# Optimization of Image Transmission in a Cooperative Semantic Communication Networks arXiv: https://arxiv.org/abs/2301.00433 ยท HTML: https://mlsanity.com/paper/2301.00433/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 47 references that are in the map. - First posted on arXiv 1 Jan 2023. ## Builds on (most cited first, 30 of 30) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018) - [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016) - [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017) - [Unbiased Scene Graph Generation from Biased Training](https://mlsanity.com/paper/2002.11949/) (2020) - [Learning to Compose Dynamic Tree Structures for Visual Contexts](https://mlsanity.com/paper/1812.01880/) (2018) - [Multiagent Cooperation and Competition with Deep Reinforcement Learning](https://mlsanity.com/paper/1511.08779/) (2015) - [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019) - [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021) - [Towards an Intelligent Edge: Wireless Communication Meets Machine Learning](https://mlsanity.com/paper/1809.00343/) (2018) - [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021) - [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022) - [From Semantic Communication to Semantic-aware Networking: Model, Architecture, and Open Problems](https://mlsanity.com/paper/2012.15405/) (2020) - [Task-Oriented Multi-User Semantic Communications for VQA Task](https://mlsanity.com/paper/2108.07357/) (2021) - [Wireless Semantic Communications for Video Conferencing](https://mlsanity.com/paper/2204.07790/) (2022) - [Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data](https://mlsanity.com/paper/2205.00271/) (2022) - [Towards Semantic Communications: Deep Learning-Based Image Semantic Coding](https://mlsanity.com/paper/2208.04094/) (2022) - [Semantic-preserved Communication System for Highly Efficient Speech Transmission](https://mlsanity.com/paper/2205.12727/) (2022) - [Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks](https://mlsanity.com/paper/2012.03158/) (2020) - [Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach](https://mlsanity.com/paper/2208.08239/) (2022) - [Task-driven Semantic Coding via Reinforcement Learning](https://mlsanity.com/paper/2106.03511/) (2021) - [Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysis](https://mlsanity.com/paper/2209.12274/) (2022) - [Reinforcement Learning-powered Semantic Communication via Semantic Similarity](https://mlsanity.com/paper/2108.12121/) (2021) - [Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text](https://mlsanity.com/paper/2106.14014/) (2021) - [Deep Learning for Optimal Deployment of UAVs with Visible Light Communications](https://mlsanity.com/paper/1912.00752/) (2019) - [Heterogeneous Semantic and Bit Communications: A Semi-NOMA Scheme](https://mlsanity.com/paper/2205.02620/) (2022) - [Sampling, Communication, and Prediction Co-Design for Synchronizing the Real-World Device and Digital Model in Metaverse](https://mlsanity.com/paper/2208.04233/) (2022) - [Semantic Communications With AI Tasks](https://mlsanity.com/paper/2109.14170/) (2021) - [Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation](https://mlsanity.com/paper/2209.15347/) (2022) - [Reliable Extraction of Semantic Information and Rate of Innovation Estimation for Graph Signals](https://mlsanity.com/paper/2211.05440/) (2022) ## Built on by (most cited first, 8 of 8) - [Distortion Resilience for Goal-Oriented Semantic Communication](https://mlsanity.com/paper/2309.14587/) (2023) - [Semantic Feature Decomposition based Semantic Communication System of Images with Large-scale Visual Generation Models](https://mlsanity.com/paper/2410.20126/) (2024) - [Goal-Oriented Semantic Communication for Wireless Visual Question Answering](https://mlsanity.com/paper/2411.02452/) (2024) - [Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks](https://mlsanity.com/paper/2502.05116/) (2025) - [Semantic Edge-Cloud Communication for Real-Time Urban Traffic Surveillance with ViT and LLMs over Mobile Networks](https://mlsanity.com/paper/2509.21259/) (2025) - [Optimization of Private Semantic Communication Performance: An Uncooperative Covert Communication Method](https://mlsanity.com/paper/2508.07586/) (2025) - [Optimizing Reinforcement Learning Training over Digital Twin Enabled Multi-fidelity Networks](https://mlsanity.com/paper/2603.08931/) (2026) - [From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity](https://mlsanity.com/paper/2609.10714/) (2026) ## Abstract (arXiv) In this paper, a semantic communication framework for image transmission is developed. In the investigated framework, a set of servers cooperatively transmit images to a set of users utilizing semantic communication techniques. To evaluate the performance of studied semantic communication system, a multimodal metric is proposed to measure the correlation between the extracted semantic information and the original image. To meet the ISS requirement of each user, each server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. We formulate this problem as an optimization problem aiming to minimize each server's transmission latency while reaching the ISS requirement. To solve this problem, a value decomposition based entropy-maximized multi-agent reinforcement learning (RL) is proposed, which enables servers to coordinate for training and execute RB allocation in a distributed manner to approach to a globally optimal performance with less training iterations. Compared to traditional multi-agent RL, the proposed RL improves the valuable action exploration of servers and the probability of finding a globally optimal RB allocation policy based on local observation. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 16.1% compared to traditional multi-agent RL.