Because of the inter-relatedness of all aspects of a basestation’s operations, self-healing procedures cannot always follow a simple and straightforward decision tree. For instance, the notion of “breathing” or expanding and contracting cell borders juxtaposes a SON’s self-healing and self-optimizing processes. An example of breathing would be when a SON basestation automatically increases its power output and thereby extends its range and its cell’s borders to offload a neighboring cell that is overloaded with call traffic and failing to connect an unacceptably high number of calls.

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This is called breathing because the basestation pushes outside its normal borders to assist with the self-healing processes in a neighboring cell. In contrast, the neighboring cell would essentially contract its borders to better serve the many callers who may have become concentrated in a certain limited area within the original cell borders.

This automatic breathing process can be quite effective at handling periods of peak call traffic. For example, rush hour traffic congestion is a routine daily occurrence in most large cities. When people are stuck in rush hour traffic, their first impulse is often to call home or wherever they are going to let someone know they are going to be late. This can quickly saturate the local macrocell basestation. In a SON, additional resources would be automatically deployed to absorb some of the peak call traffic during these periods.

The converse is also true. During the middle of the night when there is very little vehicular traffic on the highway, some of the basestation resources along the highway corridor might be automatically turned off to reduce power consumption for the service provider. Without SON technology, many wireless operators are currently using technicians to manually manipulate basestation energy consumption, channel utilization, and other operating parameters.

What’s To Optimize?

In a SON, operations can be optimized over several general operational metrics like energy consumption, signal interference conditions, coverage, and capacity. But maximizing one metric will often degrade the performance of other metrics. For example, coverage can be maximized at the expense of power consumption. In the real world, optimizing one operational metric often involves a tradeoff with others. As a result, an optimized network is one where the network as a whole is operating as efficiently as possible even though the utmost optimization of some metrics is not being achieved.

Generally speaking, several broad metrics can be applied to a network to determine how efficiently it is operating. As previously mentioned, energy consumption has increased in importance to operators in recent years. In a SON, power consumption will be reduced by automatically powering down cells when their resources are not needed or by reducing the transmit power output of certain basestations when extended range is not called for.