  Semantic communications learned on background knowledge bases (KBs) have been
identified as a promising technology for communications between intelligent
agents. Existing works assume that transceivers of semantic communications
share the same KB. However, intelligent transceivers may suffer from the
communication burden or worry about privacy leakage to exchange data in KBs.
Besides, the transceivers may independently learn from the environment and
dynamically update their KBs, leading to timely sharing of the KBs infeasible.
All these cause the mismatch between the KBs, which may result in a
semantic-level misunderstanding on the receiver side. To address this issue, we
propose a transceiver cooperative learning-assisted semantic communication
(TCL-SC) scheme against mismatched KBs. In TCL-SC, the transceivers
cooperatively train semantic encoder and decoder neuron networks (NNs) of the
same structure based on their own KBs. They periodically share the parameters
of NNs. To reduce the communication overhead of parameter sharing, parameter
quantization is adopted. Moreover, we discuss the impacts of the number of
communication rounds on the performance of semantic communication systems.
Experiments on real-world data demonstrate that our proposed TCL-SC can reduce
the semantic-level misunderstanding on the receiver side caused by the mismatch
between the KBs, especially at the low signal-to-noise (SNR) ratio regime.
