Functions and elements of self organising networks

Several studies have indicated that almost comparable performance can be achieved by relying on the use of more robust modulation and coding for UEs with low SINR combined with the interference averaging that occur in practice with bursty, nongreedy traffic, especially with low to moderate network load.

This work provides technical solutions for use cases contained in TR Comparing the advantages and disadvantages of the different SON architectures, distributed solutions seems to be the most future proof.

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In this case Functions and elements of self organising networks operator can have HeNBs from several vendors in the network, and a centralized SON solution can be advantageous in order to avoid interworking problems between different vendor specific SON algorithms.

The colors in the regions containing the red lines indicate the distances between neurons. The latter function is not only used during installation but is also an important part during normal operations. The actual decrease in OPEX is not easy to give since the corresponding installation without any self automatic features is difficult to foresee.

In both cases, the problem is to distinguish unnecessary HO from the one that was needed. In fact, the HeNB will often create a coverage hole in the macro cell. Updating automatic neighbour relations ANR is a continuous activity that may be more intense during network expansion, but is still a time consuming task in mature networks.

Triggering of each of these functions is optional and depends on implementation.

Additionally, MLB can be used to shape the system load according to operator policy, or to empty lightly loaded cells which can then be turned off in order to save energy. Here is the map after cycles. There are several useful visualizations that you can access from this window.

Circuit switched and packet switched data transport is also supported. In legacy networks, the failing base stations are at times hard to identify and a significant amount of time and resources is required to fix it. It is costly and troublesome to retain neighbour cell relation information.

There are three main reasons for this. The main drivers are essentially to reduce CAPEX and OPEX, which would otherwise increase dramatically due to increased number of network parameters that has to be monitored and set, the rapidly increasing numbers of base stations in the network and parallel operation of 2G, 3G and Evolved Packet Core EPC infrastructures.

Hence, cell edge UEs must anyway have a sufficient SINR for receiving the control channels, so reception of data channels using robust coding and modulation is also possible. The testing must include a variety of test cases from plain functional testing to large scale testing involving several SON functions where also the stability and interaction between SON components are tested also with high network load.

This can lead to severe interference between the cells, especially for cell-edge users. If there are many pico- and femto-cells this traffic will be very significant. Mobility Robustness Optimization MRO Mobility robustness optimization MRO [ 13 ] encompasses the automated optimization of parameters affecting active mode and idle mode handovers HOs to ensure good end-user quality and performance, while considering possible competing interactions with other SON features such as automatic neighbour relations ANR and mobility load balancing MLB.The concept of Self-Organizing Networks (SON) [1] emerges from a pressing need to automate the operational and maintenance tasks of mobile networks, especially in the radio access part, due to the continuing growth in their complexity.

Self-organizing network

Network Management function; Element Management function; Mainly in the network managements systems, OAM is addressed as the entity which controls, maintains and configures the network.

The term OAM is used in the 3GPP documentation for the network management functions.

Journal of Computer Networks and Communications

Self-organizing networks (SONs) are collections of control and management methods and optimization algorithms that are attracting attention as a means of achieving economical services in the next-generation mobile network.

functions in each network element. [2] 3. SELF -HEALING Self -healing is the least researched of the self -organizing functions as it is the most complex of the SON domains. Nevertheless it is still an important part of self -management as Self-Healing in Self-Organizing Networks Oliver Scheit.

The weight learning function for the self-organizing map is learnsomb. First, the network identifies the winning neuron for each input vector.

Each weight vector then moves to the average position of all of the input vectors for which it is a winner or for which it is in the neighborhood of a winner. Self-organizing networks are commonly divided into three major architectural types. Distributed SON. In this type of SON (D-SON), functions are distributed among the network elements at the edge of the network, typically the ENodeB elements.

This implies a certain degree of localization of functionality and is normally supplied by the network equipment vendor manufacturing the radio cell.

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Functions and elements of self organising networks
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