In future 6G networks, the deployment of network elements is expected to be highly
distributed, going beyond the level of distribution of existing 5G deployments. To fully exploit …

In recent years, owing to the architectural evolution of 6G towards decentralization,
distributed intelligence is being studied extensively for 6G network automation. Distributed …

In future 6G networks, distributed management of network elements is expected to be a
promising paradigm. Recent research progress in Artificial Intelligence (AI) is rapidly driving …

The rapid evolution of wireless communication technologies has led to the emergence of 6G
networks, which promise unprecedented levels of connectivity, capacity, and intelligence …

With the unprecedented rise in traffic demand and mobile subscribers, real-time fine-grained
optimization frame-works are crucial for the future of cellular networks. Indeed, rigid and …

Auto-scaling is a lifecycle management approach that automatically scales resources (CPU,
memory etc.) based on incoming load to optimize resource utilization. Centralized …

To enhance dynamic resource adaptation in fifth generation (5G) networks, network slicing
management em-powered by artificial intelligence (AI) through decision-making algorithms …

Over the last few years, the Deep Reinforcement Learning (DRL) paradigm has been widely
adopted for 5G and beyond network optimization because of its extreme adaptability to …

The management of networks is automated by closed loops. Concurrent closed loops
aiming for individual optimization cause conflicts which, left unresolved, leads to significant …

As the demand for data-driven applications and emerging technologies such as extended
reality, autonomous vehicles, and the Internet of Things (IoT) continues to grow, the …