Department of Mathematics

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Author: search.filters.author.Kulshrestha, RakheeSubject: search.filters.subject.Cognitive radio network

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Now showing 1 - 4 of 4
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    Transient analysis of energy-saving strategy for cognitive radio networks using G-queue with heterogeneity
    (Elsevier, 2024-02) Kulshrestha, Rakhee
    Energy efficiency, high data speeds, and effective spectrum utilization stand as the primary factors governing 5G and beyond networks. A base station (BS) in a cognitive radio network (CRN) that conserves energy during sleep periods is a promising contender for achieving more effective spectrum allocation. An efficient energy-saving strategy is offered in this study to achieve greener communication in wireless cellular networks. This study aims to propose and analyze an energy-saving scheme for the BS, taking into account the heterogeneity and reliability of networks. The entire system is modeled as a three-dimensional Markov chain by establishing a discrete-time preemptive priority queueing model with single vacation, heterogeneous packets, and negative packets such as viruses. Then, the transient analysis of the Markov chain is performed using the recursive method. Utilizing the transient probability distribution, we present numerical results derived from reliability analysis and queueing analysis to substantiate the validity of the proposed model. Furthermore, results based on the energy-saving degree are showcased to affirm the effectiveness of the proposed scheme. Finally, a reward cost function is obtained depending on the successful transmission of secondary packets, and the Quasi-Newton method is used to determine the optimal reward cost.
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    Comparative study on spectrum sensing and modulation techniques in cognitive radio network
    (IEEE, 2025-03) Kulshrestha, Rakhee
    This paper conducts a comparative analysis of two prominent spectrum sensing methods, energy detection (ED) and matched filter detection (MFD), under different modulation schemes, specifically binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), and 64-quadrature amplitude modulation (QAM), in an Additive White Gaussian Noise (AWGN) channel. The study evaluates the performance of ED and MFD in terms of probability of detection, false alarm, and sensitivity to signal-to-noise ratio (SNR). Simulation results indicate that 64-QAM is the preferred modulation scheme for achieving superior spectral efficiency. The analysis leverages receiver operating characteristic (ROC) curves to highlight the trade-offs between the probability of detection and probability of false alarm. These findings provide critical insights into the selection of appropriate spectrum sensing techniques, enhancing overall spectrum utilization in cognitive radio network.
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    Multi-objective optimization of advanced sleep mode for energy saving in cognitive radio network
    (Elsevier, 2025-09) Kulshrestha, Rakhee
    The Advanced Sleep Modes (ASM) concept corresponds to entering the Base Station (BS) progressively deeper and less energy-intensive states to reduce energy consumption. Introducing the ASM can mitigate energy wastage during low-traffic periods in the Cognitive Radio Network (CRN). In this study, we propose a strategy for integrating ASM within the CRN architecture to effectively handle primary and secondary traffic across varying ASM sleep states. Additionally, we study the general scenario of CRN with heterogeneous secondary users, imperfect sensing, and unreliable BS due to the arrival of negative packets (virus attack). By modeling the entire system as a three-dimensional discrete-time Markov chain, we conduct the transient analysis of the proposed model. Through numerical demonstrations involving reliability and queueing analyses, we substantiate the validity of the proposed model and examine the impact of reliability on its performance. Then, we showcased the effectiveness of the ASM strategy by comparing it with the Sleep Mode (SM) strategy in terms of the waiting time and blocking probability of the secondary user and the degree of energy savings. Also, simulation experiments are conducted to corroborate the accuracy and validity of the numerical results. Finally, we formulate the Cost Benefit Function (CBF), which depends on both the successful transmission and waiting time of secondary packets. Subsequently, we obtain the Pareto optimal solution for CBF and the degree of energy saving using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) techniques for multi-objective optimization.
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    Analysis of Spectrum Sensing and Spectrum Access in Cognitive Radio Networks With Heterogeneous Traffic and pp-Retry Buffering
    (IEEE, 2022-07) Kulshrestha, Rakhee
    It is well known that cognitive radio (CR) techniques have great potential to deal with the problem of radio spectrum scarcity. Spectrum sensing technique plays a critical role in enabling unlicensed secondary users (SUs) to utilize spectrum holes in cognitive radio networks (CRNs). However, a licensed primary user (PU) can be appropriately protected by simultaneously performing spectrum sensing and data transmission i.e., by using full-duplex (FD) mode. In this paper, we have proposed and analyzed a strategy which includes spectrum sensing and spectrum access mechanisms both. We consider heterogeneous traffic of real-time and non real-time SUs based on their different delay tolerance characteristics. We address the issue of false alarm rate (FAR) associated with FD sensing. Spectrum handoff and call buffering strategies with p -Retry policy are employed jointly so that SUs that would otherwise be blocked or forcibly dropped could be buffered and possibly served later. To evaluate the performance of the proposed strategy, five-dimensional continuous time Markov chain (CTMC) model is developed and the queueing-theoretic approach is utilized. Numerical results demonstrate the influence of spectrum sensing errors on the performance of such CRNs. Results also reveal that the provision of buffers under retrial policy increases the overall network resource utilization while decreasing blocking and dropping probabilities.