Semantic communication based on machine learning (ML) techniques emerged as a new
transmission paradigm that can significantly improve spectrum efficiency. It looks promising …

The low earth orbit (LEO) satellite-borne edge cloud (SEC) and machine learning (ML)
based semantic communication (SemCom) are both enabling technologies for 6G systems …

Recently, the autoencoder framework has shown great potential in reducing the feedback
overhead of the downlink channel state information (CSI). In this work, we further find that …

The emergence of new services and applications in emerging wireless networks (eg,
beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) …

Federated learning (FL) has been considered as a promising paradigm for enabling
distributed machine learning (ML) in wireless networks. To address the limited energy …

In this paper, federated learning (FL) over wireless networks is investigated. In each
communication round, a subset of devices is selected to participate in the aggregation with …

Federated learning (FL) is a swiftly evolving field within machine learning for collaboratively
training models at the network edge in a privacy-preserving fashion, without training data …

In cellular networks, resource allocation is usually performed in a centralized way, which
brings huge computation complexity to the base station (BS) and high transmission …

Free space optical (FSO) communication offers huge bandwidth, license-free spectrum and
a more secure channel. PIN diodes are normally used for detection, but avalanche …

As an efficient data fusion method, over-the-air computation integrates computation and
communication by exploiting the superposition property of multiple access channels. In this …