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Nonorthogonal multiple access (NOMA) has been envisaged as a potential candidate for the forthcoming 5G cellular networks and beyond 5G networks. The existing user clustering schemes in NOMA systems exploit the channel heterogeneity and channel diversity to partition the users into different clusters by grouping the same number of users to each cluster. Due to the constraint of having the fixed number of users in each cluster, the channel heterogeneity and diversity cannot be fully explored, which causes the existing user clustering scheme to perform poorly in terms of throughput performance. In this article, an efficient and dynamic clustering method termed adaptive user clustering (AUC), which flexibly group the users to different clusters based on their channel conditions regardless of the cluster size, is proposed. The channel heterogeneity and diversity are fully exploited in user grouping that maximizes the system throughput. The clustering mechanism of the proposed AUC scheme is performed using the Brute‐force search (B‐FS) method by searching through all the possible partitions for the best partition with the highest throughput. Simulation results obtained demonstrate that the proposed AUC scheme using the B‐FS method always outperforms the existing user grouping approaches in various network scenarios in terms of throughput performance.

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Software-Defined Networking (SDN) the upcoming popular, reliable, and agile networking for the future. The programmability and centralized control system of SDN attracted many researchers, scientists, and industries to do research and implement it. At the same time, the programmability of Unmanned Aerial Vehicle (UAV) and self-driving Electric Vehicle (EV) are becoming the focal point of future transportation and security. In this paper, we proposed a hypothetical model of SDNUAV-EV architecture to implement SDN with Unmanned Aerial Vehicle (UAV) and self-driving Electric Vehicle (EV) using Satellite which is cost-effective for satellite link budget and SDN architecture.

Integrated circuits (IC) are fabricated on a wafer through stacked layers of circuit patterns. To ensure proper functionality, the overlay of each pattern layer must be within the tolerance. Inspecting each wafer's overlay is unrealistic and impractical. Hence, wafers are selectively inspected at metrology stations through sampling strategies. With virtual metrology (VM), the metrology quality of the uninspected wafers can be estimated. Motivated by a real-world production environment of a 200mm semiconductor manufacturing plant (fab), a VM to estimate the overlay of the photolithography process is envisioned. Past researches on overlay VM leveraged fault detection and classification (FDC) data to estimate the overlay errors. As such, for fabs in the progress of completing their FDC development for photolithography equipment, a different modeling approach is required to realize an overlay VM that sustains the production line until FDC data can be leveraged for VM. With practical gaps that must be addressed in real fabs, this paper focuses on realizing an overlay VM for the photolithography process without leveraging FDC data. Therefore, the objectives of this paper are two folds: First, to identify the research challenges towards realizing the overlay VM. Second, to propose the future research perspectives of the envisioned overlay VM. Based on the future research perspectives, a two-steps overlay VM modeling approach utilizing data mining techniques is proposed toward realizing the envisioned overlay VM system. The proposed approach first classifies the process stability at the wafer lot level, and subsequently, performs overlay error estimations for wafers in the wafer lots classified with stable process. Linear regression models are proposed to perform overlay error estimations in this work to augment the interpretability of the overlay VM.

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