Abstract
Space-air-ground integrated networks (SAGINs) are emerging as a pivotal
element in the evolution of future wireless networks. Despite their potential,
the joint design of communication and computation within SAGINs remains a
formidable challenge. In this paper, the problem of energy efficiency in
SAGIN-enabled probabilistic semantic communication (PSC) system is
investigated. In the considered model, a satellite needs to transmit data to
multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as
a relay. During transmission, the satellite and the UAV can use PSC technique
to compress the transmitting data, while the GTs can automatically recover the
missing information. The PSC is underpinned by shared probability graphs that
serve as a common knowledge base among the transceivers, allowing for
resource-saving communication at the expense of increased computation resource.
Through analysis, the computation overhead function in PSC is a piecewise
function with respect to the semantic compression ratio. Therefore, it is
important to make a balance between communication and computation to achieve
optimal energy efficiency. The joint communication and computation problem is
formulated as an optimization problem aiming to minimize the total
communication and computation energy consumption of the network under latency,
power, computation capacity, bandwidth, semantic compression ratio, and UAV
location constraints. To solve this non-convex non-smooth problem, we propose
an iterative algorithm where the closed-form solutions for computation capacity
allocation and UAV altitude are obtained at each iteration. Numerical results
show the effectiveness of the proposed algorithm.