30,186 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
Coalitional Games for Distributed Collaborative Spectrum Sensing in Cognitive Radio Networks
Collaborative spectrum sensing among secondary users (SUs) in cognitive
networks is shown to yield a significant performance improvement. However,
there exists an inherent trade off between the gains in terms of probability of
detection of the primary user (PU) and the costs in terms of false alarm
probability. In this paper, we study the impact of this trade off on the
topology and the dynamics of a network of SUs seeking to reduce the
interference on the PU through collaborative sensing. Moreover, while existing
literature mainly focused on centralized solutions for collaborative sensing,
we propose distributed collaboration strategies through game theory. We model
the problem as a non-transferable coalitional game, and propose a distributed
algorithm for coalition formation through simple merge and split rules. Through
the proposed algorithm, SUs can autonomously collaborate and self-organize into
disjoint independent coalitions, while maximizing their detection probability
taking into account the cooperation costs (in terms of false alarm). We study
the stability of the resulting network structure, and show that a maximum
number of SUs per formed coalition exists for the proposed utility model.
Simulation results show that the proposed algorithm allows a reduction of up to
86.6% of the average missing probability per SU (probability of missing the
detection of the PU) relative to the non-cooperative case, while maintaining a
certain false alarm level. In addition, through simulations, we compare the
performance of the proposed distributed solution with respect to an optimal
centralized solution that minimizes the average missing probability per SU.
Finally, the results also show how the proposed algorithm autonomously adapts
the network topology to environmental changes such as mobility.Comment: in proceedings of IEEE INFOCOM 200
Peak to average power ratio based spatial spectrum sensing for cognitive radio systems
The recent convergence of wireless standards for incorporation of spatial dimension in wireless systems has made spatial spectrum sensing based on Peak to Average Power Ratio (PAPR) of the received signal, a promising approach. This added dimension is principally exploited for stream multiplexing, user multiplexing and spatial diversity. Considering such a wireless environment for primary users, we propose an algorithm for spectrum sensing by secondary users which are also equipped with multiple antennas. The proposed spatial spectrum sensing algorithm is based on the PAPR of the spatially received signals. Simulation results show the improved performance once the information regarding spatial diversity of the primary users is incorporated in the proposed algorithm. Moreover, through simulations a better performance is achieved by using different diversity schemes and different parameters like sensing time and scanning interval
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