In this article, José Laffitte, Head of Engineering at Cactus, explores how AI is pushing the industry toward a more connected, intelligent, and resilient future. Discover these advancements and how they are transforming the telecom landscape.
The Telecom Trends
The telecom industry is undergoing several transformative trends that are shaping the way the world connects:
- Adoption of 5G and development of 6G: 5G has become the dominant standard, paving the way for 6G, which promises faster speeds, lower latency, and enhanced connectivity.
- Integration of the non-terrestrial segment into global networks: Since the release 17 of 3GPP 5G, the non-terrestrial segment (5G-NTN) has been included to achieve global telecom capabilities, providing broader coverage in remote or underserved areas.
- Digitalization: Digitalization is revolutionizing telecom operations, making services more efficient, flexible, and customer-focused. This includes the shift towards Open Radio Access Networks (O-RAN), which use standardized, open interfaces to allow equipment from different vendors to work together. This approach fosters innovation, reduces costs, and accelerates the deployment of new technologies.
- Artificial intelligence-enabled systems allow operators to optimise resources, reduce power consumption, or deploy smarter networks, even performing physical layer critical functions from the OSI model enabling the automation of functions such as coding, decoding, and modulation. AI-driven innovations in this domain allow telecom operators to predict and address network issues proactively, allocate resources more efficiently, and deliver superior, consistent service quality.
These trends collectively drive the industry towards a more connected, intelligent, and resilient future. By embracing these advancements, telecom operators can stay ahead in the competitive landscape and deliver superior services to their customers.
Through Taurus, the project we are carrying out with the support of the European Space Agency, we are investigating among others how AI can be applied to enhance user experience, reduce power consumption and increase performance through several telecom functions. One of the purposes of our project Taurus is to fully embrace 5G NR as an implementation means to fulfil the classical satcom modem role, adopting the key functions in an O-RAN context mainly around disaggregation.
In this article, we will explain Cactus’ vision of AI applied to different telecom functions for different purposes, such as energy saving, load balancing, and spectrum resources management. This is done through orchestration, traffic steering, and differential Quality of Service (QoS), but also through the implementation employing AI of physical layer functions like coding, decoding, and modulation. Let’s first align on what a satellite modem is.
Satcom modems and systems
A satellite communication (satcom) modem is a device that modulates and demodulates signals for transmission and reception via satellite links, and performs several functions at different levels of the OSI model:
- It handles signal processing and interfaces with the satellite transceiver: the modem ensures reliable communication by performing tasks like modulation resource mapping, encapsulation, and error correction.
- It manages the data flow performing traffic optimization functions like TCP acceleration and UDP optimization, applying QoS and implementing switching/routing capabilities to ensure efficient and reliable communication.
- It interfaces with control systems for adaptive functions such as pointing, power control and beamforming.
The Open RAN architecture, as specified by O-RAN, distributes these functions across several entities within a disaggregated framework, namely the Central Unit (CU), Distributed Unit (DU), and Radio Unit (RU). Consequently, integrating AI/ML into these traditional satcom modem functions means that AI/ML processes are applied at various points and timescales within the O-RAN architecture.
Adoption of AI/ML into 3GPP
The adoption of AI is one of the key aspects of the digitalization trend explained before, by enhancing various aspects of network management and operations.
The 3GPP specification TR 37.817 provides descriptions of principles for RAN intelligence enabled by AI, the functional framework (e.g., the AI functionality and the input/output of the component for AI-enabled optimization) and use cases and solutions of AI-enabled RAN.
The 3GPP specification TR38.843 explores the benefits of augmenting the air interface with features enabling improved support of AI/ML. The 3GPP framework for AI/ML is studied for air interface corresponding to each target use case regarding aspects such as performance, complexity, and potential specification impact.
Spectrum Resources Management
Spectrum resources are the lifeblood of wireless communications, and efficient management is crucial for ensuring optimal network performance and capacity. AI can significantly enhance spectrum management through:
- Dynamic Spectrum Allocation: AI algorithms can analyze real-time data to dynamically allocate spectrum resources based on demand, usage patterns, and interference levels. This ensures efficient utilization of the available spectrum, minimizing wastage and improving network efficiency.
- Spectrum Sensing: AI-driven cognitive radio systems can sense the spectrum environment and identify unused frequencies. This capability allows for opportunistic spectrum access, where secondary users can utilize vacant channels without causing interference to primary users.
- Beamforming and MIMO Systems: Neural networks optimize beamforming techniques by dynamically adjusting the direction of signal transmission to maximize signal strength and quality.
- Error correction: Advanced error correction techniques powered by neural networks can predict and correct errors in the transmitted data, significantly reducing the bit error rate, and adapting the coding and modulation to the specific condition.
Orchestration
In the era of 5G and beyond, network orchestration is becoming increasingly complex due to the diverse range of services and devices. AI plays a pivotal role in orchestrating these intricate networks through:
- Automated Network Configuration: AI can automate the configuration and management of network elements, reducing the need for manual intervention. This includes the deployment of virtualized network functions (VNFs) and the configuration of network slices tailored to specific use cases.
- Resource Optimization: By analyzing network traffic patterns and resource utilization, AI can optimize the allocation of computational and bandwidth resources. This ensures that critical applications receive the necessary resources while maintaining overall network efficiency.
- Predictive Maintenance: AI-powered predictive analytics can foresee potential network failures and performance degradation. By proactively addressing these issues, telecom operators can reduce downtime and enhance service reliability.
Quality of Service (QoS)
In the era of 5G and beyond, network orchestration is becoming increasingly complex due to the diverse range of services and devices. AI plays a pivotal role in orchestrating these intricate networks through:
- Automated Network Configuration: AI can automate the configuration and management of network elements, reducing the need for manual intervention. This includes the deployment of virtualized network functions (VNFs) and the configuration of network slices tailored to specific use cases.
- Resource Optimization: By analyzing network traffic patterns and resource utilization, AI can optimize the allocation of computational and bandwidth resources. This ensures that critical applications receive the necessary resources while maintaining overall network efficiency.
- Predictive Maintenance: AI-powered predictive analytics can foresee potential network failures and performance degradation. By proactively addressing these issues, telecom operators can reduce downtime and enhance service reliability.
Quality of Service (QoS)
Quality of Service is a critical aspect of telecom networks, influencing user experience and satisfaction. AI enhances QoS through:
- Traffic Prioritization: AI algorithms can classify and prioritize network traffic based on the type of service and user requirements. This ensures that latency-sensitive applications, such as video conferencing and online gaming, receive higher priority over less critical traffic.
- Anomaly Detection: AI can detect anomalies in network performance, such as unexpected spikes in latency or packet loss. By identifying and addressing these issues in real-time, operators can maintain consistent QoS levels.
- User Behavior Analysis: Machine learning models can analyze user behaviour and predict future usage patterns. This information helps in anticipating network demand and adjusting resources accordingly to maintain high QoS.
Physical Layer Functions
The physical layer is the foundation of wireless communication, and AI can optimize its functions to enhance overall network performance:
- Advanced Coding Techniques: AI can develop and optimize coding schemes that improve error detection and correction. This results in more robust communication links, especially in challenging environments with high interference and noise levels.
- Efficient Modulation Schemes: AI algorithms can design and select modulation schemes that maximize data throughput while minimizing power consumption. This is particularly important for energy-efficient IoT devices and high-capacity 5G networks.
- Signal Processing: AI-driven signal processing techniques can improve the accuracy and efficiency of data transmission. This includes tasks such as channel estimation, equalization, and beamforming, which are essential for maintaining high-quality communication links.
- Interference detection and mitigation: Machine learning models can predict and identify sources of interference, enabling proactive measures to mitigate their impact. By continuously learning from the network environment, AI systems can adapt to changing conditions and maintain optimal performance.
Conclusion
The integration of AI into the telecommunications industry is revolutionizing how networks are managed and operated. From spectrum management and orchestration to QoS and physical layer functions, AI offers transformative capabilities that enhance efficiency, reliability, and user experience. As technologies like O-RAN and 5G continue to evolve, the constructive collaboration between AI and telecom will unlock new possibilities and drive the industry towards a more connected and intelligent future. By embracing these advancements, telecom operators can stay ahead in the competitive landscape and deliver superior services to their customers.
Cactus has acquired extensive expertise in this field over the last few years and has developed a trademark technology called TDR, or Tensor Defined Radio, that allows it to make the most of RF resources maximizing the power-to-compute ratio.
Cactus is prepared to support our customers in overcoming challenges to create more efficient, better-connected, and more reliable networks. By applying AI in the non-terrestrial segment, we help our clients achieve superior performance, broader connectivity, and the highest reliability.
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