High altitude platform (HAP) drones can provide broadband wireless connectivity to ground users in rural areas by establishing line-of-sight (LoS) links and exploiting effective beamforming techniques. However, at high altitudes, acquiring the channel state information (CSI) for HAPs, which is a key component to perform beamforming, is challenging. In this paper, by exploiting an interference alignment (IA) technique, a novel method for achieving the maximum sum-rate in HAP-based communications without CSI is proposed. In particular, to realize IA, a multiple-antenna tethered balloon is used as a relay between multiple HAP drones and ground stations (GSs). Here, a multiple-input multiple-output X network system is considered. The capacity of the considered M×N X network with a tethered balloon relay is derived in closed-form. Simulation results corroborate the theoretical findings and show that the proposed approach yields the maximum sum-rate in multiple HAPs-GSs communications in absence of CSI. The results also show the existence of an optimal balloon’s altitude for which the sum-rate is maximized.

The application of unmanned aerial vehicles (UAV) as base station (BS) is gaining popularity. In this paper, we consider maximization of the overall data rate by intelligent deployment of UAV BS in the downlink of a cellular system. We investigate a reinforcement learning (RL)-aided approach to optimize the position of flying BSs mounted on board UAVs to support a macro BS (MBS). We propose an algorithm to avoid collision between multiple UAVs undergoing exploratory movements and to restrict UAV BSs movement within a predefined area. Q-learning technique is used to optimize UAV BS position, where the reward is equal to sum of user equipment (UE) data rates. We consider a framework where the UAV BSs carry out exploratory movements in the beginning and exploitary movements in later stages to maximize the overall data rate. Our results show that a cellular system with three UAV BSs and one MBS serving 72 UE reaches $69.2\%$ of the best possible data rate, which is identified by brute force search. Finally, the RL algorithm is compared with a K-means algorithm to study the need of accurate UE locations. Our results show that the RL algorithm outperforms the K-means clustering algorithm when the measure of imperfection is higher. The proposed algorithm can be made use of by a practical MBS–UAV BSs–UEs system to provide protection to UAV BSs while maximizing data rate.

The fundamental issue for Beyond fifth Generation (B5G) is providing a pervasive connection to heterogeneous and various devices in smart environments. Therefore, Drones play a vital role in the B5G, allowing for wireless broadcast and high-speed communications. In addition, the drone offers several advantages compared to fixed terrestrial communications, including flexible deployment, robust Line of Sight (LoS) connections, and more design degrees of freedom due to controlled mobility. Drones can provide reliable and high data rate connectivity to users irrespective of their location. However, atmospheric disturbances impact the signal quality between drones and users and degrade the system performance. Considering practical implementation, the location of drones makes the drone–user communication susceptible to several environmental disturbances. In this paper, we evaluate the performance of drone-user connectivity during atmospheric disturbances. Further, a Machine Learning (ML)-assisted algorithm is proposed to adapt to a modulation technique that offers optimal performance during atmospheric disturbances. The results show that, with the algorithm, the system switches to a lower order modulation scheme during higher rain rate and provides reliable communication with optimized data rate and error performance.

Lung cancer, the treacherous malignancy affecting the respiratory system of a human body, has a devastating impact on the health and well-being of an individual. Due to the lack of automated and noninvasive diagnostic tools, healthcare professionals look forward toward biopsy as a gold standard for diagnosis. However, biopsy could be traumatizing and expensive process. Additionally, the limited availability of dataset and inaccuracy in diagnosis is a major drawback experienced by researchers. The objective of the proposed research is to develop an automated diagnostic tool for screening of lung cancer using optimized hyperparameters such that convolutional neural network (CNN) model generalizes well for universally obtained computerized tomography (CT) slices of lung pathologies. The aforementioned objective is achieved in the following ways: (i) Initially, a preprocessing methodology specific to lung CT scans is formulated to avoid the loss of information due to random image smoothing, and (ii) a sine cosine algorithm optimization algorithm (SCA) is integrated in the CNN model, to optimally select the tuning parameters of CNN. The error rate is used as an objective function, and the SCA algorithm tries to minimize. The proposed method successfully achieved an average classification accuracy of 99% in classification of lung scans in normal, benign, and malignant classes. Further, the generalization ability of the proposed model is tested on unseen dataset, thereby achieving promising results. The quantitative results prove the efficacy of the system to be used by radiologists in a clinical scenario.

This paper presents a novel dual-band flexible antenna, uniquely designed and extended to array as well as MIMO configurations for the Sub-6 GHz band. The single-element monopole antenna features a modified rectangular radiator with two L-strips and a reduced ground plane, enabling a compact dual-band response. The proposed four-element, two-port MIMO configuration is extended from the 1 × 2 array antenna, achieving an overall dimension of 57 × 50 × 0.1 mm³, making it exceptionally compact and flexible compared to existing rigid and bulkier designs. Operating in the 3.6–3.8 GHz and 5.65–5.95 GHz bands, the antenna delivers a high gain of 5.2 dBi and 7.7 dBi, outperforming many designs in terms of gain while maintaining the superior isolation of >22 dB utilizing a defected ground structure (DGS). The design satisfies key MIMO diversity metrics (ECC < 0.05, DG > 9.99) and demonstrates low SAR values (0.0702/0.25 W/kg at 3.75 GHz and 0.175/0.507 W/kg at 5.9 GHz), making it highly suitable for wearable and on-body communication, unlike many rigid counterparts. Fabricated on a flexible polyimide substrate, the antenna addresses challenges such as size, bandwidth, isolation, and safety in MIMO antenna design. The performance, validated through fabrication and measurement, establishes the proposed antenna as a superior alternative to existing MIMO designs for compact, high-performance Sub-6 GHz 5G applications.