Machine learning for large-scale optimization in 6g wireless networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
Recent advances in cloud radio access networks: System architectures, key techniques, and open issues
As a promising paradigm to reduce both capital and operating expenditures, the cloud radio
access network (C-RAN) has been shown to provide high spectral efficiency and energy …
access network (C-RAN) has been shown to provide high spectral efficiency and energy …
Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing
To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …
increasingly appealing to exploit distributed data communication and learning. Specifically …
Edge artificial intelligence for 6G: Vision, enabling technologies, and applications
The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …
wireless networks. It has been envisioned that 6G will be transformative and will …
Federated learning via over-the-air computation
The stringent requirements for low-latency and privacy of the emerging high-stake
applications with intelligent devices such as drones and smart vehicles make the cloud …
applications with intelligent devices such as drones and smart vehicles make the cloud …
Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems
Millimeter wave (mmWave) communications has been regarded as a key enabling
technology for 5G networks, as it offers orders of magnitude greater spectrum than current …
technology for 5G networks, as it offers orders of magnitude greater spectrum than current …
Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis
Deep learning has recently emerged as a disruptive technology to solve challenging radio
resource management problems in wireless networks. However, the neural network …
resource management problems in wireless networks. However, the neural network …
Content-centric sparse multicast beamforming for cache-enabled cloud RAN
This paper presents a content-centric transmission design in a cloud radio access network
by incorporating multicasting and caching. Users requesting the same content form a …
by incorporating multicasting and caching. Users requesting the same content form a …
Heterogeneous cloud radio access networks: A new perspective for enhancing spectral and energy efficiencies
M Peng, Y Li, J Jiang, J Li… - IEEE wireless …, 2014 - ieeexplore.ieee.org
To mitigate the severe inter-tier interference and enhance the limited cooperative gains
resulting from the constrained and non-ideal transmissions between adjacent base stations …
resulting from the constrained and non-ideal transmissions between adjacent base stations …
Software defined optical networks (SDONs): A comprehensive survey
The emerging software defined networking (SDN) paradigm separates the data plane from
the control plane and centralizes network control in an SDN controller. Applications interact …
the control plane and centralizes network control in an SDN controller. Applications interact …