Novel Approaches for the Performance Enhancement of Cognitive Radio Networks

This research is dedicated to the study of the challenges faced by Cognitive Radio (CR) networks, which include self-coexistence of the networks in the spectral environment, security and performance threats from malicious entities, and fairness in spectrum contention and utilization. We propose novel channel acquisition schemes that allow decentralized CR networks to have multiple channel access with minimal spectrum contentions. The multiple channel acquisition schemes facilitate fast spectrum access especially in cases where networks cannot communicate with each other. These schemes enable CR networks to self-organize and adapt to the dynamically changing spectral environment. We also present a self-coexistence mechanism that allows CR networks to coexist via the implementation of a risk-motivated channel selection based deference structure (DS). By forming DS coalitions, CR networks are able to have better access to preferred channels and can defer transmission to one another, thereby mitigating spectrum conflicts. CR networks are also known to be susceptible to Sybil threats from smart malicious radios with either monopolistic or disruptive intentions. We formulate novel threat and defense mechanisms to combat Sybil threats and minimize their impact on the performance of CR networks. A dynamic reputation system is proposed that considerably minimizes the effectiveness of intelligent Sybil attacks and improves the accuracy of spectrum-based decision-making processes. Finally, we present a distributed and cheat-proof spectrum contention protocol as an enhancement of the adaptive On-Demand Spectrum Contention (ODSC) protocol. The Modified On-Demand Spectrum Contention (MODSC) protocol enhances fairness and efficiency of spectrum access. We also show that there is substantial improvement in spectrum utilization with the incorporation of channel reuse into the MODSC protocol

Ingenious Method for Conducive Handoff Appliance in Cognitive Radio Networks

Wireless communications deployed in the current epoch claims ceaseless connection among its users thereby leading to the investigation of Cognitive Radio Networks (CRN) which enables to make use of unallocated spectrum optimally and provides uninterrupted connection. Establishing interminable connectivity during the handoff process in spectrum mobility of CRN is a challenging task. This paper elucidates the optimization of handoff process carried out in CRN by incorporating an intelligent method. This includes fuzzy logic wherein the handoff parameters are processed thereby indicating the need of handoff. The proffered method also comprises of a part of genetic algorithm which yields fitness value for reducing the handoff occurrences and enhancing the overall performance of the system is promoted using cuckoo search which decides the mobile node from which the handoff process has to initiate based on the priority generated. This technique ensures that decision is taken ahead of link failure rather than range failure which are the key point in comparison to the existing system. Results obtained through the simulation are satisfactory in terms of delay, throughput, number of failed handoff and handoffs performed in comparison to the existing fuzzy based handoff process in CRN

A Survey on Dynamic Spectrum Sharing Using Game Theory in Cognitive Radio Networks

Due to the tremendous increase in wireless data traffic, a usable radio spectrum is quickly being depleted. Current Fixed Spectrum Allocation (FSA) strategy give rise to the problem of spectrum scarcity and underutilization. Cognitive Radio (CR) is proposed as a new wireless paradigm to overcome the spectrum underutilization problem. CR is a promising technology which for future wireless communications. CRs can change its operating parameters intelligently in real time to account for dynamic changes in a wireless environment. CR enables a technique called Dynamic Spectrum Allocation (DSA) where the users are able to access unlicensed bands opportunistically. Since idle spectrum from PU is a valuable commodity, many cognitive users will be competing for it simultaneously. Therefore, there arises competition among the users. Users may be only concerned about maximizing their own benefits by behaving rationally and selfishly. Thus spectrum allocation problem falls under NP-hard complex based on its complexity to solve. Out of several solution approaches, Game theory is found to be an efficient mathematical tool since it deals with solving the conflicts among the users. This survey is aimed at providing a comprehensive overview on dynamic spectrum allocation using game theory

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network