2,635 research outputs found
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms
Compressive Sensing has been utilized in Cognitive Radio Networks (CRNs) to
exploit the sparse nature of the occupation of the primary users. Also,
distributed spectrum sensing has been proposed to tackle the wireless channel
problems, like node or link failures, rather than the common (centralized
approach) for spectrum sensing. In this paper, we propose a distributed
spectrum sensing framework based on consensus algorithms where SU nodes
exchange their binary decisions to take global decisions without a fusion
center to coordinate the sensing process. Each SU will share its decision with
its neighbors, and at every new iteration each SU will take a new decision
based on its current decision and the decisions it receives from its neighbors;
in the next iteration, each SU will share its new decision with its neighbors.
We show via simulations that the detection performance can tend to the
performance of majority rule Fusion Center based CRNs
Multipath streaming: fundamental limits and efficient algorithms
We investigate streaming over multiple links. A file is split into small
units called chunks that may be requested on the various links according to
some policy, and received after some random delay. After a start-up time called
pre-buffering time, received chunks are played at a fixed speed. There is
starvation if the chunk to be played has not yet arrived. We provide lower
bounds (fundamental limits) on the starvation probability of any policy. We
further propose simple, order-optimal policies that require no feedback. For
general delay distributions, we provide tractable upper bounds for the
starvation probability of the proposed policies, allowing to select the
pre-buffering time appropriately. We specialize our results to: (i) links that
employ CSMA or opportunistic scheduling at the packet level, (ii) links shared
with a primary user (iii) links that use fair rate sharing at the flow level.
We consider a generic model so that our results give insight into the design
and performance of media streaming over (a) wired networks with several paths
between the source and destination, (b) wireless networks featuring spectrum
aggregation and (c) multi-homed wireless networks.Comment: 24 page
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
Cramer-Rao Bounds for Joint RSS/DoA-Based Primary-User Localization in Cognitive Radio Networks
Knowledge about the location of licensed primary-users (PU) could enable
several key features in cognitive radio (CR) networks including improved
spatio-temporal sensing, intelligent location-aware routing, as well as aiding
spectrum policy enforcement. In this paper we consider the achievable accuracy
of PU localization algorithms that jointly utilize received-signal-strength
(RSS) and direction-of-arrival (DoA) measurements by evaluating the Cramer-Rao
Bound (CRB). Previous works evaluate the CRB for RSS-only and DoA-only
localization algorithms separately and assume DoA estimation error variance is
a fixed constant or rather independent of RSS. We derive the CRB for joint
RSS/DoA-based PU localization algorithms based on the mathematical model of DoA
estimation error variance as a function of RSS, for a given CR placement. The
bound is compared with practical localization algorithms and the impact of
several key parameters, such as number of nodes, number of antennas and
samples, channel shadowing variance and correlation distance, on the achievable
accuracy are thoroughly analyzed and discussed. We also derive the closed-form
asymptotic CRB for uniform random CR placement, and perform theoretical and
numerical studies on the required number of CRs such that the asymptotic CRB
tightly approximates the numerical integration of the CRB for a given
placement.Comment: 20 pages, 11 figures, 1 table, submitted to IEEE Transactions on
Wireless Communication
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
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