  With the aim of accomplishing intelligence tasks, semantic communications
transmit task-related information only, yielding significant performance gains
over conventional communications. To guarantee user requirements for different
types of tasks, we perform the semantic-aware resource allocation in a
multi-cell multi-task network in this paper. Specifically, an approximate
measure of semantic entropy is first developed to quantify the semantic
information for different tasks, based on which a novel quality-of-experience
(QoE) model is proposed. We formulate the QoE-aware semantic resource
allocation in terms of the number of transmitted semantic symbols, channel
assignment, and power allocation. To solve this problem, we first decouple it
into two independent subproblems. The first one is to optimize the number of
transmitted semantic symbols with given channel assignment and power
allocation, which is solved by the exhaustive searching method. The second one
is the channel assignment and power allocation subproblem, which is modeled as
a many-to-one matching game and solved by the proposed low-complexity matching
algorithm. Simulation results demonstrate the effectiveness and superiority of
the proposed method on the overall QoE.
