Next-generation Networks Research Articles

A 3.2-3.8 GHz low-noise amplifier for 5G/6G satellite-cellular convergence applications

The rapid evolution of wireless communication systems towards 5G standards has imposed stringent requirements on the performance of radio frequency front-end components. Among these, the Low-Noise Amplifier (LNA) plays a pivotal role in determining the overall noise figure and sensitivity of the receiver chain. This paper presents a comprehensive design and analysis of a 3.2-3.8 GHz LNA tailored for 5G applications, employing a 0.3 μmm gate length Gallium Arsenide (GaAs) pseudomorphic high electron transistor (pHEMT) technology process. The proposed LNA design focuses on achieving a low noise figure (NF), high gain, and robust linearity to accommodate the dense signal environment and wide bandwidth of 5G networks. The design leverages advanced matching networks and feedback topologies to enhance stability and reduce the noise contribution from the active devices. Simulation results predict a noise figure of 1.3-1.4 dB, a gain of 20-21 dB across the band of interest, and an input-referred third-order intercept point (IIP3) of 18 dBm. The LNA demonstrates excellent performance in a 5G testbed, showing a significant improvement in the signal-to-noise ratio and the potential to enhance 5G receiver sensitivity. The research substantiates the LNA’s viability for integration into 5G base stations and user equipment, underscoring its potential to contribute to the efficient and reliable operation of next-generation wireless networks. This LNA can be used for 5G New Release (NR) band of n77 and n78 (3.5-3.7 GHz)

A hybrid approach combining OFC and FSO for multichannel connectivity

Abstract This paper presents a novel approach of establishing a multichannel optical communication link, combining optical fiber cable (OFC) and free space optics (FSO) technology. By leveraging multiple lengths of optical fiber and FSO links, along with optical amplifiers to counteract attenuation, our proposed hybrid system incorporates four channels operating within the 1550 nm window. This model is specifically designed to address communication challenges in scenarios such as unplanned urban layouts, multistory buildings, or rugged terrains where traditional optical fiber deployment is impractical. By integrating FSO for the fronthaul and optical fiber for the backhaul, our system enhances transmission capacity, thereby supporting the requirements of next-generation networks and alleviating bottlenecks and connectivity issues at the last or first mile. This innovative approach holds promise for internet service providers, enterprises, and industrial networks operating in demanding environments. Furthermore, it can serve as a vital tool for restoring emergency communication links in the aftermath of disasters such as earthquakes or floods, where traditional wired optical infrastructure may be compromised.

Design of an Iterative Method for Hybrid Device-to-Device and Network Integration via Dynamic Spectrum Sharing and Intelligent Resource Management

In the evolving landscape of wireless communication, the integration of Device-to-Device (D2D) communication within traditional network infrastructures emerges as a pivotal solution to meet the burgeoning demand for efficient, scalable, and reliable communication. Despite the promise of D2D to augment network capabilities, existing frameworks often falter in optimizing resource utilization, ensuring privacy and security, and maintaining high-quality service amidst dynamic network conditions and escalating data traffic. Addressing these limitations, this work introduces a comprehensive hybrid model that synergizes D2D communication with network integration through the innovative application of Dynamic Spectrum Sharing (DSS), Reinforcement Learning (RL)-based Resource Allocation, Mobile Edge Computing (MEC), Differential Privacy Techniques, Swarm Intelligence-based Routing, Cognitive Radio Networks, Self-Healing Network Protocols, and Low-Power Communication Protocols. DSS is employed to dynamically allocate spectrum, enhancing communication flexibility between D2D and network modes. RL algorithms adaptively manage resources, optimizing network performance in real-time scenarios. MEC decentralizes data processing, slashing latency and bandwidth demands. Differential privacy fortifies data security within D2D interactions in different communication scenarios. Swarm intelligence and cognitive radios bolster network resilience and spectral efficiency through self-organized routing and adaptive spectrum access. Self-healing protocols and energy-efficient designs ensure robust, sustainable network operations. The proposed model markedly elevates network efficiency, slashes latency, and augments scalability by intelligently offloading traffic, distributing computing tasks, and preserving user privacy. Its implementation fosters a resilient, self-organizing network fabric capable of meeting contemporary demands. The impacts of this work are profound, offering a blueprint for next-generation wireless networks that are more adaptive, secure, and sustainable, thereby significantly enhancing user experience and trust in D2D communication systems.

Probing nontensorial gravitational waves with a next-generation ground-based detector network

Probing nontensorial gravitational waves with a next-generation ground-based detector network

Optical Add-Drop Multiplexers: Enhancing High Transmission Bit Rates in Next-Generation Communication Networks

The development of optical networks in the telecommunications sector is becoming much closer by considering the help of an Optical Add Drop Multiplexer (OADM) based on a novel technology called Wavelength Division Multiplexing (DWDM). The objective of the current study to examine high transmission bit rates for next-generation optical communication networks using the technology of OADM Based on DWDM. Artificial neural networks (ANNs) were developed via MATLAB software to predict three main parameters in this filed such as transmitted signal power (PT), transmitted signal bandwidth (B.Wsig), and transmission bit rate capacity (Bsh) at different fiber cable lengths, such as L=200, 250, and 300 km. The ANNs results showed that, standard error (SE) for predicting PT as a function of the number of transmitted channels (Nch) was 0.115 mW, 0.095 mW and 0.077 mW, for 200, 250 and 300 km, respectively. Additionally, the SE for predicting B.Wsig was 0.067 GHz, 0.051 GHz and 0.040 GHz for 200, 250 and 300 km, respectively. Lastly, the SE for predicting Bsh was 1.665, 1.311 Gbit/sec and 1.076Gbit/sec for 200, 250 and 300 km, respectively. The SE for predicting PT as a function of the Signal Wavelength (λ) was 0.116, 0.096 and 0.079 mW for 200, 250 and 300 km, respectively. Additionally, the SE for predicting B.Wsig was 0.067, 0.052 and 0.052 GHz for 200, 250 and 300 km, respectively. Lastly, the SE for predicting Bsh was 1.688, 1.417 and 1.110 Gbit/sec for 200, 250 and 300 km, respectively. The low SE in ANNs demonstrated the efficiency, motivating further advancements in optimizing network performance for high-bit-rate transmission.

Optimal Beamwidth for Maximizing Uplink Coverage Probability in Quasi Earth-Fixed LEO Satellite Communication System

Satellite communication is proposed to fulfill the ubiquitous coverage for next-generation wireless networks. Considering the propagation delay and path loss, low-earth orbit (LEO) satellites are widely adopted. However, since the beam boresight directions become close in quasi-earth-fixed cells (QEFC) scenarios at low elevation angles, the interference increases and causes low communication quality. This paper introduces the optimal beamwidth maximizing uplink coverage probability scheme for quasi-earth-fixed cells in LEO satellite communication systems. The proposed scheme dynamically adjusts the beamwidth to achieve max uplink coverage probability at different elevation angles. The simulation results show that the proposed scheme matches the exhaustive search method in different scenarios and target signal-to-interference-plus-noise ratios. Furthermore, the proposed scheme significantly mitigates interference and improves the uplink coverage probability. Compared with the 3GPP setting, the proposed scheme improves the uplink coverage probability by 0.93 at time 100 s, and compared with the three-color frequency reuse, the proposed scheme improves the uplink coverage probability by 0.195 at time 100 s. The dynamic beamwidth and boresight direction adjustments enable the satellite to maintain seamless and reliable communication services across a wide range of operating conditions, ultimately realizing the goal of ubiquitous communications in the QEFC LEO satellite system.

Experimentally realized physical-model-based frugal wave control in metasurface-programmable complex media

Metasurface-programmable radio environments are considered a key ingredient of next-generation wireless networks. Yet, identifying a metasurface configuration that yields a desired wireless functionality in an unknown complex environment was so far only achieved with closed-loop iterative feedback schemes. Here, we introduce open-loop wave control in metasurface-programmable complex media by estimating the parameters of a compact physics-based forward model. Our experiments demonstrate orders-of-magnitude advantages over deep-learning-based digital-twin benchmarks in terms of accuracy, compactness and required calibration examples. Strikingly, our parameter estimation also works without phase information and without providing measurements for all considered scattering coefficients. These unique generalization capabilities of our pure-physics model unlock unforeseen and previously inaccessible frugal wave control protocols that significantly alleviate the measurement complexity. For instance, we achieve coherent wave control (focusing or perfect absorption) and phase-shift-keying backscatter communications in metasurface-programmable complex media with intensity-only measurements. Our approach is also directly relevant to dynamic metasurface antennas, microwave-based signal processors and emerging in situ reconfigurable nanophotonic, optical and room-acoustical systems.

Advanced hybrid algorithms for precise multipath channel estimation in next-generation wireless networks

Multipath channels continue to present challenges in wireless communication for both 5G and 6G networks. A multipath channel is a phenomenon in wireless communications where signals traverse from the sender to the receiver along various paths. This end occurs due to the reflection, diffraction, and refraction of signals of various objects and structures in the environment. Such pathways can cause symbol interference in the transmitted signal, leading to communication issues. To this end, our paper proposes the integration of three algorithms: teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and artificial neural networks (ANN). This combination effectively analyzes and stabilizes the transmission channel, minimizing symbol interference. We have developed, simulated, and evaluated this hybrid approach for multipath fading channels. We apply it to various coding schemes, including tail-biting convolutional code, turbo codes, low-density parity-check, and polar code. Additionally, we have explored various decoding methods such as Viterbi, maximum logarithmic maximum a posteriori, minimum sum, and cyclic redundancy check soft cancellation list. Our study encompasses new channel equalization schemes and coding gains derived from simulations and mathematical analysis. Our proposed method significantly enhances channel equalization, reducing interference and improving error correction in wireless communication systems.

Intelligent resource management in 5G/6G network by adopting edge intelligence for higher education systems

The Internet of Things (IoT) is expected to have a positive impact on various sectors, such as manufacturing, users, and operators. Due to the capabilities of smart objects to share their contents and interact with their social networks, the combined Social Internet of Educational Things (SIoET) for smart education is expected to provide users with more efficient services. The primary objective of this paper is to provide an efficient resource management by adopting edge intelligence technique for higher education system workloads on next-generation networks. As the number of tasks handled by these interconnected devices continues to rise, there is an escalating concern regarding resource competition and power consumption within the network. To mitigate these challenges, we propose an innovative edge intelligence-based resource allocation approach to intelligently allocate resources while minimizing latency and optimizing power consumption with increasing task volumes. The problem is dissected into two core sub-problems: computation resource optimization and power optimization. Leveraging the concept of Lagrangian and Bisection methods, our proposed algorithm, implemented within a Device-to-Device (D2D) -assisted edge intelligence network, demonstrates remarkable performance improvements. Network latency and energy consumption is reduced by up to 57.9 % and 82.3 % compared to the benchmark schemes. These findings hold promising implications for the integration of this work into higher education systems driven by SIoET. Furthermore, simulations indicate that the system maintains robust performance even in dynamic urban environments characterized by random device movement.

Artificial Intelligence in 6G Wireless Networks: Opportunities, Applications, and Challenges

Wireless technologies are growing unprecedentedly with the advent and increasing popularity of wireless services worldwide. With the advancement in technology, profound techniques can potentially improve the performance of wireless networks. Besides, the advancement of artificial intelligence (AI) enables systems to make intelligent decisions, automation, data analysis, insights, predictive capabilities, learning, and adaptation. A sophisticated AI will be required for next-generation wireless networks to automate information delivery between smart applications simultaneously. AI technologies, such as machines and deep learning techniques, have attained tremendous success in many applications in recent years. Hances, researchers in academia and industry have turned their attention to the advanced development of AI-enabled wireless networks. This paper comprehensively surveys AI technologies for different wireless networks with various applications. Moreover, we present various AI-enabled applications that exploit the power of AI to enable the desired evolution of wireless networks. Besides, the challenges of unsolved research in this area, which represent the future research trends of AI-enabled wireless networks, are discussed in detail. We provide several suggestions and solutions that help wireless networks be more intelligent and sophisticated to handle complicated problems. In summary, this paper can help researchers deeply understand the up-to-the-minute wireless network designs based on AI technologies and identify interesting unsolved issues to be pursued in their research in a fast way.

Hybrid cell handover strategy for O-RAN-based campus networks

In recent years, the concept of Open Radio Access Network (O-RAN) has become a prominent and ongoing research area in mobile communications, as mobile operators strive to enhance the intelligence, efficiency, and vendor independence of their RAN (Radio Access Network) architectures and components. O-RAN aims to virtualize and improve RAN functions in software and therefore it supports virtualized RANs where disaggregated network components of multiple vendors are connected via open interfaces. O-RAN is a concept based on interoperability and standardization of RAN elements including a unified interconnection standard for white-box hardware and open-source software elements from different vendors. Architecture integrates a modular base station software stack on off-the-shelf hardware which allows baseband and radio unit components from different suppliers to operate seamlessly together. Analyzing and examining the O-RAN architecture in terms of various network scenarios and different network policies is crucial for next-generation networks. This study begins by discussing the O-RAN architecture and its advantages in detail. Subsequently, a campus network has been proposed for the analysis and simulation of the O-RAN architecture, and performance analyses have been performed within the scope of different scenarios and network policies. As a result of these analysis, a new hybrid algorithm for cell handovers in the O-RAN architecture has been developed and compared with traditional cell handover methods based on Reference Signal Received Power (RSRP) and Quality of Service (QoS). The results indicate that the proposed hybrid handover algorithm for O-RAN architecture yields better outcomes.

A Comprehensive Review of UAV-Assisted FSO Relay Systems

The evolving requirements of next-generation mobile communications networks can be met by leveraging vertically deployed Unmanned Aerial Vehicle (UAV) platforms integrated with Free Space Optical communications (FSO). This integration offers a flexible and scalable architecture capable of delivering high-rate communication without requiring licenses while aligning with the multi-gigabit paradigm. In recent times, the increasing availability of commercial aerial platforms has facilitated experimental demonstrations of UAV-enabled FSO systems, which play a crucial role in proposed backhaul networks and point-to-point communications by overcoming Line-of-Sight (LOS) challenges. These systems can be rapidly deployed to meet sudden demand scenarios. This document provides a comprehensive review of relevant field demonstrations of UAV-enabled FSO relay systems, with a particular focus on commercially available, free-flying platforms that are driving advancements in this domain. It categorizes the different platforms by considering the operational altitudes of these systems and their payload actuation capacity, which determines their adaptability to variables. The analysis aims to distill the design considerations that lead to optimal performance regarding communications throughput and other relevant metrics. Moreover, it also attempts to highlight areas where design choices have fallen short, indicating gaps in current research efforts toward the widespread adoption of UAV-enabled FSO relay systems. Finally, this work endeavors to outline effective design considerations, guidelines, and recommendations to bridge these identified gaps. It serves as a valuable reference guide for researchers involved in developing UAV-enabled FSO relay systems, enabling them to make informed decisions and pave the way for the successful implementation of such systems.

ABRSL: AI based bilateral RAT selection framework for next-generation wireless networks

aBRSL: AI based bilateral RAT selection framework for next-generation wireless networks

Visual Communication Design Method in Folk Art Based on Multimedia Data Transmission Technology

With the rapid development of science and technology and China's economy, the internet, big data, computers, and multimedia technology are widely used in all walks of life to promote the application and improvement of visual communication design concepts, the gradual implementation of multimedia technology, and the continuous improvement of innovation. In this paper, from the multimedia transmission technology based on network coding, some key technologies in the network communication of data and video streams are studied in depth and, based on the transmission-quality assessment model, the working effect of the jitter-buffering algorithm and WebRTC jitter-buffering algorithm is compared in different network environments. The experimental results show that the jitter-buffering algorithm proposed in this paper has better working effect. This research is of great significance for the realization of multimedia transmission technology in next-generation networks.

Privacy-Preserving Serverless Edge Learning With Decentralized Small-Scale Mobile Data

In next-generation (i.e., 6G) networking systems, the data-driven approach will play an essential role, being an efficient tool for networking system management and bringing popular user applications. With those unprecedented and novel usages, existing frameworks fail to consider the complex nature of the next-generation networking system and consequently fail to be applied to future communication systems directly. Moreover, existing frameworks also fail to support popular privacy-preserving learning strategies efficiently by presenting special designs to respond to the resource-demanding nature of the aforementioned strategies. To fill this gap, this paper extends conventional serverless platforms with serverless edge learning architectures, providing a mature and efficient distributed training framework by fully exploiting limited wireless communication and edge computation resources in the considered networking system with the following three features. Firstly, this framework dynamically orchestrates resources among heterogeneous physical units to efficiently fulfill privacy-preserving learning objectives. The design jointly considers learning task requests and underlying infrastructure heterogeneity, including last-mile transmissions, computation abilities of edge and cloud computing centers, and loading status of infrastructure. Secondly, the proposed framework can easily work with data-driven approaches to improve network management efficiency, realizing AI for network promise of next-generation networking systems to provide efficient network automation. Lastly, to significantly reduce distributed training overheads, small-scale data training is proposed by integrating with a general, simple data classifier. This low-load enhancement can seamlessly work with various distributed deep models in the proposed framework to improve communications and computation efficiencies during the training phase. Based on the above innovations, open challenges, and future research directions encourage the research community to develop efficient privacy-preserving learning techniques.

DHT-based GFDM and STC-GFDM systems for beyond 5G wireless networks

This manuscript endeavours an exploration of Generalized Frequency Division Multiplexing (GFDM) as a promising alternative to the Orthogonal Frequency Division Multiplexing (OFDM) in next-generation cellular networks. GFDM’s exceptional performance in minimizing out-of-band emissions (OOBE) and maximizing spectral efficiency (SE) makes it a compelling candidate, especially when integrated with Multi-Input Multi-Output (MIMO) technology to enhance reliability through spatial diversity. However, ensuring GFDM’s resilience against frequency-selective channels (FSCs), especially in low-latency scenarios, is crucial.This research investigates Discrete Hartley Transform (DHT) based GFDM and Space-Time Coded GFDM (STC-GFDM) systems, proposing a comprehensive framework to enhance performance metrics such as Bit Error Rate (BER) and Achievable Rate (AR). Further enhancements are achieved through a Minimum BER-Power Allocation (MBER-PA) strategy, optimizing BER and AR in QAM-based GFDM and STC-GFDM systems employing a DHT precoding scheme. Analytical expressions for BER and AR are derived and validated via Monte Carlo simulations. Additionally, a comparative analysis against traditional GFDM systems is conducted, evaluating metrics including Peak-to-Average Power Ratio (PAPR), BER, and AR.

Handover algorithm based on Bayesian-optimized LSTM and multi-attribute decision making for heterogeneous networks

Heterogeneous networks (HetNets) which can satisfy the diverse requirements of future terminal tasks through providing different network access services for terminals, have emerged as a vital technology for the development of next-generation networks. However, frequent access to different networks often leads to high handover delay and handover failure, resulting in a degradation of user experience. Therefore, maintaining seamless network connectivity has become a challenging problem for applications with high real-time and reliability requirements in HetNets. To address this problem, we propose a handover algorithm based on Bayesian-optimized Long Short-Term Memory (BO-LSTM) and multiple attribute decision making (HO-BLM) to reduce handover latency and failure, ensuring seamless network connectivity. To reduce handover delay, we designed a handover decision prediction algorithm based on BO-LSTM to trigger handover in advance by predicting the network state of users in the future. Additionally, we propose a preselection mechanism based on current network status to further reduce the calculation delay. Moreover, a multi-attribute decision making (MADM)-based selection approach is designed to select the best alternative network for a specific user, where the utility value of the best alternative network is calculated by weighing the users’ preferences for network parameters of different business types and the actual status of the candidate networks. Simulation results show that the proposed algorithm decreases the handover failure by up to 42.1% and the handover delay by up to 68% compared with traditional handover approaches.

Securing Service Instantiation on Next-generation Networks

The Next Generation of Software Defined Networks (NG-SDN) enables a whole new set of possibilities for innovation in the data plane (e.g., computing, monitoring, load-balancing). In parallel, we have been witnessing the increasing adoption of virtualized services running in light but efficient containers, which brings forth the capability to flexibly deploy services at the network edge, while providing dynamic scaling and management. As a result, operators have tap in a lot of computational real-estate as part of the adoption of telco cloud concepts, as well as to support the foreseen edge and fog computing scenarios. However, exposing the infrastructure to third parties and realizing complex services over a multi-stakeholder environment raises challenges at different levels (e.g., security, reliability, SLA guarantees). Reconfiguring the data plane for flow monitoring and handling service requests directly on the data plane could address these challenges to support the joint communication and computation vision. This is where this paper contributes by proposing a secure and reliable system for dynamic instantiation of third-party services. A proof-of-concept prototype was developed using P4 and Kubernetes showcasing the feasibility of the proposed approach.

Enhancing the power amplifier performance of an optical-OTFS modulation for optical communication system

Abstract Future wireless networks extensively employ the two-dimensional Optical-Orthogonal Time Frequency Space (O-OTFS) modulation system due to its increased dependability, flexibility, and data throughput. When there is a lot of movement, OTFS offers improved connectivity performance. While OFDM functions in the time-frequency domain, O-OTFS modulation acts in the delay-Doppler domain. This study provides an overview of how OTFS functions in the delay-Doppler domain, as well as its benefits and drawbacks. Next-generation networks utilize this modulation method in various applications, such as vehicle-to-vehicle communication, communication in mountainous areas, high-speed mobility, and train communication. This paper provides an easy-to-understand overview of the PAPR algorithms and its reduction in O-OTFS modulation.

Intelligent Seamless Handover in Next-Generation Networks

Intelligent Seamless Handover in Next-Generation Networks