Sentence examples for interference categorization from inspiring English sources

Interference categorization.

Introduction of the SU interference categorization for interference susceptibility of the FA algorithms, while reducing the communication overhead.

We propose the improved cognitive radio saturation metric for the dynamic vertex ordering and to introduce interference categorization which will reduce the communication overhead.

This novel interference categorization allows the introduction of the susceptibility of the FA protocol to the level of the interference, taking into account the probabilistic nature of the received signal as described above, as well as possible interference estimation errors.

Introducing the interference sensibility in the proposed FA algorithms with co-channel and adjacent channel interference modeling, edge weights coefficients, and interference categorization significantly contribute to the algorithm efficiency related to interference reduction compared to benchmark CSUM algorithm, which employs binary interference model.

In future work, the proposed framework can be extended to add a selection of appropriate frequency band and type of the CR spectrum access for multi-band CR operation in heterogeneous environment and to investigate implementation of the fuzzy logic membership function for the interference categorization.

This clearly shows the benefits of the interference weighting and categorization on the network performance.

In this paper, cognitive radio frequency assignment with a novel interference weighting and categorization is proposed, as an extension of the solution to the graph coloring problem.

In this paper, the CR FA is modeled as a graph coloring problem using novel dynamic vertex ordering, interference weighting, and categorization.

We can conclude that the proposed interference weighting and categorization is beneficial to the CR network performance, since it is possible to quantify the individual interference components and aggregate interference.

The CR networks using the FA algorithms with the interference weighting and categorization can accommodate almost twice as much CR users compared to the networks using algorithms with only binary interference model.