1,310 research outputs found
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
On Spectral Coexistence of CP-OFDM and FB-MC Waveforms in 5G Networks
Future 5G networks will serve a variety of applications that will coexist on
the same spectral band and geographical area, in an uncoordinated and
asynchronous manner. It is widely accepted that using CP-OFDM, the waveform
used by most current communication systems, will make it difficult to achieve
this paradigm. Especially, CP-OFDM is not adapted for spectral coexistence
because of its poor spectral localization. Therefore, it has been widely
suggested to use filter bank based multi carrier (FB-MC) waveforms with
enhanced spectral localization to replace CP-OFDM. Especially, FB-MC waveforms
are expected to facilitate coexistence with legacy CP-OFDM based systems.
However, this idea is based on the observation of the PSD of FB-MC waveforms
only. In this paper, we demonstrate that this approach is flawed and show what
metric should be used to rate interference between FB-MC and CP-OFDM systems.
Finally, our results show that using FB-MC waveforms does not facilitate
coexistence with CP-OFDM based systems to a high extent.Comment: Manuscript submitted for review to IEEE Transactions on Wireless
Communication
Spatial Wireless Channel Prediction under Location Uncertainty
Spatial wireless channel prediction is important for future wireless
networks, and in particular for proactive resource allocation at different
layers of the protocol stack. Various sources of uncertainty must be accounted
for during modeling and to provide robust predictions. We investigate two
channel prediction frameworks, classical Gaussian processes (cGP) and uncertain
Gaussian processes (uGP), and analyze the impact of location uncertainty during
learning/training and prediction/testing, for scenarios where measurements
uncertainty are dominated by large-scale fading. We observe that cGP generally
fails both in terms of learning the channel parameters and in predicting the
channel in the presence of location uncertainties.\textcolor{blue}{{} }In
contrast, uGP explicitly considers the location uncertainty. Using simulated
data, we show that uGP is able to learn and predict the wireless channel
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