Anutusha Dogra

Bio: Anutusha Dogra is an academic researcher from Shri Mata Vaishno Devi University. The author has contributed to research in topics: Computer science & Wireless network. The author has an hindex of 1, co-authored 1 publications receiving 34 citations.

A Survey on Beyond 5G Network With the Advent of 6G: Architecture and Emerging Technologies

Abstract: Nowadays, 5G is in its initial phase of commercialization. The 5G network will revolutionize the existing wireless network with its enhanced capabilities and novel features. 5G New Radio (5G NR), referred to as the global standardization of 5G, is presently under the $3^{\mathrm {rd}}$ Generation Partnership Project (3GPP) and can be operable over the wide range of frequency bands from less than 6GHz to mmWave (100GHz). 3GPP mainly focuses on the three major use cases of 5G NR that are comprised of Ultra-Reliable and Low Latency Communication (uRLLC), Massive Machine Type Communication (mMTC), Enhanced Mobile Broadband (eMBB). For meeting the targets of 5G NR, multiple features like scalable numerology, flexible spectrum, forward compatibility, and ultra-lean design are added as compared to the LTE systems. This paper presents a brief overview of the added features and key performance indicators of 5G NR. The issues related to the adaptation of higher modulation schemes and inter-RAT handover synchronization are well addressed in this paper. With the consideration of these challenges, a next-generation wireless communication architecture is proposed. The architecture acts as the platform for migration towards beyond 5G/6G networks. Along with this, various technologies and applications of 6G networks are also overviewed in this paper. 6G network will incorporate Artificial intelligence (AI) based services, edge computing, quantum computing, optical wireless communication, hybrid access, and tactile services. For enabling these diverse services, a virtualized network slicing based architecture of 6G is proposed. Various ongoing projects on 6G and its technologies are also listed in this paper.

Intelligent routing for enabling haptic communication in 6G Network

Abstract: The article proposes an efficient technique for optimal resource allocation and delay minimization in NG networks. In 6G network, the density of the users will be very high, and to handle their demand, intelligent algorithms and techniques are required. Moreover, the 6G network has promised to enable many advanced ultra-low latency applications like holographic imaging, haptic communication, Tele driving, Tele Robotics, and many more. In this article, the condition of call drop due to the non-availability of resources has been addressed. Further, this condition induces an extra delay in the network, which is not acceptable in the case of latency-sensitive applications like haptic communication. The problem can be solved by incorporating Wireless Intelligent Router (WIR) in the network. The router will monitor the network and re-route the users if network congestion occurs. The WIR is a Reinforcement Learning based trained router that intelligently handles the user's demand by connecting it to the available proximal access point. It ensures an optimal power allocation to the user based on the demanded application. The simulation results show that the WIR has reduced the average power consumption and minimized the delay in the network.

Reinforcement Learning (RL) for optimal power allocation in 6G Network

Abstract: The article introduces a Reinforcement learning (RL) based approach for optimal power allocation to the users based on the application demand. The density of users in a 6G network will be very high. The need for high data rates will also increase tremendously. Various applications like haptic communication, telerobotics, telesurgery, holographic imaging, etc., will require very high data rates and reliable connections for their realization. This paper proposes an RL-based approach in which the agent will select the access point with the highest SNR for establishing a communication link with the user. The optimal power is then allocated based on the application demand. The collection of rewards depends on the quality of service and the quality of experience. The agent always works for reward maximization by ensuring the quality of service. It is analyzed from the simulation results that the optimal power allocation enhances the energy efficiency and reduces the power utilization of the network. The proposed approach helps in effective spectrum utilization and minimizing power wastage.

Joint resource and power allocation for 5G enabled D2D networking with NOMA

Abstract: Paramount data transfer among users in social groups and concerts imposes enormous traffic at the base station (BS), depleting the available resources and forcing fifth-generation (5G) researchers to seek communion with alternative technologies. Non-Orthogonal Multiple Access (NOMA) is determined as a potential technology for supporting multiple users on the same sub-channels using power domain multiplexing. Integration of NOMA with a Device-to-Device (D2D) communication technology provides lower latency for supporting proximity communication. Significant research has been carried out till date in device pairing for resource allocation in NOMA and D2D. This paper proposes a joint resource and power allocation in the NOMA (JRPAN) algorithm for resource and power allocation to cellular and D2D users. Resources and power are adaptively assigned to the users depending upon the user's application. A proposal of group formation for resource reuse in NOMA with prioritized user application is established. The Simulations have been performed to ascertain the fair allocation of resources to the users in conformity to the application. The proposed algorithm helps in conserving power as compared to orthogonal frequency division multiplexing (OFDMA) with the Hidden Markov Model (HMM). The results demonstrate that JRPAN achieves higher system throughput and saves power while ensuring the requirement of Quality of Experience and Quality of service in comparison to OFDMA-HMM and proportional algorithm.

Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research

Abstract: Emerging applications such as Internet of Everything, Holographic Telepresence, collaborative robots, and space and deep-sea tourism are already highlighting the limitations of existing fifth-generation (5G) mobile networks. These limitations are in terms of data-rate, latency, reliability, availability, processing, connection density and global coverage, spanning over ground, underwater and space. The sixth-generation (6G) of mobile networks are expected to burgeon in the coming decade to address these limitations. The development of 6G vision, applications, technologies and standards has already become a popular research theme in academia and the industry. In this paper, we provide a comprehensive survey of the current developments towards 6G. We highlight the societal and technological trends that initiate the drive towards 6G. Emerging applications to realize the demands raised by 6G driving trends are discussed subsequently. We also elaborate the requirements that are necessary to realize the 6G applications. Then we present the key enabling technologies in detail. We also outline current research projects and activities including standardization efforts towards the development of 6G. Finally, we summarize lessons learned from state-of-the-art research and discuss technical challenges that would shed a new light on future research directions towards 6G.

Federated learning for malware detection in IoT devices

Abstract: This work investigates the possibilities enabled by federated learning concerning IoT malware detection and studies security issues inherent to this new learning paradigm. In this context, a framework that uses federated learning to detect malware affecting IoT devices is presented. N-BaIoT, a dataset modeling network traffic of several real IoT devices while affected by malware, has been used to evaluate the proposed framework. Both supervised and unsupervised federated models (multi-layer perceptron and autoencoder) able to detect malware affecting seen and unseen IoT devices of N-BaIoT have been trained and evaluated. Furthermore, their performance has been compared to two traditional approaches. The first one lets each participant locally train a model using only its own data, while the second consists of making the participants share their data with a central entity in charge of training a global model. This comparison has shown that the use of more diverse and large data, as done in the federated and centralized methods, has a considerable positive impact on the model performance. Besides, the federated models, while preserving the participant's privacy, show similar results as the centralized ones. As an additional contribution and to measure the robustness of the federated approach, an adversarial setup with several malicious participants poisoning the federated model has been considered. The baseline model aggregation averaging step used in most federated learning algorithms appears highly vulnerable to different attacks, even with a single adversary. The performance of other model aggregation functions acting as countermeasures is thus evaluated under the same attack scenarios. These functions provide a significant improvement against malicious participants, but more efforts are still needed to make federated approaches robust.

Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications

Abstract: Ever since the introduction of fifth generation (5G) mobile communications, the mobile telecommunications industry has been debating whether 5G is an “evolution” or “revolution” from the previous legacy mobile networks, but now that 5G has been commercially available for the past few years, the research direction has recently shifted towards the upcoming generation of mobile communication system, known as the sixth generation (6G), which is expected to drastically provide significant and evolutionary, if not revolutionary, improvements in mobile networks. The promise of extremely high data rates (in terabits), artificial intelligence (AI), ultra-low latency, near-zero/low energy, and immense connected devices is expected to enhance the connectivity, sustainability, and trustworthiness and provide some new services, such as truly immersive “extended reality” (XR), high-fidelity mobile hologram, and a new generation of entertainment. Sixth generation and its vision are still under research and open for developers and researchers to establish and develop their directions to realize future 6G technology, which is expected to be ready as early as 2028. This paper reviews 6G mobile technology, including its vision, requirements, enabling technologies, and challenges. Meanwhile, a total of 11 communication technologies, including terahertz (THz) communication, visible light communication (VLC), multiple access, coding, cell-free massive multiple-input multiple-output (CF-mMIMO) zero-energy interface, intelligent reflecting surface (IRS), and infusion of AI/machine learning (ML) in wireless transmission techniques, are presented. Moreover, this paper compares 5G and 6G in terms of services, key technologies, and enabling communications techniques. Finally, it discusses the crucial future directions and technology developments in 6G.

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Abstract: Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next ten years. Many references suggested that the 6G wireless network standard may arrive around 2030. Therefore, this paper presents a critical analysis of 5G wireless networks’, significant technological limitations and reviews the anticipated challenges of the 6G communication networks. In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning. To this end, we present our vision on how the 6G communication networks should look like to support the applications of these domains. This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore. The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard.

6G vision: An ultra-flexible perspective

Abstract: The upcoming sixth generation (6G) communications systems are expected to support an unprecedented variety of applications, pervading every aspect of human life. It is clearly not possible to fulfill the service requirements without actualizing a plethora of flexible options pertaining to the key enabler technologies themselves. At that point, this work presents an overview of the potential 6G key enablers from the flexibility perspective, categorizes them, and provides a general framework to incorporate them in the future networks. Furthermore, the role of artificial intelligence and integrated sensing and communications as key enablers of the presented framework is also discussed.