# Adaptable Semantic Compression and Resource Allocation for Task-Oriented Communications

arXiv: https://arxiv.org/abs/2204.08910 · HTML: https://mlsanity.com/paper/2204.08910/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2022.

## Built on by (most cited first, 15 of 15)

- [Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach](https://mlsanity.com/paper/2208.08239/) (2022)
- [OFDM-Based Digital Semantic Communication with Importance Awareness](https://mlsanity.com/paper/2401.02178/) (2024)
- [Adaptive Resource Allocation for Semantic Communication Networks](https://mlsanity.com/paper/2312.01081/) (2023)
- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [Non-Orthogonal Multiple Access Enhanced Multi-User Semantic Communication](https://mlsanity.com/paper/2303.06597/) (2023)
- [Distortion Resilience for Goal-Oriented Semantic Communication](https://mlsanity.com/paper/2309.14587/) (2023)
- [Low-Latency Task-Oriented Communications with Multi-Round, Multi-Task Deep Learning](https://mlsanity.com/paper/2411.10385/) (2024)
- [SC-GIR: Goal-oriented Semantic Communication via Invariant Representation Learning](https://mlsanity.com/paper/2509.01119/) (2025)
- [Hypergame Theory for Decentralized Resource Allocation in Multi-user Semantic Communications](https://mlsanity.com/paper/2409.17985/) (2024)
- [Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models](https://mlsanity.com/paper/2506.13243/) (2025)
- [TOAST: Task-Oriented Adaptive Semantic Transmission over Dynamic Wireless Environments](https://mlsanity.com/paper/2506.21900/) (2025)
- [Knowledge Abstraction for Knowledge-based Semantic Communication: A Generative Causality Invariant Approach](https://mlsanity.com/paper/2507.17784/) (2025)
- [Semantic Communication-Enhanced Split Federated Learning for Vehicular Networks: Architecture, Challenges, and Case Study](https://mlsanity.com/paper/2603.04936/) (2026)
- [Task-Oriented Semantic Communication for Hazard Warning and Remote Operation in Connected Vehicle Platoons](https://mlsanity.com/paper/2609.06242/) (2026)
- [Semantic-Aware Joint Source-Channel Optimization for Encoder-Agnostic Digital Video Communication](https://mlsanity.com/paper/2609.39296/) (2026)

## Abstract (arXiv)

Task-oriented communication is a new paradigm that aims at providing efficient connectivity for accomplishing intelligent tasks rather than the reception of every transmitted bit. In this paper, a deep learning-based task-oriented communication architecture is proposed where the user extracts, compresses and transmits semantics in an end-to-end (E2E) manner. Furthermore, an approach is proposed to compress the semantics according to their importance relevant to the task, namely, adaptable semantic compression (ASC). Assuming a delay-intolerant system, supporting multiple users indicates a problem that executing with the higher compression ratio requires fewer channel resources but leads to the distortion of semantics, while executing with the lower compression ratio requires more channel resources and thus may lead to a transmission failure due to delay constraint. To solve the problem, both compression ratio and resource allocation are optimized for the task-oriented communication system to maximize the success probability of tasks. Specifically, due to the nonconvexity of the problem, we propose a compression ratio and resource allocation (CRRA) algorithm by separating the problem into two subproblems and solving iteratively to obtain the convergent solution. Furthermore, considering the scenarios where users have various service levels, a compression ratio, resource allocation, and user selection (CRRAUS) algorithm is proposed to deal with the problem. In CRRAUS, users are adaptively selected to complete the corresponding intelligent tasks based on branch and bound method at the expense of higher algorithm complexity compared with CRRA. Simulation results show that the proposed CRRA and CRRAUS algorithms can obtain at least 15% and 10% success gains over baseline algorithms, respectively.