Functions and elements of self organising networks

Nodes use local information in order to choose the next hop.

self organizing network adalah

However, due to its NP-Completeness, it is incredibly hard to implement efficiently in high performance networks. Figure 5 shows how the hybrid SON architecture can be evolved using semi-distributed topology of P2P as a reference.

Functions and elements of self organising networks

This can cause conflicts between them. The neighboring base stations then automatically adjust their technical parameters such as emission power, antenna tilt, etc. Deepika, M. The main advantage of wireless sensor networks is that there is no need for a fixed network infrastructure. In potential-based routing, all nodes have a scalar value which is called potential [Kominami13]. An instance of a SON function is a single realization of that function. However, due to its NP-Completeness, it is incredibly hard to implement efficiently in high performance networks.

CPBR performance is inferior to centralized control performance. However, unlike other SONCO functions based on this approach, the outcomes of past decisions are not discarded.

Self organizing network algorithms

It can provide an appropriate approximate solution. The indexes of the resources in eNBs are stored in their super nodes. However, unlike other SONCO functions based on this approach, the outcomes of past decisions are not discarded. The neighboring base station would then re-configure their parameters in order to keep the entire area covered by the signal. Main article: Self-optimization Every base station contains hundreds of configuration parameters that control various aspects of the cell site. They are not stored in a central system. As a result efficient distributed search algorithms are required to locate resources. Each of these can be altered to change network behavior, based on observations of both the base station itself and measurements at the mobile station or handset. It is a variation of potential-based routing. However, as SON features were not initially included in the development of LTE, their integration is proving to be complex. SONCO function design can be improved using reinforcement learning with state aggregation. It can govern one or more cells in the network by tuning different network parameters to optimize some KPI. Therefore, macro-scale network problems can occur. The second approach treats SON instances as white-boxes.

Different SON instances and functions do not change the network parameters directly. SON has also been retrofitted to existing 3G networks to help reduce cost and improve service reliability. This approach is very complex to implement.

This is used to calculate the hops of multicast tree routing. The size of the state-space table scales exponentially with the number of cells in the network. The indexes of the resources in eNBs are stored in their super nodes. What is it and why is it necessary?

Potential-based routing suffers from the above mentioned problems as it is based on self-configuration. P2P search algorithms like DHT can be used to search for the resources in the super node.

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A Survey of Self