The State of AI in Telecom Services: Challenges

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State of AI
State of AI

Telecom trends: A look at the current state of AI in telecommunications

The following trends can be observed from the perspective of a communication service provider to achieve this.

Artificial intelligence and Machine Learning have been successfully implemented to improve performance and capacity in both existing functions (for example, in RAN and core networks), as well as in parts of the network that are less specific and faster. Implementation of IT technologies such as Operations and Business Support Systems (OSS/BSS) and Cloud Infrastructure.

As top telecom service providers industrialize AI technology, the trend is moving toward increasing industry-specific training for AI/ML practical architecture in both standards and open source, data management, and data collection.

With its business processes for clients, partners, and products, BSS has seen quick adoption of analytics. In addition to AI technologies, the service level agreement (SLA) management, customer care, product performance, forecasts, and subscriber management are currently being improved.

Additionally, OSS has seen a fast uptake of analytics in areas, for example, network performance, security, and experience management such as performance management, fault management, and prediction management. A trend is developing toward horizontal automation platforms that support multiple domains and multiple vendors as well as real-time AI and ML capabilities.

Traditional core networks have integrated AI and machine learning into their products along with exclusive administration and data streaming, which has resulted in a challenge and analytics ecosystem with severe probe vendor lock-in. However, lately, there has been a reasonable trend towards increasing the information about incident management data from basic network nodes and clarifying the issues surrounding AI core usage.

Taking everything into consideration, early AI/ML-based software is currently running within network functions and RAN management systems. In-network functions, AI/ML models change guidelines-based software into chosen key sub-assignments and perform better, like determining channel coding plans and beamforming control.

As part of RAN management systems, artificial intelligence/machine learning software assists with detecting incidents, providing remedial experiences, and improving suggestions. The current generation of AI/ML software guarantees more problematic enhancements with higher levels of network automation and goal-based administration, which is quite different from the current network operation that depends on configuration parameters.

Since IT and cloud-based ecosystems are solid in areas where there is a solid IT infrastructure, cloud infrastructure has been heavily reliant on true norms. However, small steps are being taken to adapt the business model for communications service providers, even without direct involvement from cloud service providers.

Telecom Businesses – Adopting Artificial intelligence online – challenges

AI adoption in telecommunications is being challenged by organizational challenges, so keeping this in mind, we will examine the network’s functional components in the next segment.

High-level AI/ML LCM process

AI’s biggest challenge for telecoms

AI/ML usage is a real-world demand, and not a function of open source or standardized industry conversations. Therefore, both telecom service providers and vendors are already incorporating AI/ML capabilities into their investment networks and organizations. Nonetheless, AI/ML is as yet at its nascent stage, so it is worthwhile considering the hurdles in its quick adoption. The following are a few examples:

  • With artificial intelligence/ML-based LCM, new perspectives are opened beyond traditional LCM software.
  • Because protection guidelines and (relevant data are needed for developing and training AI/ML models), there is a lack of access to data and real expertise.
  • Segmenting the network and interconnecting it to numerous standards and open source drives keeps spreading the business’ core, causing waves.
  • Building trust in automation technology takes time since certain conclusions are hard to clarify. To present pope guards progressively, the humans must supervise and control the process.
  • Providers generally provide their own (proprietary) tools and interfaces, which creates unlocking challenges and slows the CSP’s desire to open up, ultimately slowing deployment and maintenance.
  • For short-term investments, there are very few qualified use cases

Management of the AI/ML life cycle in the Telecom industry

Artificial intelligence/machine learning technology integrates training components, a vital requirement for model conceptual flow, integrated learning, and data security. These are all enhanced by AI/ML technology.

Life Cycle Management (LCM) adds new requirements for telecommunications software (advancement, approval, delivery, operation, and finally retirement). The role of providers and aggregators is defined by LCM processes. The CSPs essentially determine who is responsible for what and who offers what to whom when it comes to duties and delivery between partners.

The telecommunications industry has been embracing a well-established, worldwide recognized, and well-working LCM process of traditional software licensing for more than 20 years. As an industry, we need to develop AI/ML-based technology, create LCM software, realize its potential, prevent fragmentation through its variants, and maintain a clear distance of concerns.

Accessing data is a challenge for telecoms

Access to relevant data is essential to the development and training of any analysis system or AI/ML model. To do this, it is important to determine the infrastructure and processing capability of each data point.

Keeping unnecessary data exchanges to a minimum is also necessary, as the volume of data can be immense. Filtering and preprocessing in data points can drastically reduce the amount of data transmitted over the network.

The vendor has completed the initial training of the AI/ML model. This requires access to relevant data. To further improve the prediction quality in the target network, it may be necessary to retrain the AI /ML model with local data.

The CSP and vendors should be able to agree on the price, possession, and protection of data and these agreements should be part of the data ecosystem. Technical solutions must be compatible with administrative policies, reliability, and CSP policies, and the system functions should support extensive adaptability to handle differences in different countries.

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