884 research outputs found
On Improving Throughput of Multichannel ALOHA using Preamble-based Exploration
Machine-type communication (MTC) has been extensively studied to provide
connectivity for devices and sensors in the Internet-of-Thing (IoT). Thanks to
the sparse activity, random access, e.g., ALOHA, is employed for MTC to lower
signaling overhead. In this paper, we propose to adopt exploration for
multichannel ALOHA by transmitting preambles before transmitting data packets
in MTC, and show that the maximum throughput can be improved by a factor of 2 -
exp(-1) = 1.632, In the proposed approach, a base station (BS) needs to send
the feedback information to active users to inform the numbers of transmitted
preambles in multiple channels, which can be reliably estimated as in
compressive random access. A steady-state analysis is also performed with fast
retrial, which shows that the probability of packet collision becomes lower
and, as a result, the delay outage probability is greatly reduced for a lightly
loaded system. Simulation results also confirm the results from analysis.Comment: 10 pages, 7 figures, to appear in the Journal of Communications and
Networks. arXiv admin note: substantial text overlap with arXiv:2001.1111
Data-aided Sensing for Gaussian Process Regression in IoT Systems
In this paper, for efficient data collection with limited bandwidth,
data-aided sensing is applied to Gaussian process regression that is used to
learn data sets collected from sensors in Internet-of-Things systems. We focus
on the interpolation of sensors' measurements from a small number of
measurements uploaded by a fraction of sensors using Gaussian process
regression with data-aided sensing. Thanks to active sensor selection, it is
shown that Gaussian process regression with data-aided sensing can provide a
good estimate of a complete data set compared to that with random selection.
With multichannel ALOHA, data-aided sensing is generalized for distributed
selective uploading when sensors can have feedback of predictions of their
measurements so that each sensor can decide whether or not it uploads by
comparing its measurement with the predicted one. Numerical results show that
modified multichannel ALOHA with predictions can help improve the performance
of Gaussian process regression with data-aided sensing compared to conventional
multichannel ALOHA with equal uploading probability.Comment: 10 pages, 8 figures, to appear in IEEE IoT
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