协作知识共享赋能的两层语义-比特通信网络有效语义速率最大化
Collaborative Knowledge Sharing-empowered Effective Semantic Rate Maximization for Two-tier Semantic-Bit Communication Networks
AI总结：
本文针对两层语义-比特通信网络中的知识失配问题，提出协作知识共享机制，通过优化知识共享、提取比率和资源分配最大化广义有效语义传输速率，并设计分支定界和改进Kuhn-Munkres算法高效求解。
摘要：
有效的面向任务的语义通信依赖于发射机与接收机之间完美的知识对齐，以准确恢复与任务相关的语义信息，但在实际中容易受到知识失配和性能下降的影响。为解决这一问题，持续的知识更新与共享对于适应不断变化的任务和用户相关需求至关重要，尽管这会带来资源开销和增加的时延。本文提出了一种在两层边缘网络中由协作知识共享赋能的新型语义传输机制，利用边缘协作和比特通信来解决知识库失配问题。通过推导一个同时考虑语义准确性和开销的广义有效语义传输速率（GESTR），我们构建了一个混合整数非线性规划问题，通过优化知识共享决策、提取比率以及基站/子信道分配，在任务准确性和时延要求的约束下最大化所有移动设备的GESTR。所提出的基于分数规划的分支定界算法和改进的Kuhn-Munkres算法可以高效地获得联合最优解。仿真结果表明所提方案具有优越性能，尤其是在低信噪比条件下。
英文摘要：
Effective task-oriented semantic communications relies on perfect knowledge alignment between transmitters and receivers for accurate recovery of task-related semantic information, which can be susceptible to knowledge misalignment and performance degradation in practice. To tackle this issue, continual knowledge updating and sharing are crucial to adapt to evolving task and user related demands, despite the incurred resource overhead and increased latency. In this paper, we propose a novel collaborative knowledge sharing-empowered semantic transmission mechanism in a two-tier edge network, exploiting edge cooperations and bit communications to address KB mismatch. By deriving a generalized effective semantic transmission rate (GESTR) that considers both semantic accuracy and overhead, we formulate a mixed integer nonlinear programming problem to maximize GESTR of all mobile devices by optimizing knowledge sharing decisions, extraction ratios, and BS/subchannel allocations, subject to task accuracy and delay requirements. The joint optimum solution can be obtained by proposed fractional programming based branch and bound algorithm and modified Kuhn-Munkres algorithm efficiently. Simulation results demonstrate the superior performance of proposed solution, especially in low signal-to-noise conditions.
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