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

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 …

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 …

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 …

Federated Multi-Agent Deep Reinforcement Learning (F-MADRL) is gathering keen
research interests, as it may offer efficient solutions towards meeting the extreme …

Future-generation wireless networks (5G and beyond) must accommodate surging growth in
mobile data traffic and support an increasingly high density of mobile users involving a …

The deployment of network elements in 6G is expected to be significantly more distributed
than the existing 5G deployments. Distributed management paradigms are compatible with …

In the sixth-generation (6G) networks, newly emerging diversified services of massive users
in dynamic network environments are required to be satisfied by multi-dimensional …

The next-generation (6G) networks promise to provide extended 5G capabilities with
enhanced performance at high data rates, low latency, low energy consumption, and rapid …

The provision of a wide range of services each with different requirements makes next
generation wireless networks become more complex and heterogeneous which is aimed to …