Anal Paul

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• Doctor of Philosophy
• Assistant Professor at Yuan Ze University

The present work explores mitigation in end-to-end outage probability in an energy harvesting (EH) enabled cognitive radio networks (CRNs) through reinforcement learning (RL) based multi-hop Q-routing. The operation of CRN follows a frame structure that includes cooperative spectrum sensing (CSS), based on the decision of which, secondary transmit...

In this paper, we present a framework that integrates digital twin (DT) technology into space-air-ground integrated networks (SAGINs) to enhance vehicular edge computing (VEC). Our objective is to efficiently offload tasks in ultra-reliable low-latency communications (URLLC)-enabled vehicular networks, focusing on minimizing overall latency for req...

This paper presents an innovative large model framework for optimizing the task offloading efficiency in vehicular edge networks, with a focus on ultra-reliable lowlatency communication. We introduce a comprehensive model that integrates quantum computing with a deep reinforcement learning (DRL) model, supported by long short-term memory (LSTM) net...

This work proposes a new model of reconfigurable intelligent surface (RIS) called cognizable RIS (CRIS) that is specifically designed to meet the unique demands of users who require extreme-ultra-reliable and low-latency Communication (xURLLC) in the sixth generation (6G) wireless networks. The programmable elements in the proposed CRIS unit can ad...

This work proposes a quantum-aided deep reinforcement learning (DRL) framework designed to enhance the accuracy of direction-of-arrival (DoA) estimation and the efficiency of computational task offloading in integrated sensing and communication systems. Traditional DRL approaches face challenges in handling high-dimensional state spaces and ensurin...

We introduce, for the first time in wireless communication networks, a quantum gradient descent (QGD) algorithm to maximize sum data rates in non-orthogonal multiple access (NOMA)-based simultaneously transmitting and reflecting recon-figurable intelligent surface (STAR-RIS)-assisted multiple-input and multiple-output systems. The QGD algorithm uti...

This paper introduces a novel multi-agent federated deep reinforcement learning (MA-FDRL) framework designed to minimize vehicular task offloading latency in electrified road (eROAD) environments. The solution integrates inductive coil-based wireless power transfer (WPT) systems with full duplex multiple input and multiple output (MIMO) vehicular n...

In the context of ultra-reliable low-latency communication (URLLC) in Internet-of-Things (IoT) networks, conventional half-space coverage limits the flexibility of reconfigurable intelligent surface (RIS) deployment. To overcome these constraints, this paper makes use of active simultaneously transmitting and reflecting RIS (STAR-RIS), which is sea...

This paper presents a novel design for a mobile edge computing (MEC) service that integrates digital twin technology with an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). This configuration leverages edge intelligence, aiming to strengthen ultra-reliable and low-latency communications (URLLC) withi...

In this paper, we present an innovative approach to spectrum management in cognitive radio networks (CRNs) aimed at serving ultra-reliable low-latency communication (URLLC) enabled secondary users (SUs). Unmanned aerial vehicles (UAVs) are deployed for accurate and reliable spectrum sensing (SS), enhancing cooperative spectrum sensing (CSS) effecti...

The substantial power consumption attributed to the active components within fully-connected reconfigurable intelligent surface (RIS) architecture significantly hinders the efficiency and sustainability of DT-enabled MEC networks. To tackle this challenge, we present an innovative sub-connected architecture for active RIS within the digital twin (D...

This work addresses the threat of Spectrum Sensing Data Falsification (SSDF) attacks in interweave Cognitive Radio Networks (CRNs), which manipulate the spectrum sensing process and consequently affect the overall performance and reliability of CRNs. We exploit passive and active Reconfigurable Intelligent Surfaces (RIS) to counter these issues and...

This paper investigates the performance of a sub-connected active reconfigurable intelligent surface (RIS)-assisted communication system under imperfect channel state information (CSI). To ensure reliable transmission, we formulate an optimization problem aimed at maximizing the energy efficiency (EE) of the system. This optimization problem involv...

This letter investigates a reconfigurable intelligent surface (RIS)-assisted overlay cognitive radio network (CRN) that incorporates rate splitting multiple access (RSMA) and energy harvesting (EH) schemes to provide resilience during transmission power outages. In particular, we focus on maximizing the CRN's throughput using the joint allocation o...

This paper investigates the problem of spectral efficiency maximization in an underlay cognitive radio network (CRN) utilizing rate-splitting multiple access (RSMA) transmission for MISO downlink under short packet communications and imperfect channel estimation information. In particular, we focus on an effective transmit beamforming design at the...

In this study, we present a novel approach for efficient resource allocation in a digital twin (DT) framework for task offloading in a UAV-aided Internet-of-Vehicles (IoV) network. Our approach incorporates a hybrid machine learning approach that combines asynchronous federated learning (AFL) and multi-agent deep reinforcement learning (DRL) to joi...

The main challenge with Vehicular Ad-Hoc Networks (VANETs) for assisting Intelligent Transportation Services (ITSs) is ensuring effective data delivery under various network circumstances despite the scarcity of radio frequency spectrum channels. Meanwhile, Dynamic Spectrum Access (DSA) utilizing Cognitive Radio (CR) technology shows its potential...

The present work explores the scope of cognitive radio networks (CRNs) to support the spectrum demand on future diverse application specific wireless services supported by long-range multi-hop relay-based transmission. The essential requirement is dynamic route establishment where the traditional non-adaptive routing algorithms are not sufficient t...

In Spectrum Sensing Data Falsification (SSDF) attacks, malicious Secondary Users (SUs) actively send erroneous local sensing results to the Fusion Centre (FC) that influence the actual outcomes of Cooperative Spectrum Sensing (CSS). Existing trust value-based algorithms are partially successful as SUs can quickly change their characteristics from h...

Research works on cognitive radio networks (CRNs) together with energy harvesting (EH) promise to address the spectrum scarcity and limited battery power problems on the wireless communication nodes. While radio frequency (RF) signal of primary user (PU) can be used in EH, its absence (non-transmission state) offers an unused spectrum for the avail...

The present work explores a multihop cognitive radio network model that adopts energy harvesting (EH) and reinforcement learning based Q-routing. An optimization problem is formulated that maximizes the total residual energy (RE) in the secondary network under the constraints of primary user (PU) cooperation rate, secondary outage probability, and...

Cooperation among the sensing nodes in cognitive radio (CR) networks improves spectrum sensing (SS) reliability. However, determining the efficient set of secondary users (SUs) to be involved in cooperative SS (CSS) becomes challenging in vehicular networks due to the mobility of primary user (PU) and/or SU nodes. This work explores an energy detec...

Recent progress in cognitive radio networks (CRNs) promises to meet device-to-device (D2D) communication requirements for spectrum utilization and power control to support billions of machines/devices to be connected worldwide. The architecture of CRN must maintain a high data rate (throughput) at low power consumption which requires both spectrum...

Cooperative spectrum sensing (CSS) in cognitive radio network (CRN) is highly recommended to avoid the interference from secondary users (SUs) to primary user (PU). Several studies report that clustering-based CSS technique improves the system performance, among them fuzzy c-means (FCM) clustering algorithm is widely explored. However, it is observ...

Cognitive radio networks (CRNs) look promising to mitigate the spectrum underutilization problem by providing an opportunistic communication of secondary users (SUs) over the licensed or primary user (PU) band through spectrum sensing (SS). This leads to an increase in spectral efficiency (SE) of the network. Cooperative CRN (CCRN) not only enables...

This paper addresses the problem of optimal resource allocation in a multi-hop cognitive radio networks. The objective of the present work is to search for the shortest possible path from the source to the destination and explores the scope of the Bellman--Ford and the Dijkstra's algorithms due to their low runtime complexity and ease of implementa...

This paper explores joint power allocation and route selection in a multi-hop cognitive radio network consisting of secondary transmitter and receiver connected through decode-and-forward relays. A novel frame structure of radio frequency energy harvesting (EH)-cooperation-transmission is considered that operates in time switching mode. The relays...

This paper considers the problem of joint spectrum sensing and secondary data transmission in the relay-assisted cognitive radio networks. An optimization problem is formulated that searches for the cluster-wise relay selection and consequent power allocation with an objective to maximize the sum throughput of the secondary network under the constr...

Fuzzy c-means (FCM) clustering is extensively used on the energy detection based cooperative spectrum sensing (CSS) to enhance the efficiency of the system. The performance of FCM degrades at low signal-to-noise ratio (SNR) due to the non-spherical energy values at fusion center (FC). The proposed work explores the scope of possibilistic fuzzy c-me...

Cooperation in spectral sensing (SS) offers a fast and reliable detection of primary user (PU) transmission over a frequency spectrum at the expense of increased energy consumption. Since the fusion center (FC) has to handle a large set of data, a cluster based approach, specifically fuzzy c-means clustering (FCM), has been extensively used in ener...

Present research trend for image encryption mostly depends on chaos based image encryption. Chaotic behavior of a system is so much unpredictable due to its very sensitive initial parameters; a slight change in those parameters of a chaotic system will display drastic changes in output. There are a lot of research works for Gray level image encrypt...

In recent days chaos based image encryption is going through under research and implementation. Some chaos based algorithms are working well and resists many type of crypto analysis attacks, but it takes lot of time for encryption and decryption. Some of chaos based algorithms are very fast but their strength to resist attack is questionable. So th...