12,984 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
Toward Reliable Contention-aware Data Dissemination in Multi-hop Cognitive Radio Ad Hoc Networks
This paper introduces a new channel selection strategy for reliable
contentionaware data dissemination in multi-hop cognitive radio network. The
key challenge here is to select channels providing a good tradeoff between
connectivity and contention. In other words, channels with good opportunities
for communication due to (1) low primary radio nodes (PRs) activities, and (2)
limited contention of cognitive ratio nodes (CRs) acceding that channel, have
to be selected. Thus, by dynamically exploring residual resources on channels
and by monitoring the number of CRs on a particular channel, SURF allows
building a connected network with limited contention where reliable
communication can take place. Through simulations, we study the performance of
SURF when compared with three other related approaches. Simulation results
confirm that our approach is effective in selecting the best channels for
efficient and reliable multi-hop data dissemination
Decentralized Dynamic Hop Selection and Power Control in Cognitive Multi-hop Relay Systems
In this paper, we consider a cognitive multi-hop relay secondary user (SU)
system sharing the spectrum with some primary users (PU). The transmit power as
well as the hop selection of the cognitive relays can be dynamically adapted
according to the local (and causal) knowledge of the instantaneous channel
state information (CSI) in the multi-hop SU system. We shall determine a low
complexity, decentralized algorithm to maximize the average end-to-end
throughput of the SU system with dynamic spatial reuse. The problem is
challenging due to the decentralized requirement as well as the causality
constraint on the knowledge of CSI. Furthermore, the problem belongs to the
class of stochastic Network Utility Maximization (NUM) problems which is quite
challenging. We exploit the time-scale difference between the PU activity and
the CSI fluctuations and decompose the problem into a master problem and
subproblems. We derive an asymptotically optimal low complexity solution using
divide-and-conquer and illustrate that significant performance gain can be
obtained through dynamic hop selection and power control. The worst case
complexity and memory requirement of the proposed algorithm is O(M^2) and
O(M^3) respectively, where is the number of SUs
Distributed Learning in Multi-Armed Bandit with Multiple Players
We formulate and study a decentralized multi-armed bandit (MAB) problem.
There are M distributed players competing for N independent arms. Each arm,
when played, offers i.i.d. reward according to a distribution with an unknown
parameter. At each time, each player chooses one arm to play without exchanging
observations or any information with other players. Players choosing the same
arm collide, and, depending on the collision model, either no one receives
reward or the colliding players share the reward in an arbitrary way. We show
that the minimum system regret of the decentralized MAB grows with time at the
same logarithmic order as in the centralized counterpart where players act
collectively as a single entity by exchanging observations and making decisions
jointly. A decentralized policy is constructed to achieve this optimal order
while ensuring fairness among players and without assuming any pre-agreement or
information exchange among players. Based on a Time Division Fair Sharing
(TDFS) of the M best arms, the proposed policy is constructed and its order
optimality is proven under a general reward model. Furthermore, the basic
structure of the TDFS policy can be used with any order-optimal single-player
policy to achieve order optimality in the decentralized setting. We also
establish a lower bound on the system regret growth rate for a general class of
decentralized polices, to which the proposed policy belongs. This problem finds
potential applications in cognitive radio networks, multi-channel communication
systems, multi-agent systems, web search and advertising, and social networks.Comment: 31 pages, 8 figures, revised paper submitted to IEEE Transactions on
Signal Processing, April, 2010, the pre-agreement in the decentralized TDFS
policy is eliminated to achieve a complete decentralization among player
Let Cognitive Radios Imitate: Imitation-based Spectrum Access for Cognitive Radio Networks
In this paper, we tackle the problem of opportunistic spectrum access in
large-scale cognitive radio networks, where the unlicensed Secondary Users (SU)
access the frequency channels partially occupied by the licensed Primary Users
(PU). Each channel is characterized by an availability probability unknown to
the SUs. We apply evolutionary game theory to model the spectrum access problem
and develop distributed spectrum access policies based on imitation, a behavior
rule widely applied in human societies consisting of imitating successful
behavior. We first develop two imitation-based spectrum access policies based
on the basic Proportional Imitation (PI) rule and the more advanced Double
Imitation (DI) rule given that a SU can imitate any other SUs. We then adapt
the proposed policies to a more practical scenario where a SU can only imitate
the other SUs operating on the same channel. A systematic theoretical analysis
is presented for both scenarios on the induced imitation dynamics and the
convergence properties of the proposed policies to an imitation-stable
equilibrium, which is also the -optimum of the system. Simple,
natural and incentive-compatible, the proposed imitation-based spectrum access
policies can be implemented distributedly based on solely local interactions
and thus is especially suited in decentralized adaptive learning environments
as cognitive radio networks
Interference and Deployment Issues for Cognitive Radio Systems in Shadowing Environments
In this paper we describe a model for calculating the aggregate interference
encountered by primary receivers in the presence of randomly placed cognitive
radios (CRs). We show that incorporating the impact of distance attenuation and
lognormal fading on each constituent interferer in the aggregate, leads to a
composite interference that cannot be satisfactorily modeled by a lognormal.
Using the interference statistics we determine a number of key parameters
needed for the deployment of CRs. Examples of these are the exclusion zone
radius, needed to protect the primary receiver under different types of fading
environments and acceptable interference levels, and the numbers of CRs that
can be deployed. We further show that if the CRs have apriori knowledge of the
radio environment map (REM), then a much larger number of CRs can be deployed
especially in a high density environment. Given REM information, we also look
at the CR numbers achieved by two different types of techniques to process the
scheduling information.Comment: to be presented at IEEE ICC 2009. This posting is the same as the
original one. Only author's list is updated that was unfortunately not
correctly mentioned in first versio
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