2 posts tagged with "ran"

Learn more about Artificial Intelligence - AI in the workforce in this article.

Introduction#

An increase in data usage demands a network effectiveness strategy, with a primary focus on lowering overall costs. The sophistication of networks is expanding all the time. The arrival of 5G on top of existing 2G, 3G, and 4G networks, along with customers' growing demands for a user platform comparable to fibre internet, places immense strain on telecommunication operators handling day-to-day activities (Mishra, 2018). Network operators are also facing significant financial issues as a result of declining revenue per gigabyte and market share, making maximizing the impact on network investment strategies vital for existence.

How can businesses use AI to change the way businesses make network financial decisions?#

From sluggish and labor-intensive to quick, scalable, and adaptable decisions - The traditional manual planning method necessitates a significant investment of both money and time. Months of labor-intensive operations such as data gathering, aggregation of data, prediction, prompting, proportioning, and prioritizing are required for a typical medium-sized system of 10,000 nodes. Each cell is simulated separately using machine learning, depending on its special properties. Several Key performance indicators are used in multivariable modeling approaches to estimate the efficiency per unit separately. By combining diverse planning inputs into the application, operators may examine alternative possibilities due to the significant reduction in turnaround time (Raei, 2017).

Moving from a network-centric to a user-centric approach - Basic guidelines are commonly used to compare usage to bandwidth. Customer bandwidth is influenced by some parameters, including resource consumption, such as DLPRB utilization. Individual unit KPI analysis utilizing machine learning solves this inefficacy, with the major two processes involved being traffic prediction and KPI predictions. The Key performance indicator model is a useful part of cognitive planning that is specific to each cell and is trained every day using the most up-to-date data. The per-cell model's gradient and angles are governed by its unique properties, which are impacted by bandwidth, workload, broadcast strength, as well as other factors (Kibria et al., 2018). This strategy provides more granularity and precision in predicting each cell's KPI and effectiveness.

From one-dimensional to two-dimensional to three-dimensional - Availability and efficiency are frequently studied in a one-dimensional manner, with one-to-one mappings of assets such as PRB to quality and productivity. Nevertheless, additional crucial elements such as broadcast frequency or workload have a significant impact on cell quality and productivity. Optimal TCO necessitates a new method of capacity evaluation that guarantees the correct solution is implemented for each challenge (Pahlavan, 2021).

Candidate selection for improvement - Units with poor wireless reliability and effectiveness are highlighted as candidates for improvement rather than growth using additional parameters such as radio quality (CQI) and spectrum efficiency in cognitive planning. As a first resort, optimization operations can be used to solve low radio-quality cells to increase network capacity and performance. Instead of investing CAPEX in hardware expansion, cognitive planning finds low radio-quality cells where capacity may be enhanced through optimization (Athanasiadou et al., 2019).

Candidate selection for load-balancing#

Before advocating capacity increase, cognitive planning tools will always model load-balancing among co-sector operators. This is done to eliminate any potential for load-balancing-related benefits before investing. The load-balancing impact is modeled using the machine-learning-trained KPI model by assuming traffic shifts from one operator to another and then forecasting the efficiency of all operators even in the same section (He et al., 2016). If the expected performance after the test does not satisfy the defined experience requirements, an extension is suggested; alternatively, the program generates a list of suggested units for load-balancing.

Prioritization's worth for AI in the workforce#

When network operators are hesitant to spend CAPEX, a strong prioritizing technique is vital to maximizing the return on investment (ROI) while guaranteeing that even the most relevant aspects are handled. This goal is jeopardized by outdated approaches, which struggle to determine the appropriate response and have the versatility to gather all important indicators. In the case of network modeling, load corresponds to the number of consumers, utilization (DLPRB utilization) to the space occupancy levels, and quality (CQI) to the size (Maksymyuk, Brych and Masyuk, 2015). The amount of RRC users, which is near to demand as a priority measure, is put into the prioritizing procedure, taking into account the leftover areas. Further priority levels are adjusted based on cell bandwidth, resulting in a more realistic order.

Developers give ideal suggestions and growth flow (e.g. efficiency and load rebalancing ahead of growth) and generate actual value by combining all of these elements, as opposed to the conventional way, which involves a full examination of months of field data:

• Optimization activities are used as a first option wherever possible, resulting in a 25% reduction in carrier and site expansions.

• When compared to crowded cells detected by operators, congested cells found by cognitive planning had a greater user and traffic density, with an average of 21% more RRC users per cell and 19% more data volume per cell. As a result, the return on investment from the capacity increase is maximized (Pahlavan, 2021).

• Three months before the experience objective was missed, >75 percent of the field-verified accuracy in determining which cells to grow when was achieved.

• Reduce churn

Conclusion for AI in the workforce#

The radio access network (RAN) is a major component of a customer service provider's (CSP) entire mobile phone network infrastructural development, contributing to around 20% of a cellular manufacturer's capital expenditures (CapEx). According to the findings, carriers with superior connection speeds have greater average revenue per user (+31%) and lower overall turnover (-27 percent) (Mishra, 2018). As highlighted in this blog, using Machine learning and artificial intelligence for capacity management is critical for making intelligent network financial decisions that optimize total cost of ownership (TCO) while offering the highest return in terms of service quality: a critical pillar for customer service provider's (CSP) commercial viability.

Learn more about Nife to be informed about Edge Computing and its usage in different fields https://docs.nife.io/blog

Introduction#

5G has been substantially implemented, and network operators now have a huge opportunity to monetize new products and services for companies and customers. Network slicing is a critical tool for achieving customer service and assured reliability. Ericsson has created the most comprehensive network slicing platform, comprising 5G Radio Access Networks (RAN) slicing, enabling automatic and quick deployment of services of new and creative 5G use scenarios, using an edge strategy (Subedi et al., 2021). Ericsson 5G Radio Access Networks (RAN) Slicing has indeed been released, and telecom companies are enthusiastic about the possibilities of new 5G services. For mobile network operators, using system control to coordinate bespoke network slices in the personal and commercial market sectors can provide considerable income prospects. Ericsson provides unique procedures to ensure that speed and priority are maintained throughout the network slicing process. Not only do they have operational and business support systems (OSS/BSS), central, wireless, and transit systems in their portfolio, but they also have complete services like Network Support and Service Continuity (Debbabi, Jmal and Chaari Fourati, 2021).

What is 5G Radio Access Networks (RAN) Slicing?#

The concept of network slicing is incomplete without the cooperation of communication service providers. It assures that the 5G Radio Access Networks (RAN) Slicing-enabled services are both dependable and effective. Carriers can't ensure slice efficiency or meet service contracts unless they have network support and service continuity. Furthermore, if carriers fail to secure slice performance or meet the service-level agreement, they may face punishment and the dangers of losing clients (Mathew, 2020). Ericsson 5G Radio Access Networks (RAN) Slicing provides service operators with the unique and assured quality they have to make the most of their 5G resources. The novel approach was created to improve end-to-end network slicing capabilities for radio access network managing resources and coordination. As a consequence, it constantly optimizes radio resource allocation and priority throughout multiple slices to ensure service-level commitments are met. This software solution, which is based on Ericsson radio experience and has a flexible and adaptable design, will help service providers to satisfy expanding needs in sectors such as improved broadband access, network services, mission-critical connectivity, and crucial Internet of Things (IoT) (Li et al., 2017).

Ericsson Network Support#

Across complex ecosystems, such as cloud networks, Network Support enables data-driven fault isolation, which is also necessary to efficiently manage the complexity in [5G systems]. To properly manage the complexity of 5G networks, Ericsson Network Support offers data-driven fault isolation. This guarantees that system faults are quickly resolved and that networks are reliable and robust. Software, equipment, and replacement parts are divided into three categories in Network Support. By properly localizing defects and reducing catastrophic occurrences at the solution level, Ericsson can offer quick timeframes and fewer site visits. Ericsson also supports network slicing by handling multi-vendor ecosystem fault separation and resolving complications among domains (Zhang, 2019). Data-driven fault isolation from Ericsson guarantees the quick resolution of connection problems, as well as strong and effective networks, and includes the following innovative capabilities:

• Ericsson Network Support (Software) provides the carrier's software platform requirements across classic, automated, and cloud-based services in extremely sophisticated network settings. It prevents many mishaps by combining powerful data-driven support approaches with strong domain and networking experience.

• Ericsson Hardware Services provides network hardware support. Connected adds advanced technologies to remote activities, allowing for quicker problem identification and treatment. It integrates network data with past patterns to provide service personnel and network management with relevant real-time information. It is feasible to pinpoint errors with greater precision using remote scans and debugging.

• The Spare Components Management solution gives the operator's field engineers access to the parts they need to keep the network up and running (Subedi et al., 2021). Ericsson will use its broad network of logistical hubs and local parts depots to organize, warehouse, and transport the components.

Ericsson Service Continuity#

To accomplish 5G operational readiness, Service Continuity provides AI-powered, proactive assistance, backed by tight cooperation and Always-On service. Advanced analytical automation and reactive anticipatory insights provided by Ericsson Network Intelligence allow Service Continuity services. It focuses on crucial functionality to help customers reach specified business objectives while streamlining processes and ensuring service continuity (Katsalis et al., 2017). It is based on data-driven analysis and worldwide knowledge that is given directly and consists of two services:

• Ericsson Service Continuity for 5G: Enables the clients' networks to take remedial steps forward in time to prevent end-user disruption, allowing them to move from responsive to proactively network services.

• Ericsson Service Continuity for Private Networks is a smart KPI-based support product for Industry 4.0 systems and services that is targeted to the unique use of Private Networks where excellent performance is critical (Mathew, 2020).

Conclusion for 5G Network Slicing

Network slicing will be one of the most important innovations in the 5G network area, transforming the telecommunications sector. The 5G future necessitates a network that can accommodate a diverse variety of equipment and end customers. Communication service providers must act quickly as the massive network-slicing economic potential emerges (Da Silva et al., 2016). However, deciding where to begin or where to engage is difficult. Ericsson's comprehensive portfolio and end-to-end strategy include Network Support and Service Continuity services. Communication service providers across the world would then "walk the talk" for Network Slicing in the 5G age after incorporating them into their network operations plan.

References#

• Da Silva, I.L., Mildh, G., Saily, M. and Hailu, S. (2016). A novel state model for 5G Radio Access Networks. 2016 IEEE International Conference on Communications Workshops (ICC).
• Debbabi, F., Jmal, R. and Chaari Fourati, L. (2021). 5G network slicing: Fundamental concepts, architectures, algorithmics, project practices, and open issues. Concurrency and Computation: Practice and Experience, 33(20).
• Katsalis, K., Nikaein, N., Schiller, E., Ksentini, A. and Braun, T. (2017). Network Slices toward 5G Communications: Slicing the LTE Network. IEEE Communications Magazine, 55(8), pp.146–154.
• Li, X., Samaka, M., Chan, H.A., Bhamare, D., Gupta, L., Guo, C. and Jain, R. (2017). Network Slicing for 5G: Challenges and Opportunities. IEEE Internet Computing, 21(5), pp.20–27.
• Mathew, A., 2020, March. Network slicing in 5G and the security concerns. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) (pp. 75-78). IEEE.
• Subedi, P., Alsadoon, A., Prasad, P.W.C., Rehman, S., Giweli, N., Imran, M. and Arif, S. (2021). Network slicing: a next-generation 5G perspective. EURASIP Journal on Wireless Communications and Networking, 2021(1).
• Zhang, S. (2019). An Overview of Network Slicing for 5G. IEEE Wireless Communications, [online] 26(3), pp.111–117.