White Paper Modern networks are becoming increasingly characterized by a mix of subscribers using a wide variety of applications each with their own usage type and quality of service (QoS) expectations. The ways that subscribers use wireless communication networks varies dramatically between the various subscribers. Each subscriber is unique with individual characteristic uses for voice and data services. Usage patterns for subscribers tend to be determined by various demographic factors including age, occupation, whether they are corporate or commercial subscribers, whether they are pre- or post-paid, and where they live, among other factors. When it comes to services, each can be split into those offered by operators and those from OTT service providers. Devices on the networks may not meet the traditional definition for subscribers but can also be Internet of Things devices. These in turn may be fixed wireless or mobile. And, depending on what each is doing, will determine the demands it will place on the network and the resulting expectation of what constitutes satisfactory QoS. There is even a trend toward providing service to subscribers with mission- critical requirements, such as emergency-service first responders. All this adds up to a vast range of usage characteristics between the multitude of subscribers using the network. Extreme Non-uniformity in Cellular Networks The extreme variation in characteristics for the various subscribers using the network and the applications they use are two examples of the non-uniformity challenge that modern network operators face. However, other aspects of extreme non-uniformity compound this challenge for operators, as illustrated in Figure 1. Figure 1. Aspects of extreme non-uniformity in modern cellular networks Time Subscriber Location Application Solving the Challenges of Cellular RAN Management with Next-Generation SON
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White Paper
Modern networks are becoming increasingly characterized by a mix of subscribers using a wide variety of applications each with their own usage type and quality of service (QoS) expectations. The ways that subscribers use wireless communication networks varies dramatically between the various subscribers. Each subscriber is unique with individual characteristic uses for voice and data services. Usage patterns for subscribers tend to be determined by various demographic factors including age, occupation, whether they are corporate or commercial subscribers, whether they are pre- or post-paid, and where they live, among other factors.
When it comes to services, each can be split into those offered by operators and those from OTT service providers. Devices on the networks may not meet the traditional definition for subscribers but can also be Internet of Things devices. These in turn may be fixed wireless or mobile. And, depending on what each is doing, will determine the demands it will place on the network and the resulting expectation of what constitutes satisfactory QoS. There is even a trend toward providing service to subscribers with mission-critical requirements, such as emergency-service first responders. All this adds up to a vast range of usage characteristics between the multitude of subscribers using the network.
Extreme Non-uniformity in Cellular NetworksThe extreme variation in characteristics for the various subscribers using the network and the applications they use are two examples of the non-uniformity challenge that modern network operators face.
However, other aspects of extreme non-uniformity compound this challenge for operators, as illustrated in Figure 1.
Figure 1. Aspects of extreme non-uniformity in modern cellular networks
Time Subscriber
Location Application
Solving the Challenges of Cellular RAN Management with Next-Generation SON
2 Solving the Challenges of Cellular RAN Management with Next-Generation SON
For example, today’s operators typically have a highly complex network comprising different access network types spanning 2G, 3G, and 4G, and sometimes in Heterogeneous Network (HetNet) configurations. The infrastructure will often come from multiple equipment vendors, each with their own vendor-supplied performance management and optimization solutions. In some cases, some of the network elements may be virtualized and sometimes the radio access elements may be centralized, adding complexity to the challenge of managing performance. There is a risk that the networks’ heterogeneous nature means that the solutions used to manage and optimize them are also disjointed and heterogeneous. If this occurs, it also adds cost and complexity to the networks’ management and optimization.
Time is another dimension of extreme non-uniformity. Networks encounter performance issues on vastly different timescales. At one extreme, performance will fluctuate from minute to minute as the subscribers move around and utilization varies. For example, short timescale variations are also caused by equipment outages. At the other extreme, utilization will change over a course of weeks and months. This arises from the growth in demand for data driven by ever more sophistication in smartphone apps. Some of the increased demand can only be addressed by capital expenditure (CapEx) investment, but in other cases the CapEx investment can be avoided or deferred by optimizing the radio access network (RAN).
Another facet of extreme non-uniformity is location. Voice and data services consumption varies significantly by location. For example, a study performed by Viavi Solutions® evaluated how data consumption was distributed around a network. The network was divided into 50 m2 tiles and adding the total data used by all subscribers in each tile. Figure 2 shows how demand for data is distributed between the different cells.
Figure 2. Extreme non-uniformity in network usage by location
This shows that half of the data is consumed in 0.35% of the network’s geographical area. This non-uniformity adds additional complexity to optimization. Extreme demand non-uniformity means that site density will be similarly non-uniform. Often an operator must resort to HetNet solutions with micro- and pico-cells and in-building solutions, for example, adding yet another set of challenges to managing and optimizing more network layers. The parameterization of this heterogeneous RAN serving a highly non-uniform and dynamic subscriber population increases the optimization challenge more than ever before.
A Practical Approach to OptimizationA practical self-organizing network (SON) solution must have a variety of characteristics that allows it to address the challenges encountered in managing and optimizing today’s RANs. For example, a complete SON solution must be able to address the need for optimization on multiple scales. In the time domain, for example, this includes the very short timescales arising from the changing subscriber behavior during the day along with short-term infrastructure failures and impairments. It also includes the longer timescales of dealing with the trends in changing subscriber behavior. In the spatial domain, the SON solution must employ surgical precision to deal with localized phenomena, such as transient congestion or changing subscriber characteristics throughout the day. Coupled with this is the need for a wider view to find solutions that improve performance across larger clusters of hundreds of cells.
A SON solution that cannot discriminate between the varying needs of the subscriber population and different applications will have limited scope to act. The QoS expectations will vary radically between the different types of subscribers. At one extreme is smart meters, providing background readings characterized by small amounts of data infrequently and high tolerance to latency in fixed locations.
The other extreme is the critical first responder who needs higher data rates with low latency and very high reliability in unpredictable locations. When a SON solution offers visibility down to individual subscribers, it can direct performance for the best result. It can use the information about the type of subscriber, where they are, what services they are attempting to use, and what constitutes satisfactory QoS for that service. It can use that information to make decisions about how to configure the RAN for routine operations.
Coupled with the need for subscriber awareness is the ability to calculate the subscribers’ locations with sufficient accuracy to
90% of the data is consumed in less than 5% of the area
90% of the data is consumed in less than 5% of the area
50% of the data is consumed in less than 0.35% of the area
50% of the data is consumed in less than 0.35% of the area
3 Solving the Challenges of Cellular RAN Management with Next-Generation SON
determine the problem’s location. Location awareness facilitates the shaping of radio resources to deliver services where needed and in a way that subscribers will notice an enhanced service. This requires the ability to geolocate significantly more accurately than cell-level resolution and, in fact, must be to building-level accuracy, as shown in Figure 3.
Figure 3. Estimates of the mobile locations are required to building-level accuracy
As well as being able to tune performance to the subscriber, services, and locations that are most critical for the operator, subscriber visibility enables you to respond to impairments and failures to mitigate their impact on high-value subscribers, especially VIPs and emergency services workers.
The capability for self-learning is a key attribute of a SON solution, because how the network and the subscribers using it behave and respond to changes is complex. This coupled with the wide variety of networks in existence mean that the ways that each network responds to changes will, to some degree, be unique to that network and subscriber base. A SON solution must acknowledge this and be able to learn from experience, which can be achieved in a variety of ways. For example, self-learning can take into account the historic behavior of the network and the subscribers to anticipate the future. This allows it to change the configuration preemptively to deal with demand changes throughout the day, because the highest load typically occurs at a similar time each day.
It also has applications for special events, such as sports games or concerts, where behavior is unusual with respect to a normal day; but there is similarity between network behavior during the different events. Self-learning also encompasses the ability to make exploratory changes, understand the response to those changes, and use that information as part of future decision- making. This implies a stateful SON and has applications in coverage and capacity
optimization, for example. Self-configuration is another area that benefits from self-learning. One goal of self-configuration is to ensure that a new resource’s configuration, such as a site or carrier, converges to its optimum quickly. If a SON solution can determine from past experience what parameters are suited to a new resource, it will reduce the cycle time for convergence.
A flexible SON solution can redesign the network for specific operator goals which will vary from region to region, depending on such things as the subscriber numbers, terrain, available investment, and local competition. Sometimes operators place importance on certain performance measures, for example, some mix of coverage, quality, and capacity. Other goals will be more business related, such as providing the best quality of experience (QoE) for certain differentiating services. At the extreme, the goals will be financially based, for example, reducing operating expenses (OpEx) by saving energy. Ultimately operators are dependent upon revenue to underpin their business operations. In turn, a SON solution must be revenue- aware; that is, it must satisfy the subscriber’s need for service with sufficient QoE to prevent churn yet also allow them to consume, and pay for, the services they want. Thus a flexible SON is also a revenue- aware SON.
Selected SON ExamplesThere are many examples of how SON is evolving to satisfy use cases in ways that address the points described in the previous section. Here we review some of these use cases.
Subscriber-aware self-healing
A typical use case for SON systems is self-healing, which detects the failure or impairment of one or more network infrastructure elements, taking carriers or sites out of service either completely or partially. Some users previously served by the impaired infrastructure will be unable to obtain service due to being in a transient coverage hole. Other users will be able to obtain service from nearby cells that have not been taken out of service. The impact on those users who have lost service is clear and significant. The impact on the users still able to obtain service will be less serious but can still be significant. For example, the remaining infrastructure will be carrying more user traffic, which can lead to congestion that affects users not previously served by the failed infrastructure, as their serving cell is carrying more traffic than before the impairment. Another phenomenon is that some users will now get service from cells receiving lower signal strength or signal-to-noise ratio (S/N). Therefore, they may be unable to achieve the same high data throughput as they did previously.Not only can this negatively impact the user experience, it can also compound the congestion problem described above.
4 Solving the Challenges of Cellular RAN Management with Next-Generation SON
Self-healing can mitigate these outage effects by managing and extending the coverage of the remaining infrastructure to provide rescue coverage. This self-healing involves identifying donor cells and making changes to their parameters to extend their coverage into the areas not serviced due to the impairment. Increasing the power of the common pilot channel (CPICH) or reference signal will temporarily increase coverage along with uptilting antennas. Together these changes provide rescue coverage for the users that would otherwise fall into a transient coverage hole.
This traditional type of self-healing can mitigate coverage loss arising from impairments to the network infrastructure. However, this remedy is a resolution for the general population. Modern cellular networks don’t serve one subscriber type using a single service. Rather, they serve a heterogeneous mix of subscribers from pre-paid to post-paid, corporate and retail subscribers, with low and high utilization. Some networks even carry traffic with mission- critical applications like emergency services for first responders. The applications that subscribers use are now diverse with widely varying requirements on what performance measures, for example, data rates and retainability will constitute reasonable QoE. The applications that subscribers use are diverse with widely varying requirements on what performance measures will constitute reasonable QoE. For example, subscribers using e-mail are more tolerant of data rate variations and occasional dropped connections than subscribers using voice over LTE (VoLTE) services. Service degradation can also impact service level agreements (SLAs) for mission-critical users.
When self-healing responds to a network impairment without considering the subscribers it serves, it can sometimes have significant side effects. For example, a donor cell is adjusted to increase its coverage and additionally serve subscribers who otherwise no longer have service. However, if an emergency-service worker is being served by that donor cell, the effect of reconfiguring the network to mitigate the outage can induce congestion on that donor cell, resulting in congestion that negatively impacts the emergency-service first responder.
Other effects may also impact the high-value subscriber. For example, subscribers being served by less optimal cells can result in increased power in the system. The increased interference in the system often lowers S/N and impairs the ability to achieve higher data rates.
For example, Figure 4 shows a network where two sites, marked in red, experience an unplanned outage. The cells marked in green are those that self-healing identifies as donor cells. Self-healing detects an impairment in the cells’ ability to provide coverage and applies changes to the donor cells to provide rescue coverage. In this case, an emergency-service first responder subscriber is located within the coverage area of the cell marked with a red circle.
Figure 4. Helper cells (green) in the standard self-healing response to mitigate outages at the cells shown in red. Critical subscriber is served by circled cell.
Introducing subscriber awareness reduces the impact on key high- value subscribers. Subscriber-aware self-healing uses information about the active subscribers on candidate donor cells before allowing them to be modified to provide rescue coverage. Candidate donor cells serving high-value subscribers are excluded from the list of donor cells that can be optimized to provide rescue coverage. Also, self-healing addresses the risk for congestion arising from the rescue coverage and its impact on high-value subscribers. This approach of excluding cells providing coverage to high-value subscribers is shown in Figure 5. The candidate donor cell restricted from being changed is shown in orange.
Figure 5. Helper cells (green) and a cell that is blocked from being a helper cell (orange) because it is serving a high-value subscriber.
5 Solving the Challenges of Cellular RAN Management with Next-Generation SON
By identifying that a cell is serving a critical subscriber, and thus preventing that cell from helping to provide rescue coverage, the risk of that subscriber experiencing congestion is reduced. Restricting a cell from being a donor cell because it is serving a high-value subscriber has other advantages. For example, the radio signal quality often improves for high-value subscribers by reducing additional interference introduced into the system, as illustrated in Figure 6. This shows the cumulative distribution functions of the pilot-received S/N for the emergency-services first responder. This distribution is shown in various scenarios, such as for the normal, pre-outage scenario along with the unmitigated outage scenario. The impact of the outage significantly degrading the S/N is clear. This degrades the ability of the first responder to achieve higher data rates and may, in extreme circumstances, threaten the ability to maintain a connection. It shows the impact of regular self-healing for the first responder. The self- healing provides some improvement; however, there is still some degradation from the S/N achieved prior to the impairment. However, once the subscriber-aware self-healing is deployed the S/N returns to pre-impairment levels, or even improves marginally. The improved S/N occurs in addition to other positive factors for the critical subscriber, such as resilience to congestion stemming from excluding the serving cell from the list of candidate donor cells and without being modified to offer rescue coverage.
Figure 6. Cumulative distribution functions of pilot Ec/N0 for emergency-service first responders in various scenarios.
Subscriber-aware self-healing relies on a data feed from the infrastructure indicating which network elements are providing service to the critical subscribers. The feed can be monitored during an outage so that in cases where critical subscribers are moving around an impaired region of the network, the self-healing can adapt dynamically to the movement and dynamically update the candidate donor cells list in response to it.
A synthesis of SON and subscriber-centric optimization
We have described the integration of per-subscriber data with SON use cases to yield enhanced capabilities, such as the subscriber- aware self-healing. This is one example of how limited amounts of per-subscriber data can be used to enhance classic SON use cases. However, there are degrees to which per-subscriber data can be used within SON. For example, subscriber-centric optimization can predict the impact on the subscriber base of supposed parameter changes. Therefore, it can select new parameterizations across whole clusters of dozens or hundreds of cells for substantial performance improvements. This concept is described in the white paper: Harnessing Subscriber-Centric Optimization for the Next-Generation of Self-Organizing Networks. This approach can deliver double-digit improvements in a wide variety of performance measures that are critical to the subscriber experience. Operators can configure the optimization algorithms to reflect their goals for the network region.
Subscriber-centric optimization is a powerful capability that doesn’t fit neatly into traditional SON use cases, because it optimizes large clusters of cells at once leading to longer cycle times than making changes with traditional SON use cases. Using subscriber-centric optimization as part of a real-time self-healing solution is compelling because the approach has proven it can achieve coverage goals. A case study that demonstrates this deals with a cluster of over 350 3G cells on which subscriber-centric optimization was performed, and the changes to the CPICH powers and antenna tilts actuated to the network significantly improving the average RSCP for each cell.
Figure 7 compares the RSCP distribution before and after actuation. In addition to significantly improved received signal strength, service utilization increased by 23%.
Figure 7. Distribution of mean RSCP per cell in the optimization cluster before and after subscriber-centric optimization, showing a significant increase as a result of the optimization activity.
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6 Solving the Challenges of Cellular RAN Management with Next-Generation SON
Subscriber-centric optimization plays a role despite the fact that longer cycle times are required than for standard SON use cases with localized scope. In self-healing, especially when critical subscribers are involved, reacting to the impairment as soon as possible is essential, therefore, using pre-calculated subscriber-centric solutions is the solution. A key capability of subscriber-centric optimization is its ability to optimize “what if” scenarios by supplying optimized parameters to scenarios where network elements impairments are simulated. In this way the impairment is mitigated before customers actually experience them. Therefore, parameter designs can be precalculated and stored until required, using the strength of subscriber-centric optimization. As soon as the impairment is detected, the appropriate precomputed parameter design is retrieved and deployed to mitigate it instantly.
Selective optimization towards critical subscribers
As well as tuning network parameters toward the applications being used, when and where they are used, subscriber-centric optimization can tune performance so that its gains are focused or biased toward particular groups of critical subscribers. This is in contrast to the subscriber-aware self-healing which can avoid situations that often degrade performance for critical emergency-service subscribers. In contrast to degradation avoidance, subscriber-centric optimization finds configurations that improve performance and coverage for self- healing, but prefers configurations that provide optimal performance for critical subscribers.
This selective optimization technique clearly has applications in self-healing, where optimization gives preferential consideration to critical subscribers. However, there are wider applications for using subscriber-centric data. Optimization can be focused on any group of the subscriber population. For example, affording preference to subscribers depending on the services they use, the tariff they are on, whether they are roamers, where they are located, or whether they are indoors or outdoors. Table 1 gives more optimization examples based on connection type.
Table 1. Different connection type classes in which different optimization focus can be provided.
Connection Type Example Use Case
Critical subscriber Improve service for first responders, mission-critical workers
Service type Improve service for VoLTE and video connections
Location Optimize connections in specific buildings, for example, corporate headquarters
Route Ensure good service on specific roads or on trains
Subscription type Customized QoS for corporate customers, roam-ers, pre-paid, post-paid, and others
Speed More resilient connections to support subscribers in vehicles
Device capability Service tailored to those devices unable to use other network layers
One example concerns optimizing particular service types because of their resilience to adverse conditions like jitter or their high probability for dropped connections. For example, subscribers rarely notice a transient connection drop while using an e-mail application, but they usually notice connection failures that occur during a VoLTE call. Selective optimization capitalizes on the different characteristics between the critical subscribers and general subscribers, making changes to improve service where VoLTE services are often used while maintaining performance where they are seldom used. This approach can improve performance for the target application while maintaining performance for other network users.
A case study illustrates this where an operator wanted to improve VoLTE connection retainability while maintaining performance for other connections. Figure 8 shows how the optimization improved significantly the RSRQ for the VoLTE connections. Here the distribution of RSRQ (signal to noise ratio) is shifted to the right for VoLTE connections which results in better quality.
Figure 8. Cumulative distribution shift in the function of S/N (RSRQ) for VoLTE connections before and after optimization
Table 2 shows the performance measures changes for VoLTE and all connections after actuating the optimized network parameters. Notice the improved S/N after optimization, showing a 20% improvement in retainability while other measures remain flat, which was exactly what the operator wanted to accomplish.
Table 2. VoLTE performance measures before and after optimization show that optimization improvement is successfully targeted at VoLTE retainability, as required
Baseline After
Accessability 99.90% 99.92%
Accessability (VoLTE) 99.82% 99.82%
Retainability 99.44% 99.46%
Retainability (VoLTE) 97.48% 98.03%
Mean throughput 6.56 7.46
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7 Solving the Challenges of Cellular RAN Management with Next-Generation SON
Bringing subscriber-centricity to other SON use cases
The previous discussion demonstrated how subscriber-centric data can enhance the self-healing SON use case. Other use cases similarly can be enhanced with per-subscriber data. For example, a key coverage and capacity optimization (CCO) application is to minimize occurrences of locations with poor coverage. Typically this use case employs network statistics to detect coverage holes and then to take corrective action to close them. However, in the absence of per-subscriber data, the hole’s location can only be crudely located to the precision of the cell coverage area.
The algorithm employs exploratory changes to incrementally improve coverage, often requiring several iterations before finding the optimum configuration. Conversely, when data for each subscriber are available, these can be exploited using the CCO process, which calculates the locations based on where the data were generated. When the data include signal strength and quality measurements as well as events that characterize poor coverage, these locations will provide additional information about the coverage hole. By considering the coverage hole’s location along with the antenna directions, the SON algorithm can calculate which sector or sectors can best address the coverage hole. The benefit of this is that it significantly reduces the number of iterations required to find the optimum solution.
The selective-optimization concept also applies to use case enhancements where impaired coverage can be selectively addressed based on the connections they affect. For example, coverage holes for critical subscribers or particular services or locations receive higher priority than connections not meeting these criteria.
Per-subscriber data and the selective-optimization concept can enhance other SON use cases, such as the mobility robustness optimization (MRO), which selectively focus on too early, too late, and wrong cell handovers that affect critical subscribers. They also focus on specific connections rather than the whole subscriber population.
Yet another example is the automated neighbor relations (ANR) use case that creates neighbor lists to increase the likelihood that phones can find neighbor cells to which they can hand over to or add to the active set quickly to reduce instances of dropped calls. Selectively considering appropriate neighbors of critical subscribers in preference to regular subscribers increases the likelihood that critical subscribers will perform successful handovers.
Problem and opportunity detection
Modern networks are large and complex. Some of the SON actions with maximum impact also require substantial computation power. While running SON use cases across the whole network all the time may seem ideal, in reality it requires a substantial computation investment. To avoid this massive computation capability investment requires a selective optimization approach. Some scenarios are naturally selective; there is only ever value in applying self-healing when a network is experiencing an impairment, and this limits the computational investment. Other scenarios, however, require more nuance like during congestion manifesting as packet delay, loss, or exhaustion of physical radio resources across large network areas and can vary from hour to hour or even minute to minute. Given that computation resources are limited, the issue becomes determining the best way to deploy the optimization resource to mitigate the congestion.
Coping with this problem requires a solution that can detect instances of congestion, characterize the extent that it presents a problem for subscribers, and prioritize them for by being addressed by coverage and capacity optimization mitigation. For example, the degree to which the congestion is prevailing can vary from highly transient congestion to constant capacity exhaustion. A series of fleeting capacity exhaustion events will be less serious than more prolonged congestion.
Another consideration is the connection types affected by the congestion. Impacting high-value subscribers is more concerning than impacting lower-value subscribers; whereas, the impact to mission-critical subscribers is the most serious. Similarly, degradation on VoLTE connections is more serious than a similar impairment to connections used for background e-mail. It is important to consider the degree to which a problem can be mitigated. Some congestion problems can be alleviated through optimization. Others may exist in highly optimized areas where further optimization adds little or no extra capacity. The former case is an ideal target for a SON optimization. However, in the latter case, nothing is gained by applying a SON solution to the problem.
Figure 9. The critical quadrant is the target that problem and opportunity detection seeks to find.
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The capability to discern the clusters that are most deserving of optimization attention requires several factors. Subscriber-centric data are needed to understand whether high-value subscribers are affected and the types of service that are impaired. It also requires the ability to estimate the mitigation level that might be achieved, which in turn requires a predictive capability so that the need to perform full optimization up front is not required. This is the basis for problem and opportunity detection; the ability to detect network problems with the most meaningful impact on subscribers to focus mitigation efforts on areas experiencing problems with the greatest potential to significantly improve.
SON on the path to 5G
The move toward 5G forces the industry to grapple with some significant challenges. Adoption of LTE Advanced by the industry brings complex new features where SON offers significant opportunity. The Carrier Aggregation feature provides an additional dimension where optimization benefits from looking beyond each individual carrier in isolation. Here SON must consider the device’s capabilities to determine which devices can exploit the aggregation and to what extent. Doing so maximizes the value of the carrier aggregation and significantly increases the network’s capacity The Coordinated Multi-Point (CoMP) feature of LTE Advanced is an example of coordinated transmission and reception schemes that improve cell-edge performance and raise network coverage. However, these features can place large demands on a fronthaul network the more they are used.Thus the need to optimize coordinated transmission and reception utilization to achieve RF performance goals while remaining within the fronthaul cloud capacity constraints will become a capability of future SON systems.
Realizing a flexible and effective SON
In summary, a comprehensive SON solution must be able to address a range of poor network performance issues flexibly to address the operator’s business priorities. It should deal with transient impairments while maintaining and improving the network in its nominal state. It has to address problems on a range of scales, from solving localized problems with surgical precision to driving up performance across whole clusters. Furthermore, it should reduce its cycle times by predicting the impact of the changes before making them and should also learn how the network responds to optimization. The granularity of visibility down to the resolution of the individual connection event along with its location enables the solution to focus on driving performance that simultaneously gives subscribers the most appropriate QoE for the services they are using while employing the necessary revenue-awareness for the operator’s business case.
These characteristics are solid foundations for many aspects of the 5G networks of the future. Wider ranges of applications including mission-critical and high data rate, low latency applications will require a solution that can respond dynamically to which services are in demand, by which subscribers, and in what locations. SON in the access network and orchestration in software-defined networks in the core network will converge toward an end-to-end SON which recognizes and exploits the fact that changes in one part of the network effects other parts of the network. The ability to exploit this will be a hallmark of the SON of the future embedded within the 5G networks of tomorrow.
