GEOson – Self-learning, location-aware
GEOson is a unique, scalable, real-time SON solution that models decisions based on:
- Subscriber and network insight
- Location intelligence
- Real-time self-learning
By combining the power of ariesoGEO with the value of InteliSON, GEOson significantly improves network efficiency, performance, and customer QoE—accomplishments that are unattainable by manual means.
- Your network will be more efficient
- You will use fewer resources and reduce OpEx
- Your CapEx can be deferred
- Your customers will get better services
Configuration, Optimization, Healing
To deliver the self-configuration, self-optimization, and self-healing requirements of SON, GEOson uses network, subscriber, and location insight to build a self-learning SON solution.
Network optimization is all about the subscriber. GEOson uses real customer insight, capturing all events from all subscribers all the time. SON results can then reflect subscriber QoE and revenues. Resources can be balanced, and GEOson can adapt in real time to subscribers’ needs while meeting network requirements.
Network performance statistics correlated with subscriber data is the foundation for the intelligence that feeds GEOson. We interface directly to elements as well as NEM management systems: performance, configuration, fault, and event trace OSS subsystems. In addition, our Hybrid SON solution leverages any D-SON solution you may already have deployed.
With building-level accuracy, we deliver unprecedented insight into subscriber usage patterns. SON benefits greatly from granular, location-aware insight, and GEOson handles that massive non-uniformity in the network.
The initial stage of self-learning is to automatically and quickly set initial thresholds and parameters. With the massive non-uniformity in networks, you cannot use a single group of settings and apply them broadly across a network. By measuring and monitoring a broad set of insights (such as subscribers and locations) across the network over time, GEOson can adapt to changing patterns and usage to continually optimize the network. As the network evolves and GEOson continues to optimize its performance, the optimization parameters, thresholds, and partitioning into optimization clusters to maintain optimal service may require changes.
The GEOson solution is now also enabled by the recent technology acquisition of Reverb Networks.
Advanced, automatic self-optimizing network functionality in a reliable, robust, real-time production platform.
InteliSON® is a live network data-driven SON solution designed to improve the network performance. InteliSON delivers dynamic radio access network optimization through antenna and RF parameter configuration changes.
The solution is based on inputs from the Radio Access Network through Performance Management, Configuration Management, Fault Management and Call Traces OSS subsystems. The proprietary InteliSON algorithms take into account Key Performance Indicators (KPIs), configuration data, parameters, fault data, and mobile measurements in identifying, prioritizing, and executing optimization solutions in the network.
Critical Zone Detector
The Critical Zone Detector is an essential component of InteliSON and is utilized with all of its SON features.
The Critical Zone Detector provides continuous monitoring KPIs from the RAN cluster. Based on performance thresholds to these KPIs set by the operator, the Critical Zone Detector identifies Critical Sectors which are candidates from Load Balancing, Interference Reduction, or Seal-Healing optimization processes.
The Critical Zone Detector creates Critical Zones around Critical Sectors, consisting of the Critical Sectors and select neighboring sectors, upon which inteliSON optimization process are independently executed.
InteliSON Load Balancing identifies cells experiencing congestion and attempts to alleviate this congestion by a series of RF parameter or tilt change optimizations of the highly loaded cells and neighboring cells making up the Critical Zone.
The parameter or tilt changes shape cell coverage in order to move traffic from the highly loaded cell to neighboring cells with available resources.
These small, incremental changes do not create coverage holes. Frequent KPI monitoring ensures that any coverage issues reflected by a change in the average traffic and call volume will be quickly identified and rectified.
InteliSON Interference Reduction identifies cells that are propagating to other neighboring cells and creating poor C/I performance in those cells, and deploys a series of antenna tilt changes to minimize the impact of the interfering cell, thus improving overall C/I within identified Critical Zones.
InteliSON Interference Reduction uses an Interference Matrix as input, constructed from per-mobile call traces from the best server, active neighbors and other neighbors. The Critical Zone Detector analyzes the Interference Matrix and ranks problem cells by their interference-creating impact and the highest interference power.
Interference Reduction then performs optimization by making a series of downward antenna tilt changes aimed at reducing the impact of these interfering cells. This process is repeated until InteliSON’s Algorithm Engine converges to an optimal solution, and the best antenna configuration found is deployed.
InteliSON Self-Healing identifies sites and cells that demonstrate certain failure/outage indicated by alarms from the fault management (FM) system of the OSS. Based on this feedback it classifies cells as Outage Cells and attempts a series of antenna tilt changes to neighboring cells in order to restore service to the area affected by the outage.
The Self-Healing module monitors fault alarms or network KPIs (to identify instances of critical or frequent poor performance) to identify Outage Cells. The Critical Zone Detector creates Critical Zones around these cells, and the solution then begins a series of Immediate, aggressive tilt changes on the neighboring cells to restore services to the affected area.
Once the outage conditions subside, InteliSON Self-Healing reverts the antenna configurations back to their original settings.
Automatic Neighbor Relations
Reverb Networks InteliSON system uses the UMTS/HSPA Neighbor Cell Relation (NCR) list to control UE neighbor cell measurements.
Reverb Networks ANR is field-proven, and is currently optimizing cities of more than 1 million subscribers in North America.
UE neighbors can be:
- Inter-RAT (iRAT)
- Between UMTS and GSM for voice
- Between UMTS and LTE for data
Mobility Load Balancing
Reverb’s Mobility Load Balancing forces active UEs to perform Load-Based Handover (LB-HO). LB-HO is advantageous as the system has a direct measurement mechanism and knowledge of each user’s traffic requirements and radio conditions before deciding to load balance. An LB-HO reason code is included during handover messaging to provide the target cell with knowledge for admission control.
Mobility Load Balancing can be:
- Intra Carrier
- Inter Carrier
Mobility Robustness Optimization
Mobility Robustness Optimization minimizes the number of handover-related radio link failures, by automatically adapting cell parameters controlling handover, creating:-
- Better end-user experience
- Increased network capacity
Mobility Robustness features are split into several classes of behavior, including:
- Handover too late
- Handover too early
- Unnecessary handover
- Handover to wrong cell
Reverb’s SON Director offers the ability to coordinate and orchestrate self-optimizing actions across several layers of SON technology.
By considering the interaction of both centralized and distributed SON functionality, SON Director can help resolve conflicts, race conditions, bounce and glare between the various controlling layers. This creates a more harmonious, stable SON system, removing the clashes that can occur when a variety of legacy systems are forced together.
SON Director is a complete command and control system for SON within your network.