ways for UAV-based device-to-device communication: 1. Improved Hybrid Particle Swarm Optimization and Effective K-means clustering approach (IHPSO-K) 2. Hybrid Fuzzy C means (HFCM) approach, and 3—greedy algorithm to overcome the restrictions faced by UAVs in meeting the latest technological requirements. With the above considering features, we use two methods to execute the efficient performance of the proposed algorithm, namely the device-centric approach and the network-centric approach. Combining these techniques can help achieve a more accurate and efficient clustering of data points in UAV-based D2D communication. This can lead to improved performance in terms of throughput, latency, and energy consumption, which are critical factors in such communication scenarios. [ABSTRACT FROM AUTHOR]

The network design's flexibility and adaptability significantly impact the quality of service (QoS) in 5G and 6G communications. The network's service quality is simultaneously expanded because of the potential uses for the 5G vehicle network (QoS). To this aim, one of the strictest requirements for the vehicle environment has evolved into a safety–critical real-time system. Although several mathematical and computational techniques have historically been employed to improve the distribution of resources, the nonconvexity of optimization problems poses difficulties. This article informs readers how 5G vehicular network resources can be allocated to strengthen network communication using optimization and cutting-edge deep learning techniques. This paper proposes UAV path planning based on QoS constraints for minimizing the transmission power among UAVs and BS. It uses the Nonlinear Optimization method for optimizing the path and uses Multi-Agent Deep Reinforcement Learning to improve connection and enhance latency. The proposed method improves the quality of services for 5G and 6G networks and efficiently optimizes resource management. [ABSTRACT FROM AUTHOR]

Nowadays, due to the rapid growth of network service requests and popularity of IoT device deployment, wireless networks currently suffer from huge traffic produced by these requests and devices. On the other hand, stand-alone 5G networks will soon be available in the near future. People expect to have high quality streaming mechanisms to enrich and color their everyday lives. In a 5G network, data transmission between base station (BS) and user equipment (UE) is one of the biggest challenges for high-quality streaming since the bandwidth that a BS can provide is limited. Besides, the bandwidth efficiency of a BS can be further enhanced and an ideal DL:UL ratio for a residential user is 10:1 to 20:1. Also, most users of a BS are residentials. They frequently download data without generating uplink traffic or generating less uplink traffic. Therefore, in this study, we propose a 5G downlink scheduling mechanism, named the Q-learning based Scheduling and Resource Allocation Scheme (QSRAS), which deploys Q-learning techniques to effectively improve the quality of wireless transmission, and efficiently manage radio resources of a BS. This scheme dynamically adjusts radio resource allocation by referring to QoS parameters, including throughputs, delays and fairness for UEs. According to our experimental results and analyses, it can effectively trade-off the throughput and fairness of overall system in the multiple traffic. The QSRAS outperforms available state-of-the-art schemes on the summation of Fairness and Normalized spectral efficiencies. The improvements range between 2.84 and 10.51%, especially when many more users are served by a base station. [ABSTRACT FROM AUTHOR]

With the rapid development of 5G technology, studying its evolution path is crucial for innovation and development. This study explores the evolution of 5G technology using patent data analysis. By applying main path analysis and network techniques to a dataset from the IncoPat patent database, the study identifies the main path of 5G technology evolution and analyzes its trends. The Citespace is used to construct citation networks and reveal the main path of the patent citation network, regional citation network, and institutional citation network. Furthermore, the study incorporates the 5G technology lifecycle to dynamically interpret the main path and analyze the major evolution trends of 5G technology. The methodology includes data collection from the IncoPat patent database and the adoption of clustering analysis to identify community structure within citation networks. The Log Likelihood Ratio (LLR) algorithm is employed for community detection, facilitating the analysis of interrelationships among different communities. The main path analysis involves traversal counting and path searching, with the LLR algorithm selected to identify critical nodes in the evolutionary paths of technologies. Additionally, dynamic main path analysis is conducted by incorporating the temporal attribute, enabling the observation of the dynamic changes in technological evolution. The study concludes with important insights into the current development status and evolution of 5G technology, shedding light on key paths and trends in 5G technology evolution. [ABSTRACT FROM AUTHOR]

This paper proposes a novel decentralized federated reinforcement learning (DFRL) framework that integrates deep reinforcement learning (DRL) with decentralized federated learning (DFL). The DFRL framework boosts efficient virtual instance scaling in Mobile Edge Computing (MEC) environments for 5G core network automation. It enables multiple MECs to collaboratively optimize resource allocation without centralized data sharing. In this framework, DRL agents in each MEC make local scaling decisions and exchange model parameters with other MECs, rather than sharing raw data. To enhance robustness against malicious server attacks, we employ a committee mechanism that monitors the DFL process and ensures reliable aggregation of local gradients. Extensive simulations were conducted to evaluate the proposed framework, demonstrating its ability to maintain cost-effective resource usage while significantly reducing blocking rates across diverse traffic conditions. Furthermore, the framework demonstrated strong resilience against adversarial MEC nodes, ensuring reliable operation and efficient resource management. These results validate the framework's effectiveness in adaptive and efficient resource management, particularly in dynamic and varied network scenarios. [ABSTRACT FROM AUTHOR]

This study presents an advanced framework integrating LLAMA_V2, a large language model, into Open Radio Access Network (O-RAN) systems. The focus is on efficient network slicing for various services. Sensors in IoT devices generate continuous data streams, enabling resource allocation through O-RAN's dynamic slicing and LLAMA_V2's optimization. LLAMA_V2 was selected for its superior ability to capture complex network dynamics, surpassing traditional AI/ML models. The proposed method combines sophisticated mathematical models with optimization and interfacing techniques to address challenges in resource allocation and slicing. LLAMA_V2 enhances decision making by offering explanations for policy decisions within the O-RAN framework and forecasting future network conditions using a lightweight LSTM model. It outperforms baseline models in key metrics such as latency reduction, throughput improvement, and packet loss mitigation, making it a significant solution for 5G network applications in advanced industries. [ABSTRACT FROM AUTHOR]

This paper discusses a new symbol modulation scheme known as polarized amplitude and phase shift keying, (Pol$Pol$‐APSK) modulation with four rings in its basic form. The new scheme maps power‐polarized symbol pairs on its constellation in order to increase the number of data symbols. Pol$Pol$‐APSK exploits a symbol mapper based on a conjugate power splitting algorithm. Product modulation is applied, where a voltage signal of a specified amplitude and phase is multiplied by another current signal of a specified amplitude and phase, thus forming polymorphic signals in the product constellation. At the receiver, an isolated detection is performed, where the voltage signal is detected independently of the received current signal. A selection combining scheme is then used. The results depict unity peak‐to‐average power ratio and low average symbol energy, which is desirable for Green communications in 5G networks. It presents lower bit error rates when compared with the state‐of‐the art M$M$‐ary quadrature amplitude modulation, with a signal‐to‐noise ratio gap of at least 10[dB]$10\; [\text{dB}]$. The proposed analytical framework closely matches the simulations for bit error rates under M=16,32,64Pol-APSK$M=16,32,64 \ Pol\text{-APSK}$. [ABSTRACT FROM AUTHOR]

In order to meet the growing demands and extend network coverage for high‐speed railway (HSR) system, the dense deployment of a large number of small cells (SCs) is considered for 5G networks. However, the deployment of dense SCs and the high speed of trains result in challenging problems such as interference, frequent handovers (HOs), increased HO failure rate, and consequently the deteriorated overall quality of service (QoS). In order to address the challenges in handover, an improved handover decision strategy is proposed based on Support Vector Machine (SVM). The HO decision making is considered as a classification problem taking into account available states that they may have in the HSR network. From the simulation results, it is observed that the proposed scheme is capable of decreasing the number of HO, HO failure rate and enhancing the network performance remarkably. [ABSTRACT FROM AUTHOR]

In the realm of intelligent healthcare, there is a growing ambition to reshape medical services through the integration of artificial intelligence (AI). However, conventional machine learning faces inherent challenges such as privacy issues, delayed updates, and protracted training times, particularly due to the hesitance of medical institutions to directly share sensitive data, with possible noises. In response to these concerns, a Quantum-Assisted Federated Intelligent Diagnosis Algorithm (β -QuAFIDA) is proposed, applied into real medical data. Leveraging the capabilities of the 5G mobile network, this approach works the connection between Internet of Medical Things (IoMT) devices through the 5G, synchronizing training and updating the server model without disrupting their real-world applications. In our quest to safeguard patient data and enhance training efficiency, our study employs an innovative heuristic approach marked by a nested loop structure. Specifically, the inner loop is dedicated to training the beta-variational quantum eigensolver (β -VQE) to approximate the expectation values of the proposed algorithm; the outer loop trains the β -QuAFIDA to reduce the relative entropy towards the target. This approach involves a balance between privacy considerations and the urgency of training. Results demonstrate that representations with low-rank attained through β -QuAFIDA offer an effective approach for acquiring low-rank states. This research signifies a step forward in the synergy between AI and 5G technologies, presenting a novel avenue for the advancement of intelligent healthcare. [ABSTRACT FROM AUTHOR]

The design of a miniaturized dual-band bandpass filter featuring a broad bandwidth and a high center frequency ratio is demonstrated in this article. A novel symmetrically loaded shunt coupled line with dual stepped impedance stubs has been investigated for the first time and it is utilized to realize a dual-band bandpass filter with seven transmission zeros. The transmission zeros frequencies of the filter response have been theoretically verified using even-odd mode analysis. For experimental validation purposes, a dual-band filter with a center frequency ratio of 4.06 covering 5G applications is implemented using Rogers RT/duroid 5870 substrate and tested. The tested filter has wide 3 dB fractional bandwidths of 53.8% and 17.9% in the first and second passbands, respectively, and has a compact topology of 0.20 × 0.09 λg2. The full-wave simulated and tested magnitude and group delay responses of the proposed filter are in good congruence. [ABSTRACT FROM AUTHOR]

A massive MIMO (multiple-input-multiple-output) antenna system for 5G base stations is developed, featuring 240 elements and 60 ports. The system supports two modes: individual port operation and network operation. It has a rectangular shape with dimensions of 28×16.8×0.32 cm³ and consists of three layers per side, with 15 ports per layer. Each port contains a 2×2 patch sub-array operating at 3.5 GHz, with beam tilting to generate uncorrelated radiation patterns. The system provides a measured bandwidth of 252 MHz, covering the 3.39–3.64 GHz band. Gain and envelope correlation coefficients are evaluated according to mMIMO parameters. A prototype is fabricated, and the results obtained through CST simulations are validated by experimental measurements. [ABSTRACT FROM AUTHOR]

With 5G technology driving its expansion as the main infrastructure for pervasive connection, the Internet of Things (IoT) symbolises a paradigm-shifting interconnectivity of objects and devices. The increasing integration of IoT devices into our daily lives poses serious security and privacy risks. Every smart object in an urban setting is connected, which increases the vulnerability of IoT-based smart cities to various security risks. It is crucial to guarantee these digital urban settings' security and resilience, especially as cities become more computerised and have a dense population of linked devices. Ensuring the integrity and functionality of smart cities requires immediate attention to detecting and mitigating potential cyberattacks. This research presents an intrusion detection model derived from data extracted by simulating the SYNFLOOD attack scenario, a prominent form of Denial of Service attack in IoT security. The suggested detection model classifies, trains, and validates the imported data using the k-folds method and creates a unique detection model. The proposed model is fast and effectively enables all IoT networks to communicate information without compromising privacy. The model enhances the detection process by employing data preprocessing and balancing. In this work, the experiments' accuracy is stable, proving the model's success for the six used machine learning algorithms resulted in an excellent performance with an accuracy of 92.3% for the Decision Tree and Neural Network. [ABSTRACT FROM AUTHOR]

The diversification of network applications has become more comprehensive with the development of 5G mobile networks and Internet of things (IoT). Traditional intrusion detection system using rule-based anomaly technology is obviously insufficient for the changing network environment. Integrating the deep learning (DL) model can help intrusion detection system discover newly or unknown hacker's behavior. However, the quality of training dataset for DL is usually critical. If the dataset has not enough amount for training or is imbalanced for some kinds of attack categories, these situations may influence the detection accuracy. This paper aims to enhance the usage of dataset by dynamically adjusting the records of categories using Random Under-Sampling (RUS) and Generative Adversarial Network (GAN) models. The experiments show that the proposed approach has superior results in terms of the accuracy and some kinds of recall rates, compared to the evaluations of several previous studies. [ABSTRACT FROM AUTHOR]

This work analyzes the effect of positioning small cells (SC) on an existing heterogeneously scattered macro cellular (MC) network. The impact of SC deployment based on the planned blend of K-means and user density (KUD) algorithms on an existing MC network is explored. With an application of the Cox process, heterogeneous and uneven distributions of 50 and 100 users were generated for investigation. The simulated outcomes and plotted graphs illustrate a gradual increment in the average user throughput, sum rate, and energy efficiency with the overlaying of 8 SCs using the proposed KUD algorithm in an existing MC. [ABSTRACT FROM AUTHOR]

The rapid evolution of mobile and optical communication technologies is driving the transition from 5G to 6G networks. This transition inevitably brings about changes in authentication scenarios, as new security demands emerge that go beyond the capabilities of existing frameworks. Therefore, it is necessary to address these evolving requirements and the associated key challenges: ensuring Perfect Forward Secrecy (PFS) to protect communications even if long-term keys are compromised and integrating Post-Quantum Cryptography (PQC) techniques to defend against the threats posed by quantum computing. These are essential for both radio and optical communications, which are foundational elements of future 6G infrastructures. The DMRN Protocol, introduced in 2022, represents a major advancement by offering both PFS and PQC while maintaining compatibility with existing 3rd Generation Partnership Project (3GPP) standards. Given the looming quantum-era challenges, it is imperative to analyze the protocol's security architecture through formal verification. Accordingly, we formally analyze the DMRN Protocol using SVO logic and ProVerif to assess its effectiveness in mitigating attack vectors, such as malicious or compromised serving networks (SNs) and home network (HN) masquerading. Our research found that the DMRN Protocol has vulnerabilities in key areas such as mutual authentication and key exchange. In light of these findings, our study provides critical insights into the design of secure and quantum-safe authentication protocols for the transition to 6G networks. Furthermore, by identifying the vulnerabilities in and discussing countermeasures to address the DMRN Protocol, this study lays the groundwork for the future standardization of secure 6G Authentication and Key Agreement protocols. [ABSTRACT FROM AUTHOR]

Drones integrated with 5G New Radio (NR) base stations have emerged as a promising solution for efficient victim search and localization in emergency zones where cellular networks are disrupted by natural disasters. Traditional approaches relying solely on uplink Sounding Reference Signal (SRS) for localization face limitations due to User Equipment (UE) power constraints. To overcome this, our paper introduces BWSAR, a novel three-stage Search-and-Rescue (SAR) methodology leveraging 5G-NR beam sweeping technologies. BWSAR utilizes downlink Synchronization Signal Block (SSB) for coarse-grained direction estimation, guiding the drone towards potential victim locations. Subsequently, finer-grained beam sweeping with Positioning Reference Signal (PRS) is employed within the identified direction, enabling precise three-dimensional UE coordinate estimation. Furthermore, we propose a trajectory optimization algorithm to expedite the drone's navigation to emergency areas. Simulation results underscore BWSAR's efficacy in reducing positioning errors and completing SAR missions swiftly, within minutes. [ABSTRACT FROM AUTHOR]

Fifth Generation (5G) cellular networks have been adopted worldwide since the rollout began around 2019. It brought with it many innovations and new services, such as Enhanced Mobile Broadband (eMBB), Ultra Reliable and Low-Latency Communications (URLLC), and Massive Internet of Things (mIoT). Furthermore, 5G introduced a more scalable approach to network operations using fully software-based Virtualized Network Functions (VNF) in Core Networks (CN) rather than the prior hardware-based approach. However, while this shift towards a fully software-based system design provides numerous significant benefits, such as increased interoperability, scalability, and cost-effectiveness, it also brings with it an increased cybersecurity risk. Security is crucial to maintaining trust between vendors, operators, and consumers. Cyberattacks are rapidly increasing in number and sophistication, and we are seeing a shift towards zero-trust approaches. This means that even communications between VNFs inside a 5G core must be scrutinized and hardened against attacks, especially with the advent of quantum computers. The National Institute of Standards and Technology (NIST), over the past 10 years, has led efforts to standardize post-quantum cryptography (PQC) to protect against quantum attacks. This paper covers a custom implementation of the open-source free5GC CN, to expand its HTTPS capabilities for VNFs by introducing PQC Key Encapsulation Methods (KEM) for Transport Layer Security (TLS) v1.3. This paper provides the details of this integration with a focus on the latency of different PQC KEMs in initial handshakes between VNFs, on packet size, and the implications in a 5G environment. This work also conducts a security comparison between the PQC-equipped free5GC and other open-source 5G CNs. The presented results indicate a negligible increase in UE connection setup duration and a small increase in connection setup data requirements, strongly indicating that PQC KEM's benefits far outweigh any downsides when integrated into 5G and 6G core services. To the best of our knowledge, this is the first work incorporating PQC into an open-source 5G core. Furthermore, the results from this effort demonstrate that employing PQC ciphers for securing VNF communications results in only a negligible impact on latency and bandwidth usage, thus demonstrating significant benefits to 5G cybersecurity. [ABSTRACT FROM AUTHOR]

This paper presents a Doherty power amplifier (DPA) integrated circuit (IC) designed to have enhanced gain, efficiency, and AM-AM characteristics through a dynamic power dividing technique, which can control the power dividing ratio according to the input power. Since this multi-purpose dynamic power dividing network also provides the phase offset and impedance matching at the interstage network needed for appropriate DPA operation, the active IC area could be reduced. To verify the proposed technique and its analysis, the DPA was implemented with a 28 nm bulk CMOS process for the fifth-generation (5G) new radio (NR) millimeter-wave frequency band of 23.5–27.5 GHz. The measured results showed a gain of 20.3–21.9 dB, saturated output power of 14.0–15.2 dBm, power added efficiency (PAE) of 22.8–26.7% at the peak power, and PAE of 14.6–17.6% at the 6 dB output power back-off (OBO). [ABSTRACT FROM AUTHOR]

In telemedicine, the tactile internet allows surgeons to perform surgeries using robotic systems and receive tactile feedback from remote patients. However, inappropriate safety measures can put patients at risk. Recently, many lightweight authentication schemes have been developed for remote surgery applications in the tactile internet environment. These schemes forthright encrypt messages using constant keys, and store secret keys in a verifier table, so they cannot resist potential attacks. To solve these problems, this study develops a lightweight authenticated key agreement scheme for 5G networks in the Tactile Internet environment using the lightweight Physical Unclonable Function (PUF). The security of the proposed scheme is demonstrated by using formal security proofs. The proposed scheme not only overcomes the shortcomings of previously developed schemes, but also has higher efficiency and satisfies more security requirements. [ABSTRACT FROM AUTHOR]

There is an unprecedented exposure of living organisms to mobile communications radiofrequency electromagnetic field (RF‐EMF) emissions. Guidelines on exposure thresholds to limit thermal effects from these emissions are restricted to humans. However, tissue heating can occur in all living organisms that are exposed. In addition, exposure at millimetric frequencies used by 5G may impact surface tissues and organs of plants and small‐size species. It is also expected that the addition of 5G to existing networks will intensify radiofrequency absorption by living organisms. A European Parliament report proposed policy options on the effects of RF‐EMF exposure of plants, animals, and other living organisms in the context of 5G: funding more research, implementing monitoring networks, accessing more information from operators on antennas and EMF emissions, and developing compliance studies when antennas are installed. However, there is no evidence on the preferences of relevant stakeholders regarding these policy options. This paper reports the findings of a survey of key European stakeholders' policy option preferences based on the European Parliament's report. It reveals a broad consensus on funding more research on the effects of exposure of plants, animals, and other living organisms to EMFs. It also highlights the need for deliberation concerning the other policy options that could provide solutions for regulatory authorities, central administrations, the private sector, nongovernmental associations and advocates, and academics. Such deliberation would pave the way for effective solutions, focusing on long‐term output from funding research, and enabling short‐term socially and economically acceptable actions for all parties concerned. [ABSTRACT FROM AUTHOR]

Aiming at the engineering problem that 5G base station antenna is difficult to locate efficiently in complex electromagnetic environment, a two-stage positioning method of 5G base station antenna substation is proposed, which considers signal path loss and antenna RF radiation. Firstly, the path loss solution model of the 5G base station antenna signal in the substation is established, and the RF radiation solution model generated by the coupling excitation of 5G high-frequency electromagnetic wave and the metal shell of the electrical equipment is constructed based on the big element physical optics method. Secondly, combining the advantages of particle swarm optimization (PSO) and interior point algorithm, the antenna positioning method considering path loss and RF radiation is constructed. Among them, in the first stage, the minimum loss of 5G signal path in the substation is taken as the goal, and the antenna stations in the substation are searched globally to get the optimal pre-selected position. In the second stage, the antenna position is calibrated according to the pre-selected position, so that the interference of RF radiation is minimized, and the final position is output. Finally, a 500 kV 5G substation in Henan Province was taken as the simulation object. Compared with the original scheme, the simulation results ensured the minimum 5G path loss in the substation and took into account the electromagnetic compatibility of the equipment in the substation. The effectiveness of the location strategy for maintaining the safe operation of the substation is verified, and it can provide a reference for the 5G base station antenna positioning project of the substation. Article highlights: Considering the influence of 5G high-frequency electromagnetic wave on the electrical equipment in the substation, the positioning accuracy of 5G base station antenna in the substation is improved. The large area physical optics method can improve the calculation time and accuracy of radiofrequency radiation field intensity Combining the advantages of particle swarm optimization and interior point method, it can effectively avoid the problem of local optimal solution of location selection The simulation results show that the proposed method not only improves the location accuracy of 5G base station antennas in substations, but also applies to 5G base station antennas in more complex engineering environments [ABSTRACT FROM AUTHOR]

Wireless connections in 5G technology are driving the rapid growth of intelligent transport systems and vehicle communications. Wireless channels are impacted by weather, which is most noticeable in millimeter wave bands. This includes rain, fog, snow, sand, and dust. 5G networks now support diverse applications with speed and quality. In an effort to enable the use of millimeter wave frequencies, a recent study examined the impact of dust and sand on 5G channels. This paper examines the impact of heavy and frequent rainfall, along with horizontal polarization, on the propagation of millimeter waves in urban and highway settings. Using theoretical and optimization techniques, the effects of rainfall attenuation, path loss, and connection margin are evaluated at various millimeter wave frequencies. Dependencies on rainfall rate, path variation, and operating frequency are shown by the simulation results. In urban and highway situations, mean path loss and error statistics are examined with and without rainy attenuation. It is observed that the particle swarm optimization approach achieves 94% accuracy in signal propagation, which will enhance the path loss, received power, and overall system performance. [ABSTRACT FROM AUTHOR]

A compact UWB-MIMO antenna designed for sub-6 GHz, Ku-band, and millimeter-wave applications with UWB capabilities is proposed. The antenna design consists of two inverted L-shaped MIMO elements with slot etching, deliberately positioned on an FR-4 material substrate, measuring 36 × 18 × 1.6 mm³. Utilizing inverted L-shaped elements and prudently arranged slots on the substrate, the design achieves wideband characteristics. For enhanced isolation, interconnected rectangular slots with a fork shape are etched in the bottom layer, ensuring the isolation of less than -25 dB between ports. The proposed design exhibits an impedance bandwidth of approximately 54% within the frequency range of 3 GHz to 40 GHz, making it suitable for sub-6 GHz 5G bands, Ku-band, and millimeter-wave applications. The advantages of compactness and low profile of the proposed design are best suitable for 5G, Ku-band, and millimeter applications with UWB capabilities. The proposed design is successfully fabricated and tested. [ABSTRACT FROM AUTHOR]

The fifth-generation cellular network can handle extremely diverse services and user requirements including high transmission rates, low latency, and large connections. The concept of network slicing appears as an effective solution to satisfy these various needs, where different virtual networks for various service scenarios are implemented on the same physical infrastructure. The sharing technique has several issues, such as ensuring Quality of Service (QoS) satisfaction, fairness, and performance resource allocation across different slices. The variability of these slices mostly resides in their number of demands and the priority of each slice. Slices that have high demands and priority are often allocated a significant number of resources. However, due to the limitation of resources management strategies of network slicing. Moreover, the resource allocation strategy excludes the slice requests of low priority with its demands from the slice. In this scenario, some slices with low demand and priority may be suffering from starvation. The primary focus of this study is to address and overcome these issues in core network slicing. This paper proposes a resource allocation management model Based on Markov decision process and adaptive fairness resources, that considers the Quality of service of each slice based on the changing demands per slice at a specific decision epoch. The experiment results show that the proposed solution with maximizing utilization policy has outperformed by 40% compared to exist scheme in terms of QoS, which proposed a prioritized dynamic allocation scheme. Moreover, fair resource allocation ensures maximizing the overall network while maintaining the requirements of each slice and QoS guarantees. [ABSTRACT FROM AUTHOR]

Cellular networks are moving towards increasing heterogeneity by deploying more small cells into macro base station (MBS) to meet rapidly growing traffic demands. To leverage the advantages of these small cells, mobile users should be offloaded onto small base stations (BSs), which will typically be lightly populated and can give a higher data rate by presenting the mobile users with many more channels than the MBS. Likewise, a more balanced cell association will lessen the pressure on the MBS, allowing it to serve its remaining users more effectively. This paper addresses the cell association challenge for Quality of Service (QoS) provisioning in terms of throughput and load-balancing for 5G and future generation networks. This problem is quite challenging because BSs have varying backhaul capacities and users have varying QoS needs. Most of the previous studies are based on reference signal received power (RSRP), signal to interference and noise ratio (SINR) or its variants and most importantly majority of them are not load-aware. Therefore, a modified load-aware biased cell association scheme based on distance is proposed to attain better QoS provisioning in terms of throughput and load-balancing. Simulation results depict that the proposed load-aware-based method outperforms conventional cell association schemes based on RSRP and its variants, and in terms of throughput and load-balancing. Furthermore, the algorithm's complexity has been assessed through a comparison and analysis of computational time, demonstrating better performance compared to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Background: The safety of new‐generation mobile phones using 5G networks in patients with modern‐generation pacemakers has not been studied. Objectives: This study aimed to compare the risk of electromagnetic interference (EMI) generated by a new‐generation mobile phone with 5G networks when positioned at the pacemaker pocket or the contralateral ear and assess the incidence of EMI with telemetry interrogation in patients with permanent pacemakers. Methods: We enrolled 489 patients with pacemakers from three different manufacturers. The pacemaker mode was programmed for overdrive pacing and sensing mode if an intrinsic rhythm was present. A smartphone (Samsung S21 + 5G) was placed directly over the pulse generator and right ear. The phone was tested under standby mode, 5G internet connection, and incoming and outgoing calls for each location. Real‐time electrocardiography (ECG) monitoring and patient symptoms were recorded to determine the occurrence of EMI. The possibility of EMI with interrogation telemetry was also investigated. Results: A total of 4824 tests were performed on 489 patients. Most pacemakers were dual‐chamber (82%) or magnetic resonance imaging (MRI)‐compatible systems (83%). EMI was not detected with both mobile phone positions. Interference with telemetry was demonstrated in 11.5% of patients. Almost all incidences of interference (98.2%) occurred during incoming calls. Single‐chamber pacemakers, non‐MRI‐compatible systems, older pulse generators, older leads, and unipolar settings were significantly related to a higher incidence of interference with interrogation telemetry. Conclusions: The risk of EMI between modern smartphones with 5G networks and pacemakers is low. Nevertheless, interference with the interrogation telemetry may still occur. [ABSTRACT FROM AUTHOR]

In this paper, a miniaturized microstrip diplexer designed on an FR4 substrate material is presented. The diplexer operates at 2.4 and 3.5 GHz, responding to the demands of 5G and WiMAX applications, respectively. It consists of two distinct bandpass filters (BPFs) formed using the meandrous line, which are combined using a coupled-junction. An electromagnetic (EM) simulator was used to characterize the frequency responses of the designed diplexer. The results show that the proposed diplexer exhibits excellent isolation, exceeding 28.5 dB throughout the operating frequency range. Additionally, the passband insertion losses are approximately 1 dB, while the return losses measure around 39 and 57 dB for the both channels, respectively. Furthermore, the manufactured diplexer is compact with final dimensions of 28.5 × 20 mm2. These results confirm the suitability of the proposed diplexer for the intended applications. [ABSTRACT FROM AUTHOR]

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Introduction: As 5G networks become widespread and their application scenarios expand, massive amounts of traffic data are continuously generated. Properly analyzing this data is crucial for enhancing 5G services. Methods: This paper uses the visibility graph method to convert 5G traffic data into a visibility graph network, conducting a feature analysis of the 5G traffic data. Using the AfreecaTV dataset as the research object, this paper constructs visibility networks at different scales and observes the evolution of degree distribution with varying data volumes. The paper employs the Hurst index to evaluate the 5G traffic network and uses community detection to study the networks converted from 5G traffic data of different applications. Results: Experimental results reveal significant differences in node degree distribution and topological structures of 5G traffic data across different application scenarios, such as star structures and multiple subnetwork structures. It is found that the node degree distribution of 5G traffic networks exhibits heterogeneity, reflecting the uneven growth of node degrees during network expansion. The Hurst index analysis discovers that the 5G traffic network retains the long-term dependence and trends of the original data. Through community detection, it is observed that networks converted from 5G traffic data of different applications exhibit diverse community structures, such as high centrality nodes, star-like community structures, modularity, and multilayer characteristics. Discussion: These findings indicate that 5G traffic networks in different application scenarios exhibit complex and diverse characteristics. The heterogeneity of node degree distribution and differences in topological structures reflect the imbalance in node connection methods during network expansion. The results of the Hurst index show that the 5G traffic network inherits the long-term dependence of the original data, providing a basis for analyzing the dynamic characteristics of the network. The diverse community structures reveal the inherent modularity and hierarchy of the network, which helps to understand the performance and optimization directions of 5G networks in different applications. [ABSTRACT FROM AUTHOR]

This work proposes a new hybrid bidirectional standard single mode fiber (SSMF)/free space optical (FSO) dual polarization quadrature amplitude modulation (DP‐QAM) employing optical orthogonal frequency division multiplexing (OFDM). An optical multicarrier generator (OMG) source is utilized. The suggested model is simulated using VPI photonics software. Thirty‐two optical OFDM channels are used; each carrier's 30 Gbps information data with DP‐QAM are employed to improve the performance of the fifth‐generation passive optical network (5G‐PON) for downstream and upstream transmission. In addition, 16‐QAM signals are used in DP. The impact of continuous wavelength (CW) lasers and high data rates in the proposed 5G‐PON has been investigated for DP‐QAM‐OFDM. The results reveal 0.96 Tb/s and 0.1 Tb/s for the downstream and upstream directions, respectively, under different turbulence effects. Furthermore, the simulation results show that the proposed model can achieve an SSMF length and FSO propagation ranges of 20 km and 2 km, respectively, with the symbol error rate (SER) (<10−9) and error vector magnitude (EVM) (<0.186). [ABSTRACT FROM AUTHOR]

Facing the demands of different service scenarios and the large number of base stations deployed in the context of the 5G era, network slicing was introduced to support on-demand services for specific service scenarios. The base stations in this context, are utilized to provide services within different slices in each service scenario. Due to the complexity of selection of the most appropriate base stations, a multi-server polling system for 5G network slicing is proposed in this work, with a method of predicting the optimal number of base stations to be selected. In order of solve the problem of difficult mathematical analysis, low service efficiency, high delay and waste of resources due to unlimited number of base stations in the network, the selection method makes use of a Bi-directional Long Short Term Memory (BiLSTM) neural network. First, the experiment was scaled up and multidimensional variables were varied to obtain data on the average queue length and average delay of the system; Next, a neural network model is constructed for performance prediction; Finally, the optimal number of base stations is selected and a posture prediction is made for that number of base stations. The experimental results show that the system performance is best when the number of base stations is 3, which saves resources and reduces the difficulty of mathematical derivation and analysis. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, a new frontend for 5G receivers is proposed. In the presented work, a new bandwidth enhancement technique for low‐noise transconductance amplifiers and for wireless frontends has been proposed. The proposed frontend achieves low noise figure (NF) by using a noise cancelation scheme in the newly proposed LNTA. The linearity of the frontend is also improved by increasing the DC gain and the bandwidth of the opamp used in the transimpedance amplifier and by exploiting a linearity improvement method for the LNTA. The frontend has been implemented in a standard 0.18 μm CMOS process. The post‐layout simulation results prove the gain of 38 dB with less than 3 dB deviation in the pass‐band over a 6.5 GHz bandwidth. The results also show that NF is less than 8 dB, the worst‐case IIP3 is −2 dBm, the maximum S11 is equal to −9.5 dB while the frontend consumes 9.95 mW DC power from a single 1.8‐V supply, and the occupied area is 0.813 mm2. [ABSTRACT FROM AUTHOR]

The new or future communication systems require much higher flexibility and scalability to support diverse scenarios. In this paper, a high-throughput low-density parity-check (LDPC) decoder on graphics processing unit is presented to meet the flexible and scalable requirements. A memory-reduced forward/backward approach for the check node update is proposed. Moreover, elaborate on-chip memory allocations are conducted to improve memory bandwidth. The proposed (26112, 8448) 5G LDPC decoder on RTX4090 achieves 27.6 Gbps decoding throughput with five layered iterations, while the latency is less than 1 ms. Compared with related works, the throughput speedups obtained by the presented LDPC decoder are from 1.18 × to 12.4 × . [ABSTRACT FROM AUTHOR]