标题： 基于知识共享的多小区面向任务的混合语义-比特通信网络语义速率最大化 显示英文标题

标题： Knowledge Sharing-enabled Semantic Rate Maximization for Multi-cell Task-oriented Hybrid Semantic-Bit Communication Networks

摘要： 在任务导向的语义通信中，发送端被设计为向接收端传输与任务相关的语义信息，而不是每个信号位，这通过减少网络流量负载来缓解频谱压力。 有效的语义通信依赖于发送端和接收端之间共享知识的完美对齐，然而在实际中，知识对齐并不能总是得到保证。 为了解决这个挑战，我们提出了一种新的基于知识共享的任务导向混合语义和比特通信机制，其中移动设备（MD）可以主动共享并上传与任务相关的不匹配知识到关联的小型基站（SBS）。 传统的比特通信可以作为辅助手段，传输与未共享的不匹配知识相关的剩余数据，以确保目标任务的有效执行。 考虑到多小区网络中的异构收发器、目标任务需求和信道条件，制定了一个优化问题，通过联合优化知识共享、语义提取比例和SBS关联，最大化所有MD的广义有效语义传输速率，同时满足MD目标任务的语义准确性和延迟容忍度要求。 所制定的混合整数非线性规划问题被等效地分解为多个子问题。 提出了一种最优算法，并进一步利用分层类别划分和单调优化开发了另一种高效算法。 仿真结果证明了所提解决方案的有效性和优越性能。 显示英文摘要

摘要： In task-oriented semantic communications, the transmitters are designed to deliver task-related semantic information rather than every signal bit to receivers, which alleviates the spectrum pressure by reducing network traffic loads. Effective semantic communications depend on the perfect alignment of shared knowledge between transmitters and receivers, however, the alignment of knowledge cannot always be guaranteed in practice. To tackle this challenge, we propose a novel knowledge sharing-enabled task-oriented hybrid semantic and bit communications mechanism, where a mobile device (MD) can proactively share and upload the task-related mismatched knowledge to associated small base station (SBS). The traditional bit communications can be adopted as an aid to transmit the rest data related to unshared mismatched knowledge to guarantee the effective execution of target tasks. Considering the heterogeneous transceivers in multi-cell networks, target task demands, and channel conditions, an optimization problem is formulated to maximize the generalized effective semantic transmission rate of all MDs by jointly optimizing knowledge sharing, semantic extraction ratio, and SBS association, while satisfying the semantic accuracy requirements and delay tolerances of MD target tasks. The formulated mixed integer nonlinear programming problem is decomposed into multiple subproblems equivalently. An optimum algorithm is proposed and another efficient algorithm is further developed using hierarchical class partitioning and monotonic optimization. Simulation results demonstrate the validity and superior performance of proposed solutions.