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A Comparative Analysis on Machine Learning based Intelligent Forwarding Schemes in VNDN
Ayesha Siddiqa,Muhammad Toaha Raza Khan,Md.Mahmudul Islam,Muhammad Ashar Tariq,Malik Muhammad Saad,Dongkyun Kim 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
Vehicular named data networks (VNDN) schemes use predefined routing information for communication rather than dynamic network information. Therefore, the researchers encourage to integrate the machine learning (ML) algorithms in VNDN for intelligent communication. In this paper, we have discussed the machine learning (ML) based vehicular named data network (VNDN) schemes for intelligent packet forwarding in highly dynamic topology. We have discussed the intelligent ML-VNDN forwarding schemes which take dynamic network information for interest/data forwarding to increase communication efficiency. Moreover, this article explains the comparative analysis between naïve VNDN schemes and ML-based NDN schemes, also discussed their future directions.
Cell-free massive multiple-input multiple-output challenges and opportunities: A survey
Ajmal Mahnoor,Siddiqa Ayesha,Jeong Bomi,Seo Junho,Kim Dongkyun 한국통신학회 2024 ICT Express Vol.10 No.1
Cell-free (CF) massive multiple-input multiple-output (mMIMO) system is a state-of-the-art emerging technology targeted towards beyond fifth-generation (B5G) and sixth-generation (6G) communication networks. This network pertains to a dense deployment of access points (APs) dispersed over a large geographical area to serve a small number of users at the same frequency and time resources. The CF-mMIMO architecture offers resilient connectivity, interference management, power efficiency, high throughput, and macrodiversity. Moreover, this communication technique eliminates cell boundaries and facilitates the users by introducing overlapping regions, thus providing consistent quality of service (QoS) throughout the region. However, the complexity of CF-mMIMO systems increases considerably when numerous APs are dispersed over a large geographical area. Therefore, several studies have been carried out to determine the optimal solution with minimum complexity of the CF-mMIMO system. Herein, a thorough investigation of the literature on the CF-mMIMO system is presented, considering all aspects from architecture to applications. The study provides a detailed survey of CF-mMIMO architecture, fronthaul, and backhaul, as well as the challenges associated with them; deployment methodologies and challenges for practical implementation of CF-mMIMO systems are also discussed. Furthermore, we reviewed the impact of transmitter and receiver antennae on the capacity of CF-mMIMO enabled with millimeter wave (mmWave). The numerical findings indicate that the higher degree of freedom required for spatial multiplexing allows multiantenna users to surpass single-antenna users in terms of capacity. This study holds significance owing to the thorough examination of the CF-mMIMO system model, channel estimation, scalability problems, working algorithms, communication protocol, deep learning-based solutions, linkage to B5G and 6G, and key challenges. Moreover, this study presents a detailed discussion and research survey on the system model, deployment issues, deep learning, and potential applications of the CF-mMIMO system.