www.strategyanalytics.com September 2018 Guang Yang and Sue Rudd Tel: +44 1908 423662 email: [email protected]; [email protected]5G will soon enable operators to provide new and diverse services. But 5G simultaneously brings new and significant challenges for network operations and service assurance. To deploy and operate complex, scalable converged pre-5G and 5G networks, operators will need to increase efficiency, optimize performance and automate their network processes while lowering both operating and investment costs. Network automation and AI are key to achieving these goals. This report describes how Service Providers who want to become ‘5G Ready’ can leverage next generation automation and AI capabilities today and make 5G both feasible and profitable as they accelerate their digital transformation. Networks & Service Platforms (NSP) Complex Converged 5G Network Operations Demand Automation and AI
Introduction: 5G Diverse Use Cases and Performance Demand New Approach
5G is the first mobile technology that not only enhances mobile broadband use cases but also natively supports Machine Type Communication (mMTC) and Ultra-Reliable Low-Latency Communication (URLLC) use cases - see the exhibit below.
Exhibit 1: 5G Use Cases
Source: ITU-R IMT-20201 Requirements
As it supports these diverse use cases, 5G will become the important infrastructure for a future connected society, but the use cases place challenging demands on 5G network capabilities to achieve high performance. The exhibit below summarizes the requirements for each 5G use case.
Exhibit 2: Diverse Use Cases and Performance Requirements and Expected Performance Use Case Category Typical Use Cases Experienced
Data Rate E2E
Latency Users/Connection
Density Reliability
%
eMBB – enhanced Mobile
Broadband
Gigabytes per second, Virtual Reality (VR) / Augmented Reality (AR), Applications in the Cloud
Complex Network Environment – Five Challenges for 5G
Migration from 4G LTE to 5G introduces significant complexity from its first deployment since the initial 5G Phase 1 systems operate as Non-Standalone Architecture (NSA) and depend on 4G LTE signaling and Core infrastructure (e.g. EPC to provide services for 5G New Radio (NR)). 5G Release 15 Phase 2 Standalone Architecture (SA) is just being finalized with a completely new Core Architecture.
Advanced technologies and new network operations processes will be required to support the diverse 5G use cases, their demanding network performance requirements and the multiple hybrid LTE/NSA and SA deployment scenarios. 5G must therefore adopt new RF, network design and operations management approaches that support five sources of diversity:
• Multi-Technology: 4G, 5G NSA will be the mainstream option for early 5G deployments and the NSA architecture relies on 4G as signaling anchor for all services while in 5G Phase 2 Standalone (SA) a new 5G only Core will be deployed. 4G LTE and 5G NR will co-exist in the same geographic space for a long time. Thus, joint network design and simultaneous optimization for 4G and 5G – both NSA and SA – must be managed alongside any legacy 2G and 3G networks that continue to operate.
• Multi-Band: 5G NR will be deployed first in the medium and high frequency bands as initially mobile operators will continue to rely on 4G/LTE and LTE Advanced Pro in the lower bands to provide the basic network coverage for today’s services. 5G networks will be deployed in multiple frequency bands from sub 1GHz and 3GHz-6GHz to mmWave in parallel with multiple legacy networks below 1GHz. Traffic balancing must be done across all these bands according to traffic demand, network load and user behavior. Operators must also take into account the use of other frequencies for supplemental downlink and uplink carrier, and even LTE or 5G NR in unlicensed spectrum. All these planning and management will be far too complex to handle manually.
• Multi-Layer: 5G small cells will likely co-exist as an ‘underlay layer’ alongside LTE metro-cells and an ‘overlay’ alongside 4G/LTE macro-cells with service control plane from day one of the 5G network rollout. ‘Network Slicing’ may assign different services dynamically to these different layers (e.g., Low latency services to the 5G RAN and voice services to the metro area LTE network). Over time diverse ‘Network Slices’ may be assigned dynamically to virtual RAN (vRAN) resources on 5G or 4G/LTE networks based on the Quality/Class of Service required. Automation will play a major role in the dynamic service management of this multi-layer environment.
• Massive MIMO: Massive MIMO makes 5G NR a beam-based – rather than traditional cell-based – air interface. The pattern of broadcast beams will determine the coverage, neighbor list and handover parameters. When multiple broadcast beams are configured, the complexity of beam planning and optimization escalates dramatically. Automatic iterative optimization enabled by machine learning will be the only option.
• Multi-Architecture: 5G will not only bring in a new air interface with NR but also a new architecture. The separation of CU (Centralized Unit) and DU (Distributed Unit) is now standardized for 5G NR radio access network (RAN) to enable vRAN or C-RAN deployment. 5G architecture gives operators significant flexibility and supports multi-technology, multi-vendor solutions deployment with multiple configuration options for virtualized RAN (vRAN).
The new 5G Core Service Based Architecture (SBA) brings not only more flexibility than the legacy LTE Core but also far greater complexity.
• Specifically, Multi-Architecture will create new challenges because:
The ultra-high data rate of the 5G NR air interface will dramatically increase the bandwidth required for backhaul transport
New options for ‘local breakout’ to the nearest fixed broadband access require joint fixed and mobile network planning.
New Multi-Access Edge Compute (MEC) services will change load management across both access and backhaul networks
To guarantee End to End (E2E) Quality of Service (QoS) and user experience across this multi-architecture environment service providers must monitor and manage both Network KPIs and Customer Experience
To support 5G Scale and Complexity new levels of automated virtualization are required
While the 5G network will be much more complex than 4G, operators must deliver the new 5G services at the same or even lower cost than 4G, to keep their business sustainable. Below we describe three key areas where operators must leverage the benefits of automation and AI to make 5G feasible and profitable.
Three Critical Operator Requirements Leverage Automation and AI for 5G
1. Optimize 5G Efficiency across vast numbers of cells and smart antennas as well as diverse use cases.
In 5G Networks the number of cells, RF carriers and antennas will be dramatically higher than in 4G networks, and scale must constantly increase with the order of magnitude growth in traffic that is occurring every five years. The workload of network planning, optimization, configuration management and alarm management for multi-frequency HetNets and massive MIMO will escalate. In parallel, the diverse 5G use cases and new market competition will push operators to improve their flexibility and accelerate their Time-To-Market (TTM).
5G performance therefore requires automated network planning and optimization. To simplify the complexity of 5G yet remain cost competitive operators will be forced to continuously improve 5G efficiency across diverse complex use cases. Specifically, they need to:
Automate HetNet configuration and RF planning with centralized SON Optimize RAN capacity and spectrum utilization in real time Manage small cells both alone and as clusters Configure and optimize RF carriers and backhaul flexibly in real-time Manage traffic loads based on Machine Learning for specific scenarios (e.g. cell edge peaks,
variable usage by time of day).
2. Automate performance and operations processes end-to-end (E2E).
5G is not only a radio interface but an end-to-end system. To ensure performance, resources across network – such as cells, carriers, antenna beams, backhaul links or edge computing elements – must be monitored and managed in a holistic manner. Policy must drive network configuration and optimization processes to enable the end-to-end orchestration and to reduce the operations. End-to-End (E2E) slice management creates new revenue opportunities but demands new processes to
guarantee Quality of Service (QoS) for different use cases. The network and service management systems must leverage policy and automation to deliver fully orchestrated E2E services.
Operators therefore need to:
Set policy and thresholds to optimize E2E throughput and capacity utilization Apply Machine Learning (ML) based AI models to network and service KPIs Dynamically allocate database and compute resources wherever they are needed across the
network Manage standard services to ensure E2E customer quality and delivery Prioritize 5G ‘network slices’ to guarantee E2E QoS that meets SLAs with service aware
optimization
Network Automation is Key to Operator 5G Success
As E2E network intelligence and automated processes are implemented across all network layers and throughout both network operations and service management, 5G can scale to achieve its potential. These processes must leverage comprehensive inputs from all network layers, elements and use cases - from eMBB, to mMTC and URLLC - and soon from Network Slicing and Multi-Access Edge Compute (MEC) to guarantee QoS for all.
In the 5G environment deployment and operations will not be feasible without a high degree of intelligent automation, which will also support service providers’ digital transformation.
3. Lower network costs.
In a mobile network, the number of cell sites has historically been the biggest factor driving total cost of ownership (TCO) followed by maintenance and engineering costs. In a 5G network much of the focus will rightly be on reducing maintenance costs with automation. In parallel C-RAN and vRAN configurations will reduce site rental costs as Remote Radio Heads (RRHs) and small cells are deployed on towers, light poles and even cable strands. But the large number of cell sites and locations can potentially lead to enormous electricity costs dominating overall site cost as shown in the Exhibit below.
Exhibit 3: Site OPEX - Power now dominates Site Rental and Maintenance Costs
Today, in many countries as shown above electricity costs represent the biggest percentage of site costs. As massive MIMO and complex 5G baseband processing are deployed ever more widely, the energy consumption of each cell coverage area will continue to increase power consumption.
Several critical functions are already being automated to reduce these energy costs. For example, Artificial Intelligence (AI) based on predictive analytics and dynamic clustering can now be integrated with automated operations workflows to ensure efficient network power utilization, cell site consumption and load management by time of day.
RF Engineering Costs
In the context of overall RAN networking costs, engineering labor costs remain a critical item, especially for ongoing drive testing (the traditional way to test and isolate RF network problems). Drive testing absorbs a large number of engineering hours. Going forward E2E automated optimization can significantly reduce the engineering hours for drive testing, site visits, routine equipment configuration and even root cause analysis. New kinds of automated tools that leverage engineering productivity will help operators to reduce overtime costs and allow the network organization to focus on the new operations skills that 5G and future technologies demand.
Six Capabilities that create ‘5G Ready’ Networks now
To create a ‘5G ready’ network, operators must focus on the three requirements above – Optimize 5G Efficiency; Automate Performance and Operations Processes; and Lower Costs. But as the exhibit below indicates to address these requirements six next generation automation capabilities must be deployed and tested well in advance of 5G itself.
Exhibit 4: Mapping between 5G Requirements and Network Automation Features
Source: Strategy Analytics, Networks and Service Platforms
The six key capabilities operators should install to become ‘5G ready’ are: Slice Aware and Backhaul Aware Optimization, Smart Capacity Planning, Predictive Energy Saving, Advanced MIMO Optimization and Wide Area Resource Coordination. We discuss each in turn below.
• Slice Aware Optimization must be automated to match QoS requirements for diverse network slices with available physical resources. Based on the information from both physical (PNFs) and virtual network functions (VNFs) network automation can dynamically manage physical resources to optimize different slices.
P.I. Works and Turkcell recently ‘kick-started’ a research project to automate optimization of Turkcell’s network slices. The new optimization concept proposes centralized spectrum and interference management for logical network slices that share a common physical infrastructure. The initial target will be to test the concept on Turkcell’s narrowband IoT (NB-IoT) fixed wireless access (FWA) and corporate slices. With service-aware dynamic slice management, Turkcell’s service based sliced network should enable more efficient and flexible use of network resources and still guarantee SLAs.
• Backhaul Aware Optimization also needs to be an integral part of E2E 5G optimization. E2E traffic load must be balanced across both access and backhaul links. And load balancing now needs to evaluate diverse inputs together – the radio interface traffic, RAN throughput, P/GW load and backhaul link load. Dynamic sharing of bandwidth across diverse backhaul options - mobile and fixed (Wi-Fi, Carrier Ethernet etc.) – and it becomes part of the automated load balancing process. In addition, as 5G architecture supports separate deployment of CU and DU (Central Unit and Distributed Unit) to facilitate multiple vRAN and C-RAN configurations, the demand for fiber (or other very low latency) connectivity for fronthaul and Xhaul increases. This becomes a critical part of the Load Balancing equation. Similarly, to guarantee QoS for ultra-reliable low latency applications both wireless and fixed connectivity must be taken into account. To ensure a specific E2E cumulative latency, loads must be balanced across both radio access and metro area backhaul transport networks to the optimal data center.
Real time monitoring of overutilized and underutilized transport links as well as the prediction of backhaul traffic using AI methods offers a key asset for mobile operators. P.I. Works’ AI Assisted ‘Dynamic Backhaul Capacity Management’ solution incorporates data integrity checks, adaptive, configurable thresholding, anomaly detection, traffic volume prediction for variable time intervals and minimum bandwidth requirements on each backhaul link.
Exhibit 5. Tier 1 European Operator (20m subs.) Backhaul Optimization Case Example
Case Example: Recent trial of P.I. Works demonstrated that backhaul aware optimization offers: - Significant cost benefits by reducing both OpEx and CapEx. - Service quality.
P.I. Works AI assisted network automation solution analyzed bandwidth utilization of each leased backhaul link connecting radio access network (RAN) sites to core network.
Adaptive and configurable thresholding mechanism detected underutilized and overutilized leased backhaul links.
Solution recommended bandwidth upgrade on 6 backhaul links and bandwidth downgrade on 266 backhaul links.
Savings in the PoC area: $0.16 million / year
Estimated Savings in network-wide deployment: $1.52 million / year
Many operators are realizing the critical importance of fiber transport in 5G deployment. John Donovan, CEO of AT&T Communications, recently stated that “A lot of the business case on 5G is going to be how dense is your fiber network. The bigger expense tends to be monthly OpEx on the fiber, the hanging locations and the rights of way.” Tier 1 operators are already signing backhaul deals with Cable operators; and leading operators, like Orange, are investigating more efficient backhaul solutions to support 5G RAN architecture. Automated backhaul aware optimization is expected to become a standard approach for 5G network optimization.
• Smart Capacity Planning uses predictive analytics and traffic models to recommend capacity changes based on current and historical data from base stations, routers and other network elements. Today this is often limited to individual cells, but as operators move to smaller and smaller cell sites they need to look at statistics for groups of cells i.e. ‘cell clusters’ to dramatically improve capacity management across large numbers of small cells in a metro area.
In addition to real time monitoring, ‘Big Data’ can be accumulated over time in a central ‘Data Lake’ from multiple sources. Statistical analysis is then performed weekly or monthly on that ‘Data Lake’ to establish patterns or analyze variations in Radio Network KPIs, Backhaul Metrics, CEM, CDR Data, RF Propagation Data, Revenue Data, etc. From these analyses Machine Learning can develop-long range algorithms that provide increasingly accurate traffic predictions for network planning. Such forecasts can facilitate cell location selection and network infrastructure capacity planning. Network executives can then establish policy objective functions to optimize tradeoffs between improved customer
experience and lower CapEx/OpEx per Gigabyte or MHz per km2.
• Predictive Energy Saving rules or algorithms can also be based on data collected from live network usage e.g. the active numbers of user equipment (UEs) associated with a base station in a given time period and PRB utilization trend. These algorithms are able to make very precise predictions. For instance, the exhibit below indicates traffic reduction during the early morning period.
Exhibit 6: Hourly Average Number of Active UEs, Prediction of Active UE Number in Given Sector
Source: Turk Telekom Labs, P. I. Works et al, ‘Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks’
Based on policy rules, therefore, at certain times of the day some network equipment can be turned off without any impact on customer experience e.g. when the number of active UEs falls below a specific predefined threshold value. This approach can significantly lower the network energy consumption for base station equipment.
Such energy saving projects were recently implemented by P.I. Works in number of operators with multi-vendor, multi-technology networks. The results of these projects indicated strong double-digit savings in the energy consumption. energy consumption for base station equipment.
• Automated Advanced MIMO Optimization will be critical to enhancing spectrum optimization through reducing pilot contamination of advanced MIMO deployments in pre-5G and 5G networks. With the introduction of massive MIMO and 3D beamforming as part of 5G NR, the automated management of predefined coverage configurations based on traffic predictions, QoS requirements, traffic type, load per cell or per transport link will help drive even greater efficiency and enhance end user experience.
• Wide Area Resource Coordination will require intelligent automation. Inter-Cell Interference Coordination (ICIC) is already an important feature in today’s LTE networks where information is exchanged over the X2 interface to enable adjacent cells to coordinate radio resource allocation at the cell edge area for improved user experience. Going forward, as centralized network automation functions collect data and forecast traffic across cell clusters and across the metro area, network resources – such as radio resources, baseband processing units (BBUs), backhaul links, and edge computing resources – can all be coordinated over a wider area. Automated control driven by centralized network intelligence can dramatically improve the efficient utilization of network resources, especially during ‘tidal’ traffic surges.
Wide area resource coordination can also improve the service reliability particularly for the URLLC use cases in 5G networks. In LTE Advanced standardization, Co-ordinated Multi-Point (CoMP) has been standardized to improve LTE network performance through coordinating radio resources of multiple cells. A comparable mechanism can be introduced to 5G to improve service reliability through spatial diversity. The centralized intelligence and coordination can be extended – beyond radio resources – to a broader scope including backhaul, edge computing etc. to achieve E2E optimization for reliability and latency.
Benefits of ‘5G Ready’ Networks can enhance Operations today
The section above illustrates six key network automation capabilities that are fundamental to efficient 5G network management. Mobile operators will require this level of network automation to become ‘5G Ready’. But many of these approaches can be applied today to 4G LTE and LTE Advanced Pro to capture many technical, business and process transformation benefits immediately.
Technical Benefits
Network automation provides valuable benefits for complex converged network operations in each of the six areas:
Slice Awareness guarantees QoS for network slicing and multiple diverse 5G use cases. Backhaul Aware Optimization enables E2E resource management and optimization. Smart Capacity Planning accelerates network design and rollout. Predictive Energy Saving improves the efficiency of capacity utilization and reduces energy
Advanced MIMO Optimization reduces RF engineering workloads and delivers enhanced spectrum optimization at lower cost.
Wide Area Resource co-ordination both improves radio and network resource utilization and increases reliability for almost no extra cost.
A highly automated network management capability is essential for service providers to transform their operations and service management processes to become ‘5G Ready’.
By replacing a series of laborious repetitive steps with a highly automated policy driven approach, NOCC personnel will soon be able to manage ten times the traffic with greater flexibility and better performance than ever. And they will be ready for the scale and network complexity of 5G.
Business Benefits – Lower CapEx and OpEx
Smart capacity planning and wide area resource coordination will lead to better capacity utilization and more effective allocation of new network investment. Operators can capture many of the business benefits of automation on today’s LTE networks and lower both CapEx and OpEx, while improving customer experience and profitability as they prepare for 5G. Today operators can already:
Reduce OpEx to: Manage large numbers of cell sites more efficiently Lower engineering costs per Gigabyte with new tools at Network Operation and Control
Center (NOCC) Reduce drive test time and cost Save energy cost Reduce backhaul cost
Reduce CapEx to Improve spectrum utilization Optimize cell site capacity
Network automation, predictive analytics and leverage from AI algorithms are key to service provider
profitability in the near term and essential to long term survival in the coming 5G era.
Operators will see immediate Cost Reduction, faster Time to Market and accelerated Digital Transformation
These new processes will dramatically enhance operator results in three critical areas
• Process changes will put operators on a steeper cost reduction curve so that in a world of flat revenues costs will scale far less rapidly than traffic.
• Faster Time to Market (TTM) for new pre-5G and 5G services will allow operators to compete more nearly with OTTs at ‘Web-Speed’
• Automation and AI to become ‘5G Ready’ will accelerate Digital Transformation as an embedded part of day-to-day operations.
Exhibit 8. Leveraging AI for Network Transformation – Ooredoo Group
Ooredoo Group Case: At Mobile World Congress 2018, Ooredoo Group announced start of the
deployment of artificial intelligence solutions as part of a major partnership with P.I. Works to
transform its network for 5G.
Ooredoo Group is “one of the world's first communications groups using smart network optimization
to unleash the full potential of their mobile network, managing growing mobile traffic while reducing
Sheikh Saud Bin Nasser Al Thani, Group CEO noted that Ooredoo is "pioneering artificial
intelligence approaches to transform our network.”
Future Outlook: More and more mobile operators will join the network automation journey as 5G
approaches. Network automation provides the foundation for building and operation of all future
mobile and converged networks.
Source: Ooredoo Group
How Can We Help You?
Strategy Analytics provides strategic and tactical support to global clients through a range of customized solutions:
Multi-Country primary research assignments using leading edge tools and techniques User Experience Design and Innovation engagements Real Time Mobile Consumer On-Device Tracking projects B2B Consulting Projects and Whitepapers
Please contact us at [email protected] with any questions and for further details and solutions on how we can work with you to create a custom solution that addresses your specific needs.
![5G Transport Network Blueprint and Its Requirement ... · 5G-XHaul [1] is a European project working on the definition of converged Fronthaul (FH) and Backhaul (BH) networks for future](https://static.documents.pub/doc/80x56/5f31ff28401a3c096473687a/5g-transport-network-blueprint-and-its-requirement-5g-xhaul-1-is-a-european.jpg)
