Task-Oriented Semantic Information Allocation Based on Rate Splitting for Cost Minimization

Task-oriented semantic communication effectively facilitates the completion of specific tasks by conveying users' interests. Nevertheless, when a base station conveys task-oriented semantic information to multiple users, these users exhibit both shared and distinct interests, which leads to increased redundancy and communication costs. To reduce transmission redundancy and cost, in this paper, a flexible and efficient multicast and unicast transmission scheme based on task-oriented semantic information is investigated for users with overlapping interests. Specifically, a joint energy-delay cost function is adopted to measure the communication cost caused by transmission redundancy, which facilitates a trade-off between energy consumption and transmission delay. Then, to minimize the network cost, this paper leverages the inherent benefits of common and private streams in rate splitting multiple access (RSMA) and design a semantic information allocation strategy. The RSMA common stream is further divided into the super-common and user-common streams. A semantic information allocation mechanism is established according to the common and private streams to flexibly allocate multiple semantic information. Then, semantic information selection on super-common stream, the proportional strategy of user-common stream, and rate splitting are jointly designed to minimize the network cost. The problem is a mixed-integer problem. Block coordinate descent and successive convex approximation technique are adopted to address it. An algorithm, namely SURA, is proposed for semantic information allocation in RSMA networks. Simulation results demonstrate the proposed SURA algorithm can adaptively allocate semantic information and reduce the network cost.