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Robust Cell-Load Learning With a Small Sample Set

 
: Awan, D.A.; Cavalcante, R.L.G.; Stanczak, S.

:

IEEE transactions on signal processing 68 (2019), S.270-283
ISSN: 0096-3518
ISSN: 0018-9278
ISSN: 0096-1620
ISSN: 1053-587X
ISSN: 1941-0476
Englisch
Zeitschriftenaufsatz
Fraunhofer HHI ()

Abstract
Learning of the cell-load in radio access networks (RANs) has to be performed within a short time period. Therefore, we propose a learning framework that is robust against uncertainties resulting from the need for learning based on a relatively small training set. To this end, we incorporate prior knowledge about the cell-load in the learning framework. For example, an inherent property of the cell-load is that it is monotonic in downlink (data) rates. To obtain additional prior knowledge we first study the feasible rate region, i.e., the set of all vectors of user rates that can be supported by the network. We prove that the feasible rate region is compact. Moreover, we show the existence of a Lipschitz function that maps feasible rate vectors to cell-load vectors. With these results in hand, we present a learning technique that guarantees a minimum approximation error in the worst-case scenario by using prior knowledge and a small training sample set. Simulations in the network simulator NS3 demonstrate that the proposed method exhibits better robustness and accuracy than standard learning techniques, especially for small training sample sets.

: http://publica.fraunhofer.de/dokumente/N-589243.html