  In this paper, the problem of joint transmission and computation resource
allocation for probabilistic semantic communication (PSC) system with rate
splitting multiple access (RSMA) is investigated. In the considered model, the
base station (BS) needs to transmit a large amount of data to multiple users
with RSMA. Due to limited communication resources, the BS is required to
utilize semantic communication techniques to compress the large-sized data. The
semantic communication is enabled by shared probability graphs between the BS
and the users. The probability graph can be used to further compress the
transmission data at the BS, while the received compressed semantic information
can be recovered through using the same shared probability graph at each user
side. The semantic information compression progress consumes additional
computation power at the BS, which inevitably decreases the transmission power
due to limited total power budget. Considering both the effect of semantic
compression ratio and computation power, the semantic rate expression for RSMA
is first obtained. Then, based on the obtained rate expression, an optimization
problem is formulated with the aim of maximizing the sum of semantic rates of
all users under total power, semantic compression ratio, and rate allocation
constraints. To tackle this problem, an iterative algorithm is proposed, where
the rate allocation and transmit beamforming design subproblem is solved using
a successive convex approximation method, and the semantic compression ratio
subproblem is addressed using a greedy algorithm. Numerical results validate
the effectiveness of the proposed scheme.
