Semantic Knowledge Base-Enabled Zero-Shot Multi-Level Feature Transmission Optimization

Remote zero-shot object recognition, which involves offloading the zero-shot recognition task from one mobile device to a remote mobile edge computing (MEC) server or another mobile device, is crucial for 6G. To address this challenge, this paper presents a lightweight semantic knowledge base (SKB)-enabled multi-level feature extractor that projects the image into visual, semantic, and intermediate feature spaces. Then, this paper proposes a novel SKB-enabled multi-level feature transmission framework, which utilizes SKB and multi-level feature extractor at both transmitter and receiver. The semantic loss and required transmission latency at each level are characterized, and a multi-level feature transmission optimization problem is formulated to minimize the semantic loss under transmission latency constraint. However, this optimization problem is a multi-choice knapsack problem, which is challenging to solve optimally. To overcome this issue, an enhanced convex concave procedure is proposed to obtain an efficient solution. Furthermore, this paper theoretically analyzes the effects of SKBs on the communication performance when the feature extractors at both ends are the same. Numerical results demonstrate that the proposed design outperforms the benchmarks and provide insights into the impact of SKBs at both ends on performance as well as the tradeoff between transmission latency and zero-shot classification accuracy.