ENVIRONMENT MODELING AND ABSTRACTION OF NETWORK STATES FOR COGNITIVE FUNCTIONS

摘要

An EMA method of enabling CNM in communication networks comprises, for a given time instant t, extracting (S601) features from an n-dimensional input vector X^t containing at least one of continuous valued environmental parameters, network configuration values and key performance indicator values, and forming a d-dimensional feature vector Y^t from the extracted features, quantizing (S602) the formed feature vector Y^t by selecting, for the extracted vector Y^t, a single quantum corresponding to an internal state of k internal states of an internal state-space model, mapping (S603), for each dimension S_m of an m-dimensional output vector S^t, an output state bin of a number of output state bins present for dimension S_m to the selected internal state, and, for each cognitive function of f cognitive functions, selecting (S604) a subset out of the output vector S^t, each of the subsets having a dimension equal to or smaller than m and containing feature values required by the cognitive function, the f selected subsets being different in dimension from each other.