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De-risking SON deployments with network emulation

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Self-organising networks (SON) is a promising new feature defined by the 3GPP standard to help mobile network operators (MNOs) to automate several recurring tasks required for activities such as cells deployment, failure compensation and performance optimisation. The massive OPEX savings foreseen is the driver for SON demand. However, many MNOs are concerned that the introduction of such a disruptive technology could generate major revenue losses if not properly matured. This article shows how network emulation can help the mobile network ecosystem de-risk and deploy SON features efficiently.

What is SON?

The density and complexity of mobile networks is increasing rapidly to cope with exponential evolution of user traffic, mostly driven by smartphones, connected applications and video streaming. LTE is an answer to these challenges with a new, very flexible transmission technology associated with many innovations. It enables heterogeneous network architectures mixing macro cells and small cells for extended coverage and capacity. This extended flexibility compared to previous network generations comes with increased complexity for the network operation activities, in a landscape under stronger and stronger cost reduction pressure.

SON is a solution to manage the complexity and decrease OPEX. We can summarise it as a network operation automation technology which focuses on three main areas:
Self-configuration functions: This is the ability for the network to re-configure itself automatically when nodes are added, deleted or modified. One example is ANR (automatic neighbour relation), a feature that simplifies the reconfiguration process required when a new cell is added to a network. Mobile network operators see great value in this feature because with modern networks we have more and more small-cells cohabiting with macro-cells. The underlying deployment scenarios are now faster and more iterative, with associated recurring costs to be optimised with SON.
Self-optimisation functions: A recurring and automated process for the dynamic tuning of network parameters for optimal performances in changing conditions. For example, handling of traffic density migration linked to periodicity of business activities. This attractive feature is promising huge energy savings and enhanced user experience quality thanks to network capacity being provided only where it is needed.
Self-healing functions: Automatic compensation of network nodes failures, to restore the service where it has been degraded. For example, self-healing can handle the network coverage loss in case of base station outage, by dynamic reconfiguration of adjacent healthy cells. With the increase of base station sites (macro-cells and small-cells), this feature is an economical answer to the growing costs of network operations, especially linked to the demand for mastered quality of experience in any situation, even nodes failures cases.

The challenges

De-risking SON deployment with network emulation

SON is a complex technology associating network measurements with sophisticated analysis and decision algorithms reproducing human reasoning. The outputs of these algorithms are directly connected to operational levers of the network for a fully automated workflow. As networks are heterogeneous (multiple technologies, multiple vendors), SON systems must cope with the multiplicity of protocols, data formats and interfaces. They look more like a labyrinthine system than a Zen garden. Also, SON technology is so strategic that vendors keep it secret and sell black-boxes with very limited information on internal mechanisms.
This is where network emulation enters the game, as a pragmatic solution to help all SON stakeholders increase their trust and control over this new, promising technology.
We have seen previously that SON is tightly linked to dynamic aspects of the network with complex use cases involving multiple cells, multiple network nodes and SON equipments in front of many users with varying behaviours and sophisticated radio conditions. So, the validation and tuning of SON should be done at full system level, with testing conditions as close as possible to the reality, including the trickiest part which is the radio environment.
Historically, tools available for mobile network testing focused on only a few aspects of the complexity of the problem because of the limitation of available emulation technologies. Typically, up to 2009, mobile network system testing tools were spread across the following main categories:
1. System level simulators used in R&D (network vendors or research labs) with very realistic models but absolutely no means to connect to real devices and nodes
2. Radio channel emulators associated to real handsets to check system performances with a limited number of users (typically a few dozen users)
3. Load and stress test tools able to generate heavy traffic (several hundreds of handsets) but with non realistic traffic, and no radio impairments emulations
All these legacy techniques are still in use but cannot correctly cover the new test cases needed for modern telecom systems. The case of SON is critical. Proving its proper operation and stability requires using a complex system test bed composed of many different boxes coming from different vendors, with all features activated simultaneously, as in a live network. This is something that cannot be fully validated with legacy techniques.
After 2009, we have seen test tool vendors coming up with breakthrough technologies able to reproduce very realistic conditions in a lab, including the radio path under heavy traffic (several thousands of handsets). That has been possible thanks to the phenomenal increase of the CPU power and the ability to run in real-time radio propagation models on a per handset basis with a huge number of devices on chip computing platforms.
It is now possible to set up a test bed able to run the most critical SON use cases with real network nodes and heavy traffic.
As many SON mechanisms are based on radio measurements and cells radio coverage reconfiguration, the test bed should integrate a multi-channel radio path and interferences emulation component. It should be able to handle thousands of different radio conditions, simultaneously (one for each handset/eNodeB couple). This technology can easily reproduce a very complex radio scenario in the lab so that the efficiency of SON mechanisms is evaluated with the accuracy required to trust SON systems.

Details of the ideal test bed

The following diagram shows a typical setup used to stress a mobile network in the lab to assess the behaviour of the SON closed loop in front of different scenarios.

This test bed is composed of the following elements:
1. A network installed in a lab composed of all relevant real nodes and SON equipments. This example shows three eNodeBs with three sectors each, but other radio topologies can be considered easily
2. A handsets and radio channel emulator able to handle thousands of devices and reproducing very accurate traffic conditions down to interferences and fading effects
This test bed is fully equivalent with three eNodeBs deployed in the field with real handsets in mobility and using different kinds of applications such as web, VoLTE, video and email among others. The equipment to be tested (eNodeBs, core network nodes and other network elements) will not make any difference between real field and such an emulated environment if the handsets and radio path emulator have good enough realism. That is the necessary condition to get valuable results.
The most challenging part of this test bed is the radio channel emulation that should be able to reproduce many complex behaviours of the radio path linked to the coupling between the environment (terrain and buildings) and the dynamic behaviour of handsets. This equipment exists for years as standalone boxes connected between handsets and eNodeBs with coaxial cables. They provide a huge panel of complex features including 3GPP fast fading models implementation, interference simulation and slow fading scenarios. This kind of equipment has fair accuracy when running with few handsets. But trouble comes when the test scenario needs to be scaled up to hundreds or thousands of handsets: as there is one physical port per handset on the simulation box associated to complex electronics, the price increases linearly with the required capacity. For thousands of handsets, the cost will be so high that nobody would be able to afford a test platform with this technology. That is the point at which there comes a need for next generation channel simulation.

Next generation radio channel simulation

New solutions have emerged in recent years, mostly driven by the fresh needs arising with LTE and the availability of required processing capacity on a budget.
Typical solutions are based on very high performance computing platforms into which both full handset emulation and radio channel models are tightly integrated. This approach saves thousands of RF cables and associated RF/ADC/DAC electronics. Also, it opens the track to innovative software architectures able to accurately reproduce complex radio phenomena occurring with increasing numbers of handsets supporting LTE technology.

mobile phone network

You should keep in mind that LTE technology is able to reconfigure many radio transmission parameters 1,000 times per second, for each handset. Per 3GPP standards, one eNodeB, or a group of eNodeB is empowered by all required mechanisms to find an optimal allocation of the radio spectrum each millisecond, depending on traffic demand, position of handsets, interferences generated by adjacent cells and radio path degradation linked to buildings and motions. As SON contributes significantly to proper operation of underlining eNodeBs algorithms, the radio channel emulation should implement all the required dynamic behaviours of the radio path, with at least a one millisecond resolution:
1. Standard or proprietary fast fading models
2. Slow fading models
3. Interferences between devices and eNodeBs, in downlink and uplink
4. Efficient tools for complex scenario design
Implementing fast fading models for thousands of handsets with truly independent behaviour is already very challenging and requires very specific software architectures able to take advantage of modern multi-core processing boards. At this time, only a few commercial products are known to be able to handle this level of complexity and are the result of about ten years of R&D efforts.
Another challenging aspect is the dynamic emulation of interferences. The channel emulator should be able to take care of the effective transmission schema of each device for each millisecond. That occurs with unpredictable patterns because it is driven by proprietary and secret scheduling algorithms implemented in the radio access network nodes. A mix of real-time simulation and innovative algorithms is the mandatory cocktail to handle such complexity without falling down to impractical solutions.

Practical usage of the test bed

Testing SON mechanisms with real equipment in a real field can be painful because it is not possible to guarantee that all environment parameters are identical each time a test campaign is done.
The test bed is a very valuable solution able to reproduce realistic conditions with full control on all parameters, in a determinist way. Thanks to this fact, the user is able to launch the same test scenario many times so that results can be compared across the different test sessions with no doubt about the stability of test conditions.
For this purpose, the emulator is configured for different scenarios needed to assess the performance and stability of the network and SON equipments such as:
1. Emulation of traffic variation along the day (high traffic at day time, low traffic by night) to check how SON reacts to achieve minimisation of energy consumption
2. Emulation of interferences to assess the SON mechanisms in charge of coverage and capacity optimisation
3. Emulation of cell outage to assess self-healing behaviour to detect and compensate cell failure
When a test campaign is running, QoE (Quality of Experience) and QoS (Quality of Service) are automatically measured to build a picture of the level of end-to-end service experienced by users. Typically, a test which emulates an outage should show a temporary loss of service for some users followed by failover as soon as the SON system has detected and fixed the issue. When the network is reconfigured to handle the outage, the available capacity is lower because of the loss of one or several sectors, even if there is no coverage hole. So, the users should experience a degradation of QoE if the capacity demand cannot be fully satisfied in this situation. This effect can be measured objectively through QoE estimation embedded in the test bed. The following diagram shows a typical synthetic report generated by such a test bed when evaluating the self-healing feature.

On this diagram, we can understand how the self-healing feature of the network is performing:
1. At time 15, one or more sectors break down
2. We see many handsets losing connection to the network on the "No service" curve
3. The "VoLTE MOS", "Video MOS" and "Web bitrate" are degrading because of radio coverage hole appearing around the faulty eNodeB
4. At time 45, the SON has detected the failure and reconfigured the healthy sectors to compensate for the eNodeB lost
5. At time 60-90, we see "VoLTE MOS" going back near to its nominal value, meaning that voice communications are no longer significantly degraded
6. "Video MOS" is also recovering, but at a lower level because the network has lost capacity and it is configured to give priority to VoLTE
7. Web service is handled in best effort mode. As the demand is higher than the remaining capacity after the failure, it cannot recover to the initial level.
As seen, the failover mode of the network causes performance degradation linked to weaker radio coverage and an increase of interference for some users. These effects are fully emulated by the test bed for valuable performance evaluation. By analysing the curves, the MNO can verify that the SON strategy is in line with its requirements. MNOs can tune network parameters to change the strategy, depending on commercial needs. For example, another MNO may choose a different strategy and put video at higher priority than VoLTE. After having achieved the required behaviour in the lab, the MNO can safely push the new network configuration to the live network.

Other benefits of the test bed

Another application is non regression testing, which means validation of new software releases before deployment to the live network. As SON is a global and complex technology, it requires significant attention on software upgrades of any network elements. The level of interaction between nodes and SON agents is so high that a small and localised regression could compromise the stability of the whole network. In this context, a comprehensive test bed including most of the network nodes is able to provide a good picture of the impact of any software upgrade and helps de-risk maintenance operations.
The same methodology can be used in RFQ phase to compare the solutions offered by several vendors and assess the reality of the datasheet contents. This is a good way of measuring the value proposition of each vendor to achieve the proper risk/cost mix for the deployment. Also, besides de-risking the deployment, measuring the maturity of products in such a way gives good arguments for the price negotiation phase.


SON is a promising technology that should help MNOs save OPEX by decreasing the volume of manual tasks required for day to day network operation, and drive improvement in capacity, quality and network performance. However, SON deployment is being slowed down by concerns about its effective maturity and the organisational impacts on operational teams.
The diversity and complexity of SON systems implies sophisticated assessment logistics in order to achieve full confidence before effective deployment. Since major SON features are tightly linked to the radio path behaviour, the assessment is difficult to achieve accurately and efficiently with standard techniques and tools.
Up to date emulation technology, involving realistic radio channel emulation with thousands of virtual handsets, is paving the way to help all stakeholders successfully assess the SON benefits and deploy it in live networks:
1. SON providers can use network emulation to tune SON software and demonstrate the stability and benefits of their products to MNOs, with realistic and convincing traffic conditions
2. MNOs can validate proper operation of SON in a multi-vendor context with their own specific traffic model and network topology
3. Operational teams can train their employees on very realistic use cases, including simulation of emergency situations
With network emulation, the ecosystem can push the usage of SON securely, as flight simulation has helped the aeronautical industry to safely deploy next generation computerised auto-pilot technologies.
Frédéric Rible is chief technology officer of Mobipass at ERCOM.