1,991 research outputs found
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
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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
- …