59,292 research outputs found
Scaling Laws of Cognitive Networks
We consider a cognitive network consisting of n random pairs of cognitive
transmitters and receivers communicating simultaneously in the presence of
multiple primary users. Of interest is how the maximum throughput achieved by
the cognitive users scales with n. Furthermore, how far these users must be
from a primary user to guarantee a given primary outage. Two scenarios are
considered for the network scaling law: (i) when each cognitive transmitter
uses constant power to communicate with a cognitive receiver at a bounded
distance away, and (ii) when each cognitive transmitter scales its power
according to the distance to a considered primary user, allowing the cognitive
transmitter-receiver distances to grow. Using single-hop transmission, suitable
for cognitive devices of opportunistic nature, we show that, in both scenarios,
with path loss larger than 2, the cognitive network throughput scales linearly
with the number of cognitive users. We then explore the radius of a primary
exclusive region void of cognitive transmitters. We obtain bounds on this
radius for a given primary outage constraint. These bounds can help in the
design of a primary network with exclusive regions, outside of which cognitive
users may transmit freely. Our results show that opportunistic secondary
spectrum access using single-hop transmission is promising.Comment: significantly revised and extended, 30 pages, 13 figures, submitted
to IEEE Journal of Special Topics in Signal Processin
Multiuser Diversity Gain in Cognitive Networks
Dynamic allocation of resources to the \emph{best} link in large multiuser
networks offers considerable improvement in spectral efficiency. This gain,
often referred to as \emph{multiuser diversity gain}, can be cast as
double-logarithmic growth of the network throughput with the number of users.
In this paper we consider large cognitive networks granted concurrent spectrum
access with license-holding users. The primary network affords to share its
under-utilized spectrum bands with the secondary users. We assess the optimal
multiuser diversity gain in the cognitive networks by quantifying how the
sum-rate throughput of the network scales with the number of secondary users.
For this purpose we look at the optimal pairing of spectrum bands and secondary
users, which is supervised by a central entity fully aware of the instantaneous
channel conditions, and show that the throughput of the cognitive network
scales double-logarithmically with the number of secondary users () and
linearly with the number of available spectrum bands (), i.e., . We then propose a \emph{distributed} spectrum allocation scheme, which does
not necessitate a central controller or any information exchange between
different secondary users and still obeys the optimal throughput scaling law.
This scheme requires that \emph{some} secondary transmitter-receiver pairs
exchange information bits among themselves. We also show that the
aggregate amount of information exchange between secondary transmitter-receiver
pairs is {\em asymptotically} equal to . Finally, we show that our
distributed scheme guarantees fairness among the secondary users, meaning that
they are equally likely to get access to an available spectrum band.Comment: 32 pages, 3 figures, to appear in the IEEE/ACM Transactions on
Networkin
Random Access Transport Capacity
We develop a new metric for quantifying end-to-end throughput in multihop
wireless networks, which we term random access transport capacity, since the
interference model presumes uncoordinated transmissions. The metric quantifies
the average maximum rate of successful end-to-end transmissions, multiplied by
the communication distance, and normalized by the network area. We show that a
simple upper bound on this quantity is computable in closed-form in terms of
key network parameters when the number of retransmissions is not restricted and
the hops are assumed to be equally spaced on a line between the source and
destination. We also derive the optimum number of hops and optimal per hop
success probability and show that our result follows the well-known square root
scaling law while providing exact expressions for the preconstants as well.
Numerical results demonstrate that the upper bound is accurate for the purpose
of determining the optimal hop count and success (or outage) probability.Comment: Submitted to IEEE Trans. on Wireless Communications, Sept. 200
Consensus in networks of mobile communicating agents
Populations of mobile and communicating agents describe a vast array of
technological and natural systems, ranging from sensor networks to animal
groups. Here, we investigate how a group-level agreement may emerge in the
continuously evolving network defined by the local interactions of the moving
individuals. We adopt a general scheme of motion in two dimensions and we let
the individuals interact through the minimal naming game, a prototypical scheme
to investigate social consensus. We distinguish different regimes of
convergence determined by the emission range of the agents and by their
mobility, and we identify the corresponding scaling behaviors of the consensus
time. In the same way, we rationalize also the behavior of the maximum memory
used during the convergence process, which determines the minimum
cognitive/storage capacity needed by the individuals. Overall, we believe that
the simple and general model presented in this paper can represent a helpful
reference for a better understanding of the behavior of populations of mobile
agents.Comment: 7 pages, 7 figure
Proactive Resource Allocation: Harnessing the Diversity and Multicast Gains
This paper introduces the novel concept of proactive resource allocation
through which the predictability of user behavior is exploited to balance the
wireless traffic over time, and hence, significantly reduce the bandwidth
required to achieve a given blocking/outage probability. We start with a simple
model in which the smart wireless devices are assumed to predict the arrival of
new requests and submit them to the network T time slots in advance. Using
tools from large deviation theory, we quantify the resulting prediction
diversity gain} to establish that the decay rate of the outage event
probabilities increases with the prediction duration T. This model is then
generalized to incorporate the effect of the randomness in the prediction
look-ahead time T. Remarkably, we also show that, in the cognitive networking
scenario, the appropriate use of proactive resource allocation by the primary
users improves the diversity gain of the secondary network at no cost in the
primary network diversity. We also shed lights on multicasting with predictable
demands and show that the proactive multicast networks can achieve a
significantly higher diversity gain that scales super-linearly with T. Finally,
we conclude by a discussion of the new research questions posed under the
umbrella of the proposed proactive (non-causal) wireless networking framework
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