Spatial Wireless Channel Prediction under Location Uncertainty
Journal article, 2016

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. In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the wireless channel.

Author

Srikar Muppirisetty

Chalmers, Signals and Systems, Communication and Antenna Systems, Communication Systems

Tommy Svensson

Chalmers, Signals and Systems, Communication and Antenna Systems, Communication Systems

Henk Wymeersch

Chalmers, Signals and Systems, Communication and Antenna Systems, Communication Systems

IEEE Transactions on Wireless Communications

1536-1276 (ISSN)

Vol. 15 2 1031-1044 7275189

Cooperative Situational Awareness for Wireless Networks (COOPNET)

European Commission (FP7), 2011-05-01 -- 2016-04-30.

Areas of Advance

Information and Communication Technology

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/TWC.2015.2481879

More information

Created

10/7/2017