3 posts tagged with "network"

Private 5G is a special framework of 5G designed for big organizations to benefit from high speed, low latency, and seamless connectivity. Read the full article to know about the business benefits of 5G.

Introduction#

The Fifth Generation Cellular network or 5G is not only going to change the lives of individual people but it is also going to revolutionize how financial enterprises control their data in the near future. It is better than 4G in every aspect whether it is speed or connectivity. Its fast speed, seamless connectivity, and low latency make it the perfect option for big enterprises.

Private 5G will be very beneficial for large enterprises with thousands of employees and terabytes (TB) of data. To get a better understanding of private 5G architecture and its benefits, read the article till the end.

What is Private 5G?#

A private 5G/enterprise 5G is a private network setup by large organizations to work more efficiently. Large enterprises like Google, Facebook, Twitter, and many other organizations with thousands of employees and millions of terabytes of data use this network to benefit from low latency, fast speed, and seamless connectivity features.

Unlike 4G, 5G can handle millions of users in the same area. Moreover, it also can transfer big chunks of data in seconds. Different organizations use different CBRS spectrums to build their own network.

A private 5G network requires its users to set up small cellular towers built close together like wifi access points to transfer data efficiently. This type of network architecture will help universities, big plants, and warehouses to work more efficiently.

Benefits of Private 5G#

It provides many amazing features that can benefit organizations in different ways.

Firstly, the data security feature it provides is amazing. Organizations will have full control over everything that happens because they don't need to connect to a telecom now.

Apart from security, this network architecture is also cost-effective. No cables are needed, you can now get a fast and reliable wireless connection that provides seamless connectivity.

It also provides various customizations to organizations to build an infrastructure like Micro Slicing. These customizations are not available for the public version. They help these organizations to work efficiently on numerous projects.

Private 5G also allows organizations to connect various devices with latency as low as 1 millisecond. This helps them access any kind of data in seconds. Moreover, it also gives businesses the opportunity to power IoT and big data projects.

5G also provides financial enterprises to use different commercial 5G carrier services like the low band, mid-band, and high band.

Business Benefits of Enterprise 5G Network Architecture#

With its low latency and great speed, 5G can provide great benefits to financial enterprises. Here is a list of financial enterprises that will benefit from private 5G.

Health Care#

No other enterprise will benefit from 5G more than hospitals. After Covid broke, a rapid increase in demand for infrastructure emerged. After this medical emergency for the first time in decades, it was noticed that hospitals are short on ICU beds and other technologies required to treat patients. This shortage of beds and basic tech led to the death of thousands of people.

But with private 5G and its seamless connectivity and fast M2M data sharing, future breakouts can be prevented and many diseases can be cured on time. This technology will also minimize the number of deaths.

Manufacturing#

Most manufacturing plants are dependent completely on tech, some wired and some wireless. With 5G, this financial service sector can be fully automated with robots operating manufacturing plants and warehouses. This innovation is not possible with regular wifi or a 4G network because they are limited. While private 5G provides fast speed, connectivity, and low latency that will help improve efficiency in these manufacturing plants.

Private 5G opens gates to endless possibilities of advancement in the manufacturing sector like powerful IoT and a cloud computing system. Moreover, it will also help different financial enterprises increase their production.

Automated Stores#

Private 5G also provides opportunities for fully automated stores to improve their user experience with a fast M2M connection. With private 5G, data can be processed many times faster than with 4G, and results can be delivered in seconds. Fully automated store networks can greatly benefit from private 5G network architecture. As these stores work on real-time user input and 5G makes the real-time connection between devices 100 times better than 4G.

Moreover, these stores can also integrate AR or VR technologies in their stores to further enhance the user experience.

Logistics#

Logistics is another area where financial enterprises can benefit from private 5G. All e-commerce businesses use logistics to track the behavioral pattern of their consumers. Moreover, logistics are also used to track down every step of the product from product dispatch to delivery. All of these logistics work can be more efficiently done using 5G.

This technology will also help businesses automate their facilities and get rid of extra workforce. The automated devices with Private 5G will be more efficient.

Smart Facilities#

Private 5G is also beneficial to use in smart facilities like airports, offices, malls, etc. These facilities can benefit from ultra-low latency, fast speed, and seamless connectivity. Private 5G will not only improve the overall IT infrastructure of these facilities but also enhance the customer experience.

Private 5G will also enable these smart facilities to stream smooth 4K in real-time without any delay.

Conclusion#

Private 5G is going to innovate the big organization. The infrastructure of big companies will be revolutionized with it. It will provide all the big enterprises with such seamless connectivity, high speed, and ultra-low latency that all of their operations will be completed in the blink of an eye.

Private 5G is different from public 5G; it provides its users with security and many different customization options. It helps different financial enterprises benefit from its connectivity, speed, and latency. It has various useful applications in healthcare, manufacturing, IT, and smart facilities.

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#

Machine Learning-Based Techniques for observation and administration are especially suitable for sophisticated network infrastructure operations. Assume a machine learning (ML) program designed to predict mobile service disruptions. Whenever a network administrator obtains an alert about a possible imminent interruption, they can take bold measures to address bad behaviour before something affects users. The machine learning group, which constructs the underlying data processors that receive raw flows of network performance measurements and store them into such a Machine Learning (ML)-optimized databases, assisted in the development of the platform. The preliminary data analysis, feature engineering, Machine Learning (ML) modeling, and hyperparameter tuning are all done by the research team. They collaborate to build a Machine Learning (ML) service that is ready for deployment (Chen et al., 2020). Customers are satisfied because forecasts are made with the anticipated reliability, network operators can promptly repair network faults, and forecasts are produced with the anticipated precision.

What is Machine Learning (ML) Lifecycle?#

The data analyst and database administrators obtain multiple procedures (Pipeline growth, Training stage, and Inference stage) to establish, prepare, and start serving the designs using the massive amounts of data that are engaged in different apps so that the organisation can take full favor of artificial intelligence and Machine Learning (ML) methodologies to generate functional value creation (Ashmore, Calinescu and Paterson, 2021).

Monitoring allows us to understand performance concerns#

Machine Learning (ML) models are based on numbers, and they tacitly presume that the learning and interpretation data have the same probability model. Basic variables of a Machine Learning (ML) model are tuned during learning to maximise predicted efficiency on the training sample. As a result, Machine Learning (ML) models' efficiency may be sub-optimal when compared to databases with diverse properties. It is common for data ranges to alter over time considering the dynamic environment in which Machine Learning (ML) models work. This transition in cellular networks might take weeks to mature as new facility units are constructed and updated (Polyzotis et al., 2018). The datasets that ML models consume from multiple data sources and data warehouses, which are frequently developed and managed by other groups, must be regularly watched for unanticipated issues that might affect ML model results. Additionally, meaningful records of input and model versions are required to guarantee that faults may be rapidly detected and remedied.

Data monitoring can help prevent machine learning errors#

Machine Learning (ML) models have stringent data format requirements because they rely on input data. Whenever new postal codes are discovered, a model trained on data sets, such as a collection of postcodes, may not give valid forecasts. Likewise, if the source data is provided in Fahrenheit, a model trained on temperature readings in Celsius may generate inaccurate forecasts (Yang et al., 2021). These small data changes typically go unnoticed, resulting in performance loss. As a result, extra ML-specific model verification is recommended.

Variations between probability models are measured#

The steady divergence between the learning and interpretation data sets, known as idea drift, is a typical cause of efficiency degradation. This might manifest itself as a change in the mean and standard deviation of quantitative characteristics. As an area grows more crowded, the frequency of login attempts to a base transceiver station may rise. The Kolmogorov-Smirnov (KS) test is used to determine if two probability ranges are equivalent (Chen et al., 2020).

Preventing Machine Learning-Based Techniques for system engineering problems#

The danger of ML efficiency deterioration might be reduced by developing a machine learning system that specifically integrates data management and model quantitative measurement tools. Tasks including data management and [ML-specific verification] are performed at the data pipeline stage. To help with these duties, the programming group has created several public data information version control solutions. Activities for monitoring and enrolling multiple variations of ML models, as well as the facilities for having to serve them to end-users, are found at the ML model phase (Souza et al., 2019). Such activities are all part of a bigger computer science facility that includes automation supervisors, docker containers tools, VMs, as well as other cloud management software.

Data and machine learning models versioning and tracking for Machine Learning-Based Techniques#

The corporate data pipelines can be diverse and tedious, with separate elements controlled by multiple teams, each with their objectives and commitments, accurate data versioning and traceability are critical for quick debugging and root cause investigation (Jennings, Wu and Terpenny, 2016). If sudden events to data schemas, unusual variations to function production, or failures in intermediate feature transition phases are causing ML quality issues, past and present records can help pin down when the problem first showed up, what data is impacted, or which implication outcomes it may have affected.

Using current infrastructure to integrate machine learning systems#

Ultimately, the machine learning system must be adequately incorporated into the current technological framework and corporate environment. To achieve high reliability and resilience, ML-oriented datasets and content providers may need to be set up for ML-optimized inquiries, and load-managing tools may be required. Microservice frameworks, based on containers and virtual machines, are increasingly widely used to run machine learning models (Ashmore, Calinescu, and Paterson, 2021).

Conclusion for Machine Learning-Based Techniques#

The use of Machine Learning-Based Techniques could be quite common in future communication designs. At this scale, vast amounts of data streams might be recorded and stored, and traditional techniques for assessing better data and dispersion drift could become operationally inefficient. The fundamental techniques and procedures may need to be changed. Moreover, future designs are anticipated to see an expansion in the transfer of computing away from a central approach and onto the edge, closer to the final users (Hwang, Kesselheim and Vokinger, 2019). Decreased lags and Netflow are achieved at the expense of a more complicated framework that introduces new technical problems and issues. In such cases, based on regional federal regulations, data gathering and sharing may be restricted, demanding more cautious ways to programs that prepare ML models in a safe, distributed way.

References#

• Ashmore, R., Calinescu, R. and Paterson, C. (2021). Assuring the Machine Learning Lifecycle. ACM Computing Surveys, 54(5), pp.1–39.
• Chen, A., Chow, A., Davidson, A., DCunha, A., Ghodsi, A., Hong, S.A., Konwinski, A., Mewald, C., Murching, S., Nykodym, T., Ogilvie, P., Parkhe, M., Singh, A., Xie, F., Zaharia, M., Zang, R., Zheng, J. and Zumar, C. (2020). Developments in MLflow. Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning.
• Hwang, T.J., Kesselheim, A.S. and Vokinger, K.N. (2019). Lifecycle Regulation of Artificial Intelligence– and Machine Learning–Based Software Devices in Medicine. JAMA, 322(23), p.2285.
• Jennings, C., Wu, D. and Terpenny, J. (2016). Forecasting Obsolescence Risk and Product Life Cycle With Machine Learning. IEEE Transactions on Components, Packaging and Manufacturing Technology, 6(9), pp.1428–1439.
• Polyzotis, N., Roy, S., Whang, S.E. and Zinkevich, M. (2018). Data Lifecycle Challenges in Production Machine Learning. ACM SIGMOD Record, 47(2), pp.17–28.
• Souza, R., Azevedo, L., Lourenco, V., Soares, E., Thiago, R., Brandao, R., Civitarese, D., Brazil, E., Moreno, M., Valduriez, P., Mattoso, M., Cerqueira, R. and Netto, M.A.S. (2019).
• Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering. 2019 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS).
• Yang, C., Wang, W., Zhang, Y., Zhang, Z., Shen, L., Li, Y. and See, J. (2021). MLife: a lite framework for machine learning lifecycle initialization. Machine Learning.