23,399 research outputs found
Deterministic Communication in Radio Networks
In this paper we improve the deterministic complexity of two fundamental
communication primitives in the classical model of ad-hoc radio networks with
unknown topology: broadcasting and wake-up. We consider an unknown radio
network, in which all nodes have no prior knowledge about network topology, and
know only the size of the network , the maximum in-degree of any node
, and the eccentricity of the network .
For such networks, we first give an algorithm for wake-up, based on the
existence of small universal synchronizers. This algorithm runs in
time, the
fastest known in both directed and undirected networks, improving over the
previous best -time result across all ranges of parameters, but
particularly when maximum in-degree is small.
Next, we introduce a new combinatorial framework of block synchronizers and
prove the existence of such objects of low size. Using this framework, we
design a new deterministic algorithm for the fundamental problem of
broadcasting, running in time. This is
the fastest known algorithm for the problem in directed networks, improving
upon the -time algorithm of De Marco (2010) and the
-time algorithm due to Czumaj and Rytter (2003). It is also the
first to come within a log-logarithmic factor of the lower
bound due to Clementi et al.\ (2003).
Our results also have direct implications on the fastest \emph{deterministic
leader election} and \emph{clock synchronization} algorithms in both directed
and undirected radio networks, tasks which are commonly used as building blocks
for more complex procedures
Applications of Cognitive Radio Networks
The term cognitive radio (CR), originally coined in the late 1990s, envisaged a radio that is aware of its operational environment so that it can dynamically and autonomously adjust its radio-operating parameters to accordingly adapt to the different situations. Cognition is achieved through the so-called cognitive cycle, consisting of the observation of the environment, the orientation and planning that leads to making appropriate decisions in accordance with specific operation goals, and finally, the execution of these decisions (e.g., access to the appropriate channel). Decisions can be reinforced by learning procedures based on the past observations and the corresponding results of prior actuations
Broadcasting in Noisy Radio Networks
The widely-studied radio network model [Chlamtac and Kutten, 1985] is a
graph-based description that captures the inherent impact of collisions in
wireless communication. In this model, the strong assumption is made that node
receives a message from a neighbor if and only if exactly one of its
neighbors broadcasts.
We relax this assumption by introducing a new noisy radio network model in
which random faults occur at senders or receivers. Specifically, for a constant
noise parameter , either every sender has probability of
transmitting noise or every receiver of a single transmission in its
neighborhood has probability of receiving noise.
We first study single-message broadcast algorithms in noisy radio networks
and show that the Decay algorithm [Bar-Yehuda et al., 1992] remains robust in
the noisy model while the diameter-linear algorithm of Gasieniec et al., 2007
does not. We give a modified version of the algorithm of Gasieniec et al., 2007
that is robust to sender and receiver faults, and extend both this modified
algorithm and the Decay algorithm to robust multi-message broadcast algorithms.
We next investigate the extent to which (network) coding improves throughput
in noisy radio networks. We address the previously perplexing result of Alon et
al. 2014 that worst case coding throughput is no better than worst case routing
throughput up to constants: we show that the worst case throughput performance
of coding is, in fact, superior to that of routing -- by a
gap -- provided receiver faults are introduced. However, we show that any
coding or routing scheme for the noiseless setting can be transformed to be
robust to sender faults with only a constant throughput overhead. These
transformations imply that the results of Alon et al., 2014 carry over to noisy
radio networks with sender faults.Comment: Principles of Distributed Computing 201
Faster Gossiping in Bidirectional Radio Networks with Large Labels
We consider unknown ad-hoc radio networks, when the underlying network is
bidirectional and nodes can have polynomially large labels. For this model, we
present a deterministic protocol for gossiping which takes rounds. This improves upon the previous best result for deterministic
gossiping for this model by [Gasienec, Potapov, Pagourtizis, Deterministic
Gossiping in Radio Networks with Large labels, ESA (2002)], who present a
protocol of round complexity for this problem. This
resolves open problem posed in [Gasienec, Efficient gossiping in radio
networks, SIROCCO (2009)], who cite bridging gap between lower and upper bounds
for this problem as an important objective. We emphasize that a salient feature
of our protocol is its simplicity, especially with respect to the previous best
known protocol for this problem
Security in Cognitive Radio Networks
In this paper, we investigate the information-theoretic security by modeling
a cognitive radio wiretap channel under quality-of-service (QoS) constraints
and interference power limitations inflicted on primary users (PUs). We
initially define four different transmission scenarios regarding channel
sensing results and their correctness. We provide effective secure transmission
rates at which a secondary eavesdropper is refrained from listening to a
secondary transmitter (ST). Then, we construct a channel state transition
diagram that characterizes this channel model. We obtain the effective secure
capacity which describes the maximum constant buffer arrival rate under given
QoS constraints. We find out the optimal transmission power policies that
maximize the effective secure capacity, and then, we propose an algorithm that,
in general, converges quickly to these optimal policy values. Finally, we show
the performance levels and gains obtained under different channel conditions
and scenarios. And, we emphasize, in particular, the significant effect of
hidden-terminal problem on information-theoretic security in cognitive radios.Comment: Submitted to CISS 201
Adaptive Modulation in Multi-user Cognitive Radio Networks over Fading Channels
In this paper, the performance of adaptive modulation in multi-user cognitive
radio networks over fading channels is analyzed. Multi-user diversity is
considered for opportunistic user selection among multiple secondary users. The
analysis is obtained for Nakagami- fading channels. Both adaptive continuous
rate and adaptive discrete rate schemes are analysed in opportunistic spectrum
access and spectrum sharing. Numerical results are obtained and depicted to
quantify the effects of multi-user fading environments on adaptive modulation
operating in cognitive radio networks
Wideband Spectrum Sensing in Cognitive Radio Networks
Spectrum sensing is an essential enabling functionality for cognitive radio
networks to detect spectrum holes and opportunistically use the under-utilized
frequency bands without causing harmful interference to legacy networks. This
paper introduces a novel wideband spectrum sensing technique, called multiband
joint detection, which jointly detects the signal energy levels over multiple
frequency bands rather than consider one band at a time. The proposed strategy
is efficient in improving the dynamic spectrum utilization and reducing
interference to the primary users. The spectrum sensing problem is formulated
as a class of optimization problems in interference limited cognitive radio
networks. By exploiting the hidden convexity in the seemingly non-convex
problem formulations, optimal solutions for multiband joint detection are
obtained under practical conditions. Simulation results show that the proposed
spectrum sensing schemes can considerably improve the system performance. This
paper establishes important principles for the design of wideband spectrum
sensing algorithms in cognitive radio networks
Concurrent bandits and cognitive radio networks
We consider the problem of multiple users targeting the arms of a single
multi-armed stochastic bandit. The motivation for this problem comes from
cognitive radio networks, where selfish users need to coexist without any side
communication between them, implicit cooperation or common control. Even the
number of users may be unknown and can vary as users join or leave the network.
We propose an algorithm that combines an -greedy learning rule with a
collision avoidance mechanism. We analyze its regret with respect to the
system-wide optimum and show that sub-linear regret can be obtained in this
setting. Experiments show dramatic improvement compared to other algorithms for
this setting
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