This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …

In this survey, we present state-of-the-art bitrate adaptation algorithms for HTTP adaptive
streaming (HAS). As a key distinction from other streaming approaches, the bitrate …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

Led by industrialization of smart cities, numerous interconnected mobile devices, and novel
applications have emerged in the urban environment, providing great opportunities to …

Predictably, the upcoming six generation (6G) networks demand ultra-massive
interconnectivity comprising densely congested sustainable small-to-tiny networks. The …

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy
the ultra-low latency demand of future applications. In this paper, we argue that such …

We introduce a new semantic communication mechanism-SemanticRL, whose key idea is to
preserve the semantic information instead of strictly securing the bit-level precision. Unlike …

Isolated microgrids have been widely used on campuses, becoming an important part of
their power-supply infrastructure. In this study, a stochastic optimal scheduling strategy that …

Real-time video streaming is now one of the main applications in all network environments.
Due to the fluctuation of throughput under various network conditions, how to choose a …

Learning-based Adaptive Bit Rate~(ABR) method, aiming to learn outstanding strategies
without any presumptions, has become one of the research hotspots for adaptive streaming …