Wireless communication networks rely heavily on channel state information
(CSI) to make informed decision for signal processing and network operations.
However, the traditional CSI acquisition methods is facing many difficulties:
pilot-aided channel training consumes a great deal of channel resources and
reduces the opportunities for energy saving, while location-aided channel
estimation suffers from inaccurate and insufficient location information. In
this paper, we propose a novel channel learning framework, which can tackle
these difficulties by inferring unobservable CSI from the observable one. We
formulate this framework theoretically and illustrate a special case in which
the learnability of the unobservable CSI can be guaranteed. Possible
applications of channel learning are then described, including cell selection
in multi-tier networks, device discovery for device-to-device (D2D)
communications, as well as end-to-end user association for load balancing. We
also propose a neuron-network-based algorithm for the cell selection problem in
multi-tier networks. The performance of this algorithm is evaluated using
geometry-based stochastic channel model (GSCM). In settings with 5 small cells,
the average cell-selection accuracy is 73% - only a 3.9% loss compared with a
location-aided algorithm which requires genuine location information.Comment: 6 pages, 4 figures, accepted by GlobeCom'1