1,391 research outputs found
Machine-Learned Wireless Propagation Model
Generally, the present disclosure is directed to predicting wireless propagation in an area and/or at a wavelength. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict propagation data (e.g. radio signal attenuation and/or Bit Error Rate (BER)) based on connection data and terrain data
Determining Optimal Wireless Propagation Model for a Region and Wavelength
Generally, the present disclosure is directed to determining an optimal wireless propagation model for a given region and wavelength. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an optimal wireless propagation model for a region based on terrain data for the region
Coalition Formation Games for Collaborative Spectrum Sensing
Collaborative Spectrum Sensing (CSS) between secondary users (SUs) in
cognitive networks exhibits an inherent tradeoff between minimizing the
probability of missing the detection of the primary user (PU) and maintaining a
reasonable false alarm probability (e.g., for maintaining a good spectrum
utilization). In this paper, we study the impact of this tradeoff on the
network structure and the cooperative incentives of the SUs that seek to
cooperate for improving their detection performance. We model the CSS problem
as a non-transferable coalitional game, and we propose distributed algorithms
for coalition formation. First, we construct a distributed coalition formation
(CF) algorithm that allows the SUs to self-organize into disjoint coalitions
while accounting for the CSS tradeoff. Then, the CF algorithm is complemented
with a coalitional voting game for enabling distributed coalition formation
with detection probability guarantees (CF-PD) when required by the PU. The
CF-PD algorithm allows the SUs to form minimal winning coalitions (MWCs), i.e.,
coalitions that achieve the target detection probability with minimal costs.
For both algorithms, we study and prove various properties pertaining to
network structure, adaptation to mobility and stability. Simulation results
show that CF reduces the average probability of miss per SU up to 88.45%
relative to the non-cooperative case, while maintaining a desired false alarm.
For CF-PD, the results show that up to 87.25% of the SUs achieve the required
detection probability through MWCComment: IEEE Transactions on Vehicular Technology, to appea
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