TY - JOUR
T1 - Rethinking Modern Communication from Semantic Coding to Semantic Communication
AU - Lu, Kun
AU - Zhou, Qingyang
AU - Li, Rongpeng
AU - Zhao, Zhifeng
AU - Chen, Xianfu
AU - Wu, Jianjun
AU - Zhang, Honggang
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grants 61731002 and 62071425, in part by the Zhejiang Key Research and Development Plan under Grants 2019C01002, 2019C03131, and 2022C01093, in part by Huawei Cooperation Project, in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LY20F010016, and in part by the Zhejiang Lab Open Program under Grant LR23F010005.
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2023/5/9
Y1 - 2023/5/9
N2 - Modern communications are usually designed to pursue a higher bit-level precision and fewer bits while transmitting a message. This article rethinks these two major features and introduces the concept and advantage of semantics that characterizes a new kind of semantics-aware communication framework, incorporating both the semantic encoding and the semantic communication problem. After analyzing the underlying defects of existing semantics-aware techniques, we establish a confidence-based distillation mechanism for the joint semantics-noise coding (JSNC) problem and a reinforcement learning (RL) powered semantic communication paradigm that endows a system the ability to convey the semantics instead of pursuing the bit level accuracy. On top of these technical contributions, this work provides a new insight to understand how the semantics are processed and represented in a semantics-aware coding and communication system, and verifies the significant benefits of doing so. Targeted on the next generation's semantics-aware communication, some critical concerns and open challenges, such as the information overhead, semantic security, and implementation cost are also discussed and envisioned.
AB - Modern communications are usually designed to pursue a higher bit-level precision and fewer bits while transmitting a message. This article rethinks these two major features and introduces the concept and advantage of semantics that characterizes a new kind of semantics-aware communication framework, incorporating both the semantic encoding and the semantic communication problem. After analyzing the underlying defects of existing semantics-aware techniques, we establish a confidence-based distillation mechanism for the joint semantics-noise coding (JSNC) problem and a reinforcement learning (RL) powered semantic communication paradigm that endows a system the ability to convey the semantics instead of pursuing the bit level accuracy. On top of these technical contributions, this work provides a new insight to understand how the semantics are processed and represented in a semantics-aware coding and communication system, and verifies the significant benefits of doing so. Targeted on the next generation's semantics-aware communication, some critical concerns and open challenges, such as the information overhead, semantic security, and implementation cost are also discussed and envisioned.
UR - http://www.scopus.com/inward/record.url?scp=85131885002&partnerID=8YFLogxK
U2 - 10.1109/MWC.013.2100642
DO - 10.1109/MWC.013.2100642
M3 - Article
AN - SCOPUS:85131885002
SN - 1536-1284
VL - 30
SP - 158
EP - 164
JO - IEEE Wireless Communications
JF - IEEE Wireless Communications
IS - 1
ER -