Distributed Approximation of Functions over Fast Fading Channels with Applications to Distributed Learning and the Max-Consensus Problem

@article{Bjelakovic2019DistributedAO,
  title={Distributed Approximation of Functions over Fast Fading Channels with Applications to Distributed Learning and the Max-Consensus Problem},
  author={Igor Bjelakovic and Matthias Frey and Sławomir Stańczak},
  journal={2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)},
  year={2019},
  pages={1146-1153}
}
  • Igor Bjelakovic, Matthias Frey, Sławomir Stańczak
  • Published in
    57th Annual Allerton…
    2019
  • Computer Science, Mathematics
  • In this work, we consider the problem of distributed approximation of functions over multiple-access channels with additive noise. In contrast to previous works, we take fast fading into account and give explicit probability bounds for the approximation error allowing us to derive bounds on the number of channel uses that are needed to approximate a function up to a given approximation accuracy. Neither the fading nor the noise process is limited to Gaussian distributions. Instead, we consider… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 16 REFERENCES

    A Scalable Max-Consensus Protocol For Noisy Ultra-Dense Networks

    VIEW 5 EXCERPTS

    Distributed Approximation of Functions over Fast Fading Channels with Applications to Distributed Learning and the Max-Consensus Problem

    VIEW 4 EXCERPTS

    Harnessing channel collisions for efficient massive access in 5G networks: A step forward to practical implementation

    VIEW 1 EXCERPT

    Energy-efficient classification for anomaly detection: The wireless channel as a helper

    VIEW 2 EXCERPTS

    Nomographic Functions: Efficient Computation in Clustered Gaussian Sensor Networks

    VIEW 1 EXCERPT

    Harnessing Interference for Analog Function Computation in Wireless Sensor Networks

    VIEW 1 EXCERPT