AI models for green communications towards 6G
Green communications have always been a target for the information industry to alleviate
energy overhead and reduce fossil fuel usage. In the current 5G and future 6G eras, there is …
energy overhead and reduce fossil fuel usage. In the current 5G and future 6G eras, there is …
Application of machine learning in wireless networks: Key techniques and open issues
As a key technique for enabling artificial intelligence, machine learning (ML) is capable of
solving complex problems without explicit programming. Motivated by its successful …
solving complex problems without explicit programming. Motivated by its successful …
Machine learning for 5G/B5G mobile and wireless communications: Potential, limitations, and future directions
Driven by the demand to accommodate today's growing mobile traffic, 5G is designed to be
a key enabler and a leading infrastructure provider in the information and communication …
a key enabler and a leading infrastructure provider in the information and communication …
Cooperative computation offloading and resource allocation for blockchain-enabled mobile-edge computing: A deep reinforcement learning approach
Mobile-edge computing (MEC) is a promising paradigm to improve the quality of
computation experience of mobile devices because it allows mobile devices to offload …
computation experience of mobile devices because it allows mobile devices to offload …
Joint optimization of caching, computing, and radio resources for fog-enabled IoT using natural actor–critic deep reinforcement learning
The cloud-based Internet of Things (IoT) develops rapidly but suffer from large latency and
backhaul bandwidth requirement, the technology of fog computing and caching has …
backhaul bandwidth requirement, the technology of fog computing and caching has …
Power allocation in multi-user cellular networks: Deep reinforcement learning approaches
The model-based power allocation has been investigated for decades, but this approach
requires mathematical models to be analytically tractable and it has high computational …
requires mathematical models to be analytically tractable and it has high computational …
User scheduling and resource allocation in HetNets with hybrid energy supply: An actor-critic reinforcement learning approach
Densely deployment of various small-cell base stations in cellular networks to increase
capacity will lead to heterogeneous networks (HetNets), and meanwhile, embedding the …
capacity will lead to heterogeneous networks (HetNets), and meanwhile, embedding the …
A survey on deep learning for ultra-reliable and low-latency communications challenges on 6G wireless systems
The sixth generation (6G) wireless communication network presents itself as a promising
technique that can be utilized to provide a fully data-driven network evaluating and …
technique that can be utilized to provide a fully data-driven network evaluating and …
Deep reinforcement learning-based mode selection and resource management for green fog radio access networks
Fog radio access networks (F-RANs) are seen as potential architectures to support services
of Internet of Things by leveraging edge caching and edge computing. However, current …
of Internet of Things by leveraging edge caching and edge computing. However, current …
Dynamic TDD systems for 5G and beyond: A survey of cross-link interference mitigation
Dynamic time division duplex (D-TDD) dynamically allocates the transmission directions for
traffic adaptation in each cell. D-TDD systems are receiving a lot of attention because they …
traffic adaptation in each cell. D-TDD systems are receiving a lot of attention because they …