In our vision, which is in line with the views of 3GPP (3rd Generation Partnership Project) and the NGMN (Next Generation Mobile Networks) group, future networks will require minimal human involvement in the network planning and optimisation tasks. Newly added base stations are self-configured in a ‘plug-and-play’ fashion, while existing base stations continuously self-optimise their operational algorithms and parameters in response to changes in network, traffic and environmental conditions. The adaptations are performed in order to provide the targeted service availability and quality as efficiently as possible. In the event of a cell or site failure, self-healing methods are triggered to resolve the resulting coverage/capacity gap to the extent possible.
The envisioned operational process applied in self-organising radio access networks and the distinct components of self-organisation are illustrated in the Figure below.
Consider a fully configured and operational radio access network and, somewhat arbitrarily, start at the depicted 'measurements' phase. This phase indicates a continuous activity where a multitude of measurements are collected via various sources, including network counters and probes. These raw measurements of e.g. radio channel characteristics, traffic and user mobility aspects, are processed in order to provide relevant information for the various related self-optimisation tasks. The required format, accuracy and periodicity of the delivered information depend on the specific mechanism that is to be self-optimised.
In the 'self-optimisation' phase intelligent methods apply the processed measurements to derive an updated set of radio (resource management) parameters, including e.g. antenna parameters (tilt, azimuth), power settings (incl. pilot, control and traffic channels), neighbour lists (cell IDs and associated weights), and a range of radio resource management parameters (admission/congestion/handover control and packet scheduling). In case the self-optimisation methods appear to be incapable to meet the performance objectives, capacity expansion is indispensable and timely triggers with accompanying suggestions for human intervention are delivered, e.g. in terms of a recommended location for a new site.
The 'self-configuration phase, depicted as an external arm reaching into the continuous self-optimisation cycle, is triggered by 'incidental events' of an 'intentional nature'. Examples are the addition of a new site and the introduction of a service or a new network feature. These upgrades generally require an initial (re)configuration of a number of radio parameters or resource management algorithms, e.g. pilot powers and neighbour lists. These have to be set prior to operations and before they can be optimised as part of the continuous self-optimisation process.
The 'self-healing' methods are triggered by 'incidental events' of a 'non-intentional nature', such as the failure of a cell or site. These methods aim to resolve the loss of coverage/capacity induced by such events to the extent possible. This is done by appropriately adjusting the parameters and algorithms in surrounding cells. Once the actual failure has been repaired, all parameters are restored to their original settings.
The degree of self-organisation that is deployed determines the residual tasks that remain for network operators. In an ideal case, the operator merely needs to feed the self-organisation methods with a number of policy aspects, e.g. its desired balance in the apparent trade-offs that exist between the conflicting coverage, capacity, quality and cost targets. The self-organisation methods then feed the operator with
Until (if ever) such an ideal setting is achieved, we foresee a gradual introduction of self-organisation in radio access networks, characterised by different incremental upgrades which are implemented and monitored. This way, the implemented measures can be adequately assessed; the impact of potential ‘teething troubles’ can be limited and the operators’ confidence to hand over its control to automated algorithms increases.