The proposed paper presents a flexible antenna that is capable of operating in several frequency bands, namely 2.45 GHz, 5.8 GHz, and 8 GHz. The first two frequency bands are frequently utilized in industrial, scientific, and medical (ISM) as well as wireless local area network (WLAN) applications, whereas the third frequency band is associated with X-band applications. The antenna, with dimensions of 52 mm × 40 mm (0.79 λ × 0.61 λ), was designed using a 1.8 mm thick flexible kapton polyimide substrate with a permittivity of 3.5. Using CST Studio Suite, full-wave electromagnetic simulations were conducted, and the proposed design achieved a reflection coefficient below −10 dB for the intended frequency bands. Additionally, the proposed antenna achieves an efficiency value of up to 83% and appropriate values of gain in the desired frequency bands. In order to quantify the specific absorption rate (SAR), simulations were conducted by mounting the proposed antenna on a three-layered phantom. The SAR_1g values recorded for the frequency bands of 2.45 GHz, 5.8 GHz, and 8 GHz were 0.34, 1.45, and 1.57 W/Kg respectively. These SAR values were observed to be significantly lower than the 1.6 W/Kg threshold set by the Federal Communication Commission (FCC). Moreover, the performance of the antenna was evaluated by simulating various deformation tests.

Compared to 4G long-term evolution (LTE) networks, 5G and 6G networks provide fast data transmission with little delay, larger base station capacity, enhanced quality of service (QoS), and extensive multiple-input-multiple-output (MIMO) channels. Nevertheless, the attainment of mobility and handover (HO) in 5/6G networks has been hindered by substantial changes in intelligent devices and the high-definition applications of multimedia. Therefore, the existing cellular network is compared with difficulties in transmitting large amounts of data at a faster rate, ensuring high QoS, minimizing latency, and efficiently managing HOs and mobility. This paper primarily addresses the difficulties related to HO and mobility management in 5G/6G networks. The findings of this paper emphasize the importance of aligning mobility and HO strategies with sustainable development goals to reduce energy consumption and improve resource allocation. It focuses on integrating innovative technologies such as artificial intelligence and machine learning to enhance the sustainability and efficiency of mobility and HO management. The paper provides a comprehensive analysis of the current body of the literature and explores essential metrics for measuring performance (known as KPIs) and potential solutions for difficulties linked to HO and mobility. The analysis takes into account established standards in the field. Furthermore, it assesses the effectiveness of existing models in dealing with HO and mobility management problems, considering criteria such as energy efficiency, dependability, latency, and scalability. This survey concludes by highlighting key challenges associated with HO and mobility management in existing research models. It also offers comprehensive assessments of the proposed solutions, accompanied by suggestions for future research.

A revolutionary age in telecommunications is being ushered in by the confluence of machine learning (ML) with fifth-generation (5G) wireless communication technologies and beyond. This research investigates ML approaches in 5G networks for adaptive spectrum usage, quality of service (QoS) management, predictive maintenance, and network optimization. By leveraging ML algorithms, 5G networks can forecast user behavior, allocate resources optimally, and dynamically adjust to changing conditions, enhancing performance and dependability. Additionally, ML-driven methods improve cybersecurity in 5G settings. Furthermore, the integration of ML in 5G networks is pivotal for advancing intelligent transportation systems, enabling dynamic route optimization, adaptive traffic management, and enhanced vehicular communication. Intelligent networks will transform wireless communication by replacing traditional processing with end-to-end solutions, utilizing cognitive radio systems and deep reinforcement learning for optimized spectrum sharing and efficiency. Despite significant potential, challenges such as interoperability, security, scalability, and energy efficiency must be addressed. This paper discusses these challenges and highlights future trends beyond 5G, emphasizing ML’s critical role in shaping the future of wireless communication systems.

In this paper, we undertake an examination of the complicated challenges associated with bandwidth allocation and power control in multi- unmanned aerial vehicles (UAVs) network. The focal point of our investigation is the formulation and subsequent proposition of a novel algorithm aimed at optimizing bandwidth utilization and energy efficiency across multiple UAVs. The intricate joint optimization problem is cast as a nontrivial nonlinear programming (NLP) challenge; thus, to efficiently address it, we split it into two subproblems. In the first sub-problem, UAVs bandwidth resources are being optimized while considering that they are operating at their maximum transmit powers. In the second sub-problem and after optimizing bandwidth allocations, UAVs transmit powers are optimized based on their inter-links speed. Sequential quadratic programing (SQP) based on coloring graph representation is proposed to address the first sub-problem, while $\mathcal {M}$ - matrix theory is employed to address the second one. Rigorous numerical simulations are conducted to prove the effectiveness of the proposed scheme against other benchmarks in maximizing both data rate and energy efficiency of the proposed multi-UAV network.

Millimeter wave (mmWave), reconfigurable intelligent surface (RIS), and unmanned aerial vehicles (UAVs) are considered vital technologies of future six-generation (6G) communication networks. In this paper, various UAV mounted RIS are distributed to support mmWave coverage over several hotspots where numerous users exist in harsh blockage environment. UAVs should be spread among the hotspots to maximize their average achievable data rates while minimizing their hovering and flying energy consumptions. To efficiently address this non-polynomial time (NP) problem, it will be formulated as a centralized budget constraint multi-player multi-armed bandit (BCMP-MAB) game. In this formulation, UAVs will act as the players, the hotspots as the arms, and the achievable sum rates of the hotspots as the profit of the MAB game. This formulated MAB problem is different from the traditional one due to the added constraints of the limited budget of UAVs batteries as well as collision avoidance among UAVs, i.e., a hotspot should be covered by only one UAV at a time. Numerical analysis of different scenarios confirm the superior performance of the proposed BCMP-MAB algorithm over other benchmark schemes in terms of average sum rate and energy efficiency with comparable computational complexity and convergence rate.

In the context of 6G and 5G communication networks, particularly in disaster-stricken areas with a surging demand for connectivity and the rapid evolution of the internet of everything (IoE), the imperative for augmenting spectrum efficiency in unmanned aerial vehicles (UAVs) communication-enabled wireless networks becomes ever more pronounced. This work embarks on the mission of significantly enhancing spectrum efficiency by capitalizing on redundancies, such as correlations inherent in data sources like co-located video sensors. While the intuitive potential of exploiting redundancies for spectrum efficiency enhancement is apparent, our approach is systematically structured. We introduce a linear programming framework designed to meticulously allocate bandwidth to individual links based on their respective demands, while judiciously adhering to spatial spectrum reuse constraints. Subsequently, we employ this linear program to empirically quantify the improvements in spectrum efficiency engendered by source correlations. Furthermore, we elucidate various strategies for harnessing these correlations to maximize spectrum efficiency gains, while navigating the trade-off terrain between computational complexity and precision. Our findings resoundingly underscore the trans-formative power of identifying the precise set of source correlations, resulting in spectrum efficiency enhancements of up to two orders of magnitude. In terms of network performance, the judicious exploitation of source correlations grants admission to nearly 100% of delay-intolerant traffic, alongside substantial reductions in mean delay for delay-tolerant traffic.”