Nokia (Norway)

We demonstrate automatic endless optical polarization tracking over 3.8 Grad at up to 38-krad/s control speed with mean/maximum polarization errors of 0.068/0.185 rad. Without polarization fluctuations, mean/maximum polarization errors are 0.05/0.1 rad. Small-signal control time constant is about 2 mus. Function is maintained over the wavelength range 1505-1570 nm.

The escalating complexity and data volume in today’s digital networks demands innovative strategies to enhance performance and efficiency. This survey explores the possibilities of optimization techniques powered by AI to address this critical need. By utilizing artificial intelligence (AI) algorithms, including machine learning and deep learning, the study explores how AI can transform network optimization and management, to achieve superior performance and reliability. The investigation focuses on how AI algorithms can process extensive network data, recognize patterns, and make informed decisions to enhance network configurations and resource allocation methods. The review highlights key findings and insights, emphasizing the revolutionary impact of AI-based optimization for improving network performance and efficiency. It focuses on the advantages of AI-based methods in power control and EE efficiency, resource allocations, user connection, intelligent beam management, and channel estimation, by automating optimization processes, minimizing operational costs, and flexibly adjusting to evolving network conditions and user requirements. Furthermore, the review addresses the concerns and aspects associated with implementing AI-driven optimization techniques. In conclusion, the review underscores the crucial role of AI-driven optimization in tackling the growing complexities of network optimizations. It advocates for continuous exploration and innovation initiatives to fully harness the potential of AI-driven optimization, unlocking enhanced performance and operational efficiency in network systems.

Competition in telecommunications business is increasing. New business models, like Mobile Virtual Network Operators (MVNOs), shape the environment and network operators need to find cost efficient ways to offer good quality services for their end users. For operators it is important to shorten the time from network design to commercial launch. In addition they must ensure continuous service experience also when network extensions are introduced. Continuous service experience can be guaranteed only if the network is pre-analyzed and continuously monitored using measured data. In this paper measurement methods for Wideband Code Division Multiple Access (WCDMA) radio network design verification are introduced and their applicability in different stages of the network life cycle is addressed.