Next-generation Wireless Networks Research Articles

DESIGN OF A 24.5 GHZ CMOS LOW NOISE AMPLIFIER USING 0.13-µM TECHNOLOGY FOR 6G WIRELESS APPLICATIONS

The need for high-performance circuit designs is growing as wireless communication technologies continue to advance and support newer generations of wireless applications. Much emphasis has been focused on the possibility of the 24 GHz frequency band in next-generation wireless networks, including 5G and beyond. Designing a low noise amplifier (LNA) operating at 24 GHz presents several challenges. The primary concerns include achieving high gain, low power consumption, low noise figure, and while maintaining good linearity and stability. This paper presents the design, simulation, and layout of a CMOS LNA optimized for operation at 24.5 GHz frequency, targeting 6G and beyond wireless communication applications. The proposed LNA employs three stages with a cascode topology at the first stage and follow by a common source stage at second and third stage. The three stages help to achieve high gain, and the source degeneration inductor at the first stage helps to improve linearity. Extensive simulations were conducted using a 0.13-µm CMOS technology, demonstrating a peak gain of 21 dB and a noise figure of 5.6 dB at 24.5 GHz. The LNA also exhibits good linearity and stability over a wide bandwidth. The performance metrics were validated through simulation and comparison, showcasing the feasibility of the designed LNA for 6G applications. This work contributes to the advancement of CMOS-based radio frequency (RF) front-end designs for next-generation wireless communication systems.

Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach

Massive MIMO technology is recognized as a key enabler for beyond 5G (B5G) and next-generation wireless networks. By utilizing large-scale antenna arrays at the base station (BS), it significantly improves both system capacity and energy efficiency. Despite these advantages, the deployment of a high number of antennas at the BS presents considerable challenges, particularly in the design of signal detectors that can operate with low computational complexity. While the minimum mean square error (MMSE) detector offers optimal performance in these large-scale systems, it suffers from the computational burden that makes its practical implementation challenging. To mitigate this, various iterative methods and their improved versions have been introduced. However, these iterative methods often converge slowly and are less accurate. To address these challenges, this study introduces an improved variant of traditional accelerated over-relaxation (AOR), called optimized AOR (OAOR). AOR is an over-relaxation method, and its performance is highly dependent on its relaxation parameters. To find the optimal parameters, we have developed an innovative approach that integrates a nature-inspired meta-heuristic algorithm known as Particle Swarm Optimization (PSO). Specifically, we introduce a novel variant of PSO that improves upon basic PSO by enhancing the cognitive coefficients to optimize the relaxation parameters for OAOR. These key modifications to the standard PSO improve its ability to explore various solutions efficiently and help to find the optimal parameters more quickly for signal detection. It facilitates the OAOR with faster convergence towards the optimal solution by reducing the error rate, resulting in high detection accuracy and simultaneously decreasing computational complexity from O(K3) to O(K2) making it suitable for modern wireless communication systems. We conduct extensive simulations across various configurations of massive MIMO systems. The results indicate that our proposed method achieves better performance compared to existing techniques. This improvement is particularly evident in terms of both computational complexity and error rate.

Advanced security measures in coupled phase-shift STAR-RIS networks: A DRL approach

The rapid development of next-generation wireless networks has intensified the need for robust security measures, particularly in environments susceptible to eavesdropping. Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) have emerged as a transformative technology, offering full-space coverage by manipulating electromagnetic wave propagation. However, the inherent flexibility of STAR-RIS introduces new vulnerabilities, making secure communication a significant challenge. To overcome these challenges, we propose a deep reinforcement learning (DRL) based secure and efficient beamforming optimization strategy, leveraging the deep deterministic policy gradient (DDPG) algorithm. By framing the problem as a Markov decision process (MDP), our approach enables the DDPG algorithm to learn optimal strategies for beamforming and transmission and reflection coefficients (TARCs) configurations. This method is specifically designed to optimize phase-shift coefficients within the STAR-RIS environment, effectively managing the coupled phase shifts and complex interactions that are critical for enhancing physical layer security (PLS). Through extensive simulations, we demonstrate that our DRL-based strategy not only outperforms traditional optimization techniques but also achieves real-time adaptive optimization, significantly improving both confidentiality and network efficiency. This research addresses key gaps in secure wireless communication and sets a new standard for future advancements in intelligent, adaptable network technologies.

An Enhanced Particle Swarm Optimization-Based Node Deployment and Coverage in Sensor Networks.

Positioning, coverage, and connectivity play important roles in next-generation wireless network applications. The coverage in a wireless sensor network (WSN) is a measure of how effectively a region of interest (ROI) is monitored and targets are detected by the sensor nodes. The random deployment of sensor nodes results in poor coverage in WSNs. Additionally, battery depletion at the sensor nodes creates coverage holes in the ROI and affects network coverage. To enhance the coverage, determining the optimal position of the sensor nodes in the ROI is essential. The objective of this study is to define the optimal locations of sensor nodes prior to their deployment in the given network terrain and to increase the coverage area using the proposed version of an enhanced particle swarm optimization (EPSO) algorithm for different frequency bands. The EPSO algorithm avoids the deployment of sensor nodes in close proximity to each other and ensures that every target is covered by at least one sensor node. It applies a probabilistic coverage model based on the Euclidean distances to detect the coverage holes in the initial deployment of sensor nodes and guarantees a higher coverage probability. Delaunay triangulation (DT) helps to enhance the coverage of a given network terrain in the presence of targets. The combination of EPSO and DT is applied to cover the holes and optimize the position of the remaining sensor nodes in the WSN. The fitness function of the EPSO algorithm yielded converged results with the average number of iterations of 78, 82, and 80 at 3.6 GHz, 26 GHz, and 38 GHz frequency bands, respectively. The results of the sensor deployment and coverage showed that the required coverage conditions were met with a communication radius of 4 m compared with 6-120 m with the existing works.

Physical layer security for confidential transmissions in frequency hopping-based downlink NOMA networks

Facing the exponential number of Internet of Things (IoT) devices and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets in terms of providing massive access and ensuring higher spectral efficiency. In this vein, Non-Orthogonal Multiple Access (NOMA) has been widely recognized as one of the advantageous techniques to handle the proliferation of the IoT. Nevertheless, from a security standpoint, enabling a user to decode the signals of the other users, while using Successive Interference Cancellation (SIC), raises serious concerns regarding confidentiality and vulnerability to malicious attacks. Meanwhile, conventional security paradigms, such as upper-layer encryption and sophisticated authentication mechanisms, require high computational complexity and additional processing, which impose an overwhelming burden on energy-efficient IoT devices. Alternatively, Physical layer Security (PLS) has sparked a significant interest as a promising complement to cryptographic techniques. The key idea of PLS is to avail wireless communication properties to secure communications without adding complex encryption mechanisms at higher layers. In this paper, we propose a PLS approach based on a network coding technique to prevent eavesdroppers from decoding users’ information transmitted through a downlink-based NOMA system. This results in correlating the packets to be transmitted with each other, making the interception of a single packet useless. We demonstrate that the eavesdropper’s decoding complexity increases exponentially with the sequence length, making the task intractable for relatively long ones.

Dynamic resource allocation for low earth orbit satellite networks

Low earth orbit satellite networks (LSN) have been widely recognized as a key element in the development of next-generation wireless communication networks, offering extensive coverage and seamless connectivity across multiple domains. To achieve the minimum transmission latency for highly-dynamic LSN, this paper first establishes a low earth orbit (LEO) satellite downlink communication model while considering outdated channel state information and high mobility. Then, through the design of user association and satellite power allocation strategies, a dynamic problem of minimizing transmission latency is formulated and solved using successive convex approximation and alternating optimization methods. Simulation results clearly illustrate the substantial reduction in transmission latency achieved by the proposed algorithm, successfully meeting the quality of service demands in dynamic environments during the entire mobility cycle of the LEO satellite.

Energy-Efficient and Accelerated Resource Allocation in O-RAN Slicing Using Deep Reinforcement Learning and Transfer Learning

Abstract Next Generation Wireless Networks (NGWNs) have two main components: Network Slicing and Open Radio Access Networks (O-RAN). NS is needed to handle various Quality of Services (QoS). O-RAN adopts an open environment for network vendors and Mobile Network Operators (MNOs). In recent years, Deep Reinforcement Learning (DRL) approaches have been proposed to solve some key issues in NGWNs. The primary obstacles preventing the DRL deployment are being slowly converged and unstable. Additionally, these algorithms have enormous carbon emissions that negatively impact climate change. This paper tackles the dynamic allocation problem of O-RAN radio resources for better QoS, faster convergence, stability, lower energy and power consumption, and reduced carbon emissions. Firstly, we develop an agent with a newly designed latency-based reward function and a top-k filtration mechanism for actions. Then, we propose a policy Transfer Learning approach to accelerate agent convergence. We compared our model to another two models.

Security, privacy and performance concerns in ultra dense networks

Ultra Dense Networks (UDNs) have emerged as a pivotal technology in meeting the exponential growth in data demand and providing seamless connectivity in 5G and beyond networks. However, the high density of small cells in UDNs introduces significant challenges related to security, privacy, and performance. This survey paper presents a comprehensive review of the current state-of-the-art in addressing these concerns. It begins by exploring the unique security vulnerabilities inherent to UDNs, including the increased risk of eavesdropping, denial of service attacks, and unauthorized access due to the close proximity of small cells. The paper then discusses privacy issues, particularly the risks of location tracking and user data exposure, exacerbated by the dense deployment of base stations. In terms of performance, the paper evaluates the impact of interference, handover management, and resource allocation on network efficiency. Various proposed solutions, such as advanced encryption techniques, privacy-preserving algorithms, and interference mitigation strategies, are analyzed and compared. The survey concludes by identifying open research challenges and future directions, emphasizing the need for integrated approaches that simultaneously address security, privacy, and performance to ensure the robust operation of UDNs in next-generation wireless networks.

Ground-to-UAV sub-terahertz channel measurement and modeling

Unmanned aerial vehicle (UAV) assisted wireless communications have been expected to play a vital role in the next generation of wireless networks. UAVs can serve as either repeaters or data collectors within the communication link, thereby potentially augmenting the efficacy of communication systems. Despite their promise, the channel analysis and modeling specific to terahertz (THz) wireless channels leveraging UAVs remain under-explored. This work delves into a ground-to-UAV channel at 140 GHz, with a specific focus on the influence of UAV hovering behavior on channel performance. Employing experimental measurements through an unmodulated channel setup and a geometry-based stochastic model (GBSM) that integrates three-dimensional positional coordinates and beamwidth, this work evaluates the impact of UAV dynamic movements and antenna orientation on channel performance. Our findings highlight the minimal impact of UAV orientation adjustments on channel performance and underscore the diminishing necessity for precise alignment between UAVs and ground stations as beamwidth increases.

An energy-efficient JT-CoMP enabled framework with adaptive OMA/NOMA in HetNets

The increasing demand for higher data rates and improved energy efficiency (EE) in next-generation wireless networks necessitates the optimized selection of multiple-access and coordination techniques. A hybrid joint transmission (JT)-coordinated multi-point (CoMP) enabled orthogonal multiple access (OMA)/non-orthogonal multiple access (NOMA) technique, combining the spectral efficiency (SE) and capacity benefits of CoMP NOMA with the interference mitigation of CoMP OMA, offers a highly adaptable solution for future wireless networks. This paper studies the joint optimization of CoMP/non-CoMP selection, OMA/NOMA selection, power allocation, and user pairing, with the objective of maximizing the EE in the network. A Dynamic CoMP user selection with energy-efficient adaptive multiple access (DCEAMA) algorithm to solve the formulated problem is proposed. Our Monte Carlo simulations show that the DCEAMA surpasses both the pure CoMP OMA and CoMP NOMA schemes in terms of EE, with an average increase of 38% and 26% respectively. We compare our heuristic technique to an exhaustive search strategy to evaluate its efficiency. The findings indicate that our strategy produces comparable EE across various power levels with reduced computational complexity.

Efficient resource allocation using whale optimization for cell-free massive MIMO networks in 6G HetNet

The increase in data rate demands has driven the development of Cell-Free massive Multiple-Input Multiple-Output (CFmMIMO) technology in 6G Heterogeneous Networks (HetNets). This paper proposes an integration of Unmanned Aerial Vehicles (UAVs) and Device-to-Device (D2D) communication in CFmMIMO, aiming to enhance network capacity, coverage, and service delivery in next-generation wireless networks. The paper investigates uplink transmission in a network where Access Points (APs) have imperfect channel state information. It considers Cell-free User Equipment (CUE), UAVs, and D2D pairs, deriving closed-form uplink achievable rates for each user type. Moreover, two optimization problems are formulated to enhance user data rates: one focuses on maximizing the sum data rate through pilot assignment, while the other aims to achieve weighted max–min power control through power allocation. Furthermore, to address these problem, we propose a Modified Whale Optimization Algorithm (MWOA) and a novel Pilot Assignment based on Whale Optimization Algorithm (PAWOA). Performance studies demonstrate significant improvements over state-of-the-art algorithms.

Broadband Antenna Operating in Sub 6GHz Frequency for Long Range 5G Communication

Abstract: A novel broadband antenna designed for long-range 5G communication in the sub-6GHz frequency band is presented. Through rigorous optimization and simulation, the antenna achieved high gain, low side-lobe levels, and uniform radiationpatterns. Experimental validation confirms its efficacy, offering a compact and robust solution for advancing global connectivity in next-generation wireless networks

Energy efficiency and interoperability through O-RAN Rapid Transition Protocol (ORTP)

Mobile network traffic is increasing accompanied by increased energy consumption. Traditional Radio Access Networks (RANs) have been used for decades as the foundation of mobile communications, providing mobile customers with reliable voice and data services. However, they encounter obstacles in terms of scalability, adaptability, and cost-effectiveness. Next-generation wireless networks need efficient Radio Access Network (RAN) solutions to achieve low latency and high throughput. The Open RAN (O-RAN) architecture is a potential solution for 5G and beyond networks due to its open interfaces, disaggregated network entities and services, network hardware and software virtualization, and intelligent control. Traditionally standard RAN accounts for the bulk of mobile network energy consumption, leading towards higher Operational expenditure (OPEX) costs. In the context of cellular networks. Handover strategies need careful development to cater for efficient resource usage as well as overall system energy efficiency. O-RAN Rapid Transition Protocol (ORTP) presented in this paper employs minimum value of handover margin (HOM) and is aimed at increased energy efficiency. 5G based System level simulations have been performed to investigate the efficacy of ORTP for key performance indicators including handover probability, Radio Link Failure, connection density, ping pong effect and dynamic power consumption. It is found that the usage of lower HOM values provide noticeable achievement in terms of power consumption, thereby rendering ORTP around 20 % more efficient compared to 5G networks, while keeping it interoperable.

Consensus for Operating Room Multimodal Data Management: Identifying Research Priorities for Data-Driven Surgery.

This study aimed to identify research areas that demand attention in multimodal data-driven surgery for improving data management in minimally invasive surgery. New surgical procedures, high-tech equipment, and digital tools are increasingly being introduced, potentially benefiting patients and surgical teams. These innovations have resulted in operating rooms evolving into data-rich environments, which, in turn, requires a thorough understanding of the data pipeline for improved and more intelligent real-time data usage. As this new domain is vast, it is necessary to identify where efforts should be focused on developing seamless and practical data usage. A modified electronic Delphi approach was used; 53 investigators were divided into the following groups: a research group (n=9) for problem identification and a narrative literature review, a medical and technical expert group (n=14) for validation, and an invited panel (n=30) for two electronic survey rounds. Round 1 focused on a consensus regarding bottlenecks in surgical data science areas and research gaps, while round 2 prioritized the statements from round 1, and a roadmap was created based on the identified essential and very important research gaps. Consensus panelists have identified key research areas, including digitizing operating room (OR) activities, improving data streaming through advanced technologies, uniform protocols for handling multimodal data, and integrating AI for efficiency and safety. The roadmap prioritizes standardizing OR data formats, integrating OR data with patient information, ensuring regulatory compliance, standardizing surgical AI models, and securing data transfers in the next generation of wireless networks. This work is an international expert consensus regarding the current issues and key research targets in the promising field of data-driven surgery, highlighting the research needs of many operating room stakeholders with the aim of facilitating the implementation of novel patient care strategies in minimally invasive surgery.

Reconfigurable Intelligent Surfaces Between the Reality and Imagination

One interesting idea that could improve wireless communication systems is reconfigurable intelligent surfaces or RIS. To control the spread of wireless signals, RIS uses a plethora of tiny, inexpensive, and customizable reflecting devices. Signal strength, energy consumption, spectrum efficiency, and adaptability to changes in the wireless environment can all be improved by altering the phase shift of the reflecting elements. This is how RIS works. 5G and beyond, the IoT, and smart cities are just a few of the many potential uses for RIS. More study is required before this technology can reach its full potential since it is still in its infancy. Still, RIS is going to be big in the next generation of wireless networks. A great deal of excitement has enveloped the whole 6G development industry. There has been no shortage of grandiose and frequently unfounded assertions that, in reality, the creation of a full-duplex 5G technology that can introduce new services is just around the corner, even though self-interference cancellation schemes for full-duplex communications and the difficulties of building full-duplex base stations have long been known.

Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling

Multipath wireless access aims to seamlessly aggregate multiple access networks to increase data rates and decrease latency. It is currently being standardized through the ATSSS architectural framework as part of the fifth-generation (5G) cellular networks. However, facilitating efficient multi-access communication in next-generation wireless networks poses several challenges due to the complex interplay between congestion control (CC) and packet scheduling. Given that enhanced ATSSS steering functions for traffic splitting advocate the utilization of multi-access tunnels using congestion-controlled multipath network protocols between user equipment and a proxy, addressing the issue of nested CC becomes imperative. In this paper, we evaluate the impact of such nested congestion control loops on throughput over multi-access tunnels using the recently introduced Multipath DCCP (MP-DCCP) tunneling framework. We evaluate different combinations of endpoint and tunnel CC algorithms, including BBR, BBRv2, CUBIC, and NewReno. Using the Cheapest Path First scheduler, we quantify and analyze the impact of the following on the performance of tunnel-based multipath: (1) the location of the multi-access proxy relative to the user; (2) the bottleneck buffer size, and (3) the choice of the congestion control algorithms. Furthermore, our findings demonstrate the superior performance of BBRv2 as a tunnel CC algorithm.

Mobility Management in Next Generation Wireless Networks

Living in a modern society without smart devices is impossible now a days. Every sector related to human lifestyle is either smart or controlled devices which was rare a decade back. Expectations are not limited to network connection but extend to mobility as well. As a result, mobility management becomes an essential and challenging task to accomplish. The revolution in wireless technologies expects more scalability and flexibility in resource management. Handover is one of the vital parts of radio resource management. Execution with perfection and optimization of the handover technique increases the reliability of the system deployed to meet the requirement of high mobility. The cell became small as the wireless cell size adjusted with the revolution of relevant technologies like fifth generation (5G) and beyond. Traffic profile and its density are always in a growing trend. This pattern draws the attention of ultra-dense networks (UDN). The UDN of small cells requires an extra number of handovers with higher accuracy and less delay in execution. In this context, this paper proposed an algorithm where a cross-examination to reduce unnecessary handover that improves the handover performance in next-generation wireless networks.

Impact of NextGen Wireless 5G Network on Agriculture

The advent of next-generation wireless networks, particularly 5G, presents a transformative opportunity for the agriculture sector. The potential impact of 5G technology on agricultural practices, highlighting the opportunities it brings and the challenges that must be addressed for successful integration. Smart and precision farming allows farmers to be more informed and productive. The advent of 5G will considerably change the nature of jobs in farming and agriculture. The internet of things (IoT)- based cloud computing service in the 5G network provides flexible and efficient solutions for smart farming. This will allow the automated operation of various unmanned agricultural machines for the plowing, planting, and management phases of crop farming and will ultimately achieve secure, reliable, environmentally friendly, and energy-efficient operations and enable unmanned farms.

Blockchain technology integration in service migration to 6G communication networks: a comprehensive review

<p>The next generation of wireless networks, 6G is being designed with data-intensive applications. One of the key technologies that will enable 6G is blockchain technology. The emergence of blockchain technology and 6G networks has revolutionized service migration. Service migration in 6G networks is a complex process that requires the integration of new technologies, such as artificial intelligence (AI), edge computing, and network slicing. Motivated by these facts, this comprehensive review includes an overview of blockchain and service migration integration in 6G. First, state of art, development frame work and related works were introduced. Then, we used content analysis by WordStat software and bibliographic analysis by VOSviewer to analysis the current status of service migration and blockchain integration in 6G networks. Next, patterns and characteristics, benefits and challenges and potential cases were reviewed. Then, we proposed an architectural blockchain-based model including decentralized architecture, edge computing, network slicing, software-defined networking, and 5G-6G interworking in 6G. Finally, we described potential application service migration-based in 6G networks including digital twin (DT), holograms, robot avatar, high density internet of things (IoT), AR and VR in 6G and collected open research and future directions of service migration and blockchain.</p>

Hybrid TOA/AOA localization for indoor multipath-assisted next-generation wireless networks

The next-generation wireless communication systems (NGWCs) necessitate integrating effective communication and localization techniques. However, the multipath badly affects both communication and localization, especially in indoor NGWCs. Therefore, we propose a novel hybrid localization method based on time and angle-based ranging estimation to address this issue. This method employs an improved matrix enhancement matrix pencil algorithm (IMEMP) to obtain the time of arrival (TOA) and corresponding angle of arrival (AOA) of each path. Two distinct localization methods are presented: First arrival path-based maximum likelihood (ML) and scatterer-assisted. Both methods are designed to mitigate the adverse effects of serious non-line-of-sight (NLOS) scenarios. The proposed scatterer-assisted method demonstrates a noteworthy 20% improvement in performance compared to scenarios where only the first path is considered, particularly in specific areas. These findings highlight the potential of the proposed approach to enhance localization accuracy in challenging wireless environments, offering valuable insights for the advancement of NGWCs.