A tri-level PPO-based training scheme with an adaptive gradient-aware task weighting module improves multi-task semantic broadcast communication performance over conventional and prior learned baselines.
Multi-User Semantic Fusion for Semantic Communications over Degraded Broadcast Channels
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abstract
Degraded broadcast channels (DBC) are a typical multiuser communication scenario, Semantic communications over DBC still lack in-depth research. In this paper, we design a semantic communications approach based on multi-user semantic fusion for wireless image transmission over DBC. In the proposed method, the transmitter extracts semantic features for two users separately. It then effectively fuses these semantic features for broadcasting by leveraging semantic similarity. Unlike traditional allocation of time, power, or bandwidth, the semantic fusion scheme can dynamically control the weight of the semantic features of the two users to balance the performance between the two users. Considering the different channel state information (CSI) of both users over DBC, a DBC-Aware method is developed that embeds the CSI of both users into the joint source-channel coding encoder and fusion module to adapt to the channel. Experimental results show that the proposed system outperforms the traditional broadcasting schemes.
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Reinforcement Learning-Based Heterogeneous Multi-Task Optimization in Semantic Broadcast Communications
A tri-level PPO-based training scheme with an adaptive gradient-aware task weighting module improves multi-task semantic broadcast communication performance over conventional and prior learned baselines.