# Scalable Multi-task Semantic Communication System with Feature Importance Ranking

arXiv: https://arxiv.org/abs/2304.05882 · HTML: https://mlsanity.com/paper/2304.05882/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2023.

## Built on by (most cited first, 7 of 7)

- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [A Survey on Semantic Communications in Internet of Vehicles](https://mlsanity.com/paper/2503.03767/) (2025)
- [Adaptive Dual-Path Framework for Covert Semantic Communication](https://mlsanity.com/paper/2605.03423/) (2026)
- [Towards Spoken Language Understanding via Multi-level Multi-grained Contrastive Learning](https://mlsanity.com/paper/2405.20852/) (2024)
- [Two Birds with One Stone: Multi-Task Semantic Communications Systems over Relay Channel](https://mlsanity.com/paper/2410.12302/) (2024)
- [Rateless DeepJSCC for Broadcast Channels: a Rate-Distortion-Complexity Tradeoff](https://mlsanity.com/paper/2603.21616/) (2026)
- [Decentralized Federated Learning for Heterogeneous Multi-Task Semantic Communication](https://mlsanity.com/paper/2608.15256/) (2026)

## Abstract (arXiv)

Semantic communications are expected to be an innovative solution to the emerging intelligent applications in the era of connected intelligence. In this paper, a novel scalable multitask semantic communication system with feature importance ranking (SMSC-FIR) is explored. Firstly, the multi-task correlations are investigated by a joint semantic encoder to extract relevant features. Then, a new scalable coding method is proposed based on feature importance ranking, which dynamically adjusts the coding rate and guarantees that important features for semantic tasks are transmitted with higher priority. Simulation results show that SMSC-FIR achieves performance gain w.r.t. individual intelligent tasks, especially in the low SNR regime.