Environment modeling and abstraction of network states for cognitive functions

An environment modelling and abstraction, EMA, apparatus for enabling cognitive network management, CNM, in communication networks, the EMA apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the processor, cause the EMA apparatus at least to perform, for a given time instant t,

• extracting features from an n-dimensional input vector Xt containing at least one of continuous valued environmental parameters, network configuration values and key performance indicator values, and forming a d-dimensional feature vector Yt from the extracted features;
• quantizing the formed feature vector Yt by selecting, for the extracted vector Yt, a single quantum corresponding to an internal state of k internal states of an internal state-space model;
• mapping, for each dimension Sm of an m-dimensional output vector St, an output state bin of a number of output state bins present for dimension Sm to the selected internal state;
• and
• for each cognitive function of f cognitive functions, selecting a subset out of the output vector St, 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.

An environment modelling and abstraction, EMA, method of enabling cognitive network management, CNM, in communication networks, the EMA method comprising, for a given time instant t,

• extracting features from an n-dimensional input vector Xt containing at least one of continuous valued environmental parameters, network configuration values and key performance indicator values, and forming a d-dimensional feature vector Yt from the extracted features;
• quantizing the formed feature vector Yt by selecting, for the extracted vector Yt, a single quantum corresponding to an internal state of k internal states of an internal state-space model;
• mapping, for each dimension Sm of an m-dimensional output vector St, an output state bin of a number of output state bins present for dimension Sm to the selected internal state;
• and
• for each cognitive function of f cognitive functions, selecting a subset out of the output vector St, 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.

A non-transitory computer-readable medium storing a program comprising software code portions that cause a computer to perform, when the program is run on the computer:

• for a given time instant t,
• extracting features from an n-dimensional input vector Xt containing at least one of continuous valued environmental parameters, network configuration values and key performance indicator values, and forming a d-dimensional feature vector Yt from the extracted features;
• quantizing the formed feature vector Yt by selecting, for the extracted vector Yt, a single quantum corresponding to an internal state of k internal states of an internal state-space model;
• mapping, for each dimension Sm of an m-dimensional output vector St, an output state bin of a number of output state bins present for dimension Sm to the selected internal state;
• and
• for each cognitive function of f cognitive functions, selecting a subset out of the output vector St, 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.