As an enhancement of cellular networks, the future-generation 5G network can be considered an ultra-high-speed technology. The proposed 5G network might include all types of advanced dominant technologies to provide remarkable services. Consequently, new architectures and service management schemes for different applications of the emerging technologies need to be recommended to solve issues related to data traffic capacity, high data rate, and reliability for ensuring QoS. Cloud computing, Internet of things (IoT), and software-defined networking (SDN) have become some of the core technologies for the 5G network. Cloud-based services provide flexible and efficient solutions for information and communications technology by reducing the cost of investing in and managing information technology infrastructure. In terms of functionality, SDN is a promising architecture that decouples control planes and data planes to support programmability, adaptability, and flexibility in ever-changing network architectures. However, IoT combines cloud computing and SDN to achieve greater productivity for evolving technologies in 5G by facilitating interaction between the physical and human world. The major objective of this study provides a lawless vision on comprehensive works related to enabling technologies for the next generation of mobile systems and networks, mainly focusing on 5G mobile communications.

Gliomas are brain tumors starting in the glial cells. Gliomas can be low grade (slow growing) or high grade (fast growing). Physicians use the grade of a brain tumor based on gliomas to decide which treatment a patient needs. The condition of the tumor is of vital importance for the treatment. In this paper, we propose a computerized system to differentiate between normal brain and abnormal brain with tumor in the MRI images and also further classify the abnormal brain tumors into HGG or LGG tumors. The proposed computerized system uses k-means as the segmentation technique for clustering whilst Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) are the main parts of the feature extraction and feature reduction mechanisms, respectively. Support vector machine (SVM) is a major part of our proposed system as it classifies the abnormal brain tumors in the LGG and HGG after the extraction and reduction of the features.

In the realm of modern wireless networks, the integration of wireless sensing and communication systems is pivotal, especially in the context of the forthcoming 6G Internet of Things (IoT) paradigm. The popularity of integrated sensing and communication (ISAC) stems from its potential to amplify the utilization of existing network infrastructures. This study introduces an innovative fusion of joint communication and sensing (JCAS), a framework of ISAC, combined with end-to-end (E2E) network slicing (NS) techniques. The aim is to meet user Quality-of-Service (QoS) expectations within the ambit of 6G IoT applications. The prime objective is to optimize resource allocation for communication and sensing services within a bespoke network slice tailored for 6G IoT scenarios. This is achieved by minimizing E2E system latency, essential for real-time decision making in 6G IoT environments. The optimization challenge is tackled by using deep reinforcement learning (DRL) in the form of a deep $Q$ network (DQN) algorithm, which is adept at addressing nonlinear integer programming (NLIP) issues intrinsic to 6G IoT settings. Comprehensive simulations validate the approach, demonstrating its effectiveness in the context of 6G IoT networks. The amalgamation of ISAC with E2E NS emerges as a potent strategy for furnishing enhanced services customized for 6G IoT applications, successfully fulfilling consumers’ QoS requisites. This integrated approach holds substantial promise as a robust solution for addressing the exacting demands of forthcoming wireless networks, particularly those underpinned by the strides in 6G IoT technologies.

Due to the proliferation of devices and the availability of computing servers, mobile edge computing (MEC) has gained popularity in executing various computational tasks. MEC offers computing services at the network’s edge, providing user equipment (UE) with reduced latency for their applications. However, determining a suitable offloading policy for UEs in MEC, considering wireless resource allocation and power management, is computationally demanding. Additionally, the problem is NP-hard, making it challenging to find an optimal solution within a reasonable time frame. In this work, we propose a meta-reinforcement learning (MRL) based computational task offloading and power control mechanism for UEs in a resource-constrained environment of a MEC network to tackle the NP-hardness of the problem. We first develop an optimization problem to maximize UE computation efficiency by minimizing their power consumption for local computing and uplink transmission of UEs in the MEC network. We propose to use both binary offloading (full offloading or full local computing) and partial offloading schemes in the system. Our proposed MRL algorithm can figure out a suitable offloading policy for UEs within a short time. Unlike the traditional deep-reinforcement learning algorithms, our approach can resolve the issue of obtaining proper solutions in a new environment. Extensive simulation results prove the feasibility of our proposed work.

Detection of brain tumor is an intricate job that has yet to be well furnished. At present, brain tumor is rising by a large scale amidst us. To detect and segment tumor from the MRI images we have designed an automated scheme. Our scheme comprises of Otsu binarization followed by K means clustering for segmentation. To extract features and reduce the dimensions of features Discrete Wavelet Transform (DWT) followed by Principle Component Analysis (PCA) is respectively used. The reduced features were submitted to a Support Vector Machine (SVM) for classification. The performance evaluation of the proposed scheme is made by comparing the results with the ground truth of each processed image using Sensitivity, Specificity and Accuracy calculations.