Compression before Fusion: Broadcast Semantic Communication System for Heterogeneous Tasks

Shuoyao Wang

Abstract:

Semantic communication has emerged as new
paradigm shifts in 6G from the conventional syntax-oriented communications. Recently, the wireless broadcast technology has been
introduced to support semantic communication system toward
higher communication efficiency. Nevertheless, existing broadcast
semantic communication systems target on general representation
within one stage and fail to balance the inference accuracy among
users. In this paper, the broadcast encoding process is decomposed into compression and fusion to improves communication
efficiency with adaptation to tasks and channels. Particularly,
we propose multiple task-channel-aware sub-encoders (TCE)
and a channel-aware feature fusion sub-encoder (CFE) towards
compression and fusion, respectively. In TCEs, multiple localchannel-aware attention blocks are employed to extract and
compress task-relevant information for each user. In GFE, we
introduce a global-channel-aware fine-tuning block to merge
these compressed task-relevant signals into a compact broadcast
signal. Notably, we retrieve the bottleneck in DeepBroadcast
and leverage information bottleneck theory to further optimize
the parameter tuning of TCEs and CFE. We substantiate our
approach through experiments on a range of heterogeneous
tasks across various channels with additive white Gaussian
noise (AWGN) channel, Rayleigh fading channel, and Rician
fading channel. Simulation results evidence that the proposed
DeepBroadcast outperforms the state-of-the-art methods