Collaborative Knowledge Sharing-empowered Effective Semantic Rate Maximization for Two-tier Semantic-Bit Communication Networks

TL;DR

This paper introduces a collaborative knowledge sharing mechanism in two-tier semantic communication networks, optimizing semantic transmission rate through joint resource allocation and knowledge updates to improve task accuracy and reduce latency.

Contribution

It proposes a novel GESTR metric and an optimization framework with efficient algorithms to enhance semantic communication performance under knowledge mismatch conditions.

Findings

Improved semantic transmission rate in low SNR scenarios

Effective knowledge sharing reduces task accuracy degradation

Optimization algorithms outperform baseline methods

Abstract

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.