1,601 research outputs found
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
The Byzantine attack in cooperative spectrum sensing (CSS), also known as the
spectrum sensing data falsification (SSDF) attack in the literature, is one of
the key adversaries to the success of cognitive radio networks (CRNs). In the
past couple of years, the research on the Byzantine attack and defense
strategies has gained worldwide increasing attention. In this paper, we provide
a comprehensive survey and tutorial on the recent advances in the Byzantine
attack and defense for CSS in CRNs. Specifically, we first briefly present the
preliminaries of CSS for general readers, including signal detection
techniques, hypothesis testing, and data fusion. Second, we analyze the spear
and shield relation between Byzantine attack and defense from three aspects:
the vulnerability of CSS to attack, the obstacles in CSS to defense, and the
games between attack and defense. Then, we propose a taxonomy of the existing
Byzantine attack behaviors and elaborate on the corresponding attack
parameters, which determine where, who, how, and when to launch attacks. Next,
from the perspectives of homogeneous or heterogeneous scenarios, we classify
the existing defense algorithms, and provide an in-depth tutorial on the
state-of-the-art Byzantine defense schemes, commonly known as robust or secure
CSS in the literature. Furthermore, we highlight the unsolved research
challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral
A Comparative Study Of Spectrum Sensing Methods For Cognitive Radio Systems
With the increase of portable devices utilization and ever-growing demand for greater data rates in wireless transmission, an increasing demand for spectrum channels was observed since last decade. Conventionally, licensed spectrum channels are assigned for comparatively long time spans to the license holders who may not over time continuously use these channels, which creates an under-utilized spectrum. The inefficient utilization of inadequate wireless spectrum resources has motivated researchers to look for advanced and innovative technologies that enable an efficient use of the spectrum resources in a smart and efficient manner.
The notion of Cognitive Radio technology was proposed to address the problem of spectrum inefficiency by using underutilized frequency bands in an opportunistic method. A cognitive radio system (CRS) is aware of its operational and geographical surroundings and is capable of dynamically and independently adjust its functioning. Thus, CRS functionality has to be addressed with smart sensing and intelligent decision making techniques. Therefore, spectrum sensing is one of the most essential CRS components. The few sensing techniques that have been proposed are complicated and come with the price of false detection under heavy noise and jamming scenarios. Other techniques that ensure better detection performance are very sophisticated and costly in terms of both processing and hardware.
The objective of the thesis is to study and understand the three of the most basic spectrum sensing techniques i.e. energy detection, correlation based sensing, and matched filter sensing. Simulation platforms were developed for each of the three methods using GNU radio and python interpreted language. The simulated performances of the three methods have been analyzed through several test matrices and also were compared to observe and understand the corresponding strengths and weaknesses. These simulation results provide the understanding and base for the hardware implementation of spectrum sensing techniques and work towards a combined sensing approach with improved sensing performance with less complexity
Improved Double Threshold Energy Detection for Cooperative Spectrum Sensing in Cognitive Radio
In this paper, we focus on cooperative spectrum sensing (CSS) for double threshold improved energy detector. In this method, the improved energy detector compares positive power operation p of the amplitude of received signals at each secondary user (SU) with two thresholds to make binary decision about presence or absence of primary user (PU). The energies lying between upper and lower threshold are considered unreliable and are not considered in cooperation. The decisions are forwarded over an imperfect reporting channel to a fusion center where final decision on presence or absence of PU is taken. We combine double threshold approach with improved energy detection. Two step optimization is performed where cooperative probability of detection is maximized as a function of threshold difference in double threshold and then highest value of maximized cooperative probability of detectionis found as a function of power operation p, average signal-to-noise ratio at SUs, number of cooperating SUs and cooperative probability of false alarm. Also, we find the optimum fusion rule at fusion center along with optimum power corresponding p to the lowest value of the minimized total error rate using two step optimization. Then weanalyse the effect of errors introduced in reported decisions due to imperfect reporting channel.Defence Science Journal, 2013, 63(1), pp.34-40, DOI:http://dx.doi.org/10.14429/dsj.63.376
Green Cellular Networks: A Survey, Some Research Issues and Challenges
Energy efficiency in cellular networks is a growing concern for cellular
operators to not only maintain profitability, but also to reduce the overall
environment effects. This emerging trend of achieving energy efficiency in
cellular networks is motivating the standardization authorities and network
operators to continuously explore future technologies in order to bring
improvements in the entire network infrastructure. In this article, we present
a brief survey of methods to improve the power efficiency of cellular networks,
explore some research issues and challenges and suggest some techniques to
enable an energy efficient or "green" cellular network. Since base stations
consume a maximum portion of the total energy used in a cellular system, we
will first provide a comprehensive survey on techniques to obtain energy
savings in base stations. Next, we discuss how heterogeneous network deployment
based on micro, pico and femto-cells can be used to achieve this goal. Since
cognitive radio and cooperative relaying are undisputed future technologies in
this regard, we propose a research vision to make these technologies more
energy efficient. Lastly, we explore some broader perspectives in realizing a
"green" cellular network technologyComment: 16 pages, 5 figures, 2 table
A Comprehensive Survey on Networking over TV White Spaces
The 2008 Federal Communication Commission (FCC) ruling in the United States
opened up new opportunities for unlicensed operation in the TV white space
spectrum. Networking protocols over the TV white spaces promise to subdue the
shortcomings of existing short-range multi-hop wireless architectures and
protocols by offering more availability, wider bandwidth, and longer-range
communication. The TV white space protocols are the enabling technologies for
sensing and monitoring, Internet-of-Things (IoT), wireless broadband access,
real-time, smart and connected community, and smart utility applications. In
this paper, we perform a retrospective review of the protocols that have been
built over the last decade and also the new challenges and the directions for
future work. To the best of our knowledge, this is the first comprehensive
survey to present and compare existing networking protocols over the TV white
spaces.Comment: 19 page
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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