Transceiver Cooperative Learning-aided Semantic Communications Against Mismatched Background Knowledge Bases
Yanhu Wang, and Shuaishuai Guo

TL;DR
This paper introduces TCL-SC, a cooperative learning scheme for semantic communications that mitigates the effects of mismatched background knowledge bases between transceivers, reducing misunderstandings especially in low SNR conditions.
Contribution
It proposes a novel cooperative training method with parameter sharing and quantization to address KB mismatch without extensive data exchange.
Findings
Reduces semantic misunderstandings caused by KB mismatch.
Effective at low SNR regimes.
Uses parameter quantization to lower communication overhead.
Abstract
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…
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Taxonomy
TopicsWireless Signal Modulation Classification · Domain Adaptation and Few-Shot Learning · AI in cancer detection
