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The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users. Due to this popularity, there has been a huge rise in mobile data volume, applications, types of services, and number of customers. Furthermore, due to the COVID‐19 pandemic, the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home. This massive increase in demand for multi‐class services has posed numerous challenges to wireless network frameworks. The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic, such as real‐time live streaming of videos, audios, text, images etc., at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers. Next‐generation wireless networks (NGWNs, i.e. 5G networks and beyond) are being developed to accommodate the service qualities mentioned above and many more. However, achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers. It requires the analysis of a huge volume of network data (structured and unstructured) received or collected from heterogeneous devices, applications, services, and customers and the effective and dynamic management of network parameters based on this analysis in real time. In the ever‐increasing network heterogeneity and complexity, machine learning (ML) techniques may become an efficient tool for effectively managing these issues. In recent days, the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain. This study discusses current wireless network research, brief discussions on ML methods that can be effectively applied to the wireless networking domain, some tools available to support and customise efficient mobile system design, and some unresolved issues for future research directions.

The communication network of Unmanned Aerial Drones (UAD) is expected to become a vital element in the development of next-generation wireless networks, offering flexible infrastructure that extends network coverage to remote or disaster-stricken locations while enhancing capacity during critical events and large-scale emergencies. As UAD technology evolves, its role in ensuring consistent, widespread connectivity becomes more essential, though it faces challenges such as high latency, low spectral efficiency, and fairness issues across multiple drones. This research presents an optimization framework designed for multi-UAD communication networks based on Non-Orthogonal Multiple Access (NOMA) to address these difficulties. The framework focuses on optimizing ground user-to-UAD associations and drone power allocation to maximize spectral efficiency. The primary optimization problem is a mixed-integer, nonconvex, and nonlinear task, which seeks to maximize the sum-rate while addressing issues of UAD-user association and power distribution, complicated by interference and binary decision variables. To manage this complexity, we first optimize UAD-user associations under fixed NOMA power allocation and then optimize the power allocation for each NOMA-enabled ground user connected to the drones. Our numerical results show that this framework provides better performance than traditional orthogonal multiple access (OMA)-based optimization methods and other benchmark NOMA-based techniques, offering improved spectral efficiency, lower complexity, and faster convergence, making it an effective solution for enhancing UAD network performance across a range of dynamic scenarios.

To keep up with the increasing number of connected devices in people’s daily lives, it is necessary to develop intelligent mechanisms that perform the entire network management, interconnecting Wi-Fi, and the emerging beyond Fifth-Generation (5G) communications. Hence, it is essential to consider multiple usage scenarios, while managing end devices’ limitations. As a result, developing a system that allows network operators to link Wi-Fi services on their main networks becomes a critical issue. These include a new paradigm that tackles optimal and dynamic resource allocation techniques. Thus, to consider in a combined way, the applications requirements, the resources available, and the different tiers involved, mechanisms such as virtualization and slicing have emerged to handle the heterogeneous context of the next-generation wireless communications. Moreover, the allocation of Radio Access Network (RAN) resources needs to be addressed. For this purpose, Open-RAN has in mind an open environment, which relies on virtualized functions and is mostly vendor agnostic. This technology will enable high data rates while maintaining adequate Quality of Service (QoS) in wireless communications. This paper advances current literature, which mainly discusses these themes individually, by providing a comprehensive survey in Next Generation Wireless Communications, highlighting their integration with beyond 5G Communications. First, we introduce the Wi-Fi evolution and explain the main standards developed over the years. Second, we present the most recent Wi-Fi standards, Wi-Fi 6 and 7, compared with 5G and beyond. Lastly, we explain the concepts related to slicing, virtualization, RAN, Open-RAN and the open research challenges.

The article presents a miniaturized ultra-wideband (UWB) antenna tailored for modern defense and small satellite communication requirements. Designed and optimized using CST Microwave Studio, the antenna delivers superior electromagnetic performance across both Sub-6 GHz and millimeter-wave frequency ranges. Realized on an FR4 substrate with overall dimensions of 10 × 12 × 1.5 mm³, the prototype achieves an impressive operating bandwidth of 3.4–14 GHz, equivalent to an impedance bandwidth of 121.8%. The measured results highlight a peak gain of 4.56 dBi, a return loss of -28 dB, and a radiation efficiency of 82.9%, ensuring reliable performance over a broad spectrum. With an electrical size of 0.113λ × 0.136λ × 0.017λ, the proposed design demonstrates remarkable compactness while maintaining stable radiation patterns and high efficiency. These characteristics make the antenna a strong candidate for resilient, interference-resistant, and high-performance applications in defense systems and CubeSat missions.

Next-generation wireless networks (NGWNs) are moving towards a more advanced and exemplary environment encompassing data-intensive, delay-sensitive and energy-efficient services. To accommodate the stringent requirements of these radical and multifarious services, Next-generation wireless heterogeneous networks (NGWHNs) have been envisioned as an exemplary connectivity solution which enables flow based association among user devices (UDs) and radio access technologies (RATs). However, designing an intelligent RAT association scheme for NGWHNs is a significant challenge as the network heterogeneity tends to intensify in terms of access technologies and niche Quality of Service (QoS) requirements. Recently, substantial research endeavours have been carried out in this line of work but they are insufficient in sustaining adequate service levels and RAT capacity constraints concurrently. Inspired by the pitfalls in the pertinent work, an intelligent bilateral RAT selection framework has been developed. The proposed solution facilitates QoS provisioning while adhering to the RAT capacity limitations through well-defined preference functions. Within this paradigm, the proposal explores the diversity of matching game theory and double deep reinforcement learning (DDRL) that facilitates faster convergence to stable and balanced RAT selection policy. Finally, the proposed solution validated with the help of extensive simulations exhibited a significant gain of 42% and 46.35% respectively in terms of system utility and robustness in comparison to other schemes. Eventually, the performance assessment underlines the supremacy of the proposed solution by 22% in terms of system satisfaction with the varying network size.

Wireless energy transfer (WET) is a promising technology to fundamentally settle energy and lifetime problems in a wireless sensor network (WSN). In this paper, we study the operation of WSN based on WET using a mobile charging vehicle (MCV) and construct a periodic strategy to make the network operational permanently. Our goal is to decrease energy consumption of the entire system while maintaining the network operational forever. Based on the analysis of total energy consumption, we propose an energy-efficient renewable scheme (ERSVC) to achieve energy saving. Compared to previous schemes where the MCV visits and charges all nodes in each cycle, the MCV only needs to visit a portion of nodes in ERSVC. Numerical results show that our scheme can significantly decrease the total energy consumption with no performance loss. It is also validated that ERSVC can maintain the network operational forever with lower complexity than other schemes, making it more practical for real networks.

This study presents a novel evaluation framework for optimizing free-space optical (FSO) communication systems by integrating multilevel modulation with orbital angular momentum (OAM)-based data stream multiplexing to maximize spectral efficiency (SE). The proposed architecture synergizes pulse-position modulation (PPM) and dense wavelength-division multiplexing (DWDM), while mitigating impairments from optical nonlinearities and atmospheric turbulence. Key innovations include the fusion of N -encoded adaptive spatial modulation (SM) with L -ary spatial PPM (SPPM) and a diversity strategy employing mixed multiple-input multiple-output (MIMO), multiple-input single-output in an M :1 ratio, and single-input single-output (SISO) configurations. Incorporating SM into FSO systems substantially reduces bit error rate by counteracting turbulence-induced signal degradation. The performance of SPPM, MIMO, and hybrid MIMO-SPPM/MPPM schemes is rigorously analyzed across RF, FSO, and hybrid RF/FSO channels under weak turbulence and dynamic atmospheric conditions. Simulations demonstrate that the hybrid MIMO/OAM-SPPM/MPPM model achieves an average symbol error probability of 10 −10  at 20 dB signal-to-noise ratio (SNR) – outperforming conventional approaches by orders of magnitude. Optimal SE is attained via hybrid modulation over hybrid RF/FSO links, with 4 × 4 MIMO-RF/DWDM-FSO configurations achieving 49 bits/s/Hz at 25 dB SNR and 55 bits/s/Hz at 17 dB SNR – surpassing SISO RF (16 bits/s/Hz) and FSO (21 bits/s/Hz) benchmarks.

Emerging trend of ubiquitous data access is driving the demand for effective wireless communication connectivity. In essence to this, wireless local area network (WLAN) technology seems to be a reliable and cost effective access for the next-generation wireless ecosystem. But the pivotal challenge for WLAN in the next generation wireless networks is to cater the legions of heterogeneous services with characteristic sets of quality of service requirements. However, the strategies present in the existing literature are not accoutered for the application-agnostic association and are incompetent in handling the enormous WLAN state space. Realising the pitfalls of the existing strategies, a novel software-defined networking enabled artificial intelligence framework has been proposed. The proposed framework implements a novel invalid action reduction scheme and double deep reinforcement learning to guarantee the flow based association in a multi-service WLAN environment. Moreover, it allows the multi-parametric optimisation of the association decision and faster convergence to the stable solution. The analytical results validated through the extensive simulations revealed that the proposed scheme achieves high performance gain in terms of convergence, stability and network utility as compared to the other solutions in the literature.

Wireless sensor networks (WSNs) are one of the fundamental infrastructures for Internet of Things (IoTs) technology. Efficient energy consumption is one of the greatest challenges in WSNs because of its resource-constrained sensor nodes (SNs). Clustering techniques can significantly help resolve this issue and extend the network’s lifespan. In clustering, WSN is divided into various clusters, and a cluster head (CH) is selected in each cluster. The selection of appropriate CHs highly influences the clustering technique, and poor cluster structures lead toward the early death of WSNs. In this paper, we propose an energy-efficient clustering and cluster head selection technique for next-generation wireless sensor networks (NG-WSNs). The proposed clustering approach is based on the midpoint technique, considering residual energy and distance among nodes. It distributes the sensors uniformly creating balanced clusters, and uses multihop communication for distant CHs to the base station (BS). We consider a four-layer hierarchical network composed of SNs, CHs, unmanned aerial vehicle (UAV), and BS. The UAV brings the advantage of flexibility and mobility; it shortens the communication range of sensors, which leads to an extended lifetime. Finally, a simulated annealing algorithm is applied for the optimal trajectory of the UAV according to the ground sensor network. The experimental results show that the proposed approach outperforms with respect to energy efficiency and network lifetime when compared with state-of-the-art techniques from recent literature.

How to achieve energy-efficient transmission in radio frequency energy harvesting cognitive radio network (RF-CRN) is of great importance when nodes in CRN are self-maintained. This paper presents a radio frequency (RF) energy harvesting hardware-based underlay cognitive radio network (RH-CRN) structure, where a secondary transmitter (ST) first harvests energy from RF signals source originating from the primary network, and then communicates with a secondary receiver (SR) in underlay mode by using the harvested energy. The total consumed energy by the secondary user (SU) must be equal to or less than the total harvested energy referred to as energy causality constraint, In addition, the ST possesses some initial energy which may be the residual energy from the former transmission blocks, and we consider the energy loss of energy harvesting circuit as a systematic factor as well. Our goal is to achieve the maximum energy efficiency (EE) of the secondary network by jointly optimizing transmitting time and power. To guarantee the quality of service (QoS) of secondary transceiver, a minimum requirement of throughput constraint is imposed on the ST in the process of EE maximization. As the EE maximization is a nonlinear fractional programming problem, a quick iterative algorithm based on Dinkelbach’s method is proposed to achieve the optimal resource allocation. Simulation results show that the proposed strategy has fast convergence and can improve the system EE greatly while ensuring the QoS.