Probabilistic Semantic Communication over Wireless Networks with Rate Splitting

TL;DR

This paper investigates joint resource allocation for probabilistic semantic communication with rate splitting in wireless networks, optimizing semantic data transmission and compression to improve overall semantic rates under power and rate constraints.

Contribution

It introduces a novel semantic rate expression considering compression and computation, and proposes an iterative optimization algorithm for resource allocation in RSMA-based semantic communication.

Findings

The proposed scheme effectively maximizes semantic rates.

Semantic compression impacts power and rate trade-offs.

Numerical results confirm the scheme's efficiency.

Abstract

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.