Published:
Large Language Models (LLMs) have transformed how businesses operate, particularly in the telecom industry. Companies use these models to enhance customer service and streamline processes, offering significant advantages (help answering questions). This blog post explores the challenges of using LLMs in telecom and discusses how to measure their success within the telecom domain.
Published:
Large Language Models (LLMs) have transformed how businesses operate, particularly in the telecom industry. Companies use these models to enhance customer service and streamline processes, offering significant advantages (help answering questions). This blog post explores the challenges of using LLMs in telecom and discusses how to measure their success within the telecom domain.
Published:
5G non-standalone access (NSA) serves as a natural progression from LTE to a 5G upgrade. Combining user experience with data-driven insights can help achieve an optimal balance between enhanced user satisfaction and signaling costs, positioning 5G NSA as an effective intermediate step between LTE and standalone 5G. This approach supports technology leadership while keeping 5G standalone deployment as the ultimate goal.
Published:
Providing good signal strength with low interference and power consumption at the cell edge will be key to delivering multi-faceted quality of service for emerging 5G use cases. Below, we explore how uplink power control based on reinforcement learning can solve the cell edge problem.
Published:
5G non-standalone access (NSA) serves as a natural progression from LTE to a 5G upgrade. Combining user experience with data-driven insights can help achieve an optimal balance between enhanced user satisfaction and signaling costs, positioning 5G NSA as an effective intermediate step between LTE and standalone 5G. This approach supports technology leadership while keeping 5G standalone deployment as the ultimate goal.
Published:
Time-critical communication or Ultra-Reliable Low-Latency Communication (uRLLC) was envisioned as one of the fundamental use case categories in 5G network architecture. Stringent requirements on latency and reliability might require several provisions, such as robust and duplicate transmission, a method where devices can access transmission resources without prior permission, and prescheduling. These provisions, while helping to achieve optimal performance, have an impact on resource utilization. We explore and discuss how a cognitive duplication approach could be used to find a trade-off between performance and resource utilization.
Published:
Providing good signal strength with low interference and power consumption at the cell edge will be key to delivering multi-faceted quality of service for emerging 5G use cases. Below, we explore how uplink power control based on reinforcement learning can solve the cell edge problem.
Published:
Large Language Models (LLMs) have transformed how businesses operate, particularly in the telecom industry. Companies use these models to enhance customer service and streamline processes, offering significant advantages (help answering questions). This blog post explores the challenges of using LLMs in telecom and discusses how to measure their success within the telecom domain.
Published:
5G non-standalone access (NSA) serves as a natural progression from LTE to a 5G upgrade. Combining user experience with data-driven insights can help achieve an optimal balance between enhanced user satisfaction and signaling costs, positioning 5G NSA as an effective intermediate step between LTE and standalone 5G. This approach supports technology leadership while keeping 5G standalone deployment as the ultimate goal.
Published:
Time-critical communication or Ultra-Reliable Low-Latency Communication (uRLLC) was envisioned as one of the fundamental use case categories in 5G network architecture. Stringent requirements on latency and reliability might require several provisions, such as robust and duplicate transmission, a method where devices can access transmission resources without prior permission, and prescheduling. These provisions, while helping to achieve optimal performance, have an impact on resource utilization. We explore and discuss how a cognitive duplication approach could be used to find a trade-off between performance and resource utilization.
Published:
Providing good signal strength with low interference and power consumption at the cell edge will be key to delivering multi-faceted quality of service for emerging 5G use cases. Below, we explore how uplink power control based on reinforcement learning can solve the cell edge problem.
Published:
Time-critical communication or Ultra-Reliable Low-Latency Communication (uRLLC) was envisioned as one of the fundamental use case categories in 5G network architecture. Stringent requirements on latency and reliability might require several provisions, such as robust and duplicate transmission, a method where devices can access transmission resources without prior permission, and prescheduling. These provisions, while helping to achieve optimal performance, have an impact on resource utilization. We explore and discuss how a cognitive duplication approach could be used to find a trade-off between performance and resource utilization.
Published:
The evolution of 5G and ongoing research push toward 6G is enabling significant advances in scalable user-centric biosignal monitoring for assistive technology healthcare applications. While network slices cater to high-bandwidth and low latency use-cases, robustness and performance are ensured through scalable machine learning methods. In this post, we discuss the challenges and resolution for a scalable pipeline with communicating sensor devices that has the potential to transform healthcare.
Published:
The evolution of 5G and ongoing research push toward 6G is enabling significant advances in scalable user-centric biosignal monitoring for assistive technology healthcare applications. While network slices cater to high-bandwidth and low latency use-cases, robustness and performance are ensured through scalable machine learning methods. In this post, we discuss the challenges and resolution for a scalable pipeline with communicating sensor devices that has the potential to transform healthcare.
Published:
The evolution of 5G and ongoing research push toward 6G is enabling significant advances in scalable user-centric biosignal monitoring for assistive technology healthcare applications. While network slices cater to high-bandwidth and low latency use-cases, robustness and performance are ensured through scalable machine learning methods. In this post, we discuss the challenges and resolution for a scalable pipeline with communicating sensor devices that has the potential to transform healthcare.