Semantic communication (SemCom) holds promise for reducing network resource
consumption while achieving the communications goal. However, the computational …

Recently, deep learned enabled end-to-end communication systems have been developed
to merge all physical layer blocks in the traditional communication systems, which make joint …

Deep learning-empowered semantic communication is regarded as a promising candidate
for future 6G networks. Although existing semantic communication systems have achieved …

Degraded broadcast channels (DBC) are a typical multiuser communication scenario. There
exist classic transmission methods, such as superposition coding with successive …

In this paper, we address the problem of building adaptive network coding coefficients under
dynamic network conditions (eg, varying link quality and changing number of relays). In …

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 …

The last 70 years have witnessed the transition of communication from Shannon's
theoretical concept to current high-efficiency practical systems. Classical communication …

Standard digital communication techniques allow us to set aside the meaning of the
messages to concentrate on the transmission of bits efficiently and reliably. However, with …

Semantic communication, through the interpretation of the semantic meaning of transmitted
data, effectively reduces the required bandwidth. However, current deep learning-based …

Incorporating semantic analysis into image compression can significantly reduce the
repetitive computation of fundamental semantic analysis in client-side applications such as …