LTE self organizing networks

MERA follows communication technology evolution trends and invests in expertise in a new generation of wireless communication standards, including LTE and LTE Advanced.

In 2010 MERA and Fraunhofer Institute for Open Communication Systems (FOKUS), an R&D facility located in Berlin specializing in multi-domain networks and interoperable, user-centered solutions for open communication systems, have launched a joint project in the Evolved Packet Core (EPC) domain. Since then, MERA has been developing the OpenEPC (Open Evolved Packet Core) platform, e.g. Mobility Management Entity (MME) and relevant MME components and interfaces.

The company also contributes to prototyping the platform components and connecting LTE (Long Term Evolution) components to OpenEPC to create more flexible and faster communication networks. One of the most important directions of MERA's LTE expertise development is Self-Organizing Networks (SON).

Any telecom services provider is eager to have self-configuring, self-operating and self-optimizing infrastructure which is deployed quickly without special technical knowledge, automatically discovers its neighbors, automatically reconfigures around network failures and automatically optimizes the radio parameters, possesses automatically configured backhaul and interconnect, self-established and autonomously optimized QoS. These capabilities, along with many others, can be offered by SON.

SON is defined as a set of use cases that covers the entire network lifecycle: planning, deployment, operations, and optimization. In general, SON is designed to be a multi-vendor solution with standard interfaces utilized at key points to allow interoperability between different vendors. 

The key SON subjects are:

Network Planning

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The goal of SON at the stage of network planning is to eliminate as much preplanning of network configuration as possible. It does allow pre-planned network configurations, but strongly encourages as much automatically generated/discovered configuring as possible. SON allows to determine neighbor lists for networks elements, automatically assign physical cell ids and RF parameters and adjust other typically pre-planned configuration.

Network Deployment

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The goal of SON at the stage of network deployment is to radically decrease deployment period and number of procedures, especially concerning eNodeB deployment. This assumes the following eNodeB deployment model:

  • complete support of plug and play capabilities – no provisioning of hardware resources is required. Inventory information is automatically recorded and reported;
  • algorithmical computing of physical cell ID through communication with neighbor eNodeBs;
  • neighbors detection by user's equipment, optimization and refining in real-time, discovering new neighbors and deleting the expired ones;
  • automatic determination and continuous optimization of RF parameters, including antenna tilt, power output and interference control;
  • automatic setup of transport capabilities, establishing contact with the Element Management System (EMS), Mobility Management Entities (MME), etc;
  • complete self-test support, allowing easy verification after installation,
  • automatic authentication once connection to EMS is established in the network and update to the software latest version, if necessary.

Network Optimization

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The goal of SON at the stage of network optimization is to maintain the desired performance level over the life of the network as new equipment is deployed, usage patterns change, etc. SON optimization provides:

  • automatic neighbor optimization, including new neighbors detection and removal of the expired ones;
  • automatic interference reduction, including coordination of subtones and power levels across eNodeBs;
  • automatic handoff optimization, including KPIs (key performance indicators) monitoring to optimize handoffs by iteratively adjusting target C/I (carrier-to-interference) and RSSI (received-signal strength indication);
  • automatic transport QoS (quality-of-service) optimization, including monitoring KQIs to iteratively adjust QoS configuration;
  • automatic cell outage management process including KPIs monitoring to reduce the need of human control and drive testing OPEX due to adjusting antenna azimuths and tilt as well as power of the base stations transmitter;
  • automatic energy conservation by examining service loading trends and reducing the equipment power without affecting its performance.

Network Maintenance

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The goal of SON at the stage of network maintenance is to reduce Operational Expenses (OPEX) occurring over the life of the network by minimizing the level of monitoring and adjustment required by the operations staff. SON operations provide:

  • complete and standardized inventory reporting over all the components,
  • robust cell outage detection capabilities for latent faults,
  • integrated cell outage compensation capability that automatically reconfigures surrounding cells to offset the effect of the malfunctioning cell,
  • analysis of the first and second-order root causes and faults recovery,
  • real-time data to verify service capability after repair or reconfiguration,
  • multi-vendor subscriber and equipment trace to aid system troubleshooting.

SON concepts are described in the LTE (E-UTRAN) standards starting from the first release of the technology (Release 8) and expanding in scope with subsequent releases. The key goal of 3GPP standardization is support of SON features in multi-vendor network environments. 3GPP has defined a set of LTE SON use cases and associated SON functions. The standardized SON features effectively track the expected LTE network evolution stages as a function of time. With the first commercial networks launched in 2010, the initial focus of Release 8 has been functionality associated with initial equipment installation and integration (network deployment stage). The next release of SON, as standardized in Release 9, provides functionality addressing more mature networks (network optimization and maintenance stage).

Learn more about SON concepts

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SON functionality received prominent attention throughout the process of LTE and LTE Advanced standardization by 3GPP. In order to elaborate the details of the new standard several LTE SON projects have been launched. Among others, SOCRATES project can be mentioned as quite productive.

Traditional 2G/3G networks provide some elements of the desired SON functionality. However, various engineering stages are largely treated as sequential. Many operators handle them as more or less isolated tasks, where frequent responsibilities are split among different organizations. LTE SON assumes that these tasks are much more interrelated and comprise the three key elements - self-configuration, self-optimization and self-healing.

LTE networks are designed in a way to require minimal human involvement in network planning and optimization. New base stations are self-configured in a ‘plug-and-play’ mode, while existing base stations continuously self-optimize their operational algorithms and parameters in response to changes in network, traffic and environmental conditions. The adaptations are performed in order to provide targeted service availability and quality as efficient as possible. In case of cell or site failure self-healing methods are triggered to resolve coverage/capacity gap.

It is convenient to introduce specific use cases in order to evaluate performance of SON functionality. The SOCRATES project, for instance, considers the following SON use caseфthe most important:

  • Self-optimization:
    • admission control parameter optimization,
    • packet scheduling parameter optimization,
    • handover parameter optimization,
    • load balancing,
    • interference coordination,
    • self-optimization of home eNodeB.
  • Self-healing:
    • cell outage management,
    • X-map estimation (parameters value processing and performance mapping).
  • Self-configuration:
    • automatic generation of initial parameters for eNodeB insertion.

MERA's SON activities

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To enhance its expertise in LTE SON, MERA has developed  LTE system-level simulator, which is a Matlab-based simulator allowing to evaluate system performance in terms of given metrics subject to variation of different aspects such as LTE-specific PHY level parameters, propagation channel models, users' movement models, etc. The following picture shows an example of eNodeBs layout and corresponding SNIR distribution:

The one of the most important use cases of SON functionality is the handover (HO) parameter optimization. The HO is a procedure ensuring that users can move freely through the network, and its main stages (in LTE system) are shown in the following picture:

It is evident that HO performance is affected by different factors:

  • combination of HO triggers (control parameters):
    • handover hysteresis,
    • Time to Trigger (TTT).
  • handover measurement accuracy:
    • limited number of reference symbols available,
    • different approaches to L1/L3 filtering can be applied.
  • errors in transmission of control information on L1 control channels:
    • UL: measurement report or handover confirm,
    • DL: handover command.
  • rocessing Delays:
    • UE RRC Processing Delay,
    • X2 Latency.

The choice of appropriate values of HO control parameters influences the HO performance greatly. At the same time, adaptation of the HO triggers on a basis of users' speed, propagation conditions, cells size, etc. is needed. The following picture depicts the interrelation between control parameters and HO process:

The main objective of SON functionality concerning the HO is to reduce OPEX by avoiding manual tuning of the HO parameters and replacing it with automated optimization.

In order to optimize the HO performance, the corresponding metric (an objective function) should be put under consideration. The objective function (OF) may include one or more key indicators.These indicators, in accordance with the SOCRATES project, can be introduced as:

  • handover failure ratio – the ratio of failed handovers to the total number of handover attempts,
  • ping-pong handover ratio – the ratio of handed over calls that are handed back in less than critical time to the total number of handovers,
  • сall drop ratio – the ratio of existing calls that are dropped before they are finished to the number of calls that were accepted by the network.

If the values of handover hysteresis and time to trigger are the same for all eNodeBs in the network, the objective function takes a form of 2D function and can be shown graphically. The corresponding result obtained with MERA’s simulator is shown in the following picture:

The lower the value of the objective function the better the HO performance is. As it is shown above, a ditch of low values is noticeable lying in a circular shape (magenta line) around the point with a hysteresis of 0 dB and a TTT of 0 s. Hence, the optimal set of parameters does exist and the handover optimization algorithm has to drive parameters towards the area of good handover performance.

Therefore, the crucial issue of SON functionality involving the HO performance is the optimization algorithm. It should find the optimum set of control parameters under the disturbances due to the measurement errors and the finite observation time subject to keeping compution complexity at the reasonable level. MERA is carrying out intensive research in this area and will be ready to propose effective solution in the nearest future.

Another important use case of SON functionality is the Cell Outage Management. It combines the Cell Outage Detection and Compensation mechanisms to provide automatic mitigation of an eNB failures in the cases where the eNB equipment is unable to recognize being out of service and has therefore failed to notify OAM of the outage.

The main goals of Cell Outage Management are:

  • to ensure that the operator knows about the fault before the end user is out of service subject to the minimum level of human control and intervention (Cell Outage Detection);
  • to alleviate the problem caused by the loss of a Cell from service by means of automatic reconfiguration of the neighboring cells (Cell Outage Compensation).

The Cell Outage Compensation task can be solved with a cell’s coverage footprint regulation. This can be performed in different ways:

  • by tuning off the handover parameters;
  • by adjusting the antenna parameters (azimuths, tilts, beam shape) or/and the transmitting power.

MERA’s LTE system-level simulator allows to model the Cell Outage Compensation process and to see it in operation. The user of the simulator is to turn the eNB off and the corresponding network state is fixed as the initial point of simulating. The simulator assumes that the network state can be characterized by the single indicator – the value of M-dimensional objective function (OF). The OF is user-defined and, as a rule, composed of several partial metrics. The typical metrics are:

  • the distribution of Signal-to-Noise and Interference Ratios and the corresponding derivatives;
  • the Beam Loading (ratio of the number of users which are served by the least loaded eNB to the number of users, which are served by the most loaded eNB).

For any network state the simulator tries to findbest (extreme) value of the OF by adjustment of its variables (azimuths,tilts, beam shapes, transmitting power) subject to the user-defined restrictions. At the current moment, the simulator developed by MERA is able to perform optimization of up to the 63-dimensional OF.

The following pictures illustrate some features and abilities of MERA’s simulator.

The first picture shows the hexagonal network layout as an example. MERA’s simulator gives us the possibility to simulate scenarios where:

  • the network consists of 19 three-sector BSs (the central BS, 6 BSs of the first layer and 12 BSs of the second layer);
  • three parameters (azimuth, tilt of antenna beam and transmitter power) of the central BS and 6 BSs of the first layer can very during the optimization process;
  • BSs of the second layer (12 BSs) are included in the network for the “edge” effect elimination due to simulation of interference power received by central BS and 6 BSs of the first layer;
  • parameters of the second layer BSs are fixed and cannot vary during the optimization process.

The second picture shows the outage for homogeneous network (on the left), for network in the initial state (center) and network after the optimization process (on the right). The following picture shows the SNIR for homogeneous network (on the left), for network in the initial state (center) and network after the optimization process (on the right). We set the transmitter power in sector 1SE equal to zero (cell outage) and adjust 15 parameters simultaneously: azimuth direction and tilt of the antenna beam in five sectors 1SW, 2SW, 7SW, 6N, 7N as well as transmitter power in these sectors. It is implied that cell loading is full.

MERA's LTE SON research experience

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