23,107 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
Experimental detection using cyclostationary feature detectors for cognitive radios
© 2014 IEEE. Signal detection is widely used in many applications. Some examples include Cognitive Radio (CR) and military intelligence. Without guaranteed signal detection, a CR cannot reliably perform its role. Spectrum sensing is currently one of the most challenging problems in cognitive radio design because of various factors such as multi-path fading and signal to noise ratio (SNR). In this paper, we particularly focus on the detection method based on cyclostationary feature detectors (CFD) estimation. The advantage of CFD is its relative robustness against noise uncertainty compared with energy detection methods. The experimental result present in this paper show that the cyclostationary feature-based detection can be robust compared to energy-based technique for low SNR levels
Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks
Spectrum sensing, which aims at detecting spectrum holes, is the precondition
for the implementation of cognitive radio (CR). Collaborative spectrum sensing
among the cognitive radio nodes is expected to improve the ability of checking
complete spectrum usage. Due to hardware limitations, each cognitive radio node
can only sense a relatively narrow band of radio spectrum. Consequently, the
available channel sensing information is far from being sufficient for
precisely recognizing the wide range of unoccupied channels. Aiming at breaking
this bottleneck, we propose to apply matrix completion and joint sparsity
recovery to reduce sensing and transmitting requirements and improve sensing
results. Specifically, equipped with a frequency selective filter, each
cognitive radio node senses linear combinations of multiple channel information
and reports them to the fusion center, where occupied channels are then decoded
from the reports by using novel matrix completion and joint sparsity recovery
algorithms. As a result, the number of reports sent from the CRs to the fusion
center is significantly reduced. We propose two decoding approaches, one based
on matrix completion and the other based on joint sparsity recovery, both of
which allow exact recovery from incomplete reports. The numerical results
validate the effectiveness and robustness of our approaches. In particular, in
small-scale networks, the matrix completion approach achieves exact channel
detection with a number of samples no more than 50% of the number of channels
in the network, while joint sparsity recovery achieves similar performance in
large-scale networks.Comment: 12 pages, 11 figure
Enhanced Compressive Wideband Frequency Spectrum Sensing for Dynamic Spectrum Access
Wideband spectrum sensing detects the unused spectrum holes for dynamic
spectrum access (DSA). Too high sampling rate is the main problem. Compressive
sensing (CS) can reconstruct sparse signal with much fewer randomized samples
than Nyquist sampling with high probability. Since survey shows that the
monitored signal is sparse in frequency domain, CS can deal with the sampling
burden. Random samples can be obtained by the analog-to-information converter.
Signal recovery can be formulated as an L0 norm minimization and a linear
measurement fitting constraint. In DSA, the static spectrum allocation of
primary radios means the bounds between different types of primary radios are
known in advance. To incorporate this a priori information, we divide the whole
spectrum into subsections according to the spectrum allocation policy. In the
new optimization model, the minimization of the L2 norm of each subsection is
used to encourage the cluster distribution locally, while the L0 norm of the L2
norms is minimized to give sparse distribution globally. Because the L0/L2
optimization is not convex, an iteratively re-weighted L1/L2 optimization is
proposed to approximate it. Simulations demonstrate the proposed method
outperforms others in accuracy, denoising ability, etc.Comment: 23 pages, 6 figures, 4 table. arXiv admin note: substantial text
overlap with arXiv:1005.180
Cognitive Radio Networks: Realistic or Not?
A large volume of research has been conducted in the cognitive radio (CR)
area the last decade. However, the deployment of a commercial CR network is yet
to emerge. A large portion of the existing literature does not build on real
world scenarios, hence, neglecting various important interactions of the
research with commercial telecommunication networks. For instance, a lot of
attention has been paid to spectrum sensing as the front line functionality
that needs to be completed in an efficient and accurate manner to enable an
opportunistic CR network architecture. This is necessary to detect the
existence of spectrum holes without which no other procedure can be fulfilled.
However, simply sensing (cooperatively or not) the energy received from a
primary transmitter cannot enable correct dynamic spectrum access. For example,
the low strength of a primary transmitter's signal does not assure that there
will be no interference to a nearby primary receiver. In addition, the presence
of a primary transmitter's signal does not mean that CR network users cannot
access the spectrum since there might not be any primary receiver in the
vicinity. Despite the existing elegant and clever solutions to the DSA problem
no robust, implementable scheme has emerged. In this paper, we challenge the
basic premises of the proposed schemes. We further argue that addressing the
technical challenges we face in deploying robust CR networks can only be
achieved if we radically change the way we design their basic functionalities.
In support of our argument, we present a set of real-world scenarios, inspired
by realistic settings in commercial telecommunications networks, focusing on
spectrum sensing as a basic and critical functionality in the deployment of
CRs. We use these scenarios to show why existing DSA paradigms are not amenable
to realistic deployment in complex wireless environments.Comment: Work in progres
Block Outlier Methods for Malicious User Detection in Cooperative Spectrum Sensing
Block outlier detection methods, based on Tietjen-Moore (TM) and Shapiro-Wilk
(SW) tests, are proposed to detect and suppress spectrum sensing data
falsification (SSDF) attacks by malicious users in cooperative spectrum
sensing. First, we consider basic and statistical SSDF attacks, where the
malicious users attack independently. Then we propose a new SSDF attack, which
involves cooperation among malicious users by masking. In practice, the number
of malicious users is unknown. Thus, it is necessary to estimate the number of
malicious users, which is found using clustering and largest gap method.
However, we show using Monte Carlo simulations that, these methods fail to
estimate the exact number of malicious users when they cooperate. To overcome
this, we propose a modified largest gap method.Comment: Accepted in Proceedings of 79th IEEE Vehicular Technology
Conference-Spring (VTC-Spring), May 2014, Seoul, South Kore
- …