22,428 research outputs found
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
Cognitive radio has been widely considered as one of the prominent solutions
to tackle the spectrum scarcity. While the majority of existing research has
focused on single-band cognitive radio, multiband cognitive radio represents
great promises towards implementing efficient cognitive networks compared to
single-based networks. Multiband cognitive radio networks (MB-CRNs) are
expected to significantly enhance the network's throughput and provide better
channel maintenance by reducing handoff frequency. Nevertheless, the wideband
front-end and the multiband spectrum access impose a number of challenges yet
to overcome. This paper provides an in-depth analysis on the recent
advancements in multiband spectrum sensing techniques, their limitations, and
possible future directions to improve them. We study cooperative communications
for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also
investigate several limits and tradeoffs of various design parameters for
MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that
differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE
Journal, Special Issue on Future Radio Spectrum Access, March 201
Cooperative Localization under Limited Connectivity
We report two decentralized multi-agent cooperative localization algorithms
in which, to reduce the communication cost, inter-agent state estimate
correlations are not maintained but accounted for implicitly. In our first
algorithm, to guarantee filter consistency, we account for unknown inter-agent
correlations via an upper bound on the joint covariance matrix of the agents.
In the second method, we use an optimization framework to estimate the unknown
inter-agent cross-covariance matrix. In our algorithms, each agent localizes
itself in a global coordinate frame using a local filter driven by local dead
reckoning and occasional absolute measurement updates, and opportunistically
corrects its pose estimate whenever it can obtain relative measurements with
respect to other mobile agents. To process any relative measurement, only the
agent taken the measurement and the agent the measurement is taken from need to
communicate with each other. Consequently, our algorithms are decentralized
algorithms that do not impose restrictive network-wide connectivity condition.
Moreover, we make no assumptions about the type of agents or relative
measurements. We demonstrate our algorithms in simulation and a
robotic~experiment.Comment: 9 pages, 5 figure
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