Patent Application
20210326662
Some embodiments relate to environment modeling and abstraction of network states for cognitive functions. In particular, some embodiments relate to Cognitive Network Management (CNM) in 5G (radio access) networks and other (future) generations of wireless/mobile networks.
The concept of CNM has been advanced in several publications [1, 2, 3], which propose to replace SON functions with Cognitive Functions (CFs) that learn optimal behavior based on their actions on the network, the observed or measured impact thereof, and using various kinds of data, e.g., network planning, configuration, performance and quality, failure, or user/service-related data.
With the success of Self Organizing Networks (SON), but also its shortcomings in terms of flexibility and adaptability to changing and complex environments, there is a strong demand for more intelligent Operations, Administration and Management (OAM) functions to be added to the networks. The objective of CNM is thereby that OAM functions should be able to 1) learn the environment they are operating in, 2) learn their optimal behavior fitting to the specific environment, 3) learn from their experiences and that of other instances of the same or different OAM functions, and 4) learn to achieve the higher-level goals and objectives as defined by the network operator. This learning shall be based on one or more or all kinds of data available in the network (including, for example, performance information, failures, configuration data, network planning data, or user and service related data) as well as from the actions and the corresponding impact of the OAM function itself. The learning and the knowledge built from the learned information shall thereby increase the autonomy of the OAM functions.
In effect, CNM extends SON to: 1) infer higher level network and environment states from a multitude of data sources instead of the current low-level basic states recovered from KPI values 2) allow for adaptive selection and changes of NCPs (Network Configuration Parameters) depending on previous actions and operator goals. The first objective (modeling of states) is critical to the operation of CNM since CFs are expected to respond to specific states of the network. So CNM needs a module that abstracts the observed KPIs into states to which the CFs respond. Moreover, the abstraction must be consistent across multiple CFs in one or more network elements, domains or even subnetworks. And even within a single CNM instance, multiple modules need to work together (e.g. a configuration engine and a coordination engine) for the system to eventually learn the optimal network configurations. These modules should or must reference similar or the same abstract states in coordinating their responses and so they (may) require a separate module that defines these states. Meanwhile, the creation of such states should be flexible enough to allow for their online adjustment during operations, i.e., the EMA should be able to modify/split/aggregate/delete states as may be required by the subsequent entities.
Part of the learning processing is describing network states in a way that different functions have a common view of the network and that actions from different functions can be compared, correlated and coordinated. The respective function may in general terms be described as modeling and abstraction of network environment states in a way that is understandable to the different Cognitive Functions (CFs).
Some embodiments relate to the design of CFs and systems, and specifically focus on the design and realization of the Environment Modeling & Abstraction (EMA) module of a CF/CNM system.
According to some example embodiments, an EMA apparatus, a method and a non-transitory computer-readable medium are provided, that enable CNM in communication networks.
In the following the invention will be described by way of embodiments thereof with reference to the accompanying drawings.
The CF framework comprises five major components shown in
The respective components are:
In citation [3], the expected functionality of the EMA module and its deliverable to the other sub-functions are specified, i.e. to
Some embodiments to be described in the following focus on defining an EMA module explicitly.
In example embodiments described more fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments are shown, the terms “data,” “content,” “information,” and similar terms may be used interchangeably, according to some example embodiments, to refer to data capable of being transmitted, received, operated on, and/or stored. Moreover, the term “exemplary”, as may be used herein, is not provided to convey any qualitative assessment, but instead merely to convey an illustration of an example. Thus, use of any such terms should not be taken to limit the spirit and scope of embodiments of the present invention.
Referring to
EMA Input-Output System
As illustrated in
An output of the EMA module 200 is a set of CF-feature vectors S each of dimension equal to or smaller than m (m being the number of output states) and each of which contains the output states that are of interest to a specific cognitive function or engine. Each CF-feature vector S is a subset of the big network-state-vector and contains different combinations of feature values e.g. appropriate for the specific CF. The network-state vector (of dimension m) contains the states of the network along the number of prescribed (quasi-orthogonal) dimensions of interest/optimization. Such dimensions may for example be those for which the operator expects some action to be taken e.g. user mobility, cell load, energy consumption level, etc. They will be defined either by the operator or by the Network Objectives Manager through the configuration of the EMA module.
EMA Processing Steps—Environment Modeling
Referring to
At training time, the environment modeling block 310 also needs to form these internal states. This equates to transforming the n-dimensional continuous-space input into k discrete segments, through quantization. Since it can be expected that some of the input dimensions contain noise or redundant information, it is beneficial to precede the quantization step with a feature extractor, which removes these interfering parts of the data. Following this logic, according to some embodiments, the environment modeling is split into two logical functions of feature extraction in a feature extraction block 311 and quantization in a quantization block 312, which form the first two EMA steps shown in
In particular, according to some embodiments, in a first step, in feature extraction block 31 of environment modeling block 310, feature extraction is performed. For each time instant, the feature extraction block 311 compresses the input information Xt to a lower-dimensional representation Yt=[Y1t, Y2t, . . . , Ydt]T, while also removing redundant information and noise from the input Xt. According to some example implementations, this involves tasks such as combining different parameters with similar or the same underlying measure/metric (e.g. handover margins, time to trigger and cell offsets) into a single dimension (in this case handover delay). The number d of extracted features is usually much smaller than the number of input features (d<<n), but using more dimensions (d>>n) with sparsity enforced is also a viable alternative.
In a second step, in quantization block 312 of environment modeling block 310, quantization is performed. The quantization block 312 selects a single quantum from the internal state-space model 320 that best represents the current network state at the inference stage, and builds the quantization at training.
EMA Processing Steps—State Abstraction
According to some embodiments, a function of a state abstraction block of the EMA module 200 is to translate the internal state selected by the environment modeling block 310 to a representation that is useful for the CFs. The internal state-space model 320, illustrated in
In a third step, state mapping is performed by the state abstraction block 510. In the state mapping, the previously selected internal state is assigned to bins for each dimension Sm of St=[S1t, S2t, . . . , Smt]T of the output network-states. This mapping is unique for each dimension Sm, realized by a separate mapper for this dimension. According to some embodiments, mapping parameters, such as the binning, is influenced/configured by the NOM or the operator according to their global objectives.
In a fourth step, subsetting is performed by the state abstraction block 510. In subsetting, different subsets of the full network-state vector are selected to support (only) the necessary information that is required by the corresponding cognitive functions. This is done by individual subsetter elements (Subsetter1, Subsetter2, . . . , Subsetterf) unique to the specific CF of plurality of CFs comprising CF1, CF2, . . . , CFf. The subsetting can be influenced in multiple ways, as explained later on. A default subsetter (Subsetterf in
According to some embodiments, since the state abstraction can be influenced by reconfigurations of the constraints for the specific dimensions, the EMA module 200 needs to have a finely-grained internal representation of the state-space which it uses to abstract into the output states. Thereby, even with reconfiguration of constraints, it does not need to re-learn the underlying state-space model, but only adjusts the mapping between internal and external (output) states and subsets.
It is to be noted that the above-mentioned variables n, d, k, m and f are positive integers.
Now reference is made to
The EMA process of
In step S601 of
In step S602 of
In step S603 of
In step S604, for each cognitive function of f cognitive functions, a subset is selected 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. According to some embodiments, step S604 corresponds to the above-described fourth step the function of which is illustrated in
Now reference is made to
The control unit 70 comprises processing resources (processing circuitry) 71, memory resources (memory circuitry) 72 and interfaces (interface circuitry) 73, coupled by a connection 74.
As used in this application, the term “circuitry” may refer to one or more or all of the following:
(a) hardware-only circuit implementations (such as implementations in only analog and/or digital circuitry) and
(b) to combinations of circuits and software (and/or firmware), such as (as applicable): (i) to a combination of processor(s) or (ii) to portions of processor(s)/software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and
(c) to circuits, such as a microprocessor(s) or a portion of a microprocessor(s), that require software or firmware for operation, even if the software or firmware is not physically present.
This definition of “circuitry” applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term “circuitry” would also cover an implementation of merely a processor (or multiple processors) or portion of a processor and its (or their) accompanying software and/or firmware. The term “circuitry” would also cover, for example and if applicable to the particular claim element, a baseband integrated circuit or applications processor integrated circuit for a mobile phone or a similar integrated circuit in server, a cellular network device, or other network device.
The terms “connected,” “coupled,” or any variant thereof, mean any connection or coupling, either direct or indirect, between two or more elements, and may encompass the presence of one or more intermediate elements between two elements that are “connected” or “coupled” together. The coupling or connection between the elements can be physical, logical, or a combination thereof. As employed herein two elements may be considered to be “connected” or “coupled” together by the use of one or more wires, cables and printed electrical connections, as well as by the use of electromagnetic energy, such as electromagnetic energy having wavelengths in the radio frequency region, the microwave region and the optical (both visible and invisible) region, as non-limiting examples.
The memory resources (memory circuitry) 72 store a program assumed to include program instructions that, when executed by the processing resources (processing circuitry) 71 enable the control unit 70 to operate in accordance with exemplary embodiments, as detailed herein.
The memory resources (memory circuitry) 72 may be of any type suitable to the local technical environment and may be implemented using any suitable data storage technology, such as semiconductor based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory comprising a non-transitory computer-readable medium. The processing resources (processing circuitry) 71 may be of any type suitable to the local technical environment, and may include one or more of general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on a multi core processor architecture, as non limiting examples.
Training and Utility
The EMA module 200 needs to be trained before it is used as desired. The above-described first to third steps can be trained from observations of the network in different states while the fourth step requires feedback from actual CFs to train the sub-setters to learn the respective subsets. Although it is tempting to consider manually designing and constructing a mapping function that accomplishes the first to third steps, i.e., mapping each observation in continuous space to a vector of discrete values on quasi-orthogonal dimensions, it is not an obvious activity. Correspondingly, a training process is needed to ensure that the EMA module 200 learns the best matching function as described in more detail below.
A critical part of the EMA module 200 is the realization of the internal state representation as created by the environment modeling block 310. This is then the input to the state abstraction block 510 to create a CF specific output that well represents the network conditions at the time, both in general and with respect to the needs of the specific CF.
For the internal state-space model 320 to map the network's behavior regardless of user bias, environment modeling functions need to be trainable in an unsupervised manner, without labelled training data. Usually, most of the unsupervised learning algorithms do require a handful of meta-parameters, which must be set prior to training by the user, or, by the implementer. The environment modeling (EM) steps will not be reconfigurable after training, and should be trained with as much data as possible from the network to be able to form a comprehensive mapping that can be applied to one or more or all network elements and CFs.
The state abstraction (SA) functions need to be trained in a supervised or semi-supervised way owing mainly to the need for feedback from the CFs about the utility of the different dimensions for the CFs.
Multiple implementation options are foreseen for each of the four components, which will be described in the following. One of differentiators between the implementation options is whether the two logical functions in each phase (modeling or abstraction) are realized as separate steps, or can be incorporated into a single learning stage.
Feature Extraction Using Independent Component Analysis
According to an example implementation, in step S601 of
Independent Component Analysis (ICA) is a statistical technique for finding hidden factors that underlie sets of random variables. The data variables are assumed to be linear mixtures of some unknown latent, non-Gaussian and mutually independent variables mixed with an unknown mixing mechanism: i.e., X=AS, where S is the latent vector.
Pre-processing: The most basic and necessary pre-processing is to centre S, i.e. subtract its mean vector m=E{X} to make X a zero-mean variable. After estimating the mixing matrix A with centered data, the estimation can be completed by adding the mean vector of S back to the centered estimates of Sm The mean vector of S is given by A−1m, where m is the mean vector that was subtracted in the pre-processing.
A first step in many ICA algorithms is to whiten the data by removing any correlations in the data. After whitening, the separated signals can be found by an orthogonal transformation of the whitened signals y as a rotation of the joint density. There are many algorithms for performing ICA and one very efficient one is the FastICA (fixed-point) algorithm described in citation [4], which finds directions with weight vectors W1, . . . Wn, such that for each vector Wi, the projection WiTX maximizes non-Gaussianity. Thereby, the variance of WiTX must here be constrained to unity which for whitened data is equivalent to constraining the norm of W to be unity.
The FastICA is based on a fixed-point iteration scheme for finding a maximum of the non-Gaussianity of WiTX which can be derived as an approximative Newton iteration. This can be computed using an activation function g and its derivative g′ e.g. g(u)=tanh(au) and g′(u)=u exp(−u2/2), where 1≤a≤2 is some suitable constant, often as a=1.
The basic form of the FastICA algorithm is as shown below. To prevent different vectors from converging to the same maxima the outputs W1TX, . . . , WnTX have to be decorrelated after every iteration (see citation [5]) which is indicated below at step 4.
FastICA Algorithm:
Repeat until convergence:
Feature Extraction Using Autoencoders
According to another example implementation, in step S601 of
An autoencoder is an unsupervised neural network used for learning efficient encodings of a given data set. For a dataset X, the autoencoder encodes X with a function θ to an intermediate representation Z and decodes Z to X′, the estimate of X through a mapping function θ′. This is represented by
The dimension, m, of the intermediate representation depends on (and is equivalent to) the size of the hidden layer, and can be of a lower or higher dimensionality than that of the input/output layers. The autoencoder learns the encoding and decoding functions θ, θ′ by minimizing the difference between X and X′ using a specific criterion—usually the mean squared error or cross entropy loss. After training, this hidden layer encoding is utilized to compress the information, removing unnecessary and noisy information.
Quantization Using K-Means and Self-Organizing Maps
According to an additional or another example implementation, in step S602 of
For quantization, two well-used algorithms are possible: K-means and the Self-Organizing Map (SOM) algorithm (described in citation [6]). Both algorithms achieve similar or the same functionality, which is splitting the input space into segments, while simultaneously fitting this segmentation to follow the distribution of a training data-set well. Both algorithms require the number of quanta (k) to be pre-defined before training, however, techniques exist for both algorithms to figure out an optimal number for k automatically. In the context of EMA, the quantization needs to create a fine-enough segmentation so that the state-abstraction later can be done precisely. This means that a pre-set high number of quanta (100-1000) should be enough without any need to fine tune k later. Other than the parameter k, no additional parameters are required by the training, which is entirely unsupervised.
A downside of K-means and SOM algorithms is that since they try to represent the density of the data, they may underrepresent sections of the state-space, which in this use-case is undesired. The Bounding Sphere Quantization (BSQ) algorithm (described in citation [10]) could then be considered in this case. It uses similar or the same algorithmic framework as K-means, but uses a different goal function.
All-in-One State Modeling Using Sparse Autoencoders
According to an additional or another example implementation, in step S602 of
Autoencoders can have a unique regularization mechanic where various degrees of sparseness can be enforced in the middle layer(s), so that it is encouraged that only a few neurons fire at any input vector. If the user enforces extreme sparseness, the middle neurons structure themselves and the whole encoding process so that each encompasses a certain finite region of the input space, very similar to explicit quantization algorithms. However, even very sparse autoencoders do not lose the ability to extract key features from the input space. This allows using sparse or k-sparse autoencoders (described in citation [7]) as both feature selectors and quantizers in a single step. This gives a more unified approach, with an end-to-end training structure.
Mapping as Simple or Neural Networks Based Labelling
According to an additional or another example implementation, in step S603 of
In particular, the mappers shown e.g. in
An example implementation of a mapper module is similar to the example in
LSTM (Long-Short Term Memory) (described in citation [8]) Neural Networks can also be used as labellers. These functions extend on the content labelling method by adding memory to the system. This can be useful for states that exhibit complex temporal behaviour, and can not necessarily be mapped in a 1:1 manner to unique internal states. The training of LSTMs can be realized in a similar or the same way as the simple labelling, generating or manufacturing labelled observations to function as training examples.
Subsetting Using Genetic Algorithms
Subsetting modules (e.g. the subsetters shown in
A first possibility is action feedback, in which the CF (CF1 in
A second possibility is direct feedback, in which the CF (CF2 in
A third possibility is no feedback, in which the CF (CF3 in
The easier part of subsetting is in the case of direct feedback providing a numerical value of goodness. With this information, a search method such as a genetic algorithm (described in citation [9]) can be employed to figure out an optimal set of output states to be supported to each CF. However, the search requires multiple evaluations of candidate state sets, which requires an environment that detaches the search from real networks, such as a high level numerical modeling of the behaviour of the CF, or a lower level simulation of a network in which both the EMA and the CF are implemented.
Figuring out which information the CF most responds to by monitoring the actions it takes can also be done utilizing a genetic algorithm, however this solution might produce suboptimal results with regards to the CF's needs, as precise decisions can require information that is only used sparsely. The training of the subsetting module in this case can be done in a similar or the same way as in the case of direct feedback.
Offline and Online Training:
The applicable techniques for both modeling and abstraction require an amount of data with which to train the algorithms, yet this data is rarely available. For the eventual realization of the functional EMA module even without this necessary training data, the following process is proposed.
First, initial training via system simulations is performed. Data is generated from a system simulator in a large enough size and with enough detail to do an initial training.
Then, online semi-supervised training is performed. The partly trained EMA module is attached to a live system to learn from live data but without any actions being derived from its learnings. Instead, a human operator further trains it by e.g. adjusting the error calculated in the modeling step if the suggested abstract states are not those expected by the operator.
According to some embodiments, a uniform yet reconfigurable description of network states is enabled. Subsequent entities are able to reference a similar or the same state for the respective decisions. The states can also be used for reporting purposes e.g. to state how often the network was observed to be in a certain state at different times.
Further, once trained the EMA module can be used in multiple networks with minimal need for retraining.
According to an aspect, an environment modelling and abstraction, EMA, apparatus for enabling cognitive network management, CNM, in communication networks is provided. The EMA apparatus comprises means for, 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, means for 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, means for 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 means for, 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.
According to an example implementation, the means for extracting extracts the features from the input vector Xt using at least one of an independent component analysis and autoencoders.
According to an example implementation, the EMA apparatus further comprises means for acquiring d-dimensional training feature vectors, and means for learning the internal state-space model to follow a distribution of the training feature vectors, using at least one of K-means and self-organizing map algorithms with the training feature vectors as inputs.
According to another example implementation, the EMA apparatus further comprises means for acquiring n-dimensional training input vectors, and means for learning the internal state-space model having dimension d to follow a distribution of the training input vectors, using sparse autoencoders with the training input vectors as inputs.
According to an example implementation, the EMA apparatus further comprises means for forming a labelling for mapping the output state bin to the selected internal state based on training data created based at least on one of distribution and number of the output state bins.
According to an example implementation, the means for selecting selects the f different subsets by monitoring outputs from the cognitive functions, and by selecting the different subsets based on the monitored outputs.
According to an example implementation, the means for selecting selects the f different subsets by receiving numerical values from the cognitive functions indicating assessments of the subsets, and by selecting the different subsets based on the numerical values.
According to an example implementation, the EMA apparatus is implemented as a classifier configured to cluster the key performance indicator values or combinations of the key performance indicator values into the subsets that are logically distinguishable from each other.
According to an example implementation, the EMA apparatus comprises the control unit 70 shown in
It is to be understood that the above description is illustrative and is not to be construed as limiting the disclosure. Various modifications and applications may occur to those skilled in the art without departing from the true spirit and scope of the disclosure as defined by the appended claims.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2018/069638 | 7/19/2018 | WO | 00 |
