DEVELOPMENT OF ENERGY HARVESTING MODEL AND OPTIMIZATION OF IEEE 802.11 DCF MAC PROTOCOL FOR COGNITIVE RADIO NETWORK

Abstract

Cognitive Radio Networks (CRNs) have emerged as a promising solution to address the spectrum scarcity problem by enabling dynamic spectrum access. This thesis delves into the development of an energy harvesting model and optimization of IEEE 802.11 DCF MAC protocol for cognitive radio networks tailored to the unique characteristics and challenges of CRNs. Its overarching objective is to enhance the overall performance and efficiency of CRNs. The IEEE 802.11 distributed coordination function (DCF) MAC protocol is a fundamental protocol in wireless networks. Therefore, the IEEE 802.11 DCF MAC protocol requires optimization as well as synergy with energy harvesting technologies. Based on the literature survey, some objectives have been proposed for innovative enhancements. Key performance metrics such as throughput, end-to-end delay, collision probability, power allocation, and energy consumption/energy efficiency are evaluated under varying network conditions and traffic loads. The major objectives of the present work are as follows: 1. Systematic exploration of MAC protocols for cognitive radio networks (CRNs), literature survey, classification, and implementation. 2. Evaluating and optimizing the performance parameters of 802.11 DCF MAC protocol using fuzzy techniques for cognitive radio AdHoc networks. 3. Development, analysis, and performance improvement of the MAC protocol for the cognitive radio network (CRN) using Markov Chain and energy harvesting modeling. 4. Development and analysis of the listen-and-talk (LAT) MAC protocol for the full-duplex cognitive radio network (FD-CRN). The first objective aims to review and evaluate the existing literature on MAC protocols for Cognitive Radio Networks (CRNs). The study thoroughly analyzes theMAC protocols for cognitive radio that are already in use, including their classification, the available algorithms, mathematical iii models, and simulation tools linked to MAC protocols in CRNs. By carrying out this analysis, the study delineates significant trends and developments to better understand the current state of the art and develop efficient MAC protocols for cognitive radio networks. The second objective focuses on evaluating and optimizing the performance parameters of 802.11 DCF MAC protocol, specifically throughput and delay, in cognitive radio AdHoc networks using fuzzy optimization techniques. The penultimate research aims to develop, analyze, and improve the MAC protocol for CRNs using markov chain and energy harvesting modeling techniques. Since In the present study, markov chain analyzed the dynamic behavior of systems with probabilistic transitions, making it well-suited for modeling the stochastic nature of spectrum access in CRNs. Additionally, Energy harvesting modeling is employed in this study for the potential integration of energy harvesting capabilities via wireless power transfer in CRNs. It has addressed the energy constraints with the 802.11 DCF MAC protocol. Finally, the fourth objective introduces the full duplex MAC protocols for enhancing the performance of cognitive radio networks. It introduces the LAT-MAC protocol, which utilizes a fullduplex mechanism to enhance secondary users’ access performance in cognitive radio networks. LAT-MAC enables simultaneous transmission and spectrum sensing, utilizing the type 2 fuzzy cooperative spectrum sensing (T2FCSS) technique for improved energy detection. The SA-CRN algorithm is also employed to enhance spectrum sensing performance without compromising powerthroughput trade-offs and self-interference. The thesis is organized into the following chapters. Chapter 1: This chapter covers the introduction part to general concepts of cognitive radio networks and their MAC protocols, motivation, research challenges, objectives, and contributions of the thesis. It also includes the organization of the thesis. Chapter 2: This chapter includes a detailed and systematic literature survey of the different MAC protocols for cognitive radio networks. Additionally, the performance of the IEEE 802.11 (DCF) protocol for WLAN AdHoc and infrastructure modes has been studied in the context of cognitive radio networks. iv Chapter 3: This chapter discusses FIS-based optimization and outlines its challenges in cognitive radio networks. It presents the criterion to optimize 802.11 (DCF) MAC protocol parameters (Throughput and delay) using fuzzy inference systems (FIS). Moreover, aims to improve the efficiency of data transmission, i.e., throughput, and reduce communication delays, thereby optimizing the overall network performance in cognitive radio environments. Chapter 4: This chapter explores efficient energy harvesting in cognitive radio networks by applying wireless power transfer with the 802.11 (DCF) MAC protocol. The study intends to improve energy consumption by allowing secondary users (SUs) to capture power during idle periods using wireless power transfer technology. The study examines the viability and advantages of energy harvesting in cognitive radio networks, which will enhance energy performance and extend the network’s operational lifetime using IEEE 802.11 (DCF) MAC protocol. Chapter 5: The main goal of this chapter is to investigate full-duplex MAC protocols to improve the performance of cognitive radio networks. It developed a scheme inclusive of the LAT-MAC (Listen and Talk - Medium Access Control) protocol, which enables secondary users (SUs) to access the spectrum more efficiently. The LAT-MAC uses a full-duplex mechanism, allowing the SU to simultaneously transmit and sense the spectrum. The protocol incorporates the type 2 fuzzy cooperative spectrum sensing (T2FCSS) technique to improve energy detection. Additionally, the switching algorithm - cognitive radio network (SA-CRN) algorithm enhances spectrum sensing performance without compromising power-throughput trade-offs and self-interference. The developed efficient LAT-MAC protocol offers a promising solution for optimizing spectrum access and improving overall network performance in cognitive radio environments. Chapter 6: This chapter concludes the thesis, which also outlines the future scope of the work and potential prospects for further investigation.