5G rollout will open new opportunities

Operator challenges in launching 5G

5G technology has made significant progress on several fronts over the last few years. There are more 5G-compatible devices in the market like the iPhone 12 and home routers. Wireless RAN infrastructure and cloud technologies have been developed to support next-gen capabilities. And the foundation for a potentially rich business ecosystem of partnerships is also emerging. But adoption is still in the nascent stage.
Operators today have a host of concerns in transitioning their networks to 5G, which include:

• Reluctance to make the massive investment required for wide 5G coverage.
• Lack of spectrum availability in many regions and local regulatory challenges.
• Low prevalence of 5G devices, meaning operators may not be able to upsell consumers.
• Technical complications in implementing 5G alongside an existing LTE network, as it would lead to complex network infrastructure that presents difficulties in management and high maintenance costs.

How operators can swiftly launch 5G

Using a combination of mid-band and low-band spectrum

While multi-gigabit 5G speeds are seen only on high-band spectrum (mmWave), many operators have proven success in using sub-6 spectrum in low bands and mid bands for a 5G launch on a wider scale. Using a combination of spectrum in low bands and mid bands also promises larger geographical coverage with higher bandwidth. This approach will result in a balanced 5G network that is faster than 4G, ensuring the best performance indoors and outdoors.

Following a two-phase NSA approach

Technically called the eUTRA-NR Dual Connectivity (EN-DC) approach, this will allow operators to reuse and leverage their existing resources, keeping the promise of 5G intact. The 5G radio (gNodeB) can connect to the EPC (the LTE core), enabling implementation of a 5G Non-Standalone (NSA) deployment. This approach will immediately enable the enhanced mobile broadband (eMBB) use case. Voice calls will be sent over the 4G network using EPS fallback.

Issue eSIMs instead of physical SIM cards

eSIM or embedded SIM is the electronic form of a physical SIM card. eSIMs are mounted permanently to a device. A user can digitally add plans from cellular operators to their eSIM without having to physically replace the SIM card. The eSIM not only improves design flexibility, it also allows the operator to digitally distribute subscriptions in bulk. It will enable faster time-to-market for operators, as a 5G eSIM can be simply delivered in the form of a QR code via email, making it much easier to distribute than physical SIMs.

Most major operators now support eSIMs. The number of mobile devices that support them is steadily growing, and will soon be far more widespread.

Prioritize network virtualization

Arguably the best thing about 5G is its cloud-native architecture. A virtualized 5G RAN and core can reside on generic commercial off-the-shelf (COTS) hardware, eliminating dependence on proprietary hardware vendors. This approach is much more flexible and cost-efficient as it will allow operators to scale up hardware resources with increasing user traffic. A 4G virtualized RAN can enable 5G with only a software update. Enhancements to network functions can be delivered via software patches. Operators will benefit significantly from virtualizing their 4G networks on priority, as it will enable a hassle-free, easier, faster 5G rollout.

Begin with private 5G networks

Private 5G is a dedicated and standalone next-gen cellular network best suitable for relatively smaller and more confined areas like universities, airports, campuses, buildings, and more. This private network can be launched in a matter of weeks with a limited resource footprint. It provides higher bandwidth to enable all 5G business use cases over a secure and private connection for different Industry 4.0 applications. A specialized compact core makes the installation easier and faster.

Enabling private 5G networks for enterprise clients is the best fit for operators who want to soft launch a real-world 5G network on a smaller scale before moving forward with a more large-scale launch.

Forge partnerships to improve the device ecosystem

Even though 5G launches are rapidly progressing around the world, the device ecosystem is still limited. Though most of the mid-range smartphones launched in 2021 support 5G, many users still need to upgrade their existing devices to experience 5G. What operators can do here is partner with OEMs to offer 5G mobile phones and Mi-Fi devices at a subsidized price to drive consumer interest. Trade-in programs will also help. This will enable operators to gain major traction for a faster 5G rollout and drive competition around the market.

Conclusion

With its cloud-first architecture, high bandwidth, and low latency, 5G has been emerging as a platform that will drive innovation in various sectors like healthcare, automobiles, logistics, massive IoT, and more. Industrial IoT and mission-critical use cases will benefit from the always-on and real-time connectivity of this next-gen network.

Early 5G rollout will give operators more time and opportunities to explore new use cases. In an increasingly competitive and dynamically changing market, the first-mover advantage is key.

Alepo’s industry-leading 5G Core solutions enable swift and easy deployment, provide a low resource footprint, and ensure standards-compliant infrastructure for a scalable and future-proof network. They enable operators to generate more revenue from avenues that were previously unexplored in telecom. Partnering with leading technology partners, Alepo delivers end-to-end 5G solutions that have enabled commercial success for over a dozen real-world deployments globally.

To begin your next-gen journey today, email us at market.development@alepo.com.

5G analytics systems and the predictive network

Before we dive into the technical specifics, let’s first understand why predictive networks are relevant for operators.

Business benefits of predictive networks

The predictive network enables a host of benefits to service providers, including:

• Ensure Quality of Service (QoS), Quality of Experience (QoE), and Service Level Agreements (SLAs) for 5G services by intelligently tuning to network conditions
• Prevent performance issues, predicting them before they occur and taking anticipatory corrective measures
• Lower downtime by minimizing network disruptions
• Support advanced next-gen use cases
• Maximize return on network-capacity investments
• Continuously tune the network and balance network load to prevent over-engineering

Operator success will be defined by ensuring that their predictive systems can manage their networks more efficiently while ensuring a superior customer experience.

What exactly is a predictive network?

Traditionally, proactive systems, popularly known as Self-Organizing Networks (SON), have analyzed real-time network data to detect anomalies either when an event occurred, or the system was impacted. Their algorithms would then suggest a resolution or take corrective actions that involve human intervention.

In contrast, a predictive network uses months of historical data to predict the recurrence of network events. It essentially treats each event as a statistical problem that can be solved using Artificial Intelligence (AI) and Machine Learning (ML) techniques, learning from the past and predicting what it should anticipate.

The role of predictive analytics in the 5G ecosystem

5G acts as a catalyst for predictive networks because it introduces dedicated data analytics network functions (NFs). Standards bodies have defined Network Data Analytics Function (NWDAF) and Network Exposure Function (NEF) to provide a centralized predictive analytics platform for the 5G core network. These NFs collect and expose network data in real-time to machine learning applications deployed at the network edge.

3GPP has also defined standardization guidelines for data collection, predefined analytics insights, and data exposure interfaces for customers. Accordingly, the NWDAF collects data from multiple sources like user equipment, network functions, network edge, data plane, operation, administration, and maintenance (OAM) systems, and more.

The data gathered by NWDAF can be fed into an analytics engine to provide insights and take necessary actions. It is designed to defragment proprietary network analytics solutions and standardize the way mobile network data is produced and consumed.

The 5G NWDAF, combined with AI and ML, empowers proactive closed-loop network operations, ensuring the network can analyze historical data and learn from it.

The ubiquitous architecture of 5G includes an edge NWDAF co-located with core network functions and a central NWDAF. The edge NWDAF serves low and ultralow latency use cases, while the central NWDAF supports use cases that do not have real-time requirements. It also includes functions such as the data and ML models repository that help ensure the AI/ML models and continuously trained.

Along with NWDAF, 5G also introduces data analytics functions at the following layers:

• Big data, management, and orchestration (Big Data/MDAF)
• Application function level (AFDAF)
• User equipment/RAN (DAF)
• Data network (DN-DAF)

With these critical 5G functions and an edge platform, the network can meet the performance needs of more complex next-gen use cases. It will develop a system to capture network data from all functions and understand the network; measure and predict service performance; and proactively ensure high QoE, QoS, and network availability round the clock. Automating this system using AI/ML will enable operators to maximize return on investment.

Predictive analytics use cases

Some of the many use cases that can be powered by NWDAF and AI/ML include:

Analytics systems today and into the future

Key stages of a network’s analytics journey

The analytics journey can be classified into four stages:

• Context-sensitive or diagnostic analytics gathers and visualizes data, identifies patterns in historical data, and detects why the event occurred.
• Predictive analytics analyzes large volumes of data and forecasts probable future events, using machine learning techniques.
• Prescriptive analytics provides insights and options to optimize the network.
• Cognitive analytics takes optimized decisions to rectify problems without human intervention.

Broadly speaking, the industry is currently at the predictive analytics stage. With the advent of 5G analytics functions and maturity in AI/ML algorithms, we will soon see cognitive systems taking intelligent decisions without human intervention.

The future predictive network will analyze large datasets from multiple channels and identify complex network patterns, thus making near-accurate predictions. To achieve this, the operator’s monitoring and maintenance system must rely on 5G NWDAF and advanced predictive algorithms, both of which have equally innovative core functionalities.

Conclusion

Growing data is a reality of modern networks, and analyzing this data is the key to business success. It’s therefore essential for operators to devise and optimize their analytics system and continuously measure its maturity, especially as cloud technology permeates.

5G data analytics functions and AI/ML are set to disrupt the way we design and operate our networks. High compute and 5G network speeds make it easier to analyze huge amounts of data and transmit the results in real-time.

AI and ML technologies also enable the development of more sophisticated data analytics systems that can do more than analyze data and relay information. These intelligent systems can perform self-assessments, auto-adjust, and perform complex tasks on their own, without needing human intervention. As we see more widespread adoption, they will be transformative for the industry.