Next-generation Wireless Networks Research Articles

NOMA Performance Improvement with Downlink Sectorization

This study tackles the growing challenge of inter-user interference in Non-Orthogonal Multiple Access (NOMA) systems, particularly as user density increases in modern communication networks. The primary objective is to improve system performance by implementing a downlink sectorization strategy, which groups users into distinct sectors to manage interference and optimize resource allocation. A Sequential Power Allocation (SePA) algorithm was introduced to enhance power distribution within sectors, aiming to maximize both user capacity and overall sum rate. The methods employed included detailed simulations comparing the performance of traditional NOMA systems and those incorporating sectorization. The results demonstrate that sectorization can significantly boost the system’s sum rate by up to 25% and reduce decoding errors by as much as 51%, particularly when the number of users per sector is kept under 20. However, performance saturation occurs beyond this threshold, where additional users do not contribute to further improvements. The novelty of this research lies in applying spatial sectorization to NOMA, showing that spatial sectorization can minimize intra-sector interference, improve power efficiency, and maintain reliable communication in high-demand environments such as the Internet of Things (IoT). This study provides valuable insights for optimizing NOMA systems, crucial for next-generation wireless networks. Doi: 10.28991/ESJ-2025-09-01-017 Full Text: PDF

Novel techniques for efficient PAPR reduction in NOMA systems for future wireless networks

ABSTRACT Fifth-generation (5G) networks are designed to overcome critical challenges, including achieving high data rates, supporting massive device connectivity, and ensuring low latency. Non-Orthogonal Multiple Access (NOMA) significantly enhances spectral efficiency and network capacity within this framework. NOMA has a problem with a high Peak-to-Average Power Ratio (PAPR). This creates nonlinear distortion, reduces power amplifier efficiency, and increases out-of-band radiation, making it less useful in 5G and B5G networks. To address this, two PAPR reduction methods are proposed: Improved Salp Swarm Algorithm-based Partial Transmit Sequence (PTS-ISSA) and Iterative Sub-block Phase Rotation (ISPR). PTS-ISSA minimizes PAPR by phase rotation of sub-blocks, offering the best reduction but at higher computational complexity, making it ideal for systems with abundant resources. ISPR, on the other hand, employs iterative phase rotation without side information, balancing performance with lower complexity, making it suitable for resource-constrained applications. Evaluated under Rician fading and AWGN channels, both methods outperform traditional techniques in PAPR reduction and Bit Error Rate (BER) performance. While PTS-ISSA is optimal for high-performance systems, ISPR serves lightweight, real-time applications. These methodologies show potential for the next-generation wireless networks, supporting scenarios from IoT to ultra-reliable low-latency communications by optimizing the trade-off between performance and complexity.

Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges

Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges

On the Distribution of Subnetwork Size with Virtual-Cell-Based Optimal Decomposition for Next-Generation Wireless Networks

On the Distribution of Subnetwork Size with Virtual-Cell-Based Optimal Decomposition for Next-Generation Wireless Networks

PAPR Reduction Techniques in MIMO-OFDM Systems Using PSO and Advanced Modulation Schemes for Next-Generation Wireless Networks

The increasing need for rapid communication led to the development of two multicarrier regulating systems: Code Division Multiple Access (CDMA) and Orthogonal Frequency Division Multiplexing (OFDM). The OFDM is a type of frequency division multiplexing (FDM) plot used in complex multi carrier regulation. To transport information, many evenly spaced orthogonal sub-carriers are used. The data is split up into several equal bursts of streams for every sub-carrier. At a low image rate, each sub-carrier is adjusted using a traditional tweak plot (like QPSK) to maintain complete information rates that are comparable to traditional single carrier regulation methods at a similar capacity for transmission.

COMPOSITE LEFT-RIGHT HAND META SURFACE IRS FOR MODERN APPLICATIONS

A meta surface (MTS) is a 2D planner structure of an array of two dimensions. MTS structure is composed from a unit cell structure that is periodically configured to form a surface. A Composite Left-Right Hand (CLRH) Meta surface Intelligent Reflecting Surface (IRS) represents a cutting-edge approach in modern communication and sensing technologies. By integrating left-handed and right-handed materials within a meta surface, this hybrid structure leverages the unique electromagnetic properties of both to enhance signal manipulation and control. The IRS technology is pivotal in applications such as wireless communication, where it can dynamically adjust the propagation environment to optimize signal quality and reduce interference. The composite design offers enhanced bandwidth, polarization control, and tunability, making it ideal for next-generation wireless networks, radar systems, and other advanced electromagnetic applications. The integration of CLRH meta surfaces into IRS platforms presents a significant advancement in the development of reconfigurable and adaptive electromagnetic environments, essential for the growing demands of 5G/6G communication systems and beyond.

RIS-Aided coexistence in wireless networks using angular information

Owing to the ability of reconfigurable intelligent surfaces (RISs) to control the propagation environment in their vicinity, they have emerged as an appealing solution to enhance the performance of next-generation wireless networks. While the effectiveness of RISs with complete channel state information (CSI) is well-documented, their performance with limited user information remains less explored. This is particularly pronounced when an RIS interferes with nodes in a potentially non-cooperating network, making CSI acquisition challenging. This paper focuses on such scenarios, examining the interference suppression capabilities of an RIS for coexisting secondary and primary users (SUs and PUs) using angular information, achievable even with non-cooperating PUs. We address the coexistence challenge by employing semidefinite relaxation (SDR) and inner majorization minimization (iMM) for RISs with continuous phase control, and propose an enhanced SDR tailored for discrete RISs. Numerical results reveal both SDR and iMM require at least five bits of discrete phase control for moderate interference suppression. They also indicate that interference suppression weakens if the PUs are inside certain angular regions, a limitation that varies with the algorithm but can be mitigated by increasing RIS elements. Ultimately, the coexistence performance of the RIS with a non-cooperative network is validated through full-wave simulations.

Revolutionizing Free-Space Optics: A Survey of Enabling Technologies, Challenges, Trends, and Prospects of Beyond 5G Free-Space Optical (FSO) Communication Systems.

As the demand for high-speed, low-latency communication continues to grow, free-space optical (FSO) communication has gained prominence as a promising solution for supporting the next generation of wireless networks, especially in the context of the 5G and beyond era. It offers high-speed, low-latency data transmission over long distances without the need for a physical infrastructure. However, the deployment of FSO systems faces significant challenges, such as atmospheric turbulence, weather-induced signal degradation, and alignment issues, all of which can impair performance. This paper offers a comprehensive survey of the enabling technologies, challenges, trends, and future prospects for FSO communication in next-generation networks, while also providing insights into the current mitigation strategies. The survey explores the critical enabling technologies such as adaptive optics, modulation schemes, and error correction codes that are revolutionizing FSO communication and addressing the unique challenges of FSO links. Also, the integration of FSO with radio frequency, millimeter-wave, and Terahertz technologies is explored, emphasizing hybrid solutions that enhance reliability and coverage. Additionally, the paper highlights emerging trends, such as the integration of FSO with artificial intelligence-driven optimization techniques and the growing role of machine learning in enhancing FSO system performance for dynamic environments. By analyzing the current trends and identifying key challenges, this paper emphasizes the prospects of FSO communication in the evolving landscape of 5G and future networks. In this regard, it assesses the potential of FSO to meet the demands for high-speed, low-latency communication and offers insights into its scalability, reliability, and deployment strategies for 5G and beyond. The paper concludes by identifying the open challenges and future research directions critical to realizing the full potential of FSO in next-generation communication systems.

Energy-efficient and reliable dual closed-loop DC control system for intelligent electric vehicle charging infrastructure.

This study presents an innovative dual closed-loop DC control system for intelligent electric vehicle (EV) charging infrastructure, designed to address the challenges of high power factor, low harmonic pollution, and high efficiency in EV charging applications. The research implements a three-level Pulse Width Modulation (PWM) rectifier with a diode-clamped topology and Insulated-Gate Bipolar Transistors (IGBTs), achieving a power factor of 0.99, a total harmonic distortion (THD) of 1.12%, and an efficiency of 95% through rigorous simulation. These results surpass those of wireless charging technology and bidirectional DC-DC converters, demonstrating the system's superiority in key performance metrics. The dual closed-loop strategy, integrating a current inner loop and a voltage outer loop, ensures rapid response and high steady-state accuracy, with the PI regulator effectively managing phase coupling for balanced power flow. The voltage outer loop's stability is critical for the system's reliable operation. The study also discusses the challenges in the dynamic variation of midpoint source current and proposes future work to increase the system's switching frequency, improve anti-interference capabilities, and enhance the accuracy of the sampling process. Advanced computational intelligence and optimization techniques are highlighted as essential for tackling the complex challenges of modern EV charging systems. The study contributes to the development of efficient, secure technology for the next generation of wireless networks and power systems, providing a robust empirical basis for the proposed control strategies through MATLAB/Simulink simulations. This research sets a solid foundation for the performance assessment of EV charging systems, offering high-performance, environmentally friendly, and economically viable solutions for sustainable transportation.

Review on Power Control in Multi-hop Networks

This paper reviews power control algorithms through interference management in a variety of network types, such as Device-to-Device (D2D) Networks, Unmanned Aerial Vehicles (UAVs), healthcare systems, and Low Power Wide Area Networks (LPWAN). The growing requirement for effective and consistent wireless communications advanced approaches to mitigate interference, ensuring optimal power usage and network performance. We explore the exceptional interference challenges and requirements of each network type. As wireless networks progress, managing interference becomes essential to maximize power usage and maintain network performance. This paper systematically reviews the latest advancements and methodologies that adjust power levels to mitigate interference. Additionally, we analyze these techniques and discuss their impact on spectral efficiency, supported by recent case studies. Our findings aim to guide researchers and practitioners in developing more effective power control and interference management solutions for next-generation wireless networks.

Optimizing Transmit Power for User-Cooperative Backscatter-Assisted NOMA-MEC: A Green IoT Perspective

Non-orthogonal multiple access (NOMA) enables the parallel offloading of multiuser tasks, effectively enhancing throughput and reducing latency. Backscatter communication, which passively reflects radio frequency (RF) signals, improves energy efficiency and extends the operational lifespan of terminal devices. Both technologies are pivotal for the next generation of wireless networks. However, there is little research focusing on optimizing the transmit power in backscatter-assisted NOMA-MEC systems from a green IoT perspective. In this paper, we aim to minimize the transmit energy consumption of a Hybrid Access Point (HAP) while ensuring task deadlines are met. We consider the integration of Backscatter Communication (BackCom) and Active Transmission (AT), and leverage NOMA technology and user cooperation to mitigate the double near–far effect. Specifically, we formulate a transmit energy consumption minimization problem, accounting for task deadline constraints, task offloading decisions, transmit power allocation, and energy constraints. To tackle the non-convex optimization problem, we employ variable substitution and convex optimization theory to transform the original non-convex problem into a convex one, which is then efficiently solved. We deduce the semi-closed form expression of the optimal solution and propose an energy-efficient algorithm to minimize the transmit power of the entire wireless powered MEC. The extensive simulation results demonstrate that our proposed scheme significantly reduces the HAP transmit power by around 8% compared to existing schemes, validating the effectiveness of our approach. This study provides valuable insights for the design of green IoT systems by optimizing the transmit power in NOMA-MEC networks.

Co-transmission of optically-carried 5G NR signal and over 14-W power light via standard single-mode fiber

Co-transmission of optically-carried 5G NR signal and over 14-W power light via standard single-mode fiber

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.

Outage performance of users in CR-NOMA network systems

<p>Non-Orthogonal Multiple Access (NOMA) and Cognitive Radio (CR) technologies present viable solutions to mitigate spectrum scarcity in wireless communication systems. This paper focuses on evaluating the performance of CR-NOMA networks, particularly for user devices operating under a Simultaneous Wireless Information and Power Transfer (SWIPT) framework. We derive explicit mathematical expressions for key performance metrics, including outage probability (OP) and system throughput, as they relate to various power allocation coefficients. Comprehensive simulations are conducted to validate our theoretical findings, revealing that appropriate power allocation significantly impacts user fairness and overall network throughput. The analysis covers a wide range of realistic channel conditions, including Rayleigh fading, to ensure robustness. Additionally, our study addresses the challenge of limiting interference to the primary network by optimizing the transmission power of secondary users while adhering to interference constraints. The results show that the primary user device (D<sub>1</sub>) consistently outperforms the secondary user device (D<sub>2</sub>), emphasizing the importance of strategic resource management. These contributions provide deeper insights into the factors affecting outage performance in CR-NOMA systems, offering effective solutions for enhancing the robustness, fairness, and efficiency of next-generation wireless communication networks.</p>

A Comparative Analysis of F-RAN and C-RAN Architectures for Next-Generation Wireless Networks

Within the realms of software-defined networks (SDN), this study offers an in-depth analysis of F-RAN and C-RAN designs. It evaluates the essential characteristics, performance metrics, and deployment considerations of both architectures to enhance understanding of their capabilities. Additionally, the study explores the potential advantages and challenges of integrating F-RAN and C-RAN with SDN, offering insights for network architects and operators. By thoroughly comparing these architectures and analyzing their key features, performance metrics, and deployment considerations, the objective is to enhance comprehension of F-RANs and C-RANs within the framework of SDN. Additionally, it delves into examining the prospective advantages and obstacles linked with the combination of these architectural models with SDN, therefore providing network architects and operators with relevant insight. It serves to enrich the knowledge repository of SDN through meticulous analysis and furnishes pivotal insights for the deployment and refinement of network infrastructure.

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.

Design of an Improved Model for Spectrum Sharing and Resource Management Using Deep Q-Network, Federated Averaging, and Ant Colony Optimization

Indeed, the ever-evolving wireless communication requires DSS and resource management for effectiveness. The old methods of spectrum allocation have a strategic shortfall since they are static and thus not adapted to real-time network conditions. Besides, more important challenges include issues over privacy and inefficiency of resource utilization. In the light of such problems, this paper proposes a new method by bringing together three deep-learning techniques, which are Deep Q-Networks, Federated Averaging, and Ant Colony Optimisation. Each is chosen because of its various competencies and complementary capabilities. First, DQN is chosen because it has the capability to handle high-dimensional action space. DQN applies deep learning to approximate the Q Value function, which provides the optimal decision of spectrum allocation based on real-time network states, such as spectrum usage and interference levels. With this scheme, up to a 20% gain in network throughput and up to a 15% cut in wasted spectrum compared to traditional methods are achievable. The second component FedAvg tackles the model training collaboration issue with privacy. It enables FedAvg for privacy-preserving resource management, where multiple devices update the global model collaboratively by communicating model updates without exchanging raw data samples. For this approach, the resulting method leads to a further increase in data privacy by reducing data leakage as high as 40%, with an improvement in resource management accuracy of up to 15%. ACO was employed to optimize resource allocation and improve spectrum efficiency. ACO emulates the natural foraging behavior of ants to solve combinatorial optimization problems. Consequently, it has led to up to 25% increase in spectral efficiency and up to 20% improvement in resource allocation balance sets. All these methods put together will provide a wholesome solution that not only optimizes spectrum and resource management but also promises privacy and adaptability. This represents severe improvement in overcoming the weaknesses of the existing approaches and will provide a strong foundation for next-generation wireless networks.

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

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

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

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