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Article overview
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A Unified Multi-Task Semantic Communication System with Domain Adaptation | | Guangyi Zhang
; Qiyu Hu
; Zhijin Qin
; Yunlong Cai
; Guanding Yu
; | | Date: |
1 Jun 2022 | | Abstract: | The task-oriented semantic communication systems have achieved significant
performance gain, however, the paradigm that employs a model for a specific
task might be limited, since the system has to be updated once the task is
changed or multiple models are stored for serving various tasks. To address
this issue, we firstly propose a unified deep learning enabled semantic
communication system (U-DeepSC), where a unified model is developed to serve
various transmission tasks. To jointly serve these tasks in one model with
fixed parameters, we employ domain adaptation in the training procedure to
specify the task-specific features for each task. Thus, the system only needs
to transmit the task-specific features, rather than all the features, to reduce
the transmission overhead. Moreover, since each task is of different difficulty
and requires different number of layers to achieve satisfactory performance, we
develop the multi-exit architecture to provide early-exit results for
relatively simple tasks. In the experiments, we employ a proposed U-DeepSC to
serve five tasks with multi-modalities. Simulation results demonstrate that our
proposed U-DeepSC achieves comparable performance to the task-oriented semantic
communication system designed for a specific task with significant transmission
overhead reduction and much less number of model parameters. | | Source: | arXiv, 2206.00254 | | Services: | Forum | Review | PDF | Favorites | |
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