mlsanityData to 14 Oct 2026

JPPO: Joint Power and Prompt Optimization for Accelerated Large Language Model Services

arXiv 2411.18010 ↗First posted 27 Nov 2024eess.AS cs.CL cs.SDcited by 0

On the map: Efficient LLM inference and serving → Prompt and context compression

Abstract (arXiv)

Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, leading to their increasing deployment in wireless networks for a wide variety of user services. However, the growing longer prompt setting highlights the crucial issue of computational resource demands and huge communication load. To address this challenge, we propose Joint Power and Prompt Optimization (JPPO), a framework that combines Small Language Model (SLM)-based prompt compression with wireless power allocation optimization. By deploying SLM at user devices for prompt compression and employing Deep Reinforcement Learning for joint optimization of compression ratio and transmission power, JPPO effectively balances service quality with resource efficiency. Experimental results demonstrate that our framework achieves high service fidelity and low bit error rates while optimizing power usage in wireless LLM services. The system reduces response time by about 17%, with the improvement varying based on the length of the original prompt.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Prompt and context compression, one of 12 directions in Efficient LLM inference and serving. Builds on 12 of its 16 references that are in the map. First posted on arXiv 27 Nov 2024.

Builds on

12 of 16 references.

References · 12

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  3. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 463
  4. 2024Long-context LLMs Struggle with Long In-context Learningcited by 286
  5. 2023To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisiscited by 92
  6. 2023MeetingBank: A Benchmark Dataset for Meeting Summarizationcited by 84
  7. 2023Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Servicescited by 76
  8. 2023LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Modelscited by 72
  9. 2023Large Language Model Enhanced Multi-Agent Systems for 6G Communicationscited by 53
  10. 2024Efficient Prompting for LLM-based Generative Internet of Thingscited by 12
  11. 2024Deeploy: Enabling Energy-Efficient Deployment of Small Language Models On Heterogeneous Microcontrollerscited by 11
  12. 2024LLM-Slice: Dedicated Wireless Network Slicing for Large Language Modelscited by 3

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  1. 2024Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control
  2. 2024Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settings
  3. 2026Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimization
  4. 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey
  5. 2026Goodput Maximization for Large Language Model Edge Inference: A Two-Phase Maskable PPO Approach
  6. 2024Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach
  7. 2023Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
  8. 2025Split Fine-Tuning for Large Language Models in Wireless Networks