Edge caching, which offers application versatility and a range of benefits, has exerted a
significant impact on the adoption and development of fifth-generation networks and …

Future wireless networks provide research challenges with many fold increase of smart
devices and the exponential growth in mobile data traffic. The advent of highly …

In this paper, we present a deep learning-based Device-to-Device (D2D) approach that
utilizes Gated Recurrent Unit (GRU) model that is optimized through Bayesian optimization …

Deep Reinforcement Learning for Wireless Communications and Networking
Comprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless …

We propose a method to first identify users who have the most negative impact on the
overall network performance, and then offload them to an orthogonal channel. The feasibility …

Sub-THz communication is emerging as a promising technology to improve the cellular
system capacity in the sixth-generation (6G) era. Compared to conventional microwave …

The wireless communication systems, particularly the vehicle to everything (V2X) wireless
networks, take advantage of machine learning (ML) to improve and overcome the issues …

The continuous growth of data production in almost all scientific areas raises new problems
in data access and management, especially in a scenario where the end-users, as well as …

With the support of 5G technology, mobile edge computing has made the application of
industrial IoT and power IoT more and more extensive. By deploying a certain number of …

The rapid development of sixth-generation (6G) may contribute to the near-reality of
autonomous driving. The amount of work and leisure time that drivers and passengers …