  By transmitting task-related information only, semantic communications yield
significant performance gains over conventional communications. However, the
lack of mature semantic theory about semantic information quantification and
performance evaluation makes it challenging to perform resource allocation for
semantic communications, especially when multiple tasks coexist in the network.
To cope with this challenge, we propose a quality-of-experience (QoE) based
semantic-aware resource allocation method for multi-task networks in this
paper. First, semantic entropy is defined to quantify the semantic information
for different tasks, and the relationship between semantic entropy and Shannon
entropy is analyzed. Then, we develop a novel QoE model to formulate the
semantic-aware resource allocation in terms of semantic compression, channel
assignment, and transmit power. The compatibility of the formulated problem
with conventional communications is further demonstrated. To solve this
problem, we decouple it into two subproblems and solved them by a developed
deep Q-network (DQN) based method and a proposed low-complexity matching
algorithm, respectively. Finally, simulation results validate the effectiveness
and superiority of the proposed method, as well as its compatibility with
conventional communications.
